From 52518be7a646d2220e245ca2b56577a1102d51db Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sat, 4 May 2024 14:42:46 +0200 Subject: [PATCH 001/147] Extend make.predictorMatrix() with an option to subset to correlated predictors --- R/larspred.R | 3 ++ R/mice.R | 12 ++++--- R/predictorMatrix.R | 33 +++++++++++++++++--- R/quickpred.R | 14 ++++++--- man/make.predictorMatrix.Rd | 21 +++++++++++-- man/quickpred.Rd | 7 +++-- tests/testthat/test-mice.impute.jomoImpute.R | 2 +- tests/testthat/test-mice.impute.panImpute.R | 2 +- 8 files changed, 73 insertions(+), 21 deletions(-) create mode 100644 R/larspred.R diff --git a/R/larspred.R b/R/larspred.R new file mode 100644 index 000000000..4cda70dc0 --- /dev/null +++ b/R/larspred.R @@ -0,0 +1,3 @@ +larspred <- function(data, ...){ + warning("Not yet implemented") +} \ No newline at end of file diff --git a/R/mice.R b/R/mice.R index 3a2dc8061..668cc8366 100644 --- a/R/mice.R +++ b/R/mice.R @@ -341,7 +341,7 @@ mice <- function(data, if (mp & mb & mf) { # blocks lead blocks <- make.blocks(colnames(data)) - predictorMatrix <- make.predictorMatrix(data, blocks) + predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) } # case B @@ -356,7 +356,7 @@ mice <- function(data, if (mp & !mb & mf) { # blocks leads blocks <- check.blocks(blocks, data) - predictorMatrix <- make.predictorMatrix(data, blocks) + predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) } @@ -365,7 +365,7 @@ mice <- function(data, # formulas leads formulas <- check.formulas(formulas, data) blocks <- construct.blocks(formulas) - predictorMatrix <- make.predictorMatrix(data, blocks) + predictorMatrix <- make.predictorMatrix(data, blocks = blocks) } # case E @@ -384,7 +384,9 @@ mice <- function(data, formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- construct.blocks(formulas, predictorMatrix) - predictorMatrix <- make.predictorMatrix(data, blocks, predictorMatrix) + predictorMatrix <- make.predictorMatrix(data, + blocks = blocks, + predictorMatrix = predictorMatrix) } # case G @@ -392,7 +394,7 @@ mice <- function(data, # blocks lead blocks <- check.blocks(blocks, data, calltype = "formula") formulas <- check.formulas(formulas, blocks) - predictorMatrix <- make.predictorMatrix(data, blocks) + predictorMatrix <- make.predictorMatrix(data, blocks = blocks) } # case H diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 1ff6f0e3d..800ee865b 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -7,18 +7,43 @@ #' the column variable is NOT used to impute the row variable or block. #' A nonzero value indicates that it is used. #' @param data A \code{data.frame} with the source data +#' @param selection A character string specifying the method to use to +#' select predictors. The default \code{"all"} selects all variables +#' as predictors. Alternatives \code{"correlation"} and \code{"lars"} +#' select predictors using the \code{quickpred()} and \code{larspred()}, +#' respectively. #' @param blocks An optional specification for blocks of variables in #' the rows. The default assigns each variable in its own block. #' @param predictorMatrix A predictor matrix from which rows with the same -#' names are copied into the output predictor matrix. -#' @return A matrix +#' names are copied into the output predictor matrix. This is useful for +#' editing the predictor matrix. +#' @param \dots Arguments passed to \code{quickpred()} or \code{larspred()}. +#' @return A matrix with the same number of rows as the number of blocks. #' @seealso \code{\link{make.blocks}} #' @examples #' make.predictorMatrix(nhanes) #' make.predictorMatrix(nhanes, blocks = make.blocks(nhanes, "collect")) #' @export -make.predictorMatrix <- function(data, blocks = make.blocks(data), +make.predictorMatrix <- function(data, + selection = c("all", "correlation", "lars"), + ..., + blocks = make.blocks(data), predictorMatrix = NULL) { + selection <- match.arg(selection) + if (selection == "all") { + return(make.default.predictorMatrix(data, blocks, predictorMatrix)) + } + if (selection == "correlation") { + return(quickpred(data, ...) + ) + } + if (selection == "lars") { + return(larspred(data, ...)) + } + stop("Unknown selection method") +} + +make.default.predictorMatrix <- function(data, blocks, predictorMatrix) { input.predictorMatrix <- predictorMatrix data <- check.dataform(data) predictorMatrix <- matrix(1, nrow = length(blocks), ncol = ncol(data)) @@ -34,7 +59,7 @@ make.predictorMatrix <- function(data, blocks = make.blocks(data), } } } - predictorMatrix + return(predictorMatrix) } check.predictorMatrix <- function(predictorMatrix, diff --git a/R/quickpred.R b/R/quickpred.R index f785d31e4..c28c82374 100644 --- a/R/quickpred.R +++ b/R/quickpred.R @@ -54,8 +54,9 @@ #' @param method A string specifying the type of correlation. Use #' \code{'pearson'} (default), \code{'kendall'} or \code{'spearman'}. Can be #' abbreviated. +#' @param ... Not used. #' @return A square binary matrix of size \code{ncol(data)}. -#' @author Stef van Buuren, Aug 2009 +#' @author Stef van Buuren, Aug 2009, 2024 #' @seealso \code{\link{mice}}, \code{\link[=mids-class]{mids}} #' @references van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple #' imputation of missing blood pressure covariates in survival analysis. @@ -86,21 +87,24 @@ #' imp <- mice(nhanes, pred = quickpred(nhanes, minpuc = 0.25, include = "age")) #' @export quickpred <- function(data, mincor = 0.1, minpuc = 0, include = "", - exclude = "", method = "pearson") { + exclude = "", method = "pearson", ...) { data <- check.dataform(data) # initialize nvar <- ncol(data) - predictorMatrix <- matrix(0, nrow = nvar, ncol = nvar, dimnames = list(names(data), names(data))) + predictorMatrix <- matrix(0, nrow = nvar, ncol = nvar, + dimnames = list(names(data), names(data))) x <- data.matrix(data) r <- !is.na(x) # include predictors with # 1) pairwise correlation among data # 2) pairwise correlation of data with response indicator higher than mincor - suppressWarnings(v <- abs(cor(x, use = "pairwise.complete.obs", method = method))) + suppressWarnings(v <- abs(cor(x, use = "pairwise.complete.obs", + method = method))) v[is.na(v)] <- 0 - suppressWarnings(u <- abs(cor(y = x, x = r, use = "pairwise.complete.obs", method = method))) + suppressWarnings(u <- abs(cor(y = x, x = r, use = "pairwise.complete.obs", + method = method))) u[is.na(u)] <- 0 maxc <- pmax(v, u) predictorMatrix[maxc > mincor] <- 1 diff --git a/man/make.predictorMatrix.Rd b/man/make.predictorMatrix.Rd index 6e064d59e..679a76709 100644 --- a/man/make.predictorMatrix.Rd +++ b/man/make.predictorMatrix.Rd @@ -4,19 +4,34 @@ \alias{make.predictorMatrix} \title{Creates a \code{predictorMatrix} argument} \usage{ -make.predictorMatrix(data, blocks = make.blocks(data), predictorMatrix = NULL) +make.predictorMatrix( + data, + selection = c("all", "correlation", "lars"), + ..., + blocks = make.blocks(data), + predictorMatrix = NULL +) } \arguments{ \item{data}{A \code{data.frame} with the source data} +\item{selection}{A character string specifying the method to use to +select predictors. The default \code{"all"} selects all variables +as predictors. Alternatives \code{"correlation"} and \code{"lars"} +select predictors using the \code{quickpred()} and \code{larspred()}, +respectively.} + +\item{\dots}{Arguments passed to \code{quickpred()} or \code{larspred()}.} + \item{blocks}{An optional specification for blocks of variables in the rows. The default assigns each variable in its own block.} \item{predictorMatrix}{A predictor matrix from which rows with the same -names are copied into the output predictor matrix.} +names are copied into the output predictor matrix. This is useful for +editing the predictor matrix.} } \value{ -A matrix +A matrix with the same number of rows as the number of blocks. } \description{ This helper function creates a valid \code{predictMatrix}. The diff --git a/man/quickpred.Rd b/man/quickpred.Rd index 507f6d540..659fd9892 100644 --- a/man/quickpred.Rd +++ b/man/quickpred.Rd @@ -10,7 +10,8 @@ quickpred( minpuc = 0, include = "", exclude = "", - method = "pearson" + method = "pearson", + ... ) } \arguments{ @@ -35,6 +36,8 @@ excluded as a predictor.} \item{method}{A string specifying the type of correlation. Use \code{'pearson'} (default), \code{'kendall'} or \code{'spearman'}. Can be abbreviated.} + +\item{...}{Not used.} } \value{ A square binary matrix of size \code{ncol(data)}. @@ -113,6 +116,6 @@ Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} \code{\link{mice}}, \code{\link[=mids-class]{mids}} } \author{ -Stef van Buuren, Aug 2009 +Stef van Buuren, Aug 2009, 2024 } \keyword{misc} diff --git a/tests/testthat/test-mice.impute.jomoImpute.R b/tests/testthat/test-mice.impute.jomoImpute.R index 578cfb941..92ae8f7c7 100644 --- a/tests/testthat/test-mice.impute.jomoImpute.R +++ b/tests/testthat/test-mice.impute.jomoImpute.R @@ -12,7 +12,7 @@ test_that("jomoImpute returns native class", { blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("jomoImpute", "pmm") -pred <- make.predictorMatrix(nhanes, blocks) +pred <- make.predictorMatrix(nhanes, blocks = blocks) pred["B1", "hyp"] <- -2 # imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, # maxit = 1, seed = 1, print = FALSE) diff --git a/tests/testthat/test-mice.impute.panImpute.R b/tests/testthat/test-mice.impute.panImpute.R index e947f2c67..453990448 100644 --- a/tests/testthat/test-mice.impute.panImpute.R +++ b/tests/testthat/test-mice.impute.panImpute.R @@ -12,7 +12,7 @@ test_that("panImpute returns native class", { blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("panImpute", "pmm") -pred <- make.predictorMatrix(nhanes, blocks) +pred <- make.predictorMatrix(nhanes, blocks = blocks) pred["B1", "hyp"] <- -2 imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, From 03284e7ef522f62c0761b5e4c97aa793c20ff601 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 29 May 2024 23:58:57 +0200 Subject: [PATCH 002/147] Add two new predictor selection functions: 1) larspred() implements least angular regressions (LARS) as fast ways to create a predictorMatrix. 2) correlar() implements a LAR using only the correlation matrix as input, thus supporting incomplete pairwise correlation matrices. --- DESCRIPTION | 2 + NAMESPACE | 3 + R/correlar.R | 128 ++++++++++++++++++++++++++++++++++++++ R/imports.R | 1 + R/internal.R | 15 +++++ R/larspred.R | 95 +++++++++++++++++++++++++++- man/correlar.Rd | 90 +++++++++++++++++++++++++++ man/larspred.Rd | 80 ++++++++++++++++++++++++ tests/testthat/larspred.R | 10 +++ 9 files changed, 421 insertions(+), 3 deletions(-) create mode 100644 R/correlar.R create mode 100644 man/correlar.Rd create mode 100644 man/larspred.Rd create mode 100644 tests/testthat/larspred.R diff --git a/DESCRIPTION b/DESCRIPTION index 4debcd2a6..95fce82ca 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -52,6 +52,7 @@ Imports: graphics, grDevices, lattice, + Matrix, methods, mitml, nnet, @@ -67,6 +68,7 @@ Suggests: furrr, haven, knitr, + lars, lme4, MASS, miceadds, diff --git a/NAMESPACE b/NAMESPACE index 1e2b211fe..e414ca0fc 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -72,6 +72,7 @@ export(cci) export(complete) export(construct.blocks) export(convergence) +export(correlar) export(densityplot) export(estimice) export(extractBS) @@ -93,6 +94,7 @@ export(is.mids) export(is.mipo) export(is.mira) export(is.mitml.result) +export(larspred) export(lm.mids) export(make.blocks) export(make.blots) @@ -173,6 +175,7 @@ export(xyplot) exportClasses(mads) exportClasses(mira) import(methods) +importFrom(Matrix,nearPD) importFrom(Rcpp,evalCpp) importFrom(broom,glance) importFrom(broom,tidy) diff --git a/R/correlar.R b/R/correlar.R new file mode 100644 index 000000000..44cd1890b --- /dev/null +++ b/R/correlar.R @@ -0,0 +1,128 @@ +#' @title Approximate Least Angle Regression for correlation matrix +#' +#' @description +#' Finds the best predictors for dependent_var_index using an +#' approximate Least Angle Regression (LAR) algorithm applied +#' to the correlation matrix corr_matrix. Stops when the change +#' in R2 is less than crit, max_steps predictors have been +#' selected, or when R2 exceeds 1 (which can happen due to non-positivity). +#' @details +#' This is not a true LAR implementation, which requires a more +#' complex iterative process with residual updates. Instead, this +#' function uses the sweep operator to approximate the LAR algorithm, +#' and will produce slightly different R2 values, and no Mallow Cp values. +#' +#' The correlation matrix corr_matrix should be symmetric, +#' positive definite, and contain no missing data. If not, the results +#' may be incorrect, but still useful. +#' +#' If you generate the correlation matrix from an incomplete dataset +#' e.g by \code{cor(data, use = "pairwise.complete.obs")} the matrix may not +#' be positive semidefinite. Try \code{force_pf = TRUE} to ensure +#' positive definiteness, though this may introduce other problems +#' like diagonal values unequal to 1. +#' +#' If two variables have never been jointly observed in the data, it is +#' not possible to calculate the correlation. In that case, imputing +#' missing correlations by 0 neutralizes their effect on variable selection. +#' +#' The procedure may produce inflated R2 when the correlation is not a good +#' description of the relationship between the variables. The example +#' section shows a case where the correlation between a missingness +#' indicator and the original data is high, yet has low predictive power. +#' The current advice is not to include missingness indicators in the +#' correlation matrix. +#' @param corr_matrix A symmetric correlation matrix (no data frame). +#' @param dependent_var_index The location in \code{corr_matrix} indicating the +#' dependent variable. +#' @param max_steps The maximum number of predictors to select. If NULL, all +#' predictors are selected. +#' @param crit The minimum change in R^2 to stop the algorithm. +#' @param force_pd Logical. If TRUE, forces the correlation matrix to be +#' positive definite. +#' @param check_input Logical. If TRUE, checks the input for validity. +#' @return A list with two elements: +#' \item{predictors}{The indices of the selected predictors.} +#' \item{R2}{The R^2 value for each step.} +#' @author Stef van Buuren, June 2024 +#' @examples +#' # Find predictors for genital state +#' names(boys) +#' cor1 <- cor(data.matrix(boys), use = "pair") +#' correlar(cor1, dependent = 6) +#' +#' # Limit to three predictors +#' correlar(cor1, 6, max_steps = 3) +#' +#' # Problem case: Misleading correlation calculated from selective subset +#' # The correlation r(age, is.na(gen)) of -0.5 suggest strong predictive +#' # power and an R2 of 0.25. The actual R2 is only 0.03. +#' plot(y = is.na(boys$gen), x = boys$hgt) +#' cor2 <- cor(data.matrix(cbind(is.na(boys$gen), boys)), use = "pair") +#' cor2[is.na(cor2)] <- 0 +#' correlar(cor2, 1) +#' @export +correlar <- function(corr_matrix, + dependent_var_index, + max_steps = NULL, + crit = 0.005, + force_pd = FALSE, + check_input = TRUE) { + + if (check_input) { + stopifnot( + is.matrix(corr_matrix), + isTRUE(all.equal(corr_matrix, t(corr_matrix))), + all(!is.na(corr_matrix)), + is.numeric(dependent_var_index) && length(dependent_var_index) == 1L + ) + } + + if (force_pd) { + corr_matrix <- Matrix::nearPD(corr_matrix)$mat + } + + n_vars <- ncol(corr_matrix) + remaining_predictors <- setdiff(1L:n_vars, dependent_var_index) + + max_steps <- min(max_steps, n_vars - 1L) + predictors <- integer(max_steps) + R2 <- double(max_steps) + + current_matrix <- corr_matrix + steps <- 0L + converged <- FALSE + + while (length(remaining_predictors) > 0L && + (is.null(max_steps) || steps < max_steps) && + !converged) { + + # Find the predictor with the highest absolute correlation + correlations <- current_matrix[dependent_var_index, remaining_predictors] + best_predictor <- remaining_predictors[which.max(abs(correlations))] + remaining_predictors <- setdiff(remaining_predictors, best_predictor) + + # Sweep out the best predictor + current_matrix <- sweep_operator(current_matrix, best_predictor) + + # Store results + steps <- steps + 1L + predictors[steps] <- best_predictor + R2[steps] <- 1 - current_matrix[dependent_var_index, dependent_var_index] + + # stop if criterion is met + if (steps > 2L && R2[steps] - R2[steps - 1L] < crit) { + converged <- TRUE + } + + # stop if R2 is greater than 1 + if (R2[steps] > 1) { + steps <- max(steps - 1L, 1L) + converged <- TRUE + } + } + + return(list( + predictors = predictors[seq(steps)], + R2 = R2[seq(steps)])) +} diff --git a/R/imports.R b/R/imports.R index aa8a5d8b0..97e84e7b2 100644 --- a/R/imports.R +++ b/R/imports.R @@ -6,6 +6,7 @@ #' @importFrom graphics abline axis box par plot plot.new plot.window #' points rect text #' @importFrom lattice bwplot densityplot stripplot xyplot +#' @importFrom Matrix nearPD #' @importFrom mitml jomoImpute mitmlComplete panImpute testModels #' @importFrom nnet multinom #' @importFrom Rcpp evalCpp diff --git a/R/internal.R b/R/internal.R index 98cdfa138..dc76ddea1 100644 --- a/R/internal.R +++ b/R/internal.R @@ -153,3 +153,18 @@ ma_exists <- function(x, pos, n_index = 1:8) { backticks <- function(varname) { sprintf("`%s`", varname) } + +sweep_operator <- function(S, k) { + A <- S[k, k] + B <- matrix(S[-k, k], ncol = 1L) + C <- matrix(S[k, -k], nrow = 1L) + D <- S[-k, -k] - B %*% C / A + + # Update the matrix S in place + S[-k, -k] <- D + S[-k, k] <- -B / A + S[k, -k] <- -t(C) / A + S[k, k] <- 1 / A + + return(S) +} diff --git a/R/larspred.R b/R/larspred.R index 4cda70dc0..543eb4332 100644 --- a/R/larspred.R +++ b/R/larspred.R @@ -1,3 +1,92 @@ -larspred <- function(data, ...){ - warning("Not yet implemented") -} \ No newline at end of file +#' Quick selection of predictors by Least Angle Regression (LAR) algorithm +#' +#' Single imputation of incomplete data, followed by the LARS algorithm +#' for variables selection. +#' +#' This function creates a predictor matrix using the variable selection +#' procedure described in Van Buuren et al.~(1999, p.~687--688). The function is +#' designed to aid in setting up a good imputation model for data with many +#' variables. +#' +#' Basic workings: The procedure creates a single imputation of the data +#' by a random draw from the marginal, and then applies the LAR algorithm +#' twice for each incomplete variable: once with the variable as dependent, +#' and once with the missingness indicator of the variable as dependent. The +#' union of both LAR analyses is used to select the predictors for the +#' imputation model. +#' +#' The LAR algorithm is a fast approximation of the LASSO algorithm, which +#' is a penalized regression method that shrinks coefficients to zero. +#' Predictors are selected based on the absolute correlation +#' between the target and the predictor. The procedure is fast and can +#' handle large datasets. +#' +#' Problem: Imputation by random draws from the marginal distribution +#' weakens the correlation structure in the data. This may lead to +#' suboptimal predictor selection. The procedure is best used for +#' exploratory data analysis. +#' +#' Potential extensions: The function can be extended to include other +#' imputation methods, to add support for categorical variables, and +#' to select the optimal number of variables by Mallow's Cp. +#' +#' @note \code{larspred()} uses \code{\link[base]{data.matrix}} to convert +#' factors to numbers through their internal codes. For unordered factors +#' the resulting quantification may not make sense. +#' +#' @param data Matrix or data frame with incomplete data. +#' @param type A string specifying the type of LARS algorithm. Use +#' \code{'lar'} (default) or \code{'lasso'}. Can be abbreviated. +#' @param s See \code{\link{lars::predict.lars}} for details. +#' @param max.steps See \code{\link{lars::predict.lars}} for details. +#' @param ... Passed down to the \code{\link{lars::lars}} and +#' \code{\link{lars::coef.lars}} functions. +#' @return A square binary matrix of size \code{ncol(data)}. +#' @author Stef van Buuren, June 2024 +#' @examples +#' # include all variables +#' larspred(nhanes) +#' +#' # include two best variables +#' larspred(nhanes, s = 2) +#' +#' # limit to three best variables +#' larspred(boys, s = 3) +#' @export +larspred <- function(data, type = "lar", + s = ncol(data) - 1, max.steps = ncol(data) - 1, ...) { + + # use the lars package for variable selection + install.on.demand("lars", ...) + data <- check.dataform(data) + + # initialize predictor matrix + nvar <- ncol(data) + ynames <- colnames(data) + predictorMatrix <- matrix(0, nrow = nvar, ncol = nvar, + dimnames = list(ynames, ynames)) + + # fill missings with random draws from observed data + init <- mice(data, maxit = 0L, m = 1L, remove.collinear = FALSE, ...) + xy <- data.matrix(complete(init, action = 1L)) + stopifnot(all(!any(is.na(xy)))) + + # loop over incomplete variables, fit two lars models per target + # fill predictorMatrix with selected variables + for (yname in ynames) { + y <- xy[, yname] + ry <- !is.na(data[, yname]) + x <- xy[, yname != ynames] + + if (any(!ry)) { + lars_y <- lars::lars(x = x, y = y, type = type, ...) + lars_ry <- lars::lars(x = x, y = ry, type = type, ...) + coef_y <- coef(lars_y, s = min(max.steps, s), ...) + coef_ry <- coef(lars_ry, s = min(max.steps, s), ...) + preds <- union(colnames(x)[coef_y != 0], + colnames(x)[coef_ry != 0]) + if (length(preds)) predictorMatrix[yname, preds] <- 1 + } + } + return(predictorMatrix) +} diff --git a/man/correlar.Rd b/man/correlar.Rd new file mode 100644 index 000000000..d023b9aa9 --- /dev/null +++ b/man/correlar.Rd @@ -0,0 +1,90 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/correlar.R +\name{correlar} +\alias{correlar} +\title{Approximate Least Angle Regression for correlation matrix} +\usage{ +correlar( + corr_matrix, + dependent_var_index, + max_steps = NULL, + crit = 0.005, + force_pd = FALSE, + check_input = TRUE +) +} +\arguments{ +\item{corr_matrix}{A symmetric correlation matrix (no data frame).} + +\item{dependent_var_index}{The location in \code{corr_matrix} indicating the +dependent variable.} + +\item{max_steps}{The maximum number of predictors to select. If NULL, all +predictors are selected.} + +\item{crit}{The minimum change in R^2 to stop the algorithm.} + +\item{force_pd}{Logical. If TRUE, forces the correlation matrix to be +positive definite.} + +\item{check_input}{Logical. If TRUE, checks the input for validity.} +} +\value{ +A list with two elements: +\item{predictors}{The indices of the selected predictors.} +\item{R2}{The R^2 value for each step.} +} +\description{ +Finds the best predictors for dependent_var_index using an +approximate Least Angle Regression (LAR) algorithm applied +to the correlation matrix corr_matrix. Stops when the change +in R2 is less than crit, max_steps predictors have been +selected, or when R2 exceeds 1 (which can happen due to non-positivity). +} +\details{ +This is not a true LAR implementation, which requires a more +complex iterative process with residual updates. Instead, this +function uses the sweep operator to approximate the LAR algorithm, +and will produce slightly different R2 values, and no Mallow Cp values. + +The correlation matrix corr_matrix should be symmetric, +positive definite, and contain no missing data. If not, the results +may be incorrect, but still useful. + +If you generate the correlation matrix from an incomplete dataset +e.g by \code{cor(data, use = "pairwise.complete.obs")} the matrix may not +be positive semidefinite. Try \code{force_pf = TRUE} to ensure +positive definiteness, though this may introduce other problems +like diagonal values unequal to 1. + +If two variables have never been jointly observed in the data, it is +not possible to calculate the correlation. In that case, imputing +missing correlations by 0 neutralizes their effect on variable selection. + +The procedure may produce inflated R2 when the correlation is not a good +description of the relationship between the variables. The example +section shows a case where the correlation between a missingness +indicator and the original data is high, yet has low predictive power. +The current advice is not to include missingness indicators in the +correlation matrix. +} +\examples{ +# Find predictors for genital state +names(boys) +cor1 <- cor(data.matrix(boys), use = "pair") +correlar(cor1, dependent = 6) + +# Limit to three predictors +correlar(cor1, 6, max_steps = 3) + +# Problem case: Misleading correlation calculated from selective subset +# The correlation r(age, is.na(gen)) of -0.5 suggest strong predictive +# power and an R2 of 0.25. The actual R2 is only 0.03. +plot(y = is.na(boys$gen), x = boys$hgt) +cor2 <- cor(data.matrix(cbind(is.na(boys$gen), boys)), use = "pair") +cor2[is.na(cor2)] <- 0 +correlar(cor2, 1) +} +\author{ +Stef van Buuren, June 2024 +} diff --git a/man/larspred.Rd b/man/larspred.Rd new file mode 100644 index 000000000..c4c4c094f --- /dev/null +++ b/man/larspred.Rd @@ -0,0 +1,80 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/larspred.R +\name{larspred} +\alias{larspred} +\title{Quick selection of predictors by Least Angle Regression (LAR) algorithm} +\usage{ +larspred( + data, + type = "lar", + s = ncol(data) - 1, + max.steps = ncol(data) - 1, + ... +) +} +\arguments{ +\item{data}{Matrix or data frame with incomplete data.} + +\item{type}{A string specifying the type of LARS algorithm. Use +\code{'lar'} (default) or \code{'lasso'}. Can be abbreviated.} + +\item{s}{See \code{\link{lars::predict.lars}} for details.} + +\item{max.steps}{See \code{\link{lars::predict.lars}} for details.} + +\item{...}{Passed down to the \code{\link{lars::lars}} and +\code{\link{lars::coef.lars}} functions.} +} +\value{ +A square binary matrix of size \code{ncol(data)}. +} +\description{ +Single imputation of incomplete data, followed by the LARS algorithm +for variables selection. +} +\details{ +This function creates a predictor matrix using the variable selection +procedure described in Van Buuren et al.~(1999, p.~687--688). The function is +designed to aid in setting up a good imputation model for data with many +variables. + +Basic workings: The procedure creates a single imputation of the data +by a random draw from the marginal, and then applies the LAR algorithm +twice for each incomplete variable: once with the variable as dependent, +and once with the missingness indicator of the variable as dependent. The +union of both LAR analyses is used to select the predictors for the +imputation model. + +The LAR algorithm is a fast approximation of the LASSO algorithm, which +is a penalized regression method that shrinks coefficients to zero. +Predictors are selected based on the absolute correlation +between the target and the predictor. The procedure is fast and can +handle large datasets. + +Problem: Imputation by random draws from the marginal distribution +weakens the correlation structure in the data. This may lead to +suboptimal predictor selection. The procedure is best used for +exploratory data analysis. + +Potential extensions: The function can be extended to include other +imputation methods, to add support for categorical variables, and +to select the optimal number of variables by Mallow's Cp. +} +\note{ +\code{larspred()} uses \code{\link[base]{data.matrix}} to convert +factors to numbers through their internal codes. For unordered factors +the resulting quantification may not make sense. +} +\examples{ +# include all variables +larspred(nhanes) + +# include two best variables +larspred(nhanes, s = 2) + +# limit to three best variables +larspred(boys, s = 3) +} +\author{ +Stef van Buuren, June 2024 +} diff --git a/tests/testthat/larspred.R b/tests/testthat/larspred.R new file mode 100644 index 000000000..9efff11f8 --- /dev/null +++ b/tests/testthat/larspred.R @@ -0,0 +1,10 @@ +# boys3 <- dplyr::bind_cols(boys, boys, boys, .name_repair = "unique") + +imp <- mice::mice(boys3, maxit = 0L, m = 1L, remove.collinear = FALSE) +data <- data.matrix(complete(imp, action = 1L)) +v <- 7 +lrs <- lars::lars(x = data[, -v], y = data[, v], type = "lar") +plot(lrs) + +# pred <- make.predictorMatrix(boys3, selection = "lars", np = 10) +# colSums(pred) From f3a4b27c57a7fba49e8a7fa66e389c4db0395a02 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 29 Aug 2024 09:33:08 +0200 Subject: [PATCH 003/147] Repair broken links --- R/larspred.R | 9 +++++---- man/larspred.Rd | 9 +++++---- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/R/larspred.R b/R/larspred.R index 543eb4332..37040c3d3 100644 --- a/R/larspred.R +++ b/R/larspred.R @@ -37,10 +37,11 @@ #' @param data Matrix or data frame with incomplete data. #' @param type A string specifying the type of LARS algorithm. Use #' \code{'lar'} (default) or \code{'lasso'}. Can be abbreviated. -#' @param s See \code{\link{lars::predict.lars}} for details. -#' @param max.steps See \code{\link{lars::predict.lars}} for details. -#' @param ... Passed down to the \code{\link{lars::lars}} and -#' \code{\link{lars::coef.lars}} functions. +#' @param s See \code{\link[lars:predict.lars]{predict.lars}} for details. +#' @param max.steps See \code{\link[lars:predict.lars]{predict.lars}} for +#' details. +#' @param ... Passed down to the \code{\link[lars:lars]{lars}} and +#' \code{\link[lars:coef.lars]{coef.lars}} functions. #' @return A square binary matrix of size \code{ncol(data)}. #' @author Stef van Buuren, June 2024 #' @examples diff --git a/man/larspred.Rd b/man/larspred.Rd index c4c4c094f..fb4baf0db 100644 --- a/man/larspred.Rd +++ b/man/larspred.Rd @@ -18,12 +18,13 @@ larspred( \item{type}{A string specifying the type of LARS algorithm. Use \code{'lar'} (default) or \code{'lasso'}. Can be abbreviated.} -\item{s}{See \code{\link{lars::predict.lars}} for details.} +\item{s}{See \code{\link[lars:predict.lars]{predict.lars}} for details.} -\item{max.steps}{See \code{\link{lars::predict.lars}} for details.} +\item{max.steps}{See \code{\link[lars:predict.lars]{predict.lars}} for +details.} -\item{...}{Passed down to the \code{\link{lars::lars}} and -\code{\link{lars::coef.lars}} functions.} +\item{...}{Passed down to the \code{\link[lars:lars]{lars}} and +\code{\link[lars:coef.lars]{coef.lars}} functions.} } \value{ A square binary matrix of size \code{ncol(data)}. From c5177bbea0a50fe6f1fe4b9aaa67b60fcdcc03e3 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 15:31:53 +0200 Subject: [PATCH 004/147] Create a more flexible structure for selecting features that go into the imputation model. Meant to replace the rusty `remove.lindep()`. --- .Rbuildignore | 2 +- DESCRIPTION | 1 + NAMESPACE | 5 ++ R/imports.R | 1 + R/internal.R | 77 ++++------------ R/sampler.R | 7 +- R/trim.predictors.R | 199 +++++++++++++++++++++++++++++++++++++++++ man/lars.filter.Rd | 42 +++++++++ man/lars.internal.Rd | 51 +++++++++++ man/remove.lindep.Rd | 59 ++++++++++++ man/trim.predictors.Rd | 43 +++++++++ 11 files changed, 421 insertions(+), 66 deletions(-) create mode 100644 R/trim.predictors.R create mode 100644 man/lars.filter.Rd create mode 100644 man/lars.internal.Rd create mode 100644 man/remove.lindep.Rd create mode 100644 man/trim.predictors.Rd diff --git a/.Rbuildignore b/.Rbuildignore index 05ea8e9df..6f9817985 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -20,4 +20,4 @@ vignettes/ ^pkgdown$ ^LICENSE\.md$ ^\.github$ -^CRAN-SUBMISSION$ +^CRAN-SUBMISSION$ \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index 56fd5509a..941d6b66e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -53,6 +53,7 @@ Imports: glmnet, graphics, grDevices, + lars, lattice, methods, mitml, diff --git a/NAMESPACE b/NAMESPACE index 1e2b211fe..880b7ebd3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -93,6 +93,8 @@ export(is.mids) export(is.mipo) export(is.mira) export(is.mitml.result) +export(lars.filter) +export(lars.internal) export(lm.mids) export(make.blocks) export(make.blots) @@ -164,10 +166,12 @@ export(pool.syn) export(pool.table) export(quickpred) export(rbind) +export(remove.lindep) export(squeeze) export(stripplot) export(supports.transparent) export(tidy) +export(trim.predictors) export(version) export(xyplot) exportClasses(mads) @@ -205,6 +209,7 @@ importFrom(graphics,plot.window) importFrom(graphics,points) importFrom(graphics,rect) importFrom(graphics,text) +importFrom(lars,lars) importFrom(lattice,bwplot) importFrom(lattice,densityplot) importFrom(lattice,stripplot) diff --git a/R/imports.R b/R/imports.R index aa8a5d8b0..dd810235c 100644 --- a/R/imports.R +++ b/R/imports.R @@ -5,6 +5,7 @@ #' @importFrom glmnet cv.glmnet #' @importFrom graphics abline axis box par plot plot.new plot.window #' points rect text +#' @importFrom lars lars #' @importFrom lattice bwplot densityplot stripplot xyplot #' @importFrom mitml jomoImpute mitmlComplete panImpute testModels #' @importFrom nnet multinom diff --git a/R/internal.R b/R/internal.R index 98cdfa138..8ed52ea50 100644 --- a/R/internal.R +++ b/R/internal.R @@ -16,68 +16,6 @@ check.df <- function(x, y, ry) { } -remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, - allow.na = TRUE, frame = 4, ...) { - # returns a logical vector of length ncol(x) - - if (ncol(x) == 0) { - return(NULL) - } - - # setting eps = 0 bypasses remove.lindep() - if (eps == 0) { - return(rep.int(TRUE, ncol(x))) - } - if (eps < 0) { - stop("\n Argument 'eps' must be positive.") - } - - # Keep all predictors if we allow imputation of fully missing y - if (allow.na && sum(ry) == 0) { - return(rep.int(TRUE, ncol(x))) - } - - xobs <- x[ry, , drop = FALSE] - yobs <- as.numeric(y[ry]) - if (var(yobs) < eps) { - return(rep(FALSE, ncol(xobs))) - } - - keep <- unlist(apply(xobs, 2, var) > eps) - keep[is.na(keep)] <- FALSE - highcor <- suppressWarnings(unlist(apply(xobs, 2, cor, yobs) < maxcor)) - keep <- keep & highcor - if (all(!keep)) { - updateLog( - out = "All predictors are constant or have too high correlation.", - frame = frame - ) - } - - # no need to calculate correlations, so return - k <- sum(keep) - if (k <= 1L) { - return(keep) - } # at most one TRUE - - # correlation between x's - cx <- cor(xobs[, keep, drop = FALSE], use = "all.obs") - eig <- eigen(cx, symmetric = TRUE) - ncx <- cx - while (eig$values[k] / eig$values[1] < eps) { - j <- seq_len(k)[order(abs(eig$vectors[, k]), decreasing = TRUE)[1]] - keep[keep][j] <- FALSE - ncx <- cx[keep[keep], keep[keep], drop = FALSE] - k <- k - 1 - eig <- eigen(ncx) - } - if (!all(keep)) { - out <- paste(dimnames(x)[[2]][!keep], collapse = ", ") - updateLog(out = out, frame = frame) - } - return(keep) -} - ## make list of collinear variables to remove find.collinear <- function(x, threshold = 0.999, ...) { @@ -153,3 +91,18 @@ ma_exists <- function(x, pos, n_index = 1:8) { backticks <- function(varname) { sprintf("`%s`", varname) } + +sweep_operator <- function(S, k) { + A <- S[k, k] + B <- matrix(S[-k, k], ncol = 1L) + C <- matrix(S[k, -k], nrow = 1L) + D <- S[-k, -k] - B %*% C / A + + # Update the matrix S in place + S[-k, -k] <- D + S[-k, k] <- -B / A + S[k, -k] <- -t(C) / A + S[k, k] <- 1 / A + + return(S) +} diff --git a/R/sampler.R b/R/sampler.R index 95362c330..576db39f2 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -180,7 +180,8 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, sampler.univ <- function(data, r, where, pred, formula, method, yname, k, - calltype = "pred", user, ignore, ...) { + calltype = "pred", user, ignore, + trimmer = "remove.lindep", ...) { j <- yname[1L] if (calltype == "pred") { @@ -231,8 +232,8 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, cc <- wy[where[, j]] if (k == 1L) check.df(x, y, ry) - # remove linear dependencies - keep <- remove.lindep(x, y, ry, ...) + # select the features to feed into the imputation method + keep <- trim.predictors(x, y, ry, trimmer = trimmer, ...) x <- x[, keep, drop = FALSE] type <- type[keep] if (ncol(x) != length(type)) { diff --git a/R/trim.predictors.R b/R/trim.predictors.R new file mode 100644 index 000000000..dbe693057 --- /dev/null +++ b/R/trim.predictors.R @@ -0,0 +1,199 @@ +#' Trim predictors of the imputation model +#' +#' The function `trim.predictors()` filters out predictors before imputation +#' of univariate incomplete variables. The function is called by +#' `mice:::sampler.univ()`. The function is a wrapper for the trimming +#' functions `lars.filter()`, `remove.lindep()`, and a user-specified +#' trimming function. The default method is `"lars.filter"`. +#' +#' @inheritParams mice.impute.pmm +#' @param trimmer A character vector of length 1 specifying the name of the +#' trimming function. The default is "lars.filter". The other options are +#' "remove.lindep" and "none". The user can also specify a custom trimming +#' function. +#' @param ... further arguments passed to the trimming function. +#' @return A logical vector of length `ncol(x)` indicating which predictors +#' to keep. +#' @export +trim.predictors <- function( + x, y, ry, + trimmer = c("lars.filter", "remove.lindep", "none"), ...) { + + if (trimmer == "lars.filter") { + return(lars.filter(x, y, ry, ...)) + } + + if (trimmer == "remove.lindep") { + return(remove.lindep(x, y, ry, ...)) + } + + if (trimmer == "none") { + return(rep.int(TRUE, ncol(x))) + } + + # handle user-specified trimmer + args <- c(list(x = x, y = y, ry = ry), list(...)) + keep <- do.call(trimmer, args = args) + return(keep) +} + +#' Filter out predictors by least angular regression +#' +#' @inheritParams mice.impute.pmm +#' @param eps numeric. Criteriom for removing predictors with zero variance. +#' If `eps` is zero, the filter is bypassed. The default is 1e-04. +#' @param allow.na logical. If `TRUE`, allow imputation of fully missing `y`. +#' This typically only occurs for passive imputation. The default is `TRUE`. +#' @param ... further arguments passed to `lars`, like standard arguments +#' `type` and `intercept`, or custom tuning parameters like `lars.relax` and +#' `minimal.cp`. +#' @return A logical vector of length `ncol(x)` indicating which predictors +#' to keep. +#' @details +#' In the following conditions, the function skips the filter +#' 1. if there are no or only one predictors +#' 2. if user imputes a fully missing y accept all predictors +#' 3. if eps == 0 (for backward compatibility) +#' +#' @export +lars.filter <- function(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) { + # Skipping conditions + if (ncol(x) <= 1L || + allow.na && sum(ry) == 0L || + eps == 0) { + return(rep.int(TRUE, ncol(x))) + } + + # If y is constant, predict from an intercept-only model + yobs <- as.numeric(y[ry]) + if (var(yobs, na.rm = TRUE) < abs(eps)) { + return(rep(FALSE, ncol(x))) + } + + # If needed, subset data because lars() requires complete data + xobs <- x[ry, , drop = FALSE] + if (anyNA(xobs) || anyNA(yobs)) { + idx <- complete.cases(xobs, yobs) + if (sum(idx) == 0L) { + stop("The lars filter requires complete cases, but none were found.") + } + xobs <- xobs[idx, , drop = FALSE] + yobs <- yobs[idx] + } + + # Call the lars filter + keep <- lars.internal(x = xobs, y = yobs, ...) + return(keep) +} + +#' Filter out predictors by least angular regression +#' +#' @inheritParams lars::lars +#' @param lars.relax numeric. The percentage of the minimum Cp value that is +#' added to the minimum Cp to relax the inclusion threshold. The default is 5. +#' Use 1-5 percent for a more stringent filter, and 5-10 percent for a moderate +#' filter. +#' @param minimal.cp numeric. The minimum "Cp" value to consider. "Cp" may +#' become negative for small samples. `minimal_cp` is the minimum "Cp" value +#' that is is used as to define the threshold for the filter. Higher value +#' select more predictors. The default is 1. +#' @return A logical vector of length `ncol(x)` indicating which predictors +#' to keep. +#' @export +lars.internal <- function(x, y, type = "lar", intercept = TRUE, + lars.relax = 5, minimal.cp = 1) { + model <- lars(x = x, y = y, type = type, intercept = intercept) + cp <- model$Cp + min_cp <- max(min(cp), minimal.cp) # SvB small sample adjustment + threshold <- min_cp + (lars.relax * min_cp) / 100 + step <- tail(which(cp <= threshold), n = 1L) + coef_step <- coef(model, s = min(step, 1L)) + return(coef_step != 0) +} + +#' Filter out constant and multicollinear predictors before imputations +#' +#' `remove.lindep()` prevents multicollinearity +#' in the imputation model. It removes predictors that are constant or have +#' too high correlation with the target variable. The function +#' uses the eigenvalues of the correlation matrix to detect multicollinearity. +#' +#' @inheritParams mice.impute.pmm +#' @param eps numeric. The threshold for the ratio of the smallest to the +#' largest eigenvalue of the correlation matrix. The default is 1e-04. If the +#' user sets `eps = 0`, the function bypasses the filter. +#' @param maxcor numeric. The maximum correlation between a predictor and the +#' target variable. The default is 0.99. +#' @param allow.na logical. If `TRUE`, allow imputation of fully missing `y`. +#' This typically only occurs for passive imputation. The default is `TRUE`. +#' @param frame integer. The frame number for logging. The default is 4. +#' @return A logical vector of length `ncol(x)` indicating which predictors +#' to keep. +#' @details +#' This function is the classic MICE safety net to prevent multicollinearity +#' and other numerical problems. It is a far more conservative filter than +#' `lars.filter()`. The function is called by `trim.predictors()` with the +#' default method `"remove.lindep"`. The function is now deprecated, but +#' will remain part of the package for backward compatibility. +#' @export +remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, + allow.na = TRUE, frame = 4, ...) { + # returns a logical vector of length ncol(x) + + if (ncol(x) == 0) { + return(NULL) + } + + # setting eps = 0 bypasses remove.lindep() + if (eps == 0) { + return(rep.int(TRUE, ncol(x))) + } + if (eps < 0) { + stop("\n Argument 'eps' must be positive.") + } + + # Keep all predictors if we allow imputation of fully missing y + if (allow.na && sum(ry) == 0) { + return(rep.int(TRUE, ncol(x))) + } + + xobs <- x[ry, , drop = FALSE] + yobs <- as.numeric(y[ry]) + if (var(yobs) < eps) { + return(rep(FALSE, ncol(xobs))) + } + + keep <- unlist(apply(xobs, 2, var) > eps) + keep[is.na(keep)] <- FALSE + highcor <- suppressWarnings(unlist(apply(xobs, 2, cor, yobs) < maxcor)) + keep <- keep & highcor + if (all(!keep)) { + updateLog( + out = "All predictors are constant or have too high correlation.", + frame = frame + ) + } + + # no need to calculate correlations, so return + k <- sum(keep) + if (k <= 1L) { + return(keep) + } # at most one TRUE + + # correlation between x's + cx <- cor(xobs[, keep, drop = FALSE], use = "all.obs") + eig <- eigen(cx, symmetric = TRUE) + ncx <- cx + while (eig$values[k] / eig$values[1] < eps) { + j <- seq_len(k)[order(abs(eig$vectors[, k]), decreasing = TRUE)[1]] + keep[keep][j] <- FALSE + ncx <- cx[keep[keep], keep[keep], drop = FALSE] + k <- k - 1 + eig <- eigen(ncx) + } + if (!all(keep)) { + out <- paste(dimnames(x)[[2]][!keep], collapse = ", ") + updateLog(out = out, frame = frame) + } + return(keep) +} diff --git a/man/lars.filter.Rd b/man/lars.filter.Rd new file mode 100644 index 000000000..8fc335a26 --- /dev/null +++ b/man/lars.filter.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/trim.predictors.R +\name{lars.filter} +\alias{lars.filter} +\title{Filter out predictors by least angular regression} +\usage{ +lars.filter(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) +} +\arguments{ +\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for +\code{y}. Matrix \code{x} may have no missing values.} + +\item{y}{Vector to be imputed} + +\item{ry}{Logical vector of length \code{length(y)} indicating the +the subset \code{y[ry]} of elements in \code{y} to which the imputation +model is fitted. The \code{ry} generally distinguishes the observed +(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} + +\item{eps}{numeric. Criteriom for removing predictors with zero variance. +If `eps` is zero, the filter is bypassed. The default is 1e-04.} + +\item{allow.na}{logical. If `TRUE`, allow imputation of fully missing `y`. +This typically only occurs for passive imputation. The default is `TRUE`.} + +\item{...}{further arguments passed to `lars`, like standard arguments +`type` and `intercept`, or custom tuning parameters like `lars.relax` and +`minimal.cp`.} +} +\value{ +A logical vector of length `ncol(x)` indicating which predictors +to keep. +} +\description{ +Filter out predictors by least angular regression +} +\details{ +In the following conditions, the function skips the filter +1. if there are no or only one predictors +2. if user imputes a fully missing y accept all predictors +3. if eps == 0 (for backward compatibility) +} diff --git a/man/lars.internal.Rd b/man/lars.internal.Rd new file mode 100644 index 000000000..f938723fd --- /dev/null +++ b/man/lars.internal.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/trim.predictors.R +\name{lars.internal} +\alias{lars.internal} +\title{Filter out predictors by least angular regression} +\usage{ +lars.internal( + x, + y, + type = "lar", + intercept = TRUE, + lars.relax = 5, + minimal.cp = 1 +) +} +\arguments{ +\item{x}{ +matrix of predictors +} + +\item{y}{ +response +} + +\item{type}{ +One of "lasso", "lar", "forward.stagewise" or "stepwise". The names can +be abbreviated to any unique substring. Default is "lasso". +} + +\item{intercept}{ +if TRUE, an intercept is included in the model (and not penalized), +otherwise no intercept is included. Default is TRUE. +} + +\item{lars.relax}{numeric. The percentage of the minimum Cp value that is +added to the minimum Cp to relax the inclusion threshold. The default is 5. +Use 1-5 percent for a more stringent filter, and 5-10 percent for a moderate +filter.} + +\item{minimal.cp}{numeric. The minimum "Cp" value to consider. "Cp" may +become negative for small samples. `minimal_cp` is the minimum "Cp" value +that is is used as to define the threshold for the filter. Higher value +select more predictors. The default is 1.} +} +\value{ +A logical vector of length `ncol(x)` indicating which predictors +to keep. +} +\description{ +Filter out predictors by least angular regression +} diff --git a/man/remove.lindep.Rd b/man/remove.lindep.Rd new file mode 100644 index 000000000..d55a6927c --- /dev/null +++ b/man/remove.lindep.Rd @@ -0,0 +1,59 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/trim.predictors.R +\name{remove.lindep} +\alias{remove.lindep} +\title{Filter out constant and multicollinear predictors before imputations} +\usage{ +remove.lindep( + x, + y, + ry, + eps = 1e-04, + maxcor = 0.99, + allow.na = TRUE, + frame = 4, + ... +) +} +\arguments{ +\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for +\code{y}. Matrix \code{x} may have no missing values.} + +\item{y}{Vector to be imputed} + +\item{ry}{Logical vector of length \code{length(y)} indicating the +the subset \code{y[ry]} of elements in \code{y} to which the imputation +model is fitted. The \code{ry} generally distinguishes the observed +(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} + +\item{eps}{numeric. The threshold for the ratio of the smallest to the +largest eigenvalue of the correlation matrix. The default is 1e-04. If the +user sets `eps = 0`, the function bypasses the filter.} + +\item{maxcor}{numeric. The maximum correlation between a predictor and the +target variable. The default is 0.99.} + +\item{allow.na}{logical. If `TRUE`, allow imputation of fully missing `y`. +This typically only occurs for passive imputation. The default is `TRUE`.} + +\item{frame}{integer. The frame number for logging. The default is 4.} + +\item{...}{Other named arguments.} +} +\value{ +A logical vector of length `ncol(x)` indicating which predictors +to keep. +} +\description{ +`remove.lindep()` prevents multicollinearity +in the imputation model. It removes predictors that are constant or have +too high correlation with the target variable. The function +uses the eigenvalues of the correlation matrix to detect multicollinearity. +} +\details{ +This function is the classic MICE safety net to prevent multicollinearity +and other numerical problems. It is a far more conservative filter than +`lars.filter()`. The function is called by `trim.predictors()` with the +default method `"remove.lindep"`. The function is now deprecated, but +will remain part of the package for backward compatibility. +} diff --git a/man/trim.predictors.Rd b/man/trim.predictors.Rd new file mode 100644 index 000000000..6307d8e28 --- /dev/null +++ b/man/trim.predictors.Rd @@ -0,0 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/trim.predictors.R +\name{trim.predictors} +\alias{trim.predictors} +\title{Trim predictors of the imputation model} +\usage{ +trim.predictors( + x, + y, + ry, + trimmer = c("lars.filter", "remove.lindep", "none"), + ... +) +} +\arguments{ +\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for +\code{y}. Matrix \code{x} may have no missing values.} + +\item{y}{Vector to be imputed} + +\item{ry}{Logical vector of length \code{length(y)} indicating the +the subset \code{y[ry]} of elements in \code{y} to which the imputation +model is fitted. The \code{ry} generally distinguishes the observed +(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} + +\item{trimmer}{A character vector of length 1 specifying the name of the +trimming function. The default is "lars.filter". The other options are +"remove.lindep" and "none". The user can also specify a custom trimming +function.} + +\item{...}{further arguments passed to the trimming function.} +} +\value{ +A logical vector of length `ncol(x)` indicating which predictors +to keep. +} +\description{ +The function `trim.predictors()` filters out predictors before imputation +of univariate incomplete variables. The function is called by +`mice:::sampler.univ()`. The function is a wrapper for the trimming +functions `lars.filter()`, `remove.lindep()`, and a user-specified +trimming function. The default method is `"lars.filter"`. +} From 8bfbe819376034f507e0cc87abdd9c95293830ee Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 15:54:18 +0200 Subject: [PATCH 005/147] Repair dots parameter and step logic in lars.filter() --- R/trim.predictors.R | 6 ++++-- man/lars.internal.Rd | 5 ++++- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/R/trim.predictors.R b/R/trim.predictors.R index dbe693057..4f0d1ade3 100644 --- a/R/trim.predictors.R +++ b/R/trim.predictors.R @@ -97,17 +97,19 @@ lars.filter <- function(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) { #' become negative for small samples. `minimal_cp` is the minimum "Cp" value #' that is is used as to define the threshold for the filter. Higher value #' select more predictors. The default is 1. +#' @param \dots Further arguments passed to `lars`. Nothing passed down. #' @return A logical vector of length `ncol(x)` indicating which predictors #' to keep. #' @export lars.internal <- function(x, y, type = "lar", intercept = TRUE, - lars.relax = 5, minimal.cp = 1) { + lars.relax = 5, minimal.cp = 1, ...) { model <- lars(x = x, y = y, type = type, intercept = intercept) cp <- model$Cp min_cp <- max(min(cp), minimal.cp) # SvB small sample adjustment threshold <- min_cp + (lars.relax * min_cp) / 100 step <- tail(which(cp <= threshold), n = 1L) - coef_step <- coef(model, s = min(step, 1L)) + step <- ifelse(length(step), step, 1L) + coef_step <- coef(model, s = step) return(coef_step != 0) } diff --git a/man/lars.internal.Rd b/man/lars.internal.Rd index f938723fd..bac19e02f 100644 --- a/man/lars.internal.Rd +++ b/man/lars.internal.Rd @@ -10,7 +10,8 @@ lars.internal( type = "lar", intercept = TRUE, lars.relax = 5, - minimal.cp = 1 + minimal.cp = 1, + ... ) } \arguments{ @@ -41,6 +42,8 @@ filter.} become negative for small samples. `minimal_cp` is the minimum "Cp" value that is is used as to define the threshold for the filter. Higher value select more predictors. The default is 1.} + +\item{\dots}{Further arguments passed to `lars`. Nothing passed down.} } \value{ A logical vector of length `ncol(x)` indicating which predictors From f67984e53fb1e78a5e0906107a4772e325ef3cba Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 16:19:14 +0200 Subject: [PATCH 006/147] Handle case of perfect linear dependency --- R/trim.predictors.R | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/R/trim.predictors.R b/R/trim.predictors.R index 4f0d1ade3..a743a63b3 100644 --- a/R/trim.predictors.R +++ b/R/trim.predictors.R @@ -20,15 +20,18 @@ trim.predictors <- function( trimmer = c("lars.filter", "remove.lindep", "none"), ...) { if (trimmer == "lars.filter") { - return(lars.filter(x, y, ry, ...)) + keep <- lars.filter(x, y, ry, ...) + return(keep) } if (trimmer == "remove.lindep") { - return(remove.lindep(x, y, ry, ...)) + keep <- remove.lindep(x, y, ry, ...) + return(keep) } if (trimmer == "none") { - return(rep.int(TRUE, ncol(x))) + keep <- rep.int(TRUE, ncol(x)) + return(keep) } # handle user-specified trimmer @@ -104,12 +107,18 @@ lars.filter <- function(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) { lars.internal <- function(x, y, type = "lar", intercept = TRUE, lars.relax = 5, minimal.cp = 1, ...) { model <- lars(x = x, y = y, type = type, intercept = intercept) - cp <- model$Cp - min_cp <- max(min(cp), minimal.cp) # SvB small sample adjustment - threshold <- min_cp + (lars.relax * min_cp) / 100 - step <- tail(which(cp <= threshold), n = 1L) - step <- ifelse(length(step), step, 1L) - coef_step <- coef(model, s = step) + if (any(model$R2 == 1)) { + # we need a work-around because Cp gives NaN + coef_step <- coef(model, s = which(model$R2 == 1)) + } else { + # find the step where Cp is minimal + cp <- model$Cp + min_cp <- max(min(cp), minimal.cp) # SvB small sample adjustment + threshold <- min_cp + (lars.relax * min_cp) / 100 + step <- tail(which(cp <= threshold), n = 1L) + step <- ifelse(length(step), step, 1L) + coef_step <- coef(model, s = step) + } return(coef_step != 0) } From e93aa8b677137afdb2c0b9b733b7c09bb12ed740 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 16:54:47 +0200 Subject: [PATCH 007/147] Tweak tests so that they work with trimmer = "lars.filter" --- tests/testthat/test-mice.impute.norm.R | 6 +++--- tests/testthat/test-pool.R | 2 +- tests/testthat/test-rbind.R | 8 +++++--- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/tests/testthat/test-mice.impute.norm.R b/tests/testthat/test-mice.impute.norm.R index 1704f06e1..763d3c450 100644 --- a/tests/testthat/test-mice.impute.norm.R +++ b/tests/testthat/test-mice.impute.norm.R @@ -64,9 +64,9 @@ test_that("Correct estimation method used", { # TEST 3: correct imputation model # ##################################### -expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE)) -expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE)) -expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE)) +expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "remove.lindep")) +expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "remove.lindep")) +expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "remove.lindep")) test_that("Imputations are equal", { expect_equal(imp.qr$imp, imp.svd$imp) diff --git a/tests/testthat/test-pool.R b/tests/testthat/test-pool.R index b443ed9bd..eb02d59d0 100644 --- a/tests/testthat/test-pool.R +++ b/tests/testthat/test-pool.R @@ -8,7 +8,7 @@ context("pool") # https://stefvanbuuren.name/fimd/ suppressWarnings(RNGversion("3.5.0")) -imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE) +imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE, trimmer = "remove.lindep") fit <- with(imp, lm(bmi ~ chl + age + hyp)) est <- pool(fit) # fitlist <- fit$analyses diff --git a/tests/testthat/test-rbind.R b/tests/testthat/test-rbind.R index ba4b04bd5..ef88e0d82 100644 --- a/tests/testthat/test-rbind.R +++ b/tests/testthat/test-rbind.R @@ -1,12 +1,13 @@ context("rbind.mids") -expect_warning(imp1 <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE)) test_that("Constant variables are not imputed by default", { + expect_warning(imp1 <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE)) expect_equal(sum(is.na(complete(imp1))), 6L) }) -expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE)) test_that("Constant variables are imputed for remove.constant = FALSE", { + expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, + remove.constant = FALSE, trimmer = "remove.lindep")) expect_equal(sum(is.na(complete(imp1b))), 0L) }) @@ -14,7 +15,8 @@ imp2 <- mice(nhanes[14:25, ], m = 2, maxit = 1, print = FALSE) imp3 <- mice(nhanes2, m = 2, maxit = 1, print = FALSE) imp4 <- mice(nhanes2, m = 1, maxit = 1, print = FALSE) expect_warning(imp5 <<- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE)) -expect_error(imp6 <<- mice(nhanes[1:13, 2:3], m = 2, maxit = 2, print = FALSE), "`mice` detected constant and/or collinear variables. No predictors were left after their removal.") +expect_error(imp6 <<- mice(nhanes[1:13, 2:3], m = 2, maxit = 2, print = FALSE), + "`mice` detected constant and/or collinear variables. No predictors were left after their removal.") nh3 <- nhanes colnames(nh3) <- c("AGE", "bmi", "hyp", "chl") imp7 <- mice(nh3[14:25, ], m = 2, maxit = 2, print = FALSE) From 0a22f2727792893024715cd77578ac9055f4fac4 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 16:56:08 +0200 Subject: [PATCH 008/147] Update documentation of trim.predictors(), lars.filter(), lars.internal() and remove.lindep() --- R/trim.predictors.R | 60 ++++++++++++++++++++++++------------------ man/lars.filter.Rd | 18 +++++++------ man/lars.internal.Rd | 6 ++--- man/remove.lindep.Rd | 19 ++++++------- man/trim.predictors.Rd | 18 +++++++++---- 5 files changed, 71 insertions(+), 50 deletions(-) diff --git a/R/trim.predictors.R b/R/trim.predictors.R index a743a63b3..10e9e1825 100644 --- a/R/trim.predictors.R +++ b/R/trim.predictors.R @@ -1,10 +1,10 @@ #' Trim predictors of the imputation model #' -#' The function `trim.predictors()` filters out predictors before imputation +#' The function \code{trim.predictors()} filters out predictors before imputation #' of univariate incomplete variables. The function is called by -#' `mice:::sampler.univ()`. The function is a wrapper for the trimming -#' functions `lars.filter()`, `remove.lindep()`, and a user-specified -#' trimming function. The default method is `"lars.filter"`. +#' \code{mice:::sampler.univ()}. The function is a wrapper for the trimming +#' functions \code{lars.filter()}, \code{remove.lindep()}, and a user-specified +#' trimming function. The default method is \code{trimmer = "lars.filter"}. #' #' @inheritParams mice.impute.pmm #' @param trimmer A character vector of length 1 specifying the name of the @@ -12,8 +12,15 @@ #' "remove.lindep" and "none". The user can also specify a custom trimming #' function. #' @param ... further arguments passed to the trimming function. -#' @return A logical vector of length `ncol(x)` indicating which predictors +#' @return A logical vector of length \code{ncol(x)} indicating which predictors #' to keep. +#' @details +#' The function \code{lars.filter()} changes the behavior of the MICE algorithm. +#' It is far more agressive in removing predictors than the classic +#' \code{remove.lindep()} function. The feature is experimental and may be +#' subject to change in future versions. For backward compatibility, +#' add \code{trimmer = "remove.lindep"} to your call +#' \code{mice(..., trimmer = "remove.lindep")}. #' @export trim.predictors <- function( x, y, ry, @@ -44,16 +51,18 @@ trim.predictors <- function( #' #' @inheritParams mice.impute.pmm #' @param eps numeric. Criteriom for removing predictors with zero variance. -#' If `eps` is zero, the filter is bypassed. The default is 1e-04. -#' @param allow.na logical. If `TRUE`, allow imputation of fully missing `y`. -#' This typically only occurs for passive imputation. The default is `TRUE`. -#' @param ... further arguments passed to `lars`, like standard arguments -#' `type` and `intercept`, or custom tuning parameters like `lars.relax` and -#' `minimal.cp`. -#' @return A logical vector of length `ncol(x)` indicating which predictors +#' If \code{eps} is zero, the filter is bypassed. The default is 1e-04. +#' Not passed down to \code{lars::lars}. +#' @param allow.na logical. If \code{TRUE}, allow imputation of fully +#' missing \code{y}. This typically only occurs for passive imputation. +#' The default is \code{TRUE}. +#' @param ... further arguments passed to \code{lars::lars}, like standard +#' arguments \code{type} and \code{intercept}, or custom tuning parameters +#' like \code{lars.relax} and \code{minimal.cp}. +#' @return A logical vector of length \code{ncol(x)} indicating which predictors #' to keep. #' @details -#' In the following conditions, the function skips the filter +#' In the following conditions, the function skips the filter: #' 1. if there are no or only one predictors #' 2. if user imputes a fully missing y accept all predictors #' 3. if eps == 0 (for backward compatibility) @@ -97,11 +106,11 @@ lars.filter <- function(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) { #' Use 1-5 percent for a more stringent filter, and 5-10 percent for a moderate #' filter. #' @param minimal.cp numeric. The minimum "Cp" value to consider. "Cp" may -#' become negative for small samples. `minimal_cp` is the minimum "Cp" value +#' become negative for small samples. \code{minimal_cp} is the minimum "Cp" value #' that is is used as to define the threshold for the filter. Higher value #' select more predictors. The default is 1. -#' @param \dots Further arguments passed to `lars`. Nothing passed down. -#' @return A logical vector of length `ncol(x)` indicating which predictors +#' @param \dots Further arguments passed to \code{lars::lars}. Nothing passed down. +#' @return A logical vector of length \code{ncol(x)} indicating which predictors #' to keep. #' @export lars.internal <- function(x, y, type = "lar", intercept = TRUE, @@ -124,7 +133,7 @@ lars.internal <- function(x, y, type = "lar", intercept = TRUE, #' Filter out constant and multicollinear predictors before imputations #' -#' `remove.lindep()` prevents multicollinearity +#' \code{remove.lindep()} prevents multicollinearity #' in the imputation model. It removes predictors that are constant or have #' too high correlation with the target variable. The function #' uses the eigenvalues of the correlation matrix to detect multicollinearity. @@ -132,19 +141,20 @@ lars.internal <- function(x, y, type = "lar", intercept = TRUE, #' @inheritParams mice.impute.pmm #' @param eps numeric. The threshold for the ratio of the smallest to the #' largest eigenvalue of the correlation matrix. The default is 1e-04. If the -#' user sets `eps = 0`, the function bypasses the filter. +#' user sets \code{eps = 0}, the function bypasses the filter. #' @param maxcor numeric. The maximum correlation between a predictor and the #' target variable. The default is 0.99. -#' @param allow.na logical. If `TRUE`, allow imputation of fully missing `y`. -#' This typically only occurs for passive imputation. The default is `TRUE`. +#' @param allow.na logical. If \code{TRUE}, allow imputation of fully +#' missing \code{y}. This typically only occurs for passive imputation. +#' The default is \code{TRUE}. #' @param frame integer. The frame number for logging. The default is 4. -#' @return A logical vector of length `ncol(x)` indicating which predictors +#' @return A logical vector of length \code{ncol(x)} indicating which predictors #' to keep. #' @details -#' This function is the classic MICE safety net to prevent multicollinearity -#' and other numerical problems. It is a far more conservative filter than -#' `lars.filter()`. The function is called by `trim.predictors()` with the -#' default method `"remove.lindep"`. The function is now deprecated, but +#' \code{remove.lindep()} is the classic MICE safety net to prevent +#' multicollinearity and other numerical problems. It is a far more +#' conservative filter than \code{lars.filter()}. The function is called +#' by \code{trim.predictors()}. The function is now deprecated, but #' will remain part of the package for backward compatibility. #' @export remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, diff --git a/man/lars.filter.Rd b/man/lars.filter.Rd index 8fc335a26..45e944d58 100644 --- a/man/lars.filter.Rd +++ b/man/lars.filter.Rd @@ -18,24 +18,26 @@ model is fitted. The \code{ry} generally distinguishes the observed (\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} \item{eps}{numeric. Criteriom for removing predictors with zero variance. -If `eps` is zero, the filter is bypassed. The default is 1e-04.} +If \code{eps} is zero, the filter is bypassed. The default is 1e-04. +Not passed down to \code{lars::lars}.} -\item{allow.na}{logical. If `TRUE`, allow imputation of fully missing `y`. -This typically only occurs for passive imputation. The default is `TRUE`.} +\item{allow.na}{logical. If \code{TRUE}, allow imputation of fully +missing \code{y}. This typically only occurs for passive imputation. +The default is \code{TRUE}.} -\item{...}{further arguments passed to `lars`, like standard arguments -`type` and `intercept`, or custom tuning parameters like `lars.relax` and -`minimal.cp`.} +\item{...}{further arguments passed to \code{lars::lars}, like standard +arguments \code{type} and \code{intercept}, or custom tuning parameters +like \code{lars.relax} and \code{minimal.cp}.} } \value{ -A logical vector of length `ncol(x)` indicating which predictors +A logical vector of length \code{ncol(x)} indicating which predictors to keep. } \description{ Filter out predictors by least angular regression } \details{ -In the following conditions, the function skips the filter +In the following conditions, the function skips the filter: 1. if there are no or only one predictors 2. if user imputes a fully missing y accept all predictors 3. if eps == 0 (for backward compatibility) diff --git a/man/lars.internal.Rd b/man/lars.internal.Rd index bac19e02f..9f4ba2626 100644 --- a/man/lars.internal.Rd +++ b/man/lars.internal.Rd @@ -39,14 +39,14 @@ Use 1-5 percent for a more stringent filter, and 5-10 percent for a moderate filter.} \item{minimal.cp}{numeric. The minimum "Cp" value to consider. "Cp" may -become negative for small samples. `minimal_cp` is the minimum "Cp" value +become negative for small samples. \code{minimal_cp} is the minimum "Cp" value that is is used as to define the threshold for the filter. Higher value select more predictors. The default is 1.} -\item{\dots}{Further arguments passed to `lars`. Nothing passed down.} +\item{\dots}{Further arguments passed to \code{lars::lars}. Nothing passed down.} } \value{ -A logical vector of length `ncol(x)` indicating which predictors +A logical vector of length \code{ncol(x)} indicating which predictors to keep. } \description{ diff --git a/man/remove.lindep.Rd b/man/remove.lindep.Rd index d55a6927c..1635f891a 100644 --- a/man/remove.lindep.Rd +++ b/man/remove.lindep.Rd @@ -28,32 +28,33 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{eps}{numeric. The threshold for the ratio of the smallest to the largest eigenvalue of the correlation matrix. The default is 1e-04. If the -user sets `eps = 0`, the function bypasses the filter.} +user sets \code{eps = 0}, the function bypasses the filter.} \item{maxcor}{numeric. The maximum correlation between a predictor and the target variable. The default is 0.99.} -\item{allow.na}{logical. If `TRUE`, allow imputation of fully missing `y`. -This typically only occurs for passive imputation. The default is `TRUE`.} +\item{allow.na}{logical. If \code{TRUE}, allow imputation of fully +missing \code{y}. This typically only occurs for passive imputation. +The default is \code{TRUE}.} \item{frame}{integer. The frame number for logging. The default is 4.} \item{...}{Other named arguments.} } \value{ -A logical vector of length `ncol(x)` indicating which predictors +A logical vector of length \code{ncol(x)} indicating which predictors to keep. } \description{ -`remove.lindep()` prevents multicollinearity +\code{remove.lindep()} prevents multicollinearity in the imputation model. It removes predictors that are constant or have too high correlation with the target variable. The function uses the eigenvalues of the correlation matrix to detect multicollinearity. } \details{ -This function is the classic MICE safety net to prevent multicollinearity -and other numerical problems. It is a far more conservative filter than -`lars.filter()`. The function is called by `trim.predictors()` with the -default method `"remove.lindep"`. The function is now deprecated, but +\code{remove.lindep()} is the classic MICE safety net to prevent +multicollinearity and other numerical problems. It is a far more +conservative filter than \code{lars.filter()}. The function is called +by \code{trim.predictors()}. The function is now deprecated, but will remain part of the package for backward compatibility. } diff --git a/man/trim.predictors.Rd b/man/trim.predictors.Rd index 6307d8e28..44035bc3f 100644 --- a/man/trim.predictors.Rd +++ b/man/trim.predictors.Rd @@ -31,13 +31,21 @@ function.} \item{...}{further arguments passed to the trimming function.} } \value{ -A logical vector of length `ncol(x)` indicating which predictors +A logical vector of length \code{ncol(x)} indicating which predictors to keep. } \description{ -The function `trim.predictors()` filters out predictors before imputation +The function \code{trim.predictors()} filters out predictors before imputation of univariate incomplete variables. The function is called by -`mice:::sampler.univ()`. The function is a wrapper for the trimming -functions `lars.filter()`, `remove.lindep()`, and a user-specified -trimming function. The default method is `"lars.filter"`. +\code{mice:::sampler.univ()}. The function is a wrapper for the trimming +functions \code{lars.filter()}, \code{remove.lindep()}, and a user-specified +trimming function. The default method is \code{trimmer = "lars.filter"}. +} +\details{ +The function \code{lars.filter()} changes the behavior of the MICE algorithm. +It is far more agressive in removing predictors than the classic +\code{remove.lindep()} function. The feature is experimental and may be +subject to change in future versions. For backward compatibility, +add \code{trimmer = "remove.lindep"} to your call +\code{mice(..., trimmer = "remove.lindep")}. } From 8f913aa084ddd0a6649b52c67a86846bda569f23 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 16:57:18 +0200 Subject: [PATCH 009/147] Set default trimmer = "lars.filter" in sampler.univ() --- R/sampler.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/sampler.R b/R/sampler.R index 576db39f2..b5b8cceaf 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -181,7 +181,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, sampler.univ <- function(data, r, where, pred, formula, method, yname, k, calltype = "pred", user, ignore, - trimmer = "remove.lindep", ...) { + trimmer = "lars.filter", ...) { j <- yname[1L] if (calltype == "pred") { From 7234e30ed26b93a40cc65660378f87178fb4db1a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 17:03:21 +0200 Subject: [PATCH 010/147] Add news item --- NEWS.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/NEWS.md b/NEWS.md index b94f8f621..426f4ab77 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +* Adds a new method to filter features from the design matrix just before fitting the imputation model. A new function `lars.filter()` takes over duties of old `remove.lindep()` that has acted as a safety net for many years. The new method fits a LARS model, and keep the subset of variables that contribute to the model. The method is more robust and faster than `remove.lindep()` and can handle more complex situations. + # mice 3.16.14 * Fixes a bug during initialization of factor values From 74a2bd0341d655f1dbb411a420ca95aab0762ee3 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 18 Sep 2024 23:36:48 +0200 Subject: [PATCH 011/147] Reorganise functionality of trim.predictors() and refine documentation --- R/trim.predictors.R | 205 ++++++++++++++++++++++------------------- man/lars.filter.Rd | 44 --------- man/lars.internal.Rd | 54 ----------- man/remove.lindep.Rd | 60 ------------ man/trim.predictors.Rd | 136 ++++++++++++++++++++++----- 5 files changed, 225 insertions(+), 274 deletions(-) delete mode 100644 man/lars.filter.Rd delete mode 100644 man/lars.internal.Rd delete mode 100644 man/remove.lindep.Rd diff --git a/R/trim.predictors.R b/R/trim.predictors.R index 10e9e1825..c47ca98cd 100644 --- a/R/trim.predictors.R +++ b/R/trim.predictors.R @@ -1,84 +1,111 @@ -#' Trim predictors of the imputation model +#' Trim the predictor set before univariate imputation #' -#' The function \code{trim.predictors()} filters out predictors before imputation -#' of univariate incomplete variables. The function is called by -#' \code{mice:::sampler.univ()}. The function is a wrapper for the trimming -#' functions \code{lars.filter()}, \code{remove.lindep()}, and a user-specified +#' The function \code{trim.predictors()} filters out predictors before +#' univariate imputation. The function is a wrapper for the trimming +#' functions \code{lars.filter()}, \code{remove.lindep()}, or a user-specified #' trimming function. The default method is \code{trimmer = "lars.filter"}. +#' The function is called by \code{mice:::sampler.univ()} and not intended +#' for direct application by the user. #' -#' @inheritParams mice.impute.pmm +#' @param x Numeric design matrix with predictors, for example, as produced +#' by \code{model.matrix()}. The matrix must have the same number of rows as +#' \code{length(y)} and \code{length(ry)}. +#' @param y Numeric vector of length \code{length(y)} with the target variable. +#' If not numeric, it will be converted to numeric. +#' @param ry Logical vector of length \code{length(ry)} indicating which +#' observations are observed for the target variable. #' @param trimmer A character vector of length 1 specifying the name of the -#' trimming function. The default is "lars.filter". The other options are -#' "remove.lindep" and "none". The user can also specify a custom trimming -#' function. -#' @param ... further arguments passed to the trimming function. +#' trimming function. The default is \code{"lars.filter"}. Other options are +#' \code{"remove.lindep"} or \code{""}. The user can also specify the name of a +#' custom trimming function. +#' @param allow.na Logical. If \code{TRUE}, allow imputation of fully +#' missing \code{y}. This typically only occurs for passive imputation. +#' The default is \code{TRUE}. #' @return A logical vector of length \code{ncol(x)} indicating which predictors #' to keep. #' @details #' The function \code{lars.filter()} changes the behavior of the MICE algorithm. #' It is far more agressive in removing predictors than the classic -#' \code{remove.lindep()} function. The feature is experimental and may be -#' subject to change in future versions. For backward compatibility, +#' \code{remove.lindep()} function. For backward compatibility, #' add \code{trimmer = "remove.lindep"} to your call #' \code{mice(..., trimmer = "remove.lindep")}. +#' +#' Observe that filtering works on the design matrix \code{x}. If this +#' matrix contains dummy codings of categorical variables, the filter +#' will test the contribution of separate dummy variables to the model. +#' Implicitly, this practice changes the categories of the ancestor +#' factors. A neater approach that avoids this problem would be to include +#' all dummy codings of a categorical variable as a block. This is currently +#' not implemented. +#' +#' The current implementation only supports a global trimmer that applies +#' to all variables. +#' @rdname trim.predictors +#' @examples +#' # Impute using the default (LARS filter) +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE) +#' +#' # LARS with no more than two predictors per variable +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, max.predictors = 2) +#' +#' # Impute using the remove.lindep filter (classic MICE) +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "remove.lindep") +#' +#' # Impute without a filter +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") #' @export trim.predictors <- function( x, y, ry, - trimmer = c("lars.filter", "remove.lindep", "none"), ...) { - - if (trimmer == "lars.filter") { + trimmer = "lars.filter", + allow.na = TRUE, + ...) { + stopifnot(is.matrix(x), is.logical(ry)) + stopifnot(length(y) == length(ry), nrow(x) == length(y)) + + if (allow.na && sum(ry) == 0L || + sum(ry) <= 1L || + ncol(x) <= 1L || + trimmer == "") { + # Exception 1: Keep all if we allow for a fully missing y + # Exception 2: Keep all if there are no or only one observed y + # Exception 3: Keep all if there is only zero or one predictor + # Exception 4: Keep all if the user specifies trimmer = "" + keep <- rep.int(TRUE, ncol(x)) + } else if (trimmer == "lars.filter") { keep <- lars.filter(x, y, ry, ...) - return(keep) - } - - if (trimmer == "remove.lindep") { + } else if (trimmer == "remove.lindep") { keep <- remove.lindep(x, y, ry, ...) - return(keep) - } - - if (trimmer == "none") { - keep <- rep.int(TRUE, ncol(x)) - return(keep) + } else { + # handle user-specified trimmer + args <- c(list(x = x, y = y, ry = ry), list(...)) + keep <- do.call(trimmer, args = args) } - # handle user-specified trimmer - args <- c(list(x = x, y = y, ry = ry), list(...)) - keep <- do.call(trimmer, args = args) return(keep) } -#' Filter out predictors by least angular regression +#' Filter out predictors by least angle regression (LARS) +#' +#' \code{lars.filter()} is a fast way to filter out predictors by +#' least angle regression (LARS). #' #' @inheritParams mice.impute.pmm -#' @param eps numeric. Criteriom for removing predictors with zero variance. -#' If \code{eps} is zero, the filter is bypassed. The default is 1e-04. -#' Not passed down to \code{lars::lars}. -#' @param allow.na logical. If \code{TRUE}, allow imputation of fully -#' missing \code{y}. This typically only occurs for passive imputation. -#' The default is \code{TRUE}. -#' @param ... further arguments passed to \code{lars::lars}, like standard -#' arguments \code{type} and \code{intercept}, or custom tuning parameters -#' like \code{lars.relax} and \code{minimal.cp}. -#' @return A logical vector of length \code{ncol(x)} indicating which predictors -#' to keep. +#' @param ... Further arguments passed to \code{lars::lars}, like standard +#' LARS arguments \code{type}, \code{intercept}, \code{eps} and +#' \code{max.steps}, or tuning parameters \code{lars.relax} and +#' \code{minimal.cp}. #' @details -#' In the following conditions, the function skips the filter: -#' 1. if there are no or only one predictors -#' 2. if user imputes a fully missing y accept all predictors -#' 3. if eps == 0 (for backward compatibility) -#' +#' \code{lars.filter} fits the LARS model to the elements of the design matrix +#' \code{x} and the target variable \code{y}, as indicated by \code{ry}. +#' If these data contain missing data, the function will remove the relevant +#' rows before calling \code{lars()}. +#' @rdname trim.predictors #' @export -lars.filter <- function(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) { - # Skipping conditions - if (ncol(x) <= 1L || - allow.na && sum(ry) == 0L || - eps == 0) { - return(rep.int(TRUE, ncol(x))) - } +lars.filter <- function(x, y, ry, ...) { # If y is constant, predict from an intercept-only model yobs <- as.numeric(y[ry]) - if (var(yobs, na.rm = TRUE) < abs(eps)) { + if (var(yobs, na.rm = TRUE) < 1000 * .Machine$double.eps) { return(rep(FALSE, ncol(x))) } @@ -101,23 +128,26 @@ lars.filter <- function(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) { #' Filter out predictors by least angular regression #' #' @inheritParams lars::lars -#' @param lars.relax numeric. The percentage of the minimum Cp value that is +#' @param type Character. The type of LARS model to fit. The default is "lar". +#' @param max.predictors Integer. The maximum number of variables to include +#' in the LARS model. The default is 20. +#' @param lars.relax Numeric. The percentage of the minimum Cp value that is #' added to the minimum Cp to relax the inclusion threshold. The default is 5. -#' Use 1-5 percent for a more stringent filter, and 5-10 percent for a moderate -#' filter. -#' @param minimal.cp numeric. The minimum "Cp" value to consider. "Cp" may +#' Use 1-5 percent for a slightly more permissive filter, 5-10 percent +#' for a moderate permissive filter, and 10-20 percent for very permissive. +#' @param minimal.cp Numeric. The minimum "Cp" value to consider. "Cp" may #' become negative for small samples. \code{minimal_cp} is the minimum "Cp" value -#' that is is used as to define the threshold for the filter. Higher value +#' that is is used as to define the threshold for the filter. Higher values #' select more predictors. The default is 1. -#' @param \dots Further arguments passed to \code{lars::lars}. Nothing passed down. -#' @return A logical vector of length \code{ncol(x)} indicating which predictors -#' to keep. +#' @rdname trim.predictors #' @export -lars.internal <- function(x, y, type = "lar", intercept = TRUE, - lars.relax = 5, minimal.cp = 1, ...) { - model <- lars(x = x, y = y, type = type, intercept = intercept) +lars.internal <- function( + x, y, type = "lar", intercept = TRUE, max.predictors = 20, eps = 1e-12, + lars.relax = 5, minimal.cp = 1, ...) { + model <- lars(x = x, y = y, type = type, intercept = intercept, + eps = eps, max.steps = min(ncol(x), max.predictors)) if (any(model$R2 == 1)) { - # we need a work-around because Cp gives NaN + # work-around because Cp gives NaN for perfect fits coef_step <- coef(model, s = which(model$R2 == 1)) } else { # find the step where Cp is minimal @@ -131,7 +161,7 @@ lars.internal <- function(x, y, type = "lar", intercept = TRUE, return(coef_step != 0) } -#' Filter out constant and multicollinear predictors before imputations +#' Filter out constant and multi-collinear predictors before imputation #' #' \code{remove.lindep()} prevents multicollinearity #' in the imputation model. It removes predictors that are constant or have @@ -139,52 +169,37 @@ lars.internal <- function(x, y, type = "lar", intercept = TRUE, #' uses the eigenvalues of the correlation matrix to detect multicollinearity. #' #' @inheritParams mice.impute.pmm -#' @param eps numeric. The threshold for the ratio of the smallest to the -#' largest eigenvalue of the correlation matrix. The default is 1e-04. If the -#' user sets \code{eps = 0}, the function bypasses the filter. -#' @param maxcor numeric. The maximum correlation between a predictor and the +#' @param eps Numeric. Used by \code{remove.lindep()} as the threshold for +#' the ratio of the smallest to the largest eigenvalue of the correlation +#' matrix. The default is 1e-04. If the user sets \code{eps = 0}, +#' all variables are returned (for backward compatibility). Used by +#' \code{lars.internal()} as an argument to the \code{lars::lars()} function. +#' @param maxcor Numeric. The maximum correlation between a predictor and the #' target variable. The default is 0.99. -#' @param allow.na logical. If \code{TRUE}, allow imputation of fully -#' missing \code{y}. This typically only occurs for passive imputation. -#' The default is \code{TRUE}. -#' @param frame integer. The frame number for logging. The default is 4. -#' @return A logical vector of length \code{ncol(x)} indicating which predictors -#' to keep. +#' @param frame Integer. The frame number for logging. Do not alter. #' @details #' \code{remove.lindep()} is the classic MICE safety net to prevent #' multicollinearity and other numerical problems. It is a far more #' conservative filter than \code{lars.filter()}. The function is called -#' by \code{trim.predictors()}. The function is now deprecated, but +#' by \code{trim.predictors()}. The function is not the default anymore, but #' will remain part of the package for backward compatibility. +#' @rdname trim.predictors #' @export remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, - allow.na = TRUE, frame = 4, ...) { - # returns a logical vector of length ncol(x) - - if (ncol(x) == 0) { - return(NULL) - } - + frame = 4, ...) { # setting eps = 0 bypasses remove.lindep() + # for compatibility with previous versions if (eps == 0) { return(rep.int(TRUE, ncol(x))) } - if (eps < 0) { - stop("\n Argument 'eps' must be positive.") - } - - # Keep all predictors if we allow imputation of fully missing y - if (allow.na && sum(ry) == 0) { - return(rep.int(TRUE, ncol(x))) - } xobs <- x[ry, , drop = FALSE] yobs <- as.numeric(y[ry]) - if (var(yobs) < eps) { + if (var(yobs) < abs(eps)) { return(rep(FALSE, ncol(xobs))) } - keep <- unlist(apply(xobs, 2, var) > eps) + keep <- unlist(apply(xobs, 2, var) > 1000 * .Machine$double.eps) keep[is.na(keep)] <- FALSE highcor <- suppressWarnings(unlist(apply(xobs, 2, cor, yobs) < maxcor)) keep <- keep & highcor @@ -205,7 +220,7 @@ remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, cx <- cor(xobs[, keep, drop = FALSE], use = "all.obs") eig <- eigen(cx, symmetric = TRUE) ncx <- cx - while (eig$values[k] / eig$values[1] < eps) { + while (eig$values[k] / eig$values[1] < abs(eps)) { j <- seq_len(k)[order(abs(eig$vectors[, k]), decreasing = TRUE)[1]] keep[keep][j] <- FALSE ncx <- cx[keep[keep], keep[keep], drop = FALSE] diff --git a/man/lars.filter.Rd b/man/lars.filter.Rd deleted file mode 100644 index 45e944d58..000000000 --- a/man/lars.filter.Rd +++ /dev/null @@ -1,44 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trim.predictors.R -\name{lars.filter} -\alias{lars.filter} -\title{Filter out predictors by least angular regression} -\usage{ -lars.filter(x, y, ry, eps = 1e-04, allow.na = TRUE, ...) -} -\arguments{ -\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for -\code{y}. Matrix \code{x} may have no missing values.} - -\item{y}{Vector to be imputed} - -\item{ry}{Logical vector of length \code{length(y)} indicating the -the subset \code{y[ry]} of elements in \code{y} to which the imputation -model is fitted. The \code{ry} generally distinguishes the observed -(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} - -\item{eps}{numeric. Criteriom for removing predictors with zero variance. -If \code{eps} is zero, the filter is bypassed. The default is 1e-04. -Not passed down to \code{lars::lars}.} - -\item{allow.na}{logical. If \code{TRUE}, allow imputation of fully -missing \code{y}. This typically only occurs for passive imputation. -The default is \code{TRUE}.} - -\item{...}{further arguments passed to \code{lars::lars}, like standard -arguments \code{type} and \code{intercept}, or custom tuning parameters -like \code{lars.relax} and \code{minimal.cp}.} -} -\value{ -A logical vector of length \code{ncol(x)} indicating which predictors -to keep. -} -\description{ -Filter out predictors by least angular regression -} -\details{ -In the following conditions, the function skips the filter: -1. if there are no or only one predictors -2. if user imputes a fully missing y accept all predictors -3. if eps == 0 (for backward compatibility) -} diff --git a/man/lars.internal.Rd b/man/lars.internal.Rd deleted file mode 100644 index 9f4ba2626..000000000 --- a/man/lars.internal.Rd +++ /dev/null @@ -1,54 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trim.predictors.R -\name{lars.internal} -\alias{lars.internal} -\title{Filter out predictors by least angular regression} -\usage{ -lars.internal( - x, - y, - type = "lar", - intercept = TRUE, - lars.relax = 5, - minimal.cp = 1, - ... -) -} -\arguments{ -\item{x}{ -matrix of predictors -} - -\item{y}{ -response -} - -\item{type}{ -One of "lasso", "lar", "forward.stagewise" or "stepwise". The names can -be abbreviated to any unique substring. Default is "lasso". -} - -\item{intercept}{ -if TRUE, an intercept is included in the model (and not penalized), -otherwise no intercept is included. Default is TRUE. -} - -\item{lars.relax}{numeric. The percentage of the minimum Cp value that is -added to the minimum Cp to relax the inclusion threshold. The default is 5. -Use 1-5 percent for a more stringent filter, and 5-10 percent for a moderate -filter.} - -\item{minimal.cp}{numeric. The minimum "Cp" value to consider. "Cp" may -become negative for small samples. \code{minimal_cp} is the minimum "Cp" value -that is is used as to define the threshold for the filter. Higher value -select more predictors. The default is 1.} - -\item{\dots}{Further arguments passed to \code{lars::lars}. Nothing passed down.} -} -\value{ -A logical vector of length \code{ncol(x)} indicating which predictors -to keep. -} -\description{ -Filter out predictors by least angular regression -} diff --git a/man/remove.lindep.Rd b/man/remove.lindep.Rd deleted file mode 100644 index 1635f891a..000000000 --- a/man/remove.lindep.Rd +++ /dev/null @@ -1,60 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trim.predictors.R -\name{remove.lindep} -\alias{remove.lindep} -\title{Filter out constant and multicollinear predictors before imputations} -\usage{ -remove.lindep( - x, - y, - ry, - eps = 1e-04, - maxcor = 0.99, - allow.na = TRUE, - frame = 4, - ... -) -} -\arguments{ -\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for -\code{y}. Matrix \code{x} may have no missing values.} - -\item{y}{Vector to be imputed} - -\item{ry}{Logical vector of length \code{length(y)} indicating the -the subset \code{y[ry]} of elements in \code{y} to which the imputation -model is fitted. The \code{ry} generally distinguishes the observed -(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} - -\item{eps}{numeric. The threshold for the ratio of the smallest to the -largest eigenvalue of the correlation matrix. The default is 1e-04. If the -user sets \code{eps = 0}, the function bypasses the filter.} - -\item{maxcor}{numeric. The maximum correlation between a predictor and the -target variable. The default is 0.99.} - -\item{allow.na}{logical. If \code{TRUE}, allow imputation of fully -missing \code{y}. This typically only occurs for passive imputation. -The default is \code{TRUE}.} - -\item{frame}{integer. The frame number for logging. The default is 4.} - -\item{...}{Other named arguments.} -} -\value{ -A logical vector of length \code{ncol(x)} indicating which predictors -to keep. -} -\description{ -\code{remove.lindep()} prevents multicollinearity -in the imputation model. It removes predictors that are constant or have -too high correlation with the target variable. The function -uses the eigenvalues of the correlation matrix to detect multicollinearity. -} -\details{ -\code{remove.lindep()} is the classic MICE safety net to prevent -multicollinearity and other numerical problems. It is a far more -conservative filter than \code{lars.filter()}. The function is called -by \code{trim.predictors()}. The function is now deprecated, but -will remain part of the package for backward compatibility. -} diff --git a/man/trim.predictors.Rd b/man/trim.predictors.Rd index 44035bc3f..ca476b606 100644 --- a/man/trim.predictors.Rd +++ b/man/trim.predictors.Rd @@ -2,50 +2,144 @@ % Please edit documentation in R/trim.predictors.R \name{trim.predictors} \alias{trim.predictors} -\title{Trim predictors of the imputation model} +\alias{lars.filter} +\alias{lars.internal} +\alias{remove.lindep} +\title{Trim the predictor set before univariate imputation} \usage{ -trim.predictors( +trim.predictors(x, y, ry, trimmer = "lars.filter", allow.na = TRUE, ...) + +lars.filter(x, y, ry, ...) + +lars.internal( x, y, - ry, - trimmer = c("lars.filter", "remove.lindep", "none"), + type = "lar", + intercept = TRUE, + max.predictors = 20, + eps = 1e-12, + lars.relax = 5, + minimal.cp = 1, ... ) + +remove.lindep(x, y, ry, eps = 1e-04, maxcor = 0.99, frame = 4, ...) } \arguments{ -\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for -\code{y}. Matrix \code{x} may have no missing values.} +\item{x}{Numeric design matrix with predictors, for example, as produced +by \code{model.matrix()}. The matrix must have the same number of rows as +\code{length(y)} and \code{length(ry)}.} -\item{y}{Vector to be imputed} +\item{y}{Numeric vector of length \code{length(y)} with the target variable. +If not numeric, it will be converted to numeric.} -\item{ry}{Logical vector of length \code{length(y)} indicating the -the subset \code{y[ry]} of elements in \code{y} to which the imputation -model is fitted. The \code{ry} generally distinguishes the observed -(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} +\item{ry}{Logical vector of length \code{length(ry)} indicating which +observations are observed for the target variable.} \item{trimmer}{A character vector of length 1 specifying the name of the -trimming function. The default is "lars.filter". The other options are -"remove.lindep" and "none". The user can also specify a custom trimming -function.} +trimming function. The default is \code{"lars.filter"}. Other options are +\code{"remove.lindep"} or \code{""}. The user can also specify the name of a +custom trimming function.} + +\item{allow.na}{Logical. If \code{TRUE}, allow imputation of fully +missing \code{y}. This typically only occurs for passive imputation. +The default is \code{TRUE}.} + +\item{...}{Further arguments passed to \code{lars::lars}, like standard +LARS arguments \code{type}, \code{intercept}, \code{eps} and +\code{max.steps}, or tuning parameters \code{lars.relax} and +\code{minimal.cp}.} + +\item{type}{Character. The type of LARS model to fit. The default is "lar".} -\item{...}{further arguments passed to the trimming function.} +\item{intercept}{ +if TRUE, an intercept is included in the model (and not penalized), +otherwise no intercept is included. Default is TRUE. +} + +\item{max.predictors}{Integer. The maximum number of variables to include +in the LARS model. The default is 20.} + +\item{eps}{Numeric. Used by \code{remove.lindep()} as the threshold for +the ratio of the smallest to the largest eigenvalue of the correlation +matrix. The default is 1e-04. If the user sets \code{eps = 0}, +all variables are returned (for backward compatibility). Used by +\code{lars.internal()} as an argument to the \code{lars::lars()} function.} + +\item{lars.relax}{Numeric. The percentage of the minimum Cp value that is +added to the minimum Cp to relax the inclusion threshold. The default is 5. +Use 1-5 percent for a slightly more permissive filter, 5-10 percent +for a moderate permissive filter, and 10-20 percent for very permissive.} + +\item{minimal.cp}{Numeric. The minimum "Cp" value to consider. "Cp" may +become negative for small samples. \code{minimal_cp} is the minimum "Cp" value +that is is used as to define the threshold for the filter. Higher values +select more predictors. The default is 1.} + +\item{maxcor}{Numeric. The maximum correlation between a predictor and the +target variable. The default is 0.99.} + +\item{frame}{Integer. The frame number for logging. Do not alter.} } \value{ A logical vector of length \code{ncol(x)} indicating which predictors to keep. } \description{ -The function \code{trim.predictors()} filters out predictors before imputation -of univariate incomplete variables. The function is called by -\code{mice:::sampler.univ()}. The function is a wrapper for the trimming -functions \code{lars.filter()}, \code{remove.lindep()}, and a user-specified +The function \code{trim.predictors()} filters out predictors before +univariate imputation. The function is a wrapper for the trimming +functions \code{lars.filter()}, \code{remove.lindep()}, or a user-specified trimming function. The default method is \code{trimmer = "lars.filter"}. +The function is called by \code{mice:::sampler.univ()} and not intended +for direct application by the user. + +\code{lars.filter()} is a fast way to filter out predictors by +least angle regression (LARS). + +\code{remove.lindep()} prevents multicollinearity +in the imputation model. It removes predictors that are constant or have +too high correlation with the target variable. The function +uses the eigenvalues of the correlation matrix to detect multicollinearity. } \details{ The function \code{lars.filter()} changes the behavior of the MICE algorithm. It is far more agressive in removing predictors than the classic -\code{remove.lindep()} function. The feature is experimental and may be -subject to change in future versions. For backward compatibility, +\code{remove.lindep()} function. For backward compatibility, add \code{trimmer = "remove.lindep"} to your call \code{mice(..., trimmer = "remove.lindep")}. + +Observe that filtering works on the design matrix \code{x}. If this +matrix contains dummy codings of categorical variables, the filter +will test the contribution of separate dummy variables to the model. +Implicitly, this practice changes the categories of the ancestor +factors. A neater approach that avoids this problem would be to include +all dummy codings of a categorical variable as a block. This is currently +not implemented. + +The current implementation only supports a global trimmer that applies +to all variables. + +\code{lars.filter} fits the LARS model to the elements of the design matrix +\code{x} and the target variable \code{y}, as indicated by \code{ry}. +If these data contain missing data, the function will remove the relevant +rows before calling \code{lars()}. + +\code{remove.lindep()} is the classic MICE safety net to prevent +multicollinearity and other numerical problems. It is a far more +conservative filter than \code{lars.filter()}. The function is called +by \code{trim.predictors()}. The function is not the default anymore, but +will remain part of the package for backward compatibility. +} +\examples{ +# Impute using the default (LARS filter) +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE) + +# LARS with no more than two predictors per variable +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, max.predictors = 2) + +# Impute using the remove.lindep filter (classic MICE) +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "remove.lindep") + +# Impute without a filter +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") } From 2ea6abe81ca4e78eda6ae35f795ac43b92d3dc1f Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sat, 21 Sep 2024 21:44:44 +0200 Subject: [PATCH 012/147] Add glmnet and cv.glmnet predictor selectors --- NAMESPACE | 2 ++ R/imports.R | 2 +- R/sampler.R | 2 +- R/trim.predictors.R | 82 +++++++++++++++++++++++++++++++++--------- man/trim.predictors.Rd | 29 ++++++++------- 5 files changed, 86 insertions(+), 31 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 880b7ebd3..7a9a54203 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -85,6 +85,7 @@ export(getfit) export(getqbar) export(glance) export(glm.mids) +export(glmnet.internal) export(ibind) export(ic) export(ici) @@ -197,6 +198,7 @@ importFrom(dplyr,summarize) importFrom(generics,glance) importFrom(generics,tidy) importFrom(glmnet,cv.glmnet) +importFrom(glmnet,glmnet) importFrom(grDevices,dev.off) importFrom(graphics,abline) importFrom(graphics,axis) diff --git a/R/imports.R b/R/imports.R index dd810235c..6bbc12186 100644 --- a/R/imports.R +++ b/R/imports.R @@ -2,7 +2,7 @@ #' @importFrom broom glance tidy #' @importFrom dplyr %>% any_of bind_cols bind_rows filter group_by lead #' mutate n pull relocate row_number select summarize -#' @importFrom glmnet cv.glmnet +#' @importFrom glmnet cv.glmnet glmnet #' @importFrom graphics abline axis box par plot plot.new plot.window #' points rect text #' @importFrom lars lars diff --git a/R/sampler.R b/R/sampler.R index b5b8cceaf..576db39f2 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -181,7 +181,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, sampler.univ <- function(data, r, where, pred, formula, method, yname, k, calltype = "pred", user, ignore, - trimmer = "lars.filter", ...) { + trimmer = "remove.lindep", ...) { j <- yname[1L] if (calltype == "pred") { diff --git a/R/trim.predictors.R b/R/trim.predictors.R index c47ca98cd..0d941f5bf 100644 --- a/R/trim.predictors.R +++ b/R/trim.predictors.R @@ -42,21 +42,18 @@ #' to all variables. #' @rdname trim.predictors #' @examples -#' # Impute using the default (LARS filter) +#' # Impute using the default #' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE) #' -#' # LARS with no more than two predictors per variable -#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, max.predictors = 2) -#' -#' # Impute using the remove.lindep filter (classic MICE) -#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "remove.lindep") +#' # Impute using LARS +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") #' #' # Impute without a filter #' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") #' @export trim.predictors <- function( x, y, ry, - trimmer = "lars.filter", + trimmer = "remove.lindep", allow.na = TRUE, ...) { stopifnot(is.matrix(x), is.logical(ry)) @@ -71,8 +68,12 @@ trim.predictors <- function( # Exception 3: Keep all if there is only zero or one predictor # Exception 4: Keep all if the user specifies trimmer = "" keep <- rep.int(TRUE, ncol(x)) - } else if (trimmer == "lars.filter") { - keep <- lars.filter(x, y, ry, ...) + } else if (trimmer == "lars") { + keep <- lars.filter(x, y, ry, method = "lars", ...) + } else if (trimmer == "glmnet") { + keep <- lars.filter(x, y, ry, method = "glmnet", ...) + } else if (trimmer == "cv.glmnet") { + keep <- lars.filter(x, y, ry, method = "cv.glmnet", ...) } else if (trimmer == "remove.lindep") { keep <- remove.lindep(x, y, ry, ...) } else { @@ -90,6 +91,7 @@ trim.predictors <- function( #' least angle regression (LARS). #' #' @inheritParams mice.impute.pmm +#' @param method The selection method #' @param ... Further arguments passed to \code{lars::lars}, like standard #' LARS arguments \code{type}, \code{intercept}, \code{eps} and #' \code{max.steps}, or tuning parameters \code{lars.relax} and @@ -101,7 +103,8 @@ trim.predictors <- function( #' rows before calling \code{lars()}. #' @rdname trim.predictors #' @export -lars.filter <- function(x, y, ry, ...) { +lars.filter <- function(x, y, ry, + method = c("lars", "glmnet", "cv.glmnet"), ...) { # If y is constant, predict from an intercept-only model yobs <- as.numeric(y[ry]) @@ -114,14 +117,21 @@ lars.filter <- function(x, y, ry, ...) { if (anyNA(xobs) || anyNA(yobs)) { idx <- complete.cases(xobs, yobs) if (sum(idx) == 0L) { - stop("The lars filter requires complete cases, but none were found.") + stop("The filter requires complete cases, but none were found.") } xobs <- xobs[idx, , drop = FALSE] yobs <- yobs[idx] } - # Call the lars filter - keep <- lars.internal(x = xobs, y = yobs, ...) + # Call the filter + if (method == "lars") { + keep <- lars.internal(x = xobs, y = yobs, ...) + } else if (method == "glmnet") { + keep <- glmnet.internal(x = xobs, y = yobs, method = "glmnet", ...) + } else if (method == "cv.glmnet") { + keep <- glmnet.internal(x = xobs, y = yobs, method = "cv.glmnet", ...) + } + return(keep) } @@ -142,10 +152,11 @@ lars.filter <- function(x, y, ry, ...) { #' @rdname trim.predictors #' @export lars.internal <- function( - x, y, type = "lar", intercept = TRUE, max.predictors = 20, eps = 1e-12, - lars.relax = 5, minimal.cp = 1, ...) { + x, y, type = "lar", intercept = TRUE, eps = 1e-12, + max.predictors = NULL, lars.relax = 5, minimal.cp = 1, ...) { + max.steps <- ifelse(is.null(max.predictors), ncol(x), max.predictors) model <- lars(x = x, y = y, type = type, intercept = intercept, - eps = eps, max.steps = min(ncol(x), max.predictors)) + eps = eps, max.steps = max.steps) if (any(model$R2 == 1)) { # work-around because Cp gives NaN for perfect fits coef_step <- coef(model, s = which(model$R2 == 1)) @@ -158,7 +169,44 @@ lars.internal <- function( step <- ifelse(length(step), step, 1L) coef_step <- coef(model, s = step) } - return(coef_step != 0) + keep <- coef_step != 0 + + # ind <- which(keep) + # cat("length indices: ", length(ind), "\n") + return(keep) +} + +#' Filter out predictors by glmnet +#' +#' @inheritParams glmnet::glmnet +#' @rdname trim.predictors +#' @export +glmnet.internal <- function(x, y, method = "cv.glmnet", + dfmax = NULL, ...) { + + dfmax <- ifelse(is.null(dfmax), ncol(x), dfmax) + + if (method == "glmnet") { + # select max.predictors predictors with LASSO + fit <- glmnet::glmnet(x = x, y = y, dfmax = dfmax, ...) + valid_indices <- which(fit$df <= dfmax) + closest_index <- valid_indices[which.max(fit$df[valid_indices])] + lambda <- fit$lambda[closest_index] + } else if (method == "cv.glmnet") { + # select predictors with cross-validation + fit <- glmnet::cv.glmnet(x = x, y = y, dfmax = dfmax, ...) + lambda <- fit$lambda.min + } else { + stop("Unknown method.") + } + + # select predictors + coefs <- coef(fit, s = lambda) + keep <- as.logical(coefs[-1, ] != 0) + + # ind <- which(keep) + # cat("length indices: ", length(ind), "\n") + return(keep) } #' Filter out constant and multi-collinear predictors before imputation diff --git a/man/trim.predictors.Rd b/man/trim.predictors.Rd index ca476b606..52345bdf7 100644 --- a/man/trim.predictors.Rd +++ b/man/trim.predictors.Rd @@ -4,25 +4,28 @@ \alias{trim.predictors} \alias{lars.filter} \alias{lars.internal} +\alias{glmnet.internal} \alias{remove.lindep} \title{Trim the predictor set before univariate imputation} \usage{ -trim.predictors(x, y, ry, trimmer = "lars.filter", allow.na = TRUE, ...) +trim.predictors(x, y, ry, trimmer = "remove.lindep", allow.na = TRUE, ...) -lars.filter(x, y, ry, ...) +lars.filter(x, y, ry, method = c("lars", "glmnet", "cv.glmnet"), ...) lars.internal( x, y, type = "lar", intercept = TRUE, - max.predictors = 20, eps = 1e-12, + max.predictors = NULL, lars.relax = 5, minimal.cp = 1, ... ) +glmnet.internal(x, y, method = "cv.glmnet", dfmax = NULL, ...) + remove.lindep(x, y, ry, eps = 1e-04, maxcor = 0.99, frame = 4, ...) } \arguments{ @@ -50,6 +53,8 @@ LARS arguments \code{type}, \code{intercept}, \code{eps} and \code{max.steps}, or tuning parameters \code{lars.relax} and \code{minimal.cp}.} +\item{method}{The selection method} + \item{type}{Character. The type of LARS model to fit. The default is "lar".} \item{intercept}{ @@ -57,15 +62,15 @@ if TRUE, an intercept is included in the model (and not penalized), otherwise no intercept is included. Default is TRUE. } -\item{max.predictors}{Integer. The maximum number of variables to include -in the LARS model. The default is 20.} - \item{eps}{Numeric. Used by \code{remove.lindep()} as the threshold for the ratio of the smallest to the largest eigenvalue of the correlation matrix. The default is 1e-04. If the user sets \code{eps = 0}, all variables are returned (for backward compatibility). Used by \code{lars.internal()} as an argument to the \code{lars::lars()} function.} +\item{max.predictors}{Integer. The maximum number of variables to include +in the LARS model. The default is 20.} + \item{lars.relax}{Numeric. The percentage of the minimum Cp value that is added to the minimum Cp to relax the inclusion threshold. The default is 5. Use 1-5 percent for a slightly more permissive filter, 5-10 percent @@ -76,6 +81,9 @@ become negative for small samples. \code{minimal_cp} is the minimum "Cp" value that is is used as to define the threshold for the filter. Higher values select more predictors. The default is 1.} +\item{dfmax}{Limit the maximum number of variables in the model. Useful for +very large \code{nvars}, if a partial path is desired.} + \item{maxcor}{Numeric. The maximum correlation between a predictor and the target variable. The default is 0.99.} @@ -131,14 +139,11 @@ by \code{trim.predictors()}. The function is not the default anymore, but will remain part of the package for backward compatibility. } \examples{ -# Impute using the default (LARS filter) +# Impute using the default imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE) -# LARS with no more than two predictors per variable -imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, max.predictors = 2) - -# Impute using the remove.lindep filter (classic MICE) -imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "remove.lindep") +# Impute using LARS +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") # Impute without a filter imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") From b6de52c03ba664b5cc59cc1f494d5a6b6fbefac9 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 20 Oct 2024 17:29:19 +0200 Subject: [PATCH 013/147] Non-functional commit to save work --- NAMESPACE | 9 +- R/design.R | 2 + R/sampler.R | 45 ++-- R/trim.data.R | 318 +++++++++++++++++++++++++ R/trim.predictors.R | 283 ---------------------- man/trim.data.Rd | 156 ++++++++++++ man/trim.predictors.Rd | 150 ------------ tests/testthat/test-blocks.R | 2 +- tests/testthat/test-mice.impute.norm.R | 6 +- tests/testthat/test-pool.R | 2 +- tests/testthat/test-rbind.R | 2 +- 11 files changed, 510 insertions(+), 465 deletions(-) create mode 100644 R/trim.data.R delete mode 100644 R/trim.predictors.R create mode 100644 man/trim.data.Rd delete mode 100644 man/trim.predictors.Rd diff --git a/NAMESPACE b/NAMESPACE index 7a9a54203..234fa8d1a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -85,7 +85,6 @@ export(getfit) export(getqbar) export(glance) export(glm.mids) -export(glmnet.internal) export(ibind) export(ic) export(ici) @@ -94,8 +93,6 @@ export(is.mids) export(is.mipo) export(is.mira) export(is.mitml.result) -export(lars.filter) -export(lars.internal) export(lm.mids) export(make.blocks) export(make.blots) @@ -147,6 +144,10 @@ export(mice.impute.ri) export(mice.impute.sample) export(mice.mids) export(mice.theme) +export(mice.trim.cv.glmnet) +export(mice.trim.glmnet) +export(mice.trim.lars) +export(mice.trim.lindep) export(mids2mplus) export(mids2spss) export(mipo) @@ -172,7 +173,7 @@ export(squeeze) export(stripplot) export(supports.transparent) export(tidy) -export(trim.predictors) +export(trim.data) export(version) export(xyplot) exportClasses(mads) diff --git a/R/design.R b/R/design.R index d9e9c87d7..69400064d 100644 --- a/R/design.R +++ b/R/design.R @@ -1,4 +1,6 @@ obtain.design <- function(data, formula = ~.) { + # try out the following + # formula <- update(formula, . ~ . - 1) mf <- model.frame(formula, data = data, na.action = na.pass) model.matrix(formula, data = mf) } diff --git a/R/sampler.R b/R/sampler.R index 576db39f2..8f62249e2 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -114,13 +114,13 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, )) } else { stop("Cannot call function of type ", calltype, - call. = FALSE + call. = FALSE ) } if (is.null(imputes)) { stop("No imputations from ", theMethod, - h, - call. = FALSE + h, + call. = FALSE ) } for (j in names(imputes)) { @@ -136,7 +136,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, wy <- where[, j] ry <- r[, j] imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], - na.action = na.pass + na.action = na.pass ) data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] } @@ -181,9 +181,14 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, sampler.univ <- function(data, r, where, pred, formula, method, yname, k, calltype = "pred", user, ignore, - trimmer = "remove.lindep", ...) { + trimmer = "lindep", ...) { j <- yname[1L] + # nothing to impute + if (!any(where[, j])) { + return(numeric(0)) + } + if (calltype == "pred") { vars <- colnames(data)[pred != 0] xnames <- setdiff(vars, j) @@ -219,33 +224,29 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, names(type) <- colnames(x) } - # define y, ry and wy - y <- data[, j] - ry <- complete.cases(x, y) & r[, j] & !ignore - wy <- complete.cases(x) & where[, j] - - # nothing to impute - if (all(!wy)) { - return(numeric(0)) - } - - cc <- wy[where[, j]] - if (k == 1L) check.df(x, y, ry) - # select the features to feed into the imputation method - keep <- trim.predictors(x, y, ry, trimmer = trimmer, ...) - x <- x[, keep, drop = FALSE] - type <- type[keep] + keep <- trim.data(y = data[, j], + ry = r[, j] & !ignore, + x = x, + trimmer = trimmer, ...) + # set up the data for the imputation method + wy <- complete.cases(x) & where[, j] + xt <- x[, names(keep$cols), drop = FALSE] + type <- type[names(keep$cols)] if (ncol(x) != length(type)) { stop("Internal error: length(type) != number of predictors") } + cc <- wy[where[, j]] + if (k == 1L) check.df(x = xt, y = data[, j], ry = r[, j]) + # here we go f <- paste("mice.impute", method, sep = ".") imputes <- data[wy, j] imputes[!cc] <- NA - args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type), user, list(...)) + args <- c(list(y = data[, j], ry = r[, j], x = xt, wy = wy, type = type), + user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } diff --git a/R/trim.data.R b/R/trim.data.R new file mode 100644 index 000000000..a1ad3359e --- /dev/null +++ b/R/trim.data.R @@ -0,0 +1,318 @@ +#' Trims rows and columns of predictors before univariate imputation +#' +#' \code{trim.data()} returns two array of logicals, one for rows and one +#' for columns. The function filters out predictors that do not contribute to +#' the prediction of the target variable. +#' The user can select one of the following trimming functions: +#' \code{mice.trim.lindep()} (a wrapper of \code{remove.lindep()}), +#' \code{mice.trim.lars()}, \code{mice.trim.glmnet()}, +#' \code{mice.trim.cv.glmnet()} or call a custom trimming function. +#' +#' @param y Numeric vector of length \code{length(y)} with the target variable. +#' If not numeric, it will be converted to numeric. +#' @param ry Logical vector of length \code{length(ry)} indicating which +#' observations are observed for the target variable. +#' @param x Numeric design matrix with predictors, for example, as produced +#' by \code{model.matrix()}. The matrix must have the same number of rows as +#' \code{length(y)} and \code{length(ry)}. +#' @param trimmer A string identifying the trimming function. The default is +#' \code{"lindep"}, which call \code{mice.trim.lindep()}. Other trimmers +#' include \code{"lars"}, \code{"glmnet"}, \code{"cv.glmnet"} or a custom +#' trimming function. Turn off trimming by \code{trimmer = ""}. +#' @param allow.na Logical. If \code{TRUE}, allow imputation of fully +#' missing \code{y}. Typically, this only occurs for passive imputation. +#' The default is \code{TRUE}. +#' @return A list with elements named \code{"rows"} and \code{"cols"}, +#' logical vectors of lengths \code{nrow(x)} and \code{ncol(x)}, respectively. +#' @details +#' Filtering works on the design matrix \code{x}. The filter excludes columns +#' that do not contribute to the prediction of \code{y[ry]}. The filter +#' may omit columns of \code{x}. Removing a column that corresponds to a factor +#' level is equivalent to collapsing that level to the reference category. +#' +#' The function bypasses the column trimmer in the following cases: +#' \describe{ +#' \item{1}{If \code{y} is allowed to be fully missing.} +#' \item{2}{If there are zero or 1 entries of \code{y} observed.} +#' \item{3}{If there are zero or 1 predictors.} +#' \item{4}{If the user specifies \code{trimmer = ""}.} +#' } +#' +#' Trimmers like \code{mice.trim.lars()} change the behaviour of the +#' MICE algorithm. They are more aggressive in removing predictors than +#' the classic \code{remove.lindep()} function. For backward compatibility, +#' set \code{trimmer = "lindep"} to your call like +#' \code{mice(..., trimmer = "lindep")}. +#' @rdname trim.data +#' @examples +#' # Impute according to old baheviour (remove.lindep()) +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lindep") +#' +#' # Trim predictors using LARS +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") +#' +#' # Impute without a trim function +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") +#' @export +trim.data <- function( + y, ry, x, trimmer = "lindep", allow.na = TRUE, ...) { + stopifnot(is.matrix(x), is.logical(ry)) + stopifnot(length(y) == length(ry), nrow(x) == length(y)) + + # handle exceptions to bypass trimming + if (allow.na && sum(ry) == 0L || + sum(ry) <= 1L || + ncol(x) <= 1L || + trimmer == "") { + keep <- list(rows = ry & complete.cases(x, y), + cols = !logical(ncol(x))) + } else if (trimmer == "lindep") { + keep <- mice.trim.lindep(y, ry, x, ...) + } else if (trimmer == "lars") { + keep <- mice.trim.lars(y, ry, x, ...) + } else if (trimmer == "glmnet") { + keep <- mice.trim.glmnet(y, ry, x, ...) + } else if (trimmer == "cv.glmnet") { + keep <- mice.trim.cv.glmnet(y, ry, x, ...) + } else { + # handle user-specified trimmer + args <- c(list(y = y, ry = ry, x = x), list(...)) + keep <- do.call(paste0("mice.trim.", trimmer), args = args) + } + + return(keep) +} + +#' Removes constant and multi-collinear predictors +#' +#' \code{mice.trim.lindep()} is a wrapper of \code{remove.lindep()}, the +#' classic MICE safety net to prevent multicollinearity and other numerical +#' problems. +#' +#' @param frame Integer. The frame number for logging. Do not alter. +#' @rdname trim.data +#' @export +mice.trim.lindep <- function(y, ry, x, frame = 5, ...) { + xobs <- x[ry, , drop = FALSE] + yobs <- as.numeric(y[ry]) + + keep <- list( + rows = complete.cases(x, y) & ry, + cols = remove.lindep(x, y, ry, frame = frame, ...) + ) + return(keep) +} + +#' +#' @inheritParams mice.impute.pmm +#' @param ... Further arguments passed to \code{lars::lars}, like standard +#' LARS arguments \code{type}, \code{intercept}, \code{eps} and +#' \code{max.steps}, or tuning parameters \code{lars.relax} and +#' \code{minimal.cp}. +#' @details +#' @rdname trim.data +#' @export + +#' Include predictors by least angle regression (LARS) +#' +#' \code{trim.lars()} is a fast way to filter predictors by least angle +#' regression (LARS). +#' +#' @inheritParams lars::lars +#' @inheritParams mice::mice.impute.pmm +#' @param lars.type Character. The type of model to fit. Could be +#' \code{"lar"}, \code{"lasso"}, \code{"forward.stagewise"} or +#' \code{"stepwise"}. +#' @param max.predictors Integer. The maximum number of variables to include. +#' The default \code{NULL} does not use a maximum. +#' @param lars.relax Numeric. Percent minimum Cp value that is +#' added to the minimum Cp to relax the inclusion threshold. The default is 5. +#' Use 1-5 percent for a slightly more permissive filter, 5-10 percent +#' for a moderate permissive filter, and 10-20 percent for very permissive. +#' @param minimal.cp Numeric. The minimum \code{"Cp"} value to consider. +#' \code{"Cp"} may become negative for small samples. +#' \code{minimal_cp} is the minimum \code{"Cp"} value used as to define the +#' threshold for the filter. Higher values select more predictors. +#' The default is 1. +#' @returns A logical vector of length \code{ncol(x)} indicating the predictors +#' to keep. +#' @details +#' \code{mice.trim.lars()} fits a LARS model to the elements of the design +#' matrix \code{x} and the target variable \code{y[ry]}. The procedure +#' removes rows with missing data before calling \code{lars()}. +#' @return A logical vector of length \code{ncol(x)} indicating which predictors +#' to keep. +#' @rdname trim.data +#' @export +mice.trim.lars <- function( + y, ry, x, + lars.type = c("lar", "lasso", "forward.stagewise", "stepwise"), + max.predictors = NULL, lars.relax = 5, minimal.cp = 1, ...) { + + keep <- trim.preprocess(y, ry, x) + if (!any(keep$cols) || !any(keep$rows)) { + return(keep) + } + yobs <- as.numeric(y[keep$rows]) + xobs <- x[keep$rows, keep$cols, drop = FALSE] + + max.steps <- ifelse(is.null(max.predictors), ncol(xobs), max.predictors) + model <- lars(x = xobs, y = yobs, type = lars.type, eps = eps, + max.steps = max.steps) + if (any(model$R2 == 1)) { + # work-around because Cp gives NaN for perfect fits + coef_step <- coef(model, s = which(model$R2 == 1)) + } else { + # find the step where Cp is minimal + cp <- model$Cp + min_cp <- max(min(cp), minimal.cp) # SvB small sample adjustment + threshold <- min_cp + (lars.relax * min_cp) / 100 + step <- tail(which(cp <= threshold), n = 1L) + step <- ifelse(length(step), step, 1L) + coef_step <- coef(model, s = step) + } + + keep$cols <- coef_step != 0 + return(keep) +} + +#' Filter out predictors by glmnet +#' +#' @inheritParams glmnet::glmnet +#' @rdname trim.data +#' @export +mice.trim.glmnet <- function(y, ry, x, dfmax = NULL, ...) { + + keep <- trim.preprocess(y, ry, x) + if (!any(keep$cols) || !any(keep$rows)) { + return(keep) + } + yobs <- as.numeric(y[keep$rows]) + xobs <- x[keep$rows, keep$cols, drop = FALSE] + + # select max.predictors predictors with LASSO + dfmax <- ifelse(is.null(dfmax), ncol(x), dfmax) + fit <- glmnet::glmnet(x = xobs, y = yobs, dfmax = dfmax, ...) + valid_indices <- which(fit$df <= dfmax) + closest_index <- valid_indices[which.max(fit$df[valid_indices])] + lambda <- fit$lambda[closest_index] + + # select predictors + coefs <- coef(fit, s = lambda) + keep$cols <- as.logical(coefs[-1, ] != 0) + return(keep) +} + +#' Filter out predictors by cv.glmnet +#' +#' @inheritParams glmnet::glmnet +#' @rdname trim.data +#' @export +mice.trim.cv.glmnet <- function(y, ry, x, dfmax = NULL, ...) { + + keep <- trim.preprocess(y, ry, x) + if (!any(keep$cols) || !any(keep$rows)) { + return(keep) + } + yobs <- as.numeric(y[keep$rows]) + xobs <- x[keep$rows, keep$cols, drop = FALSE] + + # select predictors with cross-validation + dfmax <- ifelse(is.null(dfmax), ncol(x), dfmax) + fit <- glmnet::cv.glmnet(x = xobs, y = yobs, dfmax = dfmax, ...) + lambda <- fit$lambda.min + + # select predictors + coefs <- coef(fit, s = lambda) + keep$cols <- as.logical(coefs[-1, ] != 0) + return(keep) +} + +trim.preprocess <- function(y, ry, x) { + # Common actions for trimmers + # 1) if y is constant, remove all predictors + # 2) subset rows to obtain complete cases + # Returns: rows, cols + cols <- !logical(ncol(x)) + + # Safety: If yobs is constant, predict from an intercept-only model + yobs <- as.numeric(y[ry]) + if (var(yobs, na.rm = TRUE) < 1000 * .Machine$double.eps) { + cols <- logical(ncol(x)) + } + + return(list(rows = ry & complete.cases(x, y), + cols = cols)) +} + +#' Filter out constant and multi-collinear predictors before imputation +#' +#' \code{remove.lindep()} prevents multicollinearity +#' in the imputation model. It removes predictors that are constant or have +#' too high correlation with the target variable. The function +#' uses the eigenvalues of the correlation matrix to detect multicollinearity. +#' +#' @inheritParams mice.impute.pmm +#' @param eps Numeric. Used by \code{remove.lindep()} as the threshold for +#' the ratio of the smallest to the largest eigenvalue of the correlation +#' matrix. The default is 1e-04. Setting \code{eps = 0} bypasses +#' \code{remove.lindep()} and returns all variables. +#' Note: In \code{lars.trimmer()} the \code{eps} argument has a different +#' meaning. +#' @param maxcor Numeric. The maximum correlation between a predictor and the +#' target variable. The default is 0.99. +#' @param frame Integer. The frame number for logging. Do not alter. +#' @details +#' \code{remove.lindep()} is the classic MICE safety net to prevent +#' multicollinearity and other numerical problems. It is a more +#' conservative filter than \code{trim.lars()}. +#' @rdname trim.data +#' @export +remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, + frame = 4, ...) { + # setting eps = 0 bypasses remove.lindep() + # for compatibility with previous versions + if (eps == 0) { + return(rep.int(TRUE, ncol(x))) + } + + xobs <- x[ry, , drop = FALSE] + yobs <- as.numeric(y[ry]) + if (var(yobs) < abs(eps)) { + return(rep(FALSE, ncol(xobs))) + } + + cols <- unlist(apply(xobs, 2, var) > 1000 * .Machine$double.eps) + cols[is.na(cols)] <- FALSE + highcor <- suppressWarnings(unlist(apply(xobs, 2, cor, yobs) < maxcor)) + cols <- cols & highcor + if (all(!cols)) { + updateLog( + out = "All predictors are constant or have too high correlation.", + frame = frame + ) + } + + # no need to calculate correlations, so return + k <- sum(cols) + if (k <= 1L) { + return(cols) + } # at most one TRUE + + # correlation between x's + cx <- cor(xobs[, cols, drop = FALSE], use = "all.obs") + eig <- eigen(cx, symmetric = TRUE) + ncx <- cx + while (eig$values[k] / eig$values[1] < abs(eps)) { + j <- seq_len(k)[order(abs(eig$vectors[, k]), decreasing = TRUE)[1]] + cols[cols][j] <- FALSE + ncx <- cx[cols[cols], cols[cols], drop = FALSE] + k <- k - 1 + eig <- eigen(ncx) + } + if (!all(cols)) { + out <- paste(dimnames(x)[[2]][!cols], collapse = ", ") + updateLog(out = out, frame = frame) + } + return(cols) +} diff --git a/R/trim.predictors.R b/R/trim.predictors.R deleted file mode 100644 index 0d941f5bf..000000000 --- a/R/trim.predictors.R +++ /dev/null @@ -1,283 +0,0 @@ -#' Trim the predictor set before univariate imputation -#' -#' The function \code{trim.predictors()} filters out predictors before -#' univariate imputation. The function is a wrapper for the trimming -#' functions \code{lars.filter()}, \code{remove.lindep()}, or a user-specified -#' trimming function. The default method is \code{trimmer = "lars.filter"}. -#' The function is called by \code{mice:::sampler.univ()} and not intended -#' for direct application by the user. -#' -#' @param x Numeric design matrix with predictors, for example, as produced -#' by \code{model.matrix()}. The matrix must have the same number of rows as -#' \code{length(y)} and \code{length(ry)}. -#' @param y Numeric vector of length \code{length(y)} with the target variable. -#' If not numeric, it will be converted to numeric. -#' @param ry Logical vector of length \code{length(ry)} indicating which -#' observations are observed for the target variable. -#' @param trimmer A character vector of length 1 specifying the name of the -#' trimming function. The default is \code{"lars.filter"}. Other options are -#' \code{"remove.lindep"} or \code{""}. The user can also specify the name of a -#' custom trimming function. -#' @param allow.na Logical. If \code{TRUE}, allow imputation of fully -#' missing \code{y}. This typically only occurs for passive imputation. -#' The default is \code{TRUE}. -#' @return A logical vector of length \code{ncol(x)} indicating which predictors -#' to keep. -#' @details -#' The function \code{lars.filter()} changes the behavior of the MICE algorithm. -#' It is far more agressive in removing predictors than the classic -#' \code{remove.lindep()} function. For backward compatibility, -#' add \code{trimmer = "remove.lindep"} to your call -#' \code{mice(..., trimmer = "remove.lindep")}. -#' -#' Observe that filtering works on the design matrix \code{x}. If this -#' matrix contains dummy codings of categorical variables, the filter -#' will test the contribution of separate dummy variables to the model. -#' Implicitly, this practice changes the categories of the ancestor -#' factors. A neater approach that avoids this problem would be to include -#' all dummy codings of a categorical variable as a block. This is currently -#' not implemented. -#' -#' The current implementation only supports a global trimmer that applies -#' to all variables. -#' @rdname trim.predictors -#' @examples -#' # Impute using the default -#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE) -#' -#' # Impute using LARS -#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") -#' -#' # Impute without a filter -#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") -#' @export -trim.predictors <- function( - x, y, ry, - trimmer = "remove.lindep", - allow.na = TRUE, - ...) { - stopifnot(is.matrix(x), is.logical(ry)) - stopifnot(length(y) == length(ry), nrow(x) == length(y)) - - if (allow.na && sum(ry) == 0L || - sum(ry) <= 1L || - ncol(x) <= 1L || - trimmer == "") { - # Exception 1: Keep all if we allow for a fully missing y - # Exception 2: Keep all if there are no or only one observed y - # Exception 3: Keep all if there is only zero or one predictor - # Exception 4: Keep all if the user specifies trimmer = "" - keep <- rep.int(TRUE, ncol(x)) - } else if (trimmer == "lars") { - keep <- lars.filter(x, y, ry, method = "lars", ...) - } else if (trimmer == "glmnet") { - keep <- lars.filter(x, y, ry, method = "glmnet", ...) - } else if (trimmer == "cv.glmnet") { - keep <- lars.filter(x, y, ry, method = "cv.glmnet", ...) - } else if (trimmer == "remove.lindep") { - keep <- remove.lindep(x, y, ry, ...) - } else { - # handle user-specified trimmer - args <- c(list(x = x, y = y, ry = ry), list(...)) - keep <- do.call(trimmer, args = args) - } - - return(keep) -} - -#' Filter out predictors by least angle regression (LARS) -#' -#' \code{lars.filter()} is a fast way to filter out predictors by -#' least angle regression (LARS). -#' -#' @inheritParams mice.impute.pmm -#' @param method The selection method -#' @param ... Further arguments passed to \code{lars::lars}, like standard -#' LARS arguments \code{type}, \code{intercept}, \code{eps} and -#' \code{max.steps}, or tuning parameters \code{lars.relax} and -#' \code{minimal.cp}. -#' @details -#' \code{lars.filter} fits the LARS model to the elements of the design matrix -#' \code{x} and the target variable \code{y}, as indicated by \code{ry}. -#' If these data contain missing data, the function will remove the relevant -#' rows before calling \code{lars()}. -#' @rdname trim.predictors -#' @export -lars.filter <- function(x, y, ry, - method = c("lars", "glmnet", "cv.glmnet"), ...) { - - # If y is constant, predict from an intercept-only model - yobs <- as.numeric(y[ry]) - if (var(yobs, na.rm = TRUE) < 1000 * .Machine$double.eps) { - return(rep(FALSE, ncol(x))) - } - - # If needed, subset data because lars() requires complete data - xobs <- x[ry, , drop = FALSE] - if (anyNA(xobs) || anyNA(yobs)) { - idx <- complete.cases(xobs, yobs) - if (sum(idx) == 0L) { - stop("The filter requires complete cases, but none were found.") - } - xobs <- xobs[idx, , drop = FALSE] - yobs <- yobs[idx] - } - - # Call the filter - if (method == "lars") { - keep <- lars.internal(x = xobs, y = yobs, ...) - } else if (method == "glmnet") { - keep <- glmnet.internal(x = xobs, y = yobs, method = "glmnet", ...) - } else if (method == "cv.glmnet") { - keep <- glmnet.internal(x = xobs, y = yobs, method = "cv.glmnet", ...) - } - - return(keep) -} - -#' Filter out predictors by least angular regression -#' -#' @inheritParams lars::lars -#' @param type Character. The type of LARS model to fit. The default is "lar". -#' @param max.predictors Integer. The maximum number of variables to include -#' in the LARS model. The default is 20. -#' @param lars.relax Numeric. The percentage of the minimum Cp value that is -#' added to the minimum Cp to relax the inclusion threshold. The default is 5. -#' Use 1-5 percent for a slightly more permissive filter, 5-10 percent -#' for a moderate permissive filter, and 10-20 percent for very permissive. -#' @param minimal.cp Numeric. The minimum "Cp" value to consider. "Cp" may -#' become negative for small samples. \code{minimal_cp} is the minimum "Cp" value -#' that is is used as to define the threshold for the filter. Higher values -#' select more predictors. The default is 1. -#' @rdname trim.predictors -#' @export -lars.internal <- function( - x, y, type = "lar", intercept = TRUE, eps = 1e-12, - max.predictors = NULL, lars.relax = 5, minimal.cp = 1, ...) { - max.steps <- ifelse(is.null(max.predictors), ncol(x), max.predictors) - model <- lars(x = x, y = y, type = type, intercept = intercept, - eps = eps, max.steps = max.steps) - if (any(model$R2 == 1)) { - # work-around because Cp gives NaN for perfect fits - coef_step <- coef(model, s = which(model$R2 == 1)) - } else { - # find the step where Cp is minimal - cp <- model$Cp - min_cp <- max(min(cp), minimal.cp) # SvB small sample adjustment - threshold <- min_cp + (lars.relax * min_cp) / 100 - step <- tail(which(cp <= threshold), n = 1L) - step <- ifelse(length(step), step, 1L) - coef_step <- coef(model, s = step) - } - keep <- coef_step != 0 - - # ind <- which(keep) - # cat("length indices: ", length(ind), "\n") - return(keep) -} - -#' Filter out predictors by glmnet -#' -#' @inheritParams glmnet::glmnet -#' @rdname trim.predictors -#' @export -glmnet.internal <- function(x, y, method = "cv.glmnet", - dfmax = NULL, ...) { - - dfmax <- ifelse(is.null(dfmax), ncol(x), dfmax) - - if (method == "glmnet") { - # select max.predictors predictors with LASSO - fit <- glmnet::glmnet(x = x, y = y, dfmax = dfmax, ...) - valid_indices <- which(fit$df <= dfmax) - closest_index <- valid_indices[which.max(fit$df[valid_indices])] - lambda <- fit$lambda[closest_index] - } else if (method == "cv.glmnet") { - # select predictors with cross-validation - fit <- glmnet::cv.glmnet(x = x, y = y, dfmax = dfmax, ...) - lambda <- fit$lambda.min - } else { - stop("Unknown method.") - } - - # select predictors - coefs <- coef(fit, s = lambda) - keep <- as.logical(coefs[-1, ] != 0) - - # ind <- which(keep) - # cat("length indices: ", length(ind), "\n") - return(keep) -} - -#' Filter out constant and multi-collinear predictors before imputation -#' -#' \code{remove.lindep()} prevents multicollinearity -#' in the imputation model. It removes predictors that are constant or have -#' too high correlation with the target variable. The function -#' uses the eigenvalues of the correlation matrix to detect multicollinearity. -#' -#' @inheritParams mice.impute.pmm -#' @param eps Numeric. Used by \code{remove.lindep()} as the threshold for -#' the ratio of the smallest to the largest eigenvalue of the correlation -#' matrix. The default is 1e-04. If the user sets \code{eps = 0}, -#' all variables are returned (for backward compatibility). Used by -#' \code{lars.internal()} as an argument to the \code{lars::lars()} function. -#' @param maxcor Numeric. The maximum correlation between a predictor and the -#' target variable. The default is 0.99. -#' @param frame Integer. The frame number for logging. Do not alter. -#' @details -#' \code{remove.lindep()} is the classic MICE safety net to prevent -#' multicollinearity and other numerical problems. It is a far more -#' conservative filter than \code{lars.filter()}. The function is called -#' by \code{trim.predictors()}. The function is not the default anymore, but -#' will remain part of the package for backward compatibility. -#' @rdname trim.predictors -#' @export -remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, - frame = 4, ...) { - # setting eps = 0 bypasses remove.lindep() - # for compatibility with previous versions - if (eps == 0) { - return(rep.int(TRUE, ncol(x))) - } - - xobs <- x[ry, , drop = FALSE] - yobs <- as.numeric(y[ry]) - if (var(yobs) < abs(eps)) { - return(rep(FALSE, ncol(xobs))) - } - - keep <- unlist(apply(xobs, 2, var) > 1000 * .Machine$double.eps) - keep[is.na(keep)] <- FALSE - highcor <- suppressWarnings(unlist(apply(xobs, 2, cor, yobs) < maxcor)) - keep <- keep & highcor - if (all(!keep)) { - updateLog( - out = "All predictors are constant or have too high correlation.", - frame = frame - ) - } - - # no need to calculate correlations, so return - k <- sum(keep) - if (k <= 1L) { - return(keep) - } # at most one TRUE - - # correlation between x's - cx <- cor(xobs[, keep, drop = FALSE], use = "all.obs") - eig <- eigen(cx, symmetric = TRUE) - ncx <- cx - while (eig$values[k] / eig$values[1] < abs(eps)) { - j <- seq_len(k)[order(abs(eig$vectors[, k]), decreasing = TRUE)[1]] - keep[keep][j] <- FALSE - ncx <- cx[keep[keep], keep[keep], drop = FALSE] - k <- k - 1 - eig <- eigen(ncx) - } - if (!all(keep)) { - out <- paste(dimnames(x)[[2]][!keep], collapse = ", ") - updateLog(out = out, frame = frame) - } - return(keep) -} diff --git a/man/trim.data.Rd b/man/trim.data.Rd new file mode 100644 index 000000000..b86ce29b3 --- /dev/null +++ b/man/trim.data.Rd @@ -0,0 +1,156 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/trim.data.R +\name{trim.data} +\alias{trim.data} +\alias{mice.trim.lindep} +\alias{mice.trim.lars} +\alias{mice.trim.glmnet} +\alias{mice.trim.cv.glmnet} +\alias{remove.lindep} +\title{Trims rows and columns of predictors before univariate imputation} +\usage{ +trim.data(y, ry, x, trimmer = "lindep", allow.na = TRUE, ...) + +mice.trim.lindep(y, ry, x, frame = 5, ...) + +mice.trim.lars( + y, + ry, + x, + lars.type = c("lar", "lasso", "forward.stagewise", "stepwise"), + max.predictors = NULL, + lars.relax = 5, + minimal.cp = 1, + ... +) + +mice.trim.glmnet(y, ry, x, dfmax = NULL, ...) + +mice.trim.cv.glmnet(y, ry, x, dfmax = NULL, ...) + +remove.lindep(x, y, ry, eps = 1e-04, maxcor = 0.99, frame = 4, ...) +} +\arguments{ +\item{y}{Numeric vector of length \code{length(y)} with the target variable. +If not numeric, it will be converted to numeric.} + +\item{ry}{Logical vector of length \code{length(ry)} indicating which +observations are observed for the target variable.} + +\item{x}{Numeric design matrix with predictors, for example, as produced +by \code{model.matrix()}. The matrix must have the same number of rows as +\code{length(y)} and \code{length(ry)}.} + +\item{trimmer}{A string identifying the trimming function. The default is +\code{"lindep"}, which call \code{mice.trim.lindep()}. Other trimmers +include \code{"lars"}, \code{"glmnet"}, \code{"cv.glmnet"} or a custom +trimming function. Turn off trimming by \code{trimmer = ""}.} + +\item{allow.na}{Logical. If \code{TRUE}, allow imputation of fully +missing \code{y}. Typically, this only occurs for passive imputation. +The default is \code{TRUE}.} + +\item{...}{Further arguments passed to \code{lars::lars}, like standard +LARS arguments \code{type}, \code{intercept}, \code{eps} and +\code{max.steps}, or tuning parameters \code{lars.relax} and +\code{minimal.cp}.} + +\item{frame}{Integer. The frame number for logging. Do not alter.} + +\item{lars.type}{Character. The type of model to fit. Could be +\code{"lar"}, \code{"lasso"}, \code{"forward.stagewise"} or +\code{"stepwise"}.} + +\item{max.predictors}{Integer. The maximum number of variables to include. +The default \code{NULL} does not use a maximum.} + +\item{lars.relax}{Numeric. Percent minimum Cp value that is +added to the minimum Cp to relax the inclusion threshold. The default is 5. +Use 1-5 percent for a slightly more permissive filter, 5-10 percent +for a moderate permissive filter, and 10-20 percent for very permissive.} + +\item{minimal.cp}{Numeric. The minimum \code{"Cp"} value to consider. +\code{"Cp"} may become negative for small samples. +\code{minimal_cp} is the minimum \code{"Cp"} value used as to define the +threshold for the filter. Higher values select more predictors. +The default is 1.} + +\item{dfmax}{Limit the maximum number of variables in the model. Useful for +very large \code{nvars}, if a partial path is desired.} + +\item{eps}{Numeric. Used by \code{remove.lindep()} as the threshold for +the ratio of the smallest to the largest eigenvalue of the correlation +matrix. The default is 1e-04. Setting \code{eps = 0} bypasses +\code{remove.lindep()} and returns all variables. +Note: In \code{lars.trimmer()} the \code{eps} argument has a different +meaning.} + +\item{maxcor}{Numeric. The maximum correlation between a predictor and the +target variable. The default is 0.99.} +} +\value{ +A list with elements named \code{"rows"} and \code{"cols"}, +logical vectors of lengths \code{nrow(x)} and \code{ncol(x)}, respectively. + +A logical vector of length \code{ncol(x)} indicating the predictors +to keep. + +A logical vector of length \code{ncol(x)} indicating which predictors +to keep. +} +\description{ +\code{trim.data()} returns two array of logicals, one for rows and one +for columns. The function filters out predictors that do not contribute to +the prediction of the target variable. +The user can select one of the following trimming functions: +\code{mice.trim.lindep()} (a wrapper of \code{remove.lindep()}), +\code{mice.trim.lars()}, \code{mice.trim.glmnet()}, +\code{mice.trim.cv.glmnet()} or call a custom trimming function. + +\code{mice.trim.lindep()} is a wrapper of \code{remove.lindep()}, the +classic MICE safety net to prevent multicollinearity and other numerical +problems. + +\code{remove.lindep()} prevents multicollinearity +in the imputation model. It removes predictors that are constant or have +too high correlation with the target variable. The function +uses the eigenvalues of the correlation matrix to detect multicollinearity. +} +\details{ +Filtering works on the design matrix \code{x}. The filter excludes columns +that do not contribute to the prediction of \code{y[ry]}. The filter +may omit columns of \code{x}. Removing a column that corresponds to a factor +level is equivalent to collapsing that level to the reference category. + +The function bypasses the column trimmer in the following cases: +\describe{ + \item{1}{If \code{y} is allowed to be fully missing.} + \item{2}{If there are zero or 1 entries of \code{y} observed.} + \item{3}{If there are zero or 1 predictors.} + \item{4}{If the user specifies \code{trimmer = ""}.} +} + +Trimmers like \code{mice.trim.lars()} change the behaviour of the +MICE algorithm. They are more aggressive in removing predictors than +the classic \code{remove.lindep()} function. For backward compatibility, +set \code{trimmer = "lindep"} to your call like +\code{mice(..., trimmer = "lindep")}. + +\code{mice.trim.lars()} fits a LARS model to the elements of the design +matrix \code{x} and the target variable \code{y[ry]}. The procedure +removes rows with missing data before calling \code{lars()}. + +\code{remove.lindep()} is the classic MICE safety net to prevent +multicollinearity and other numerical problems. It is a more +conservative filter than \code{trim.lars()}. +} +\examples{ +# Impute according to old baheviour (remove.lindep()) +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lindep") + +# Trim predictors using LARS +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") + +# Impute without a trim function +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") +} diff --git a/man/trim.predictors.Rd b/man/trim.predictors.Rd deleted file mode 100644 index 52345bdf7..000000000 --- a/man/trim.predictors.Rd +++ /dev/null @@ -1,150 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/trim.predictors.R -\name{trim.predictors} -\alias{trim.predictors} -\alias{lars.filter} -\alias{lars.internal} -\alias{glmnet.internal} -\alias{remove.lindep} -\title{Trim the predictor set before univariate imputation} -\usage{ -trim.predictors(x, y, ry, trimmer = "remove.lindep", allow.na = TRUE, ...) - -lars.filter(x, y, ry, method = c("lars", "glmnet", "cv.glmnet"), ...) - -lars.internal( - x, - y, - type = "lar", - intercept = TRUE, - eps = 1e-12, - max.predictors = NULL, - lars.relax = 5, - minimal.cp = 1, - ... -) - -glmnet.internal(x, y, method = "cv.glmnet", dfmax = NULL, ...) - -remove.lindep(x, y, ry, eps = 1e-04, maxcor = 0.99, frame = 4, ...) -} -\arguments{ -\item{x}{Numeric design matrix with predictors, for example, as produced -by \code{model.matrix()}. The matrix must have the same number of rows as -\code{length(y)} and \code{length(ry)}.} - -\item{y}{Numeric vector of length \code{length(y)} with the target variable. -If not numeric, it will be converted to numeric.} - -\item{ry}{Logical vector of length \code{length(ry)} indicating which -observations are observed for the target variable.} - -\item{trimmer}{A character vector of length 1 specifying the name of the -trimming function. The default is \code{"lars.filter"}. Other options are -\code{"remove.lindep"} or \code{""}. The user can also specify the name of a -custom trimming function.} - -\item{allow.na}{Logical. If \code{TRUE}, allow imputation of fully -missing \code{y}. This typically only occurs for passive imputation. -The default is \code{TRUE}.} - -\item{...}{Further arguments passed to \code{lars::lars}, like standard -LARS arguments \code{type}, \code{intercept}, \code{eps} and -\code{max.steps}, or tuning parameters \code{lars.relax} and -\code{minimal.cp}.} - -\item{method}{The selection method} - -\item{type}{Character. The type of LARS model to fit. The default is "lar".} - -\item{intercept}{ -if TRUE, an intercept is included in the model (and not penalized), -otherwise no intercept is included. Default is TRUE. -} - -\item{eps}{Numeric. Used by \code{remove.lindep()} as the threshold for -the ratio of the smallest to the largest eigenvalue of the correlation -matrix. The default is 1e-04. If the user sets \code{eps = 0}, -all variables are returned (for backward compatibility). Used by -\code{lars.internal()} as an argument to the \code{lars::lars()} function.} - -\item{max.predictors}{Integer. The maximum number of variables to include -in the LARS model. The default is 20.} - -\item{lars.relax}{Numeric. The percentage of the minimum Cp value that is -added to the minimum Cp to relax the inclusion threshold. The default is 5. -Use 1-5 percent for a slightly more permissive filter, 5-10 percent -for a moderate permissive filter, and 10-20 percent for very permissive.} - -\item{minimal.cp}{Numeric. The minimum "Cp" value to consider. "Cp" may -become negative for small samples. \code{minimal_cp} is the minimum "Cp" value -that is is used as to define the threshold for the filter. Higher values -select more predictors. The default is 1.} - -\item{dfmax}{Limit the maximum number of variables in the model. Useful for -very large \code{nvars}, if a partial path is desired.} - -\item{maxcor}{Numeric. The maximum correlation between a predictor and the -target variable. The default is 0.99.} - -\item{frame}{Integer. The frame number for logging. Do not alter.} -} -\value{ -A logical vector of length \code{ncol(x)} indicating which predictors -to keep. -} -\description{ -The function \code{trim.predictors()} filters out predictors before -univariate imputation. The function is a wrapper for the trimming -functions \code{lars.filter()}, \code{remove.lindep()}, or a user-specified -trimming function. The default method is \code{trimmer = "lars.filter"}. -The function is called by \code{mice:::sampler.univ()} and not intended -for direct application by the user. - -\code{lars.filter()} is a fast way to filter out predictors by -least angle regression (LARS). - -\code{remove.lindep()} prevents multicollinearity -in the imputation model. It removes predictors that are constant or have -too high correlation with the target variable. The function -uses the eigenvalues of the correlation matrix to detect multicollinearity. -} -\details{ -The function \code{lars.filter()} changes the behavior of the MICE algorithm. -It is far more agressive in removing predictors than the classic -\code{remove.lindep()} function. For backward compatibility, -add \code{trimmer = "remove.lindep"} to your call -\code{mice(..., trimmer = "remove.lindep")}. - -Observe that filtering works on the design matrix \code{x}. If this -matrix contains dummy codings of categorical variables, the filter -will test the contribution of separate dummy variables to the model. -Implicitly, this practice changes the categories of the ancestor -factors. A neater approach that avoids this problem would be to include -all dummy codings of a categorical variable as a block. This is currently -not implemented. - -The current implementation only supports a global trimmer that applies -to all variables. - -\code{lars.filter} fits the LARS model to the elements of the design matrix -\code{x} and the target variable \code{y}, as indicated by \code{ry}. -If these data contain missing data, the function will remove the relevant -rows before calling \code{lars()}. - -\code{remove.lindep()} is the classic MICE safety net to prevent -multicollinearity and other numerical problems. It is a far more -conservative filter than \code{lars.filter()}. The function is called -by \code{trim.predictors()}. The function is not the default anymore, but -will remain part of the package for backward compatibility. -} -\examples{ -# Impute using the default -imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE) - -# Impute using LARS -imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") - -# Impute without a filter -imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") -} diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 8b57529a3..4dc13209a 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -1,7 +1,7 @@ context("blocks") -imp <- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 10, print = FALSE) +imp <- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 1, print = FALSE) # plot(imp) test_that("removes variables from 'where'", { diff --git a/tests/testthat/test-mice.impute.norm.R b/tests/testthat/test-mice.impute.norm.R index 763d3c450..8227db8ad 100644 --- a/tests/testthat/test-mice.impute.norm.R +++ b/tests/testthat/test-mice.impute.norm.R @@ -64,9 +64,9 @@ test_that("Correct estimation method used", { # TEST 3: correct imputation model # ##################################### -expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "remove.lindep")) -expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "remove.lindep")) -expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "remove.lindep")) +expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) test_that("Imputations are equal", { expect_equal(imp.qr$imp, imp.svd$imp) diff --git a/tests/testthat/test-pool.R b/tests/testthat/test-pool.R index eb02d59d0..8a9b91d78 100644 --- a/tests/testthat/test-pool.R +++ b/tests/testthat/test-pool.R @@ -8,7 +8,7 @@ context("pool") # https://stefvanbuuren.name/fimd/ suppressWarnings(RNGversion("3.5.0")) -imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE, trimmer = "remove.lindep") +imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE, trimmer = "lindep") fit <- with(imp, lm(bmi ~ chl + age + hyp)) est <- pool(fit) # fitlist <- fit$analyses diff --git a/tests/testthat/test-rbind.R b/tests/testthat/test-rbind.R index ef88e0d82..ba7134e19 100644 --- a/tests/testthat/test-rbind.R +++ b/tests/testthat/test-rbind.R @@ -7,7 +7,7 @@ test_that("Constant variables are not imputed by default", { test_that("Constant variables are imputed for remove.constant = FALSE", { expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, - remove.constant = FALSE, trimmer = "remove.lindep")) + remove.constant = FALSE, trimmer = "lindep")) expect_equal(sum(is.na(complete(imp1b))), 0L) }) From 0bb29280128e7c4662e7aca3e42af36b90bbf971 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 09:36:07 +0200 Subject: [PATCH 014/147] Bypass trimming only if there a zero predictors --- R/trim.data.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/trim.data.R b/R/trim.data.R index a1ad3359e..9986fe75b 100644 --- a/R/trim.data.R +++ b/R/trim.data.R @@ -33,7 +33,7 @@ #' The function bypasses the column trimmer in the following cases: #' \describe{ #' \item{1}{If \code{y} is allowed to be fully missing.} -#' \item{2}{If there are zero or 1 entries of \code{y} observed.} +#' \item{2}{If there are zero entries of \code{y} observed.} #' \item{3}{If there are zero or 1 predictors.} #' \item{4}{If the user specifies \code{trimmer = ""}.} #' } @@ -62,7 +62,7 @@ trim.data <- function( # handle exceptions to bypass trimming if (allow.na && sum(ry) == 0L || sum(ry) <= 1L || - ncol(x) <= 1L || + ncol(x) < 1L || trimmer == "") { keep <- list(rows = ry & complete.cases(x, y), cols = !logical(ncol(x))) From a94942f88c32004a6177397f6a9f65335cbb7618 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 10:24:07 +0200 Subject: [PATCH 015/147] Extends remove.lindep() with argument expects.complete.x for more flexible handling of incomplete x --- R/trim.data.R | 41 +++++++++++++++++++++++++---------------- man/trim.data.Rd | 26 ++++++++++++++++++++------ 2 files changed, 45 insertions(+), 22 deletions(-) diff --git a/R/trim.data.R b/R/trim.data.R index 9986fe75b..91ba27fef 100644 --- a/R/trim.data.R +++ b/R/trim.data.R @@ -98,28 +98,19 @@ mice.trim.lindep <- function(y, ry, x, frame = 5, ...) { keep <- list( rows = complete.cases(x, y) & ry, - cols = remove.lindep(x, y, ry, frame = frame, ...) + cols = remove.lindep(x, y, ry, frame = frame, + expects.complete.x = FALSE, ...) ) return(keep) } -#' -#' @inheritParams mice.impute.pmm -#' @param ... Further arguments passed to \code{lars::lars}, like standard -#' LARS arguments \code{type}, \code{intercept}, \code{eps} and -#' \code{max.steps}, or tuning parameters \code{lars.relax} and -#' \code{minimal.cp}. -#' @details -#' @rdname trim.data -#' @export - #' Include predictors by least angle regression (LARS) #' #' \code{trim.lars()} is a fast way to filter predictors by least angle #' regression (LARS). #' #' @inheritParams lars::lars -#' @inheritParams mice::mice.impute.pmm +#' @inheritParams mice.impute.pmm #' @param lars.type Character. The type of model to fit. Could be #' \code{"lar"}, \code{"lasso"}, \code{"forward.stagewise"} or #' \code{"stepwise"}. @@ -262,6 +253,10 @@ trim.preprocess <- function(y, ry, x) { #' @param maxcor Numeric. The maximum correlation between a predictor and the #' target variable. The default is 0.99. #' @param frame Integer. The frame number for logging. Do not alter. +#' @param expects.complete.x Logical. If \code{TRUE}, the function expects +#' the data in \code{x} to be complete (used in mice <= 3.17.0). +#' If \code{FALSE}, the function uses only the observed values to calculate +#' (co)variances from incomplete data in \code{x}. #' @details #' \code{remove.lindep()} is the classic MICE safety net to prevent #' multicollinearity and other numerical problems. It is a more @@ -269,7 +264,21 @@ trim.preprocess <- function(y, ry, x) { #' @rdname trim.data #' @export remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, - frame = 4, ...) { + frame = 4, expects.complete.x = TRUE, ...) { + + # handle.incomplete is a flag to indicate what to do with incomplete x + if (expects.complete.x) { + # classic remove.lindep + na.rm <- FALSE + use1 <- "everything" + use2 <- "all.obs" + } else { + # more permissive remove.lindep + na.rm <- TRUE + use1 <- "pairwise.complete.obs" + use2 <- "pairwise.complete.obs" + } + # setting eps = 0 bypasses remove.lindep() # for compatibility with previous versions if (eps == 0) { @@ -282,9 +291,9 @@ remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, return(rep(FALSE, ncol(xobs))) } - cols <- unlist(apply(xobs, 2, var) > 1000 * .Machine$double.eps) + cols <- apply(xobs, 2, var, na.rm = na.rm) > 1000 * .Machine$double.eps cols[is.na(cols)] <- FALSE - highcor <- suppressWarnings(unlist(apply(xobs, 2, cor, yobs) < maxcor)) + highcor <- suppressWarnings(apply(xobs, 2, cor, yobs, use = use1) < maxcor) cols <- cols & highcor if (all(!cols)) { updateLog( @@ -300,7 +309,7 @@ remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, } # at most one TRUE # correlation between x's - cx <- cor(xobs[, cols, drop = FALSE], use = "all.obs") + cx <- cor(xobs[, cols, drop = FALSE], use = use2) eig <- eigen(cx, symmetric = TRUE) ncx <- cx while (eig$values[k] / eig$values[1] < abs(eps)) { diff --git a/man/trim.data.Rd b/man/trim.data.Rd index b86ce29b3..e6123d487 100644 --- a/man/trim.data.Rd +++ b/man/trim.data.Rd @@ -28,7 +28,16 @@ mice.trim.glmnet(y, ry, x, dfmax = NULL, ...) mice.trim.cv.glmnet(y, ry, x, dfmax = NULL, ...) -remove.lindep(x, y, ry, eps = 1e-04, maxcor = 0.99, frame = 4, ...) +remove.lindep( + x, + y, + ry, + eps = 1e-04, + maxcor = 0.99, + frame = 4, + expects.complete.x = TRUE, + ... +) } \arguments{ \item{y}{Numeric vector of length \code{length(y)} with the target variable. @@ -50,10 +59,7 @@ trimming function. Turn off trimming by \code{trimmer = ""}.} missing \code{y}. Typically, this only occurs for passive imputation. The default is \code{TRUE}.} -\item{...}{Further arguments passed to \code{lars::lars}, like standard -LARS arguments \code{type}, \code{intercept}, \code{eps} and -\code{max.steps}, or tuning parameters \code{lars.relax} and -\code{minimal.cp}.} +\item{...}{Other named arguments.} \item{frame}{Integer. The frame number for logging. Do not alter.} @@ -87,6 +93,11 @@ meaning.} \item{maxcor}{Numeric. The maximum correlation between a predictor and the target variable. The default is 0.99.} + +\item{expects.complete.x}{Logical. If \code{TRUE}, the function expects +the data in \code{x} to be complete (used in mice <= 3.17.0). +If \code{FALSE}, the function uses only the observed values to calculate +(co)variances from incomplete data in \code{x}.} } \value{ A list with elements named \code{"rows"} and \code{"cols"}, @@ -111,6 +122,9 @@ The user can select one of the following trimming functions: classic MICE safety net to prevent multicollinearity and other numerical problems. +\code{trim.lars()} is a fast way to filter predictors by least angle +regression (LARS). + \code{remove.lindep()} prevents multicollinearity in the imputation model. It removes predictors that are constant or have too high correlation with the target variable. The function @@ -125,7 +139,7 @@ level is equivalent to collapsing that level to the reference category. The function bypasses the column trimmer in the following cases: \describe{ \item{1}{If \code{y} is allowed to be fully missing.} - \item{2}{If there are zero or 1 entries of \code{y} observed.} + \item{2}{If there are zero entries of \code{y} observed.} \item{3}{If there are zero or 1 predictors.} \item{4}{If the user specifies \code{trimmer = ""}.} } From 821fada07268ad814b93c8e0847b97cd068b68fd Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 10:25:41 +0200 Subject: [PATCH 016/147] Evade creation of superfluous working variables --- R/sampler.R | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/R/sampler.R b/R/sampler.R index 8f62249e2..b8e01ecb2 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -231,21 +231,26 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, trimmer = trimmer, ...) # set up the data for the imputation method wy <- complete.cases(x) & where[, j] - xt <- x[, names(keep$cols), drop = FALSE] - type <- type[names(keep$cols)] + type <- type[keep$cols] if (ncol(x) != length(type)) { stop("Internal error: length(type) != number of predictors") } cc <- wy[where[, j]] - if (k == 1L) check.df(x = xt, y = data[, j], ry = r[, j]) + if (k == 1L) check.df(x = x[, names(keep$cols), drop = FALSE], + y = data[, j], + ry = keep$rows) # here we go f <- paste("mice.impute", method, sep = ".") imputes <- data[wy, j] imputes[!cc] <- NA - args <- c(list(y = data[, j], ry = r[, j], x = xt, wy = wy, type = type), + args <- c(list(y = data[, j], + ry = keep$rows, + x = x[, names(keep$cols), drop = FALSE], + wy = wy, + type = type), user, list(...)) imputes[cc] <- do.call(f, args = args) imputes From fcb336dafbf11150925abadfc752868566eee531 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 11:11:46 +0200 Subject: [PATCH 017/147] Correct the variable selection syntax for x --- R/sampler.R | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/R/sampler.R b/R/sampler.R index b8e01ecb2..61dc51ec9 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -230,14 +230,15 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, x = x, trimmer = trimmer, ...) # set up the data for the imputation method + cx <- names(keep$cols)[keep$cols] wy <- complete.cases(x) & where[, j] - type <- type[keep$cols] - if (ncol(x) != length(type)) { + type <- type[cx] + if (ncol(x[, cx, drop = FALSE]) != length(type)) { stop("Internal error: length(type) != number of predictors") } cc <- wy[where[, j]] - if (k == 1L) check.df(x = x[, names(keep$cols), drop = FALSE], + if (k == 1L) check.df(x = x[, cx, drop = FALSE], y = data[, j], ry = keep$rows) @@ -248,7 +249,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, args <- c(list(y = data[, j], ry = keep$rows, - x = x[, names(keep$cols), drop = FALSE], + x = x[, cx, drop = FALSE], wy = wy, type = type), user, list(...)) From 48c8bc6c6b4619d17b302c7416d301bea7731fa7 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 13:20:25 +0200 Subject: [PATCH 018/147] Clean up --- R/sampler.R | 63 ++++++++++++++++++++++++++++------------------------- 1 file changed, 33 insertions(+), 30 deletions(-) diff --git a/R/sampler.R b/R/sampler.R index 61dc51ec9..1bea99067 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -114,13 +114,13 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, )) } else { stop("Cannot call function of type ", calltype, - call. = FALSE + call. = FALSE ) } if (is.null(imputes)) { stop("No imputations from ", theMethod, - h, - call. = FALSE + h, + call. = FALSE ) } for (j in names(imputes)) { @@ -135,8 +135,10 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, for (j in b) { wy <- where[, j] ry <- r[, j] - imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], - na.action = na.pass + imp[[j]][, i] <- model.frame( + as.formula(theMethod), + data[wy, ], + na.action = na.pass ) data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] } @@ -225,34 +227,35 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, } # select the features to feed into the imputation method - keep <- trim.data(y = data[, j], - ry = r[, j] & !ignore, - x = x, - trimmer = trimmer, ...) - # set up the data for the imputation method - cx <- names(keep$cols)[keep$cols] + keep <- trim.data( + y = data[, j], + ry = r[, j] & !ignore, + x = x, + trimmer = trimmer, ... + ) + + # set up univariate imputation method + # wy: entries we wish to impute (length(y) elements) + # iy: entries we will impute (sum(wy) elements) wy <- complete.cases(x) & where[, j] - type <- type[cx] - if (ncol(x[, cx, drop = FALSE]) != length(type)) { - stop("Internal error: length(type) != number of predictors") - } + iy <- wy[where[, j]] - cc <- wy[where[, j]] - if (k == 1L) check.df(x = x[, cx, drop = FALSE], - y = data[, j], - ry = keep$rows) + # wipe out previous values + imputes <- data[wy, j] + imputes[!iy] <- NA # here we go f <- paste("mice.impute", method, sep = ".") - imputes <- data[wy, j] - imputes[!cc] <- NA - - args <- c(list(y = data[, j], - ry = keep$rows, - x = x[, cx, drop = FALSE], - wy = wy, - type = type), - user, list(...)) - imputes[cc] <- do.call(f, args = args) - imputes + args <- c( + list( + y = data[, j], + ry = keep$rows, + x = x[, keep$cols, drop = FALSE], + wy = wy, + type = type[keep$cols] + ), + user, list(...) + ) + imputes[iy] <- do.call(f, args = args) + return(imputes) } From af5e33bb30423ecd2337537b172291787c652527 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 13:25:08 +0200 Subject: [PATCH 019/147] Remove check.df() --- R/internal.R | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/R/internal.R b/R/internal.R index 8ed52ea50..878058e80 100644 --- a/R/internal.R +++ b/R/internal.R @@ -5,18 +5,6 @@ keep.in.model <- function(y, ry, x, wy) { impute.with.na <- function(x, wy) !complete.cases(x) & wy - -check.df <- function(x, y, ry) { - # if needed, writes the df warning message to the log - df <- sum(ry) - ncol(x) - 1 - mess <- paste("df set to 1. # observed cases:", sum(ry), " # predictors:", ncol(x) + 1) - if (df < 1 && sum(ry) > 0) { - updateLog(out = mess, frame = 4) - } -} - - - ## make list of collinear variables to remove find.collinear <- function(x, threshold = 0.999, ...) { nvar <- ncol(x) From 3e202280618f6695183decbf3126b4113876828f Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 13:48:41 +0200 Subject: [PATCH 020/147] Create a NEWS entry --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 8153ffce5..0d1782a48 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -* Adds a new method to filter features from the design matrix just before fitting the imputation model. A new function `lars.filter()` takes over duties of old `remove.lindep()` that has acted as a safety net for many years. The new method fits a LARS model, and keep the subset of variables that contribute to the model. The method is more robust and faster than `remove.lindep()` and can handle more complex situations. +* Adds a mechanism for filtering rows and selecting columns during the MICE iterations with univariate imputations. The method simplifies univariate imputation models by removing redundant predictors. The method is implemented in the top-level function `trim.data()`, which takes as input the design matrix `x`, the target variable `y` and the response `ry`, and returns a list of two logical vectors named `"rows"` (which filters rows of `x`) and `"cols"` (which selects columns of `x`). The user can choose among several low-level trimmers, including least angular regression, lasso, elastic net, and linear dependencies removal. It is also possible to specify your own low-level `mice.trim.mytrim()` function and call it from `mice()` using the `trimmer == "mytrim"` argument. The method is more robust and faster than `remove.lindep()` and can handle datasets with many variables. # mice 3.16.16 From bf35e577b77a7b7cbe8699b4ab97768e05e2da3e Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 21 Oct 2024 16:23:13 +0200 Subject: [PATCH 021/147] Update trim.data() documentation --- R/trim.data.R | 59 ++++++++++++++++++++++++++-------------------- man/trim.data.Rd | 61 ++++++++++++++++++++++++++++-------------------- 2 files changed, 70 insertions(+), 50 deletions(-) diff --git a/R/trim.data.R b/R/trim.data.R index 91ba27fef..bbd3c4256 100644 --- a/R/trim.data.R +++ b/R/trim.data.R @@ -1,12 +1,18 @@ #' Trims rows and columns of predictors before univariate imputation #' -#' \code{trim.data()} returns two array of logicals, one for rows and one -#' for columns. The function filters out predictors that do not contribute to -#' the prediction of the target variable. -#' The user can select one of the following trimming functions: -#' \code{mice.trim.lindep()} (a wrapper of \code{remove.lindep()}), -#' \code{mice.trim.lars()}, \code{mice.trim.glmnet()}, -#' \code{mice.trim.cv.glmnet()} or call a custom trimming function. +#' \code{trim.data()} filters out predictors that do not contribute to +#' the prediction of the target variable. It is called from within the +#' MICE algorithm. The user can select one of the following trimming functions +#' using the `trimmer` argument of the `mice()` function: +#' \describe{ +#' \item{\code{"lindep"}}{Remove constant and multi-collinear predictors.} +#' \item{\code{"lars"}}{Include predictors by least angle regression (LARS).} +#' \item{\code{"glmnet"}}{Filter out predictors by elastic net.} +#' \item{\code{"cv.glmnet"}}{Filter out predictors by cross validated elastic net.} +#' } +#' The relevant functions are \code{mice.trim.lindep()}, \code{mice.trim.lars()}, +#' \code{mice.trim.glmnet()} and \code{mice.trim.cv.glmnet()}. The user can +#' also specify and call a custom trimming function. #' #' @param y Numeric vector of length \code{length(y)} with the target variable. #' If not numeric, it will be converted to numeric. @@ -16,14 +22,14 @@ #' by \code{model.matrix()}. The matrix must have the same number of rows as #' \code{length(y)} and \code{length(ry)}. #' @param trimmer A string identifying the trimming function. The default is -#' \code{"lindep"}, which call \code{mice.trim.lindep()}. Other trimmers -#' include \code{"lars"}, \code{"glmnet"}, \code{"cv.glmnet"} or a custom -#' trimming function. Turn off trimming by \code{trimmer = ""}. +#' \code{"lindep"}. Other trimmers include \code{"lars"}, \code{"glmnet"}, +#' \code{"cv.glmnet"} or a custom trimming function. Turn off all trimming +#' by \code{trimmer = ""}. #' @param allow.na Logical. If \code{TRUE}, allow imputation of fully -#' missing \code{y}. Typically, this only occurs for passive imputation. -#' The default is \code{TRUE}. -#' @return A list with elements named \code{"rows"} and \code{"cols"}, -#' logical vectors of lengths \code{nrow(x)} and \code{ncol(x)}, respectively. +#' missing \code{y} (typically only useful for passive imputation). +#' The default is \code{allow.na = TRUE}. +#' @return A list with two elements named \code{"rows"} and \code{"cols"}, +#' logical vectors of lengths \code{nrow(x)} and \code{ncol(x)}. #' @details #' Filtering works on the design matrix \code{x}. The filter excludes columns #' that do not contribute to the prediction of \code{y[ry]}. The filter @@ -34,24 +40,26 @@ #' \describe{ #' \item{1}{If \code{y} is allowed to be fully missing.} #' \item{2}{If there are zero entries of \code{y} observed.} -#' \item{3}{If there are zero or 1 predictors.} +#' \item{3}{If there are zero predictors.} #' \item{4}{If the user specifies \code{trimmer = ""}.} #' } #' #' Trimmers like \code{mice.trim.lars()} change the behaviour of the #' MICE algorithm. They are more aggressive in removing predictors than -#' the classic \code{remove.lindep()} function. For backward compatibility, -#' set \code{trimmer = "lindep"} to your call like -#' \code{mice(..., trimmer = "lindep")}. +#' the classic \code{remove.lindep()} method. For datasets with many +#' columns, our current recommendation is \code{trimmer = "lars"} in +#' combination with the `max.predictors` argument. #' @rdname trim.data +#' @author Stef van Buuren, Oct 2024 #' @examples -#' # Impute according to old baheviour (remove.lindep()) +#' # Remove only linear dependencies (remove.lindep()) #' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lindep") #' -#' # Trim predictors using LARS -#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") +#' # Filter predictors using LARS +#' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars", +#' max.predictors = 2) #' -#' # Impute without a trim function +#' # No trimming #' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") #' @export trim.data <- function( @@ -114,6 +122,7 @@ mice.trim.lindep <- function(y, ry, x, frame = 5, ...) { #' @param lars.type Character. The type of model to fit. Could be #' \code{"lar"}, \code{"lasso"}, \code{"forward.stagewise"} or #' \code{"stepwise"}. +#' @param lars.eps Numeric. An effective zero, with default 1e-12. #' @param max.predictors Integer. The maximum number of variables to include. #' The default \code{NULL} does not use a maximum. #' @param lars.relax Numeric. Percent minimum Cp value that is @@ -138,7 +147,9 @@ mice.trim.lindep <- function(y, ry, x, frame = 5, ...) { mice.trim.lars <- function( y, ry, x, lars.type = c("lar", "lasso", "forward.stagewise", "stepwise"), + lars.eps = 1e-12, max.predictors = NULL, lars.relax = 5, minimal.cp = 1, ...) { + lars.type <- match.arg(lars.type) keep <- trim.preprocess(y, ry, x) if (!any(keep$cols) || !any(keep$rows)) { @@ -148,7 +159,7 @@ mice.trim.lars <- function( xobs <- x[keep$rows, keep$cols, drop = FALSE] max.steps <- ifelse(is.null(max.predictors), ncol(xobs), max.predictors) - model <- lars(x = xobs, y = yobs, type = lars.type, eps = eps, + model <- lars(x = xobs, y = yobs, type = lars.type, eps = lars.eps, max.steps = max.steps) if (any(model$R2 == 1)) { # work-around because Cp gives NaN for perfect fits @@ -248,8 +259,6 @@ trim.preprocess <- function(y, ry, x) { #' the ratio of the smallest to the largest eigenvalue of the correlation #' matrix. The default is 1e-04. Setting \code{eps = 0} bypasses #' \code{remove.lindep()} and returns all variables. -#' Note: In \code{lars.trimmer()} the \code{eps} argument has a different -#' meaning. #' @param maxcor Numeric. The maximum correlation between a predictor and the #' target variable. The default is 0.99. #' @param frame Integer. The frame number for logging. Do not alter. diff --git a/man/trim.data.Rd b/man/trim.data.Rd index e6123d487..2898fd0b3 100644 --- a/man/trim.data.Rd +++ b/man/trim.data.Rd @@ -18,6 +18,7 @@ mice.trim.lars( ry, x, lars.type = c("lar", "lasso", "forward.stagewise", "stepwise"), + lars.eps = 1e-12, max.predictors = NULL, lars.relax = 5, minimal.cp = 1, @@ -51,13 +52,13 @@ by \code{model.matrix()}. The matrix must have the same number of rows as \code{length(y)} and \code{length(ry)}.} \item{trimmer}{A string identifying the trimming function. The default is -\code{"lindep"}, which call \code{mice.trim.lindep()}. Other trimmers -include \code{"lars"}, \code{"glmnet"}, \code{"cv.glmnet"} or a custom -trimming function. Turn off trimming by \code{trimmer = ""}.} +\code{"lindep"}. Other trimmers include \code{"lars"}, \code{"glmnet"}, +\code{"cv.glmnet"} or a custom trimming function. Turn off all trimming +by \code{trimmer = ""}.} \item{allow.na}{Logical. If \code{TRUE}, allow imputation of fully -missing \code{y}. Typically, this only occurs for passive imputation. -The default is \code{TRUE}.} +missing \code{y} (typically only useful for passive imputation). +The default is \code{allow.na = TRUE}.} \item{...}{Other named arguments.} @@ -67,6 +68,8 @@ The default is \code{TRUE}.} \code{"lar"}, \code{"lasso"}, \code{"forward.stagewise"} or \code{"stepwise"}.} +\item{lars.eps}{Numeric. An effective zero, with default 1e-12.} + \item{max.predictors}{Integer. The maximum number of variables to include. The default \code{NULL} does not use a maximum.} @@ -87,9 +90,7 @@ very large \code{nvars}, if a partial path is desired.} \item{eps}{Numeric. Used by \code{remove.lindep()} as the threshold for the ratio of the smallest to the largest eigenvalue of the correlation matrix. The default is 1e-04. Setting \code{eps = 0} bypasses -\code{remove.lindep()} and returns all variables. -Note: In \code{lars.trimmer()} the \code{eps} argument has a different -meaning.} +\code{remove.lindep()} and returns all variables.} \item{maxcor}{Numeric. The maximum correlation between a predictor and the target variable. The default is 0.99.} @@ -100,8 +101,8 @@ If \code{FALSE}, the function uses only the observed values to calculate (co)variances from incomplete data in \code{x}.} } \value{ -A list with elements named \code{"rows"} and \code{"cols"}, -logical vectors of lengths \code{nrow(x)} and \code{ncol(x)}, respectively. +A list with two elements named \code{"rows"} and \code{"cols"}, +logical vectors of lengths \code{nrow(x)} and \code{ncol(x)}. A logical vector of length \code{ncol(x)} indicating the predictors to keep. @@ -110,13 +111,19 @@ A logical vector of length \code{ncol(x)} indicating which predictors to keep. } \description{ -\code{trim.data()} returns two array of logicals, one for rows and one -for columns. The function filters out predictors that do not contribute to -the prediction of the target variable. -The user can select one of the following trimming functions: -\code{mice.trim.lindep()} (a wrapper of \code{remove.lindep()}), -\code{mice.trim.lars()}, \code{mice.trim.glmnet()}, -\code{mice.trim.cv.glmnet()} or call a custom trimming function. +\code{trim.data()} filters out predictors that do not contribute to +the prediction of the target variable. It is called from within the +MICE algorithm. The user can select one of the following trimming functions +using the `trimmer` argument of the `mice()` function: +\describe{ + \item{\code{"lindep"}}{Remove constant and multi-collinear predictors.} + \item{\code{"lars"}}{Include predictors by least angle regression (LARS).} + \item{\code{"glmnet"}}{Filter out predictors by elastic net.} + \item{\code{"cv.glmnet"}}{Filter out predictors by cross validated elastic net.} +} +The relevant functions are \code{mice.trim.lindep()}, \code{mice.trim.lars()}, +\code{mice.trim.glmnet()} and \code{mice.trim.cv.glmnet()}. The user can +also specify and call a custom trimming function. \code{mice.trim.lindep()} is a wrapper of \code{remove.lindep()}, the classic MICE safety net to prevent multicollinearity and other numerical @@ -140,15 +147,15 @@ The function bypasses the column trimmer in the following cases: \describe{ \item{1}{If \code{y} is allowed to be fully missing.} \item{2}{If there are zero entries of \code{y} observed.} - \item{3}{If there are zero or 1 predictors.} + \item{3}{If there are zero predictors.} \item{4}{If the user specifies \code{trimmer = ""}.} } Trimmers like \code{mice.trim.lars()} change the behaviour of the MICE algorithm. They are more aggressive in removing predictors than -the classic \code{remove.lindep()} function. For backward compatibility, -set \code{trimmer = "lindep"} to your call like -\code{mice(..., trimmer = "lindep")}. +the classic \code{remove.lindep()} method. For datasets with many +columns, our current recommendation is \code{trimmer = "lars"} in +combination with the `max.predictors` argument. \code{mice.trim.lars()} fits a LARS model to the elements of the design matrix \code{x} and the target variable \code{y[ry]}. The procedure @@ -159,12 +166,16 @@ multicollinearity and other numerical problems. It is a more conservative filter than \code{trim.lars()}. } \examples{ -# Impute according to old baheviour (remove.lindep()) +# Remove only linear dependencies (remove.lindep()) imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lindep") -# Trim predictors using LARS -imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars") +# Filter predictors using LARS +imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "lars", +max.predictors = 2) -# Impute without a trim function +# No trimming imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") } +\author{ +Stef van Buuren, Oct 2024 +} From 436ee1e2983bfa1a4e8f57d8370dd890b24ca0ae Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 22 Nov 2024 09:25:08 +0100 Subject: [PATCH 022/147] Prepare to add trim details --- R/imports.R | 2 +- R/mice.R | 10 ++++++++++ R/sampler.R | 5 +++++ 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/R/imports.R b/R/imports.R index 6bbc12186..37c0b219b 100644 --- a/R/imports.R +++ b/R/imports.R @@ -10,7 +10,7 @@ #' @importFrom mitml jomoImpute mitmlComplete panImpute testModels #' @importFrom nnet multinom #' @importFrom Rcpp evalCpp -#' @importFrom rlang .data syms +#' @importFrom rlang .data env syms #' @importFrom rpart rpart rpart.control #' @importFrom stats C aggregate as.formula binomial cancor coef #' complete.cases confint diff --git a/R/mice.R b/R/mice.R index 3a2dc8061..29a05c09c 100644 --- a/R/mice.R +++ b/R/mice.R @@ -322,12 +322,22 @@ mice <- function(data, printFlag = TRUE, seed = NA, data.init = NULL, +# saveDetails = FALSE, ...) { call <- match.call() check.deprecated(...) if (!is.na(seed)) set.seed(seed) + # # create details environment + # if (saveDetails) { + # details <- rlang::env( + # call = call, + # seed = seed, + # version = packageVersion("mice"), + # date = Sys.Date()) + # assign("details", details, envir = globalenv()) + # check form of data and m data <- check.dataform(data) m <- check.m(m) diff --git a/R/sampler.R b/R/sampler.R index 1bea99067..16bcc88e5 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -217,6 +217,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, # expand pred vector to model matrix, remove intercept if (calltype == "pred") { type <- pred[labels(terms(formula))][attr(x, "assign")] + # xnames <- names(type) x <- x[, -1L, drop = FALSE] names(type) <- colnames(x) } @@ -234,6 +235,10 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, trimmer = trimmer, ... ) + # store the names of the features + # xj <- unique(xnames[keep$cols]) + # print(xj) + # set up univariate imputation method # wy: entries we wish to impute (length(y) elements) # iy: entries we will impute (sum(wy) elements) From b5c08e3071654f8319ae18e440a5a0dbc53f1dab Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 3 Jan 2025 15:58:38 +0100 Subject: [PATCH 023/147] Remove lars from suggests --- DESCRIPTION | 1 - NAMESPACE | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index a5a94d311..299f059bd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -71,7 +71,6 @@ Suggests: furrr, haven, knitr, - lars, literanger, lme4, MASS, diff --git a/NAMESPACE b/NAMESPACE index 93311bbc0..b717a703d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -226,6 +226,7 @@ importFrom(mitml,panImpute) importFrom(mitml,testModels) importFrom(nnet,multinom) importFrom(rlang,.data) +importFrom(rlang,env) importFrom(rlang,syms) importFrom(rpart,rpart) importFrom(rpart,rpart.control) From 0af1feb7c3e010a7bbb6587a91135a10838d6bc2 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 5 Jan 2025 15:13:35 +0100 Subject: [PATCH 024/147] Add mice.impute.lasso.pmm() to select features and generate imputations in high-dimensional (p > n) data --- NAMESPACE | 1 + R/mice.impute.lasso.pmm.R | 230 +++++++++++++++++++++++++ man/mice.impute.cart.Rd | 1 + man/mice.impute.lasso.logreg.Rd | 1 + man/mice.impute.lasso.norm.Rd | 1 + man/mice.impute.lasso.pmm.Rd | 186 ++++++++++++++++++++ man/mice.impute.lasso.select.logreg.Rd | 1 + man/mice.impute.lasso.select.norm.Rd | 1 + man/mice.impute.lda.Rd | 1 + man/mice.impute.logreg.Rd | 1 + man/mice.impute.logreg.boot.Rd | 1 + man/mice.impute.mean.Rd | 1 + man/mice.impute.midastouch.Rd | 1 + man/mice.impute.mnar.Rd | 1 + man/mice.impute.mpmm.Rd | 1 + man/mice.impute.norm.Rd | 1 + man/mice.impute.norm.boot.Rd | 1 + man/mice.impute.norm.nob.Rd | 1 + man/mice.impute.norm.predict.Rd | 1 + man/mice.impute.pmm.Rd | 1 + man/mice.impute.polr.Rd | 1 + man/mice.impute.polyreg.Rd | 1 + man/mice.impute.quadratic.Rd | 1 + man/mice.impute.rf.Rd | 1 + man/mice.impute.ri.Rd | 1 + 25 files changed, 439 insertions(+) create mode 100644 R/mice.impute.lasso.pmm.R create mode 100644 man/mice.impute.lasso.pmm.Rd diff --git a/NAMESPACE b/NAMESPACE index 28fea4e2f..6a1c736fe 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -116,6 +116,7 @@ export(mice.impute.cart) export(mice.impute.jomoImpute) export(mice.impute.lasso.logreg) export(mice.impute.lasso.norm) +export(mice.impute.lasso.pmm) export(mice.impute.lasso.select.logreg) export(mice.impute.lasso.select.norm) export(mice.impute.lda) diff --git a/R/mice.impute.lasso.pmm.R b/R/mice.impute.lasso.pmm.R new file mode 100644 index 000000000..d921bf914 --- /dev/null +++ b/R/mice.impute.lasso.pmm.R @@ -0,0 +1,230 @@ +#' Imputation by predictive mean matching +#' +#' @aliases mice.impute.lasso.pmm +#' @param y Vector to be imputed +#' @param ry Logical vector of length \code{length(y)} indicating the +#' the subset \code{y[ry]} of elements in \code{y} to which the imputation +#' model is fitted. The \code{ry} generally distinguishes the observed +#' (\code{TRUE}) and missing values (\code{FALSE}) in \code{y}. +#' @param x Numeric design matrix with \code{length(y)} rows with predictors for +#' \code{y}. Matrix \code{x} may have no missing values. +#' @param remove.values Dependent values to exclude from the imputation model +#' and the collection of donor values +#' @param quantify Logical. If \code{TRUE}, factor levels are replaced +#' by the first canonical variate before fitting the imputation model. +#' If false, the procedure reverts to the old behaviour and takes the +#' integer codes (which may lack a sensible interpretation). +#' Relevant only of \code{y} is a factor. +#' @param trim Scalar integer. Minimum number of observations required in a +#' category in order to be considered as a potential donor value. +#' Relevant only of \code{y} is a factor. +#' @param wy Logical vector of length \code{length(y)}. A \code{TRUE} value +#' indicates locations in \code{y} for which imputations are created. +#' @param donors The size of the donor pool among which a draw is made. +#' The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects +#' the closest match, but is not recommended. Values between 3L and 10L +#' provide the best results in most cases (Morris et al, 2015). +#' @param matchtype Type of matching distance. The default choice +#' (\code{matchtype = 1L}) calculates the distance between +#' the \emph{predicted} value of \code{yobs} and +#' the \emph{drawn} values of \code{ymis} (called type-1 matching). +#' Other choices are \code{matchtype = 0L} +#' (distance between predicted values) and \code{matchtype = 2L} +#' (distance between drawn values). The functions sets \code{matchtype = 1L} +#' for sample sizes to 1000, and \code{matchtype = 0L} otherwise. +#' @param dfmax Maximum number of non-zero coefficients in the LASSO path. +#' @param \dots Other named arguments. +#' @return Vector with imputed data, same type as \code{y}, and of length +#' \code{sum(wy)} +#' @note +#' This version uses glmnet with \code{dfmax} to speed up the process +#' @author Stef van Buuren +#' @references Little, R.J.A. (1988), Missing data adjustments in large surveys +#' (with discussion), Journal of Business Economics and Statistics, 6, 287--301. +#' +#' Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive +#' mean matching and local residual draws. BMC Med Res Methodol. ;14:75. +#' +#' Van Buuren, S. (2018). +#' \href{https://stefvanbuuren.name/fimd/sec-pmm.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' Chapman & Hall/CRC. Boca Raton, FL. +#' +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate +#' Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical +#' Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} +#' @family univariate imputation functions +#' @keywords datagen +#' @examples +#' # We normally call mice.impute.lasso.pmm() from within mice() +#' # But we may call it directly as follows (not recommended) +#' +#' set.seed(53177) +#' xname <- c("age", "hgt", "wgt") +#' r <- stats::complete.cases(boys[, xname]) +#' x <- boys[r, xname] +#' y <- boys[r, "tv"] +#' ry <- !is.na(y) +#' table(ry) +#' +#' # percentage of missing data in tv +#' sum(!ry) / length(ry) +#' +#' # Impute missing tv data +#' yimp <- mice.impute.lasso.pmm(y, ry, x) +#' length(yimp) +#' hist(yimp, xlab = "Imputed missing tv") +#' +#' # Impute all tv data +#' yimp <- mice.impute.lasso.pmm(y, ry, x, wy = rep(TRUE, length(y))) +#' length(yimp) +#' hist(yimp, xlab = "Imputed missing and observed tv") +#' plot(jitter(y), jitter(yimp), +#' main = "Predictive mean matching on age, height and weight", +#' xlab = "Observed tv (n = 224)", +#' ylab = "Imputed tv (n = 224)" +#' ) +#' abline(0, 1) +#' cor(y, yimp, use = "pair") +#' +#' # Use blots to exclude different values per column +#' # Create blots object +#' blots <- make.blots(boys) +#' # Exclude ml 1 through 5 from tv donor pool +#' blots$tv$remove.values <- c(1:5) +#' # Exclude 100 random observed heights from tv donor pool +#' blots$hgt$remove.values <- sample(unique(boys$hgt), 100) +#' imp <- mice(boys, method = "lasso.pmm", m = 1, maxit = 1, print = FALSE, +#' blots = blots, seed = 123) +#' #' Check if all values are removed +#' all(!blots$hgt$remove.values %in% unlist(c(imp$imp$hgt))) +#' all(!blots$tv$remove.values %in% unlist(c(imp$imp$tv))) +#' +#' # Factor quantification +#' xname <- c("age", "hgt", "wgt") +#' br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] +#' r <- stats::complete.cases(br[, xname]) +#' x <- br[r, xname] +#' y <- factor(br[r, "tv"]) +#' ry <- !is.na(y) +#' table(y, useNA = "always") +#' +#' # impute 38 NA's in factor by optimal scaling of y | x +#' table(mice.impute.lasso.pmm(y, ry, x)) +#' +#' # only categories with at least 2 cases can be donor +#' table(mice.impute.lasso.pmm(y, ry, x, trim = 2L)) +#' +#' # in addition, eliminate category 20 +#' table(mice.impute.lasso.pmm(y, ry, x, trim = 2L, remove.value = 20)) +#' +#' # to get old behavior: as.integer(y)) +#' table(mice.impute.lasso.pmm(y, ry, x, quantify = FALSE)) +#' @export +mice.impute.lasso.pmm <- function(y, ry, x, wy = NULL, + donors = 5L, + matchtype = ifelse(sum(ry) >= 1000L, 0L, 1L), + remove.values = NULL, + quantify = TRUE, + trim = 1L, + dfmax = NULL, + ...) { + stopifnot(ncol(x) >= 2L) + x <- as.matrix(x) + + dots <- list(...) + if ("type" %in% names(dots)) { + dots[["type"]] <- NULL + } + if ("exclude" %in% names(dots)) { + dots[["exclude"]] <- NULL + } + + if (is.null(wy)) { + wy <- !ry + } + + # Remove rare categories from the outcome variable + if (is.factor(y)) { + active <- !ry | y %in% (levels(y)[table(y) >= trim]) + y <- y[active] + ry <- ry[active] + x <- x[active, , drop = FALSE] + wy <- wy[active] + } + + # Remove donor values from the outcome variable + if (!is.null(remove.values)) { + active <- !ry | !y %in% remove.values + y <- y[active] + ry <- ry[active] + x <- x[active, , drop = FALSE] + wy <- wy[active] + } + + # Quantify categories of factors + ynum <- y + if (is.factor(y)) { + if (quantify) { + ynum <- quantify(y, ry, x) + } else { + # as.integer() may not make sense for unordered factors + ynum <- as.integer(y) + } + } + + # Define yobs and xobs for modeling + xobs <- x[ry & complete.cases(x, y), , drop = FALSE] + yobs <- as.numeric(ynum[ry]) + + # If yobs is near constant, return imputations as random draws from yobs + if (var(yobs, na.rm = TRUE) < 1000 * .Machine$double.eps) { + return(sample(yobs, sum(wy), replace = TRUE)) + } + + # Create partial LASSO path with dfmax non-zero coefficients + dfmax <- ifelse(is.null(dfmax), ncol(x), dfmax) + fit <- do.call(glmnet::glmnet, + c(list(x = xobs, y = yobs, dfmax = dfmax), dots)) + + # Find lambda from partial LASSO path + indices <- which(fit$df <= dfmax) + closest <- indices[which.max(fit$df[indices])] + lambda <- fit$lambda[closest] + + # Calculate predicted values for ximp + ximp <- x[wy, , drop = FALSE] + if (matchtype == 0L) { + # One model for observed and missing data + yhatobs <- predict(fit, newx = xobs, s = lambda) + yhatmis <- predict(fit, newx = ximp, s = lambda) + } + if (matchtype == 1L) { + # Two different models for observed and missing data + # Bootstrap observed records to account for sampling uncertainty + n <- sum(ry) + s <- sample(n, n, replace = TRUE) + xobs1 <- x[ry & complete.cases(x, y), , drop = FALSE][s, , drop = FALSE] + yobs1 <- as.numeric(ynum[ry][s]) + fit1 <- do.call(glmnet::glmnet, + c(list(x = xobs1, y = yobs1, lambda = lambda), dots)) + yhatobs <- predict(fit, newx = xobs, s = lambda) + yhatmis <- predict(fit1, newx = ximp, s = lambda) + } + if (matchtype == 2L) { + # One refitted model for observed and missing data + n <- sum(ry) + s <- sample(n, n, replace = TRUE) + xobs1 <- x[ry & complete.cases(x, y), , drop = FALSE][s, , drop = FALSE] + yobs1 <- as.numeric(ynum[ry][s]) + fit1 <- do.call(glmnet::glmnet, + c(list(x = xobs1, y = yobs1, lambda = lambda), dots)) + yhatobs <- predict(fit1, newx = xobs, s = lambda) + yhatmis <- predict(fit1, newx = ximp, s = lambda) + } + + # Predictive mean matching + idx <- matchindex(yhatobs, yhatmis, donors) + yimp <- y[ry][idx] + + return(yimp) +} diff --git a/man/mice.impute.cart.Rd b/man/mice.impute.cart.Rd index 19a1767d4..e90b3d146 100644 --- a/man/mice.impute.cart.Rd +++ b/man/mice.impute.cart.Rd @@ -73,6 +73,7 @@ Chapman & Hall/CRC. Boca Raton, FL. Other univariate imputation functions: \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.lasso.logreg.Rd b/man/mice.impute.lasso.logreg.Rd index d102536d9..39f0ca6af 100644 --- a/man/mice.impute.lasso.logreg.Rd +++ b/man/mice.impute.lasso.logreg.Rd @@ -64,6 +64,7 @@ high-dimensional data. Statistical Methods in Medical Research, 25(5), Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.lasso.norm.Rd b/man/mice.impute.lasso.norm.Rd index 6e6fb86e2..34a8b0c62 100644 --- a/man/mice.impute.lasso.norm.Rd +++ b/man/mice.impute.lasso.norm.Rd @@ -64,6 +64,7 @@ high-dimensional data. Statistical Methods in Medical Research, 25(5), Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.lasso.pmm.Rd b/man/mice.impute.lasso.pmm.Rd new file mode 100644 index 000000000..ab27c687f --- /dev/null +++ b/man/mice.impute.lasso.pmm.Rd @@ -0,0 +1,186 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mice.impute.lasso.pmm.R +\name{mice.impute.lasso.pmm} +\alias{mice.impute.lasso.pmm} +\title{Imputation by predictive mean matching} +\usage{ +mice.impute.lasso.pmm( + y, + ry, + x, + wy = NULL, + donors = 5L, + matchtype = ifelse(sum(ry) >= 1000L, 0L, 1L), + remove.values = NULL, + quantify = TRUE, + trim = 1L, + dfmax = 30, + ... +) +} +\arguments{ +\item{y}{Vector to be imputed} + +\item{ry}{Logical vector of length \code{length(y)} indicating the +the subset \code{y[ry]} of elements in \code{y} to which the imputation +model is fitted. The \code{ry} generally distinguishes the observed +(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} + +\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for +\code{y}. Matrix \code{x} may have no missing values.} + +\item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value +indicates locations in \code{y} for which imputations are created.} + +\item{donors}{The size of the donor pool among which a draw is made. +The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects +the closest match, but is not recommended. Values between 3L and 10L +provide the best results in most cases (Morris et al, 2015).} + +\item{matchtype}{Type of matching distance. The default choice +(\code{matchtype = 1L}) calculates the distance between +the \emph{predicted} value of \code{yobs} and +the \emph{drawn} values of \code{ymis} (called type-1 matching). +Other choices are \code{matchtype = 0L} +(distance between predicted values) and \code{matchtype = 2L} +(distance between drawn values). The functions sets \code{matchtype = 1L} +for sample sizes to 1000, and \code{matchtype = 0L} otherwise.} + +\item{remove.values}{Dependent values to exclude from the imputation model +and the collection of donor values} + +\item{quantify}{Logical. If \code{TRUE}, factor levels are replaced +by the first canonical variate before fitting the imputation model. +If false, the procedure reverts to the old behaviour and takes the +integer codes (which may lack a sensible interpretation). +Relevant only of \code{y} is a factor.} + +\item{trim}{Scalar integer. Minimum number of observations required in a +category in order to be considered as a potential donor value. +Relevant only of \code{y} is a factor.} + +\item{dfmax}{Maximum number of non-zero coefficients in the LASSO path.} + +\item{\dots}{Other named arguments.} +} +\value{ +Vector with imputed data, same type as \code{y}, and of length +\code{sum(wy)} +} +\description{ +Imputation by predictive mean matching +} +\note{ +This version uses glmnet with \code{dfmax} to speed up the process +} +\examples{ +# We normally call mice.impute.lasso.pmm() from within mice() +# But we may call it directly as follows (not recommended) + +set.seed(53177) +xname <- c("age", "hgt", "wgt") +r <- stats::complete.cases(boys[, xname]) +x <- boys[r, xname] +y <- boys[r, "tv"] +ry <- !is.na(y) +table(ry) + +# percentage of missing data in tv +sum(!ry) / length(ry) + +# Impute missing tv data +yimp <- mice.impute.lasso.pmm(y, ry, x) +length(yimp) +hist(yimp, xlab = "Imputed missing tv") + +# Impute all tv data +yimp <- mice.impute.lasso.pmm(y, ry, x, wy = rep(TRUE, length(y))) +length(yimp) +hist(yimp, xlab = "Imputed missing and observed tv") +plot(jitter(y), jitter(yimp), + main = "Predictive mean matching on age, height and weight", + xlab = "Observed tv (n = 224)", + ylab = "Imputed tv (n = 224)" +) +abline(0, 1) +cor(y, yimp, use = "pair") + +# Use blots to exclude different values per column +# Create blots object +blots <- make.blots(boys) +# Exclude ml 1 through 5 from tv donor pool +blots$tv$remove.values <- c(1:5) +# Exclude 100 random observed heights from tv donor pool +blots$hgt$remove.values <- sample(unique(boys$hgt), 100) +imp <- mice(boys, method = "lasso.pmm", m = 1, maxit = 1, print = FALSE, + blots = blots, seed = 123) +#' Check if all values are removed +all(!blots$hgt$remove.values \%in\% unlist(c(imp$imp$hgt))) +all(!blots$tv$remove.values \%in\% unlist(c(imp$imp$tv))) + +# Factor quantification +xname <- c("age", "hgt", "wgt") +br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] +r <- stats::complete.cases(br[, xname]) +x <- br[r, xname] +y <- factor(br[r, "tv"]) +ry <- !is.na(y) +table(y, useNA = "always") + +# impute 38 NA's in factor by optimal scaling of y | x +table(mice.impute.lasso.pmm(y, ry, x)) + +# only categories with at least 2 cases can be donor +table(mice.impute.lasso.pmm(y, ry, x, trim = 2L)) + +# in addition, eliminate category 20 +table(mice.impute.lasso.pmm(y, ry, x, trim = 2L, remove.value = 20)) + +# to get old behavior: as.integer(y)) +table(mice.impute.lasso.pmm(y, ry, x, quantify = FALSE)) +} +\references{ +Little, R.J.A. (1988), Missing data adjustments in large surveys +(with discussion), Journal of Business Economics and Statistics, 6, 287--301. + +Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive +mean matching and local residual draws. BMC Med Res Methodol. ;14:75. + +Van Buuren, S. (2018). +\href{https://stefvanbuuren.name/fimd/sec-pmm.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +Chapman & Hall/CRC. Boca Raton, FL. + +Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate +Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical +Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} +} +\seealso{ +Other univariate imputation functions: +\code{\link{mice.impute.cart}()}, +\code{\link{mice.impute.lasso.logreg}()}, +\code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.select.logreg}()}, +\code{\link{mice.impute.lasso.select.norm}()}, +\code{\link{mice.impute.lda}()}, +\code{\link{mice.impute.logreg}()}, +\code{\link{mice.impute.logreg.boot}()}, +\code{\link{mice.impute.mean}()}, +\code{\link{mice.impute.midastouch}()}, +\code{\link{mice.impute.mnar.logreg}()}, +\code{\link{mice.impute.mpmm}()}, +\code{\link{mice.impute.norm}()}, +\code{\link{mice.impute.norm.boot}()}, +\code{\link{mice.impute.norm.nob}()}, +\code{\link{mice.impute.norm.predict}()}, +\code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.polr}()}, +\code{\link{mice.impute.polyreg}()}, +\code{\link{mice.impute.quadratic}()}, +\code{\link{mice.impute.rf}()}, +\code{\link{mice.impute.ri}()} +} +\author{ +Stef van Buuren +} +\concept{univariate imputation functions} +\keyword{datagen} diff --git a/man/mice.impute.lasso.select.logreg.Rd b/man/mice.impute.lasso.select.logreg.Rd index 027e2a513..eb0be0f33 100644 --- a/man/mice.impute.lasso.select.logreg.Rd +++ b/man/mice.impute.lasso.select.logreg.Rd @@ -73,6 +73,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, \code{\link{mice.impute.logreg}()}, diff --git a/man/mice.impute.lasso.select.norm.Rd b/man/mice.impute.lasso.select.norm.Rd index e825a028c..88a532836 100644 --- a/man/mice.impute.lasso.select.norm.Rd +++ b/man/mice.impute.lasso.select.norm.Rd @@ -75,6 +75,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lda}()}, \code{\link{mice.impute.logreg}()}, diff --git a/man/mice.impute.lda.Rd b/man/mice.impute.lda.Rd index e46b23505..29c699826 100644 --- a/man/mice.impute.lda.Rd +++ b/man/mice.impute.lda.Rd @@ -73,6 +73,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.logreg}()}, diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index 8427031d2..d54aa8997 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -69,6 +69,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.logreg.boot.Rd b/man/mice.impute.logreg.boot.Rd index 2076dc202..1c4e29e0c 100644 --- a/man/mice.impute.logreg.boot.Rd +++ b/man/mice.impute.logreg.boot.Rd @@ -49,6 +49,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.mean.Rd b/man/mice.impute.mean.Rd index 3c3435bc4..2db726aed 100644 --- a/man/mice.impute.mean.Rd +++ b/man/mice.impute.mean.Rd @@ -55,6 +55,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.midastouch.Rd b/man/mice.impute.midastouch.Rd index cfa5c310a..4e758b997 100644 --- a/man/mice.impute.midastouch.Rd +++ b/man/mice.impute.midastouch.Rd @@ -129,6 +129,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.mnar.Rd b/man/mice.impute.mnar.Rd index 3568786ae..eb6517c3d 100644 --- a/man/mice.impute.mnar.Rd +++ b/man/mice.impute.mnar.Rd @@ -176,6 +176,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.mpmm.Rd b/man/mice.impute.mpmm.Rd index 4d82409bd..de74a3974 100644 --- a/man/mice.impute.mpmm.Rd +++ b/man/mice.impute.mpmm.Rd @@ -68,6 +68,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.norm.Rd b/man/mice.impute.norm.Rd index a082d1ffc..945ffce95 100644 --- a/man/mice.impute.norm.Rd +++ b/man/mice.impute.norm.Rd @@ -61,6 +61,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.norm.boot.Rd b/man/mice.impute.norm.boot.Rd index b426a7139..e0e16617e 100644 --- a/man/mice.impute.norm.boot.Rd +++ b/man/mice.impute.norm.boot.Rd @@ -45,6 +45,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.norm.nob.Rd b/man/mice.impute.norm.nob.Rd index 683170dc9..7ba53fc87 100644 --- a/man/mice.impute.norm.nob.Rd +++ b/man/mice.impute.norm.nob.Rd @@ -67,6 +67,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.norm.predict.Rd b/man/mice.impute.norm.predict.Rd index 86b2f7ecb..b29adb5a4 100644 --- a/man/mice.impute.norm.predict.Rd +++ b/man/mice.impute.norm.predict.Rd @@ -64,6 +64,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index ff0336988..394f98a5a 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -190,6 +190,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.polr.Rd b/man/mice.impute.polr.Rd index 21f17912b..6fb48a826 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -102,6 +102,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index 30cf4f435..738e07649 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -89,6 +89,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.quadratic.Rd b/man/mice.impute.quadratic.Rd index b8e7d441b..19e20c38a 100644 --- a/man/mice.impute.quadratic.Rd +++ b/man/mice.impute.quadratic.Rd @@ -104,6 +104,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.rf.Rd b/man/mice.impute.rf.Rd index ee288b634..bf389ae9f 100644 --- a/man/mice.impute.rf.Rd +++ b/man/mice.impute.rf.Rd @@ -96,6 +96,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, diff --git a/man/mice.impute.ri.Rd b/man/mice.impute.ri.Rd index 8a5b6ccf8..95bd457bc 100644 --- a/man/mice.impute.ri.Rd +++ b/man/mice.impute.ri.Rd @@ -52,6 +52,7 @@ Other univariate imputation functions: \code{\link{mice.impute.cart}()}, \code{\link{mice.impute.lasso.logreg}()}, \code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.pmm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, From 980bf81b64a9b6a61473fa771930e48d258254f0 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 6 Jan 2025 08:55:33 +0100 Subject: [PATCH 025/147] Update man page --- man/mice.impute.lasso.pmm.Rd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/man/mice.impute.lasso.pmm.Rd b/man/mice.impute.lasso.pmm.Rd index ab27c687f..e25579ed3 100644 --- a/man/mice.impute.lasso.pmm.Rd +++ b/man/mice.impute.lasso.pmm.Rd @@ -14,7 +14,7 @@ mice.impute.lasso.pmm( remove.values = NULL, quantify = TRUE, trim = 1L, - dfmax = 30, + dfmax = NULL, ... ) } From 78aabec29f201bee0d808c8a5fcdf76077281e0a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 27 Feb 2025 11:50:33 +0100 Subject: [PATCH 026/147] Provide a few optimizations to the `matchindex` C++ function: - Use d[x] instead of d(x) for NumericVector - Use std::clamp() instead of manual if conditions for k - Remove redundant std::vector ysort(n1) - Use std::distance() instead of unnecessary iterator assignments - Use seq_len(k) directly instead of creating an extra IntegerVector kv(k) - Avoid redundant copies of sampled indices - Replace unnecessary lambda [yshuf] capture with direct reference //' # - Use std::clamp() for restricting k values In typical use cases, changes result in a speed-up of about 10%. --- R/RcppExports.R | 1 + man/matchindex.Rd | 1 + src/matchindex.cpp | 83 ++++++++++++++++------------------------------ 3 files changed, 31 insertions(+), 54 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 07c19a46d..83c6508eb 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -87,6 +87,7 @@ matcher <- function(obs, mis, k) { #' # twelve mpg entries in test, and count how many times the true value #' # (which we know here) is located within the inter-quartile range of each #' # distribution. Is your count anywhere close to 500? Why? Why not? +#' #' @author Stef van Buuren, Nasinski Maciej, Alexander Robitzsch #' @export matchindex <- function(d, t, k = 5L) { diff --git a/man/matchindex.Rd b/man/matchindex.Rd index 1e7aa519b..5a28f1630 100644 --- a/man/matchindex.Rd +++ b/man/matchindex.Rd @@ -89,6 +89,7 @@ train$mpg[idx] # twelve mpg entries in test, and count how many times the true value # (which we know here) is located within the inter-quartile range of each # distribution. Is your count anywhere close to 500? Why? Why not? + } \author{ Stef van Buuren, Nasinski Maciej, Alexander Robitzsch diff --git a/src/matchindex.cpp b/src/matchindex.cpp index 0db2f81fe..1d391aeaf 100644 --- a/src/matchindex.cpp +++ b/src/matchindex.cpp @@ -82,6 +82,7 @@ using namespace Rcpp; //' # twelve mpg entries in test, and count how many times the true value //' # (which we know here) is located within the inter-quartile range of each //' # distribution. Is your count anywhere close to 500? Why? Why not? +//' //' @author Stef van Buuren, Nasinski Maciej, Alexander Robitzsch //' @export // [[Rcpp::export]] @@ -90,86 +91,60 @@ IntegerVector matchindex(NumericVector d, NumericVector t, int k = 5) { Environment base("package:base"); Function sample = base["sample"]; - // declarations int n1 = d.size(); int n0 = t.size(); - // 1. Shuffle records to remove effects of ties - // Suggested by Alexander Robitzsch - // https://github.com/stefvanbuuren/mice/issues/236 - // Call base::sample() to advance .Random.seed - IntegerVector ishuf= sample(n1); - ishuf = ishuf - 1; + // 1. Shuffle donor data + IntegerVector ishuf = as(sample(seq_len(n1))) - 1; NumericVector yshuf(n1); - for (int i = 0; i < n1; i++) {yshuf(i) = d(ishuf(i));} + for (int i = 0; i < n1; i++) yshuf[i] = d[ishuf[i]]; - // 2. Obtain sorting order on shuffled data - // https://stackoverflow.com/questions/1577475/c-sorting-and-keeping-track-of-indexes + // 2. Sorting donor indices IntegerVector isort(n1); iota(isort.begin(), isort.end(), 0); stable_sort(isort.begin(), isort.end(), - [yshuf](int i1, int i2) {return yshuf[i1] < yshuf[i2];}); + [&yshuf](int i1, int i2) { return yshuf[i1] < yshuf[i2]; }); - // 3. Calculate index on input data and sort + // 3. Mapping back to shuffled indices IntegerVector id(n1); - std::vector ysort(n1); + NumericVector ysort(n1); for (int i = 0; i < n1; i++) { - id(i) = ishuf(isort(i)); - ysort[i] = d(id(i)); + id[i] = ishuf[isort[i]]; + ysort[i] = d[id[i]]; } - // 4. Pre-sample n0 values between 1 and k - // restrict 1 <= k <= n1 - k = (k <= n1) ? k : n1; - k = (k >= 1) ? k : 1; - IntegerVector kv(k); - iota(kv.begin(), kv.end(), 1); - IntegerVector h = sample(kv, n0, Rcpp::_["replace"] = true); + // 4. Pre-sample h + k = std::clamp(k, 1, n1); + IntegerVector h = as(sample(seq_len(k), n0, + Rcpp::_["replace"] = true)); IntegerVector idx(n0); - // loop over the target units + // 5. Find closest neighbors for (int i = 0; i < n0; i++) { - double val = t(i); - int hi = h(i); - int count = 0; + double val = t[i]; + int hi = h[i], count = 0; - // 5. find the two adjacent neighbours - std::vector::iterator iter; - iter = std::lower_bound(ysort.begin(), ysort.end(), val); - int r = iter - ysort.begin(); + // Binary search for nearest neighbor + int r = std::distance(ysort.begin(), + std::lower_bound(ysort.begin(), ysort.end(), val)); int l = r - 1; - // 6. find the h_i'th nearest neighbour - // 7. store the index of that neighbour - - // Compare elements on left and right of crossover - // point to find the h'th closest match - // Inspired on Polkas: https://github.com/Polkas/miceFast/issues/10 - while (count < hi && l >= 0 && r < n1) - { - if (val - ysort[l] < ysort[r] - val) - { - idx(i) = id[l--]; + // 6. Find h-th nearest neighbor + while (count < hi && l >= 0 && r < n1) { + if (val - ysort[l] < ysort[r] - val) { + idx[i] = id[l--]; } else { - idx(i) = id[r++]; + idx[i] = id[r++]; } count++; } - // If right side is exhausted, take left elements - while (count < hi && l >= 0) - { - idx(i) = id[l--]; - count++; - } + // If left side is exhausted, take from right side + while (count < hi && l >= 0) idx[i] = id[l--], count++; - // If left side is exhausted, take right elements - while (count < hi && r < n1) - { - idx(i) = id[r++]; - count++; - } + // If right side is exhausted, take from left side + while (count < hi && r < n1) idx[i] = id[r++], count++; } return idx + 1; From b44fd1a286c868ca02c28479b75a85e70c4bce22 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 5 Mar 2025 10:36:39 +0100 Subject: [PATCH 027/147] Add pmmsplit() that splits the calculations in mice.impute.pmm() by means of a stored object --- NAMESPACE | 2 + R/imports.R | 2 +- R/mice.impute.pmmsplit.R | 269 +++++++++++++++++++++ man/mice.impute.pmmsplit.Rd | 191 +++++++++++++++ tests/testthat/test-mice.impute.pmmsplit.R | 114 +++++++++ tests/testthat/test-quantify.R | 48 ++++ 6 files changed, 625 insertions(+), 1 deletion(-) create mode 100644 R/mice.impute.pmmsplit.R create mode 100644 man/mice.impute.pmmsplit.Rd create mode 100644 tests/testthat/test-mice.impute.pmmsplit.R create mode 100644 tests/testthat/test-quantify.R diff --git a/NAMESPACE b/NAMESPACE index 28fea4e2f..6b13cca89 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -133,6 +133,7 @@ export(mice.impute.norm.predict) export(mice.impute.panImpute) export(mice.impute.passive) export(mice.impute.pmm) +export(mice.impute.pmmsplit) export(mice.impute.polr) export(mice.impute.polyreg) export(mice.impute.quadratic) @@ -251,6 +252,7 @@ importFrom(stats,rgamma) importFrom(stats,rnorm) importFrom(stats,runif) importFrom(stats,sd) +importFrom(stats,setNames) importFrom(stats,spline) importFrom(stats,summary.glm) importFrom(stats,terms) diff --git a/R/imports.R b/R/imports.R index f3bd89a8c..4e600f17e 100644 --- a/R/imports.R +++ b/R/imports.R @@ -18,7 +18,7 @@ #' na.exclude na.omit na.pass #' pf predict pt qt quantile quasibinomial #' rbinom rchisq reformulate rgamma rnorm runif -#' sd summary.glm terms update var vcov +#' sd setNames summary.glm terms update var vcov #' @importFrom tidyr complete #' @importFrom utils askYesNo flush.console hasName head install.packages #' methods packageDescription packageVersion diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R new file mode 100644 index 000000000..1afb15aee --- /dev/null +++ b/R/mice.impute.pmmsplit.R @@ -0,0 +1,269 @@ +#' Imputation by predictive mean matching +#' +#' +#' \code{pmmsplit()} is an implementation of pmm that saves the imputation model +#' and generates imputations from the saved model. +#' @aliases pmmsplit +#' @param num_bins The number of bins used to store the predictive mean matching +#' model. The default is 50. +#' @inheritParams mice.impute.pmm +#' @return Vector with imputed data, same type as \code{y}, and of length +#' \code{sum(wy)} +#' @author Stef van Buuren +#' +#' @references Little, R.J.A. (1988), Missing data adjustments in large surveys +#' (with discussion), Journal of Business Economics and Statistics, 6, 287--301. +#' +#' Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive +#' mean matching and local residual draws. BMC Med Res Methodol. ;14:75. +#' +#' Van Buuren, S. (2018). +#' \href{https://stefvanbuuren.name/fimd/sec-pmm.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' Chapman & Hall/CRC. Boca Raton, FL. +#' +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate +#' Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical +#' Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} +#' @family univariate imputation functions +#' @keywords datagen +#' @examples +#' # We normally call mice.impute.pmmsplit() from within mice() +#' # But we may call it directly as follows (not recommended) +#' +#' set.seed(53177) +#' xname <- c("age", "hgt", "wgt") +#' r <- stats::complete.cases(boys[, xname]) +#' x <- boys[r, xname] +#' y <- boys[r, "tv"] +#' ry <- !is.na(y) +#' table(ry) +#' +#' # percentage of missing data in tv +#' sum(!ry) / length(ry) +#' +#' # Impute missing tv data +#' yimp <- mice.impute.pmmsplit(y, ry, x) +#' length(yimp) +#' hist(yimp, xlab = "Imputed missing tv") +#' +#' # Impute all tv data +#' yimp <- mice.impute.pmmsplit(y, ry, x, wy = rep(TRUE, length(y))) +#' length(yimp) +#' hist(yimp, xlab = "Imputed missing and observed tv") +#' plot(jitter(y), jitter(yimp), +#' main = "Predictive mean matching on age, height and weight", +#' xlab = "Observed tv (n = 224)", +#' ylab = "Imputed tv (n = 224)" +#' ) +#' abline(0, 1) +#' cor(y, yimp, use = "pair") +#' +#' # Use blots to exclude different values per column +#' # Create blots object +#' blots <- make.blots(boys) +#' # Exclude ml 1 through 5 from tv donor pool +#' blots$tv$exclude <- c(1:5) +#' # Exclude 100 random observed heights from tv donor pool +#' blots$hgt$exclude <- sample(unique(boys$hgt), 100) +#' imp <- mice(boys, method = "pmmsplit", print = FALSE, blots = blots, seed=123) +#' blots$hgt$exclude %in% unlist(c(imp$imp$hgt)) # MUST be all FALSE +#' blots$tv$exclude %in% unlist(c(imp$imp$tv)) # MUST be all FALSE +#' +#' # Factor quantification +#' xname <- c("age", "hgt", "wgt") +#' br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] +#' r <- stats::complete.cases(br[, xname]) +#' x <- br[r, xname] +#' y <- factor(br[r, "tv"]) +#' ry <- !is.na(y) +#' table(y) +#' +#' # impute factor by optimizing canonical correlation y, x +#' mice.impute.pmmsplit(y, ry, x) +#' +#' # only categories with at least 2 cases can be donor +#' mice.impute.pmmsplit(y, ry, x, trim = 2L) +#' +#' # in addition, eliminate category 20 +#' mice.impute.pmmsplit(y, ry, x, trim = 2L, exclude = 20) +#' +#' # to get old behavior: as.integer(y)) +#' mice.impute.pmmsplit(y, ry, x, quantify = FALSE) +#' @export +mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = 10L, + matchtype = 1L, exclude = NULL, + quantify = TRUE, trim = 1L, + ridge = 1e-05, + num_bins = 50, ...) { + if (is.null(wy)) { + wy <- !ry + } + + # Reformulate the imputation problem such that + # 1. the imputation model disregards records with excluded y-values + # 2. the donor set does not contain excluded y-values + + # Keep sparse categories out of the imputation model + if (is.factor(y)) { + active <- !ry | y %in% (levels(y)[table(y) >= trim]) + y <- y[active] + ry <- ry[active] + x <- x[active, , drop = FALSE] + wy <- wy[active] + } + # Keep excluded values out of the imputation model + if (!is.null(exclude)) { + active <- !ry | !y %in% exclude + y <- y[active] + ry <- ry[active] + x <- x[active, , drop = FALSE] + wy <- wy[active] + } + + x <- cbind(1, as.matrix(x)) + + # quantify categories for factors + ynum <- y + if (is.factor(y)) { + if (quantify) { + ynum <- quantify(y, ry, x) + } else { + ynum <- as.integer(y) + } + } + + # train the imputation model using the data + # parameter estimation + parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) + + if (matchtype %in% c(0L, 1L)) { + yhatobs <- x[ry, , drop = FALSE] %*% parm$coef + } else { + yhatobs <- x[ry, , drop = FALSE] %*% parm$beta + } + yhatobs <- as.vector(yhatobs) + + # save predictive beta, bin_edge, and lookup table + stored <- preprocess_yhat(yhatobs, ynum[ry], k = donors, num_bins = num_bins) + stored$beta <- parm$beta + if (matchtype == 0L) { + stored$beta <- parm$coef + } + + # impute the missing data + if (matchtype == 0L) { + yhatmis <- x[wy, , drop = FALSE] %*% stored$beta + } else { + yhatmis <- x[wy, , drop = FALSE] %*% stored$beta + } + + impy <- draw_neighbors_pmm(yhatmis, + bin_edges = stored$bin_edges, + lookup_table = stored$lookup_table, + m = 1) + # convert back to factor + impy <- unquantify(impy, y) + + return(impy) + # idx <- matchindex(yhatobs, yhatmis, donors) + # return(y[ry][idx]) +} + + +preprocess_yhat <- function(yhat, y, k = 10, num_bins = 50) { + stopifnot(length(yhat) == length(y)) + + # Ensure valid k + n <- length(yhat) + k <- max(1, min(k, n)) # Clamp k between 1 and n + + # Determine unique yhat values and adjust num_bins accordingly + unique_yhat <- unique(yhat) + num_unique <- length(unique_yhat) + + if (num_bins > num_unique) { + # message("Warning: num_bins (", num_bins, ") exceeds unique yhat values (", num_unique, "). Adjusting to ", num_unique, ".") + num_bins <- num_unique + } + num_bins <- max(2, num_bins) # Ensure at least 2 bins + + # Compute percentile-based bin edges + bin_edges <- quantile(yhat, probs = seq(0, 1, length.out = num_bins + 1), type = 7, na.rm = TRUE) + + # Sort yhat and y together + sort_order <- order(yhat) + yhat_sorted <- yhat[sort_order] + y_sorted <- y[sort_order] + + # Initialize lookup table + lookup_table <- matrix(NA_real_, nrow = num_bins, ncol = k) + + # Assign values to bins + bin_idx <- findInterval(yhat_sorted, vec = bin_edges, all.inside = TRUE) + + # Split y_sorted by bins + bin_values_list <- split(y_sorted, bin_idx) + + # Fill lookup table + lookup_table <- t(sapply(seq_len(num_bins), function(b) { + bin_values <- bin_values_list[[as.character(b)]] + + if (length(bin_values) > 0) { + # If more than k values are available, randomly sample k + sample(bin_values, size = k, replace = length(bin_values) < k) + } else { + # If bin is empty, sample from entire y_sorted to avoid NA values + sample(y_sorted, size = k, replace = TRUE) + } + })) + + return(list(bin_edges = bin_edges, lookup_table = lookup_table)) +} + +draw_neighbors_pmm <- function(yhat_query, bin_edges, lookup_table, m = 1) { + num_queries <- length(yhat_query) + num_bins <- length(bin_edges) - 1 # Bins are defined by edges[i] and edges[i+1] + + # Initialize result matrix: rows = number of queries, columns = m draws per query + imputed_values <- matrix(NA_real_, nrow = num_queries, ncol = m) + + # Find the bin for each query value + bin_idx <- findInterval(yhat_query, bin_edges, rightmost.closed = TRUE, all.inside = TRUE) + + # Compute probability of selecting from left bin (smooth transition) + t0 <- bin_edges[pmax(bin_idx, 1)] + t1 <- bin_edges[pmin(bin_idx + 1, num_bins)] + p_left <- ifelse(t1 > t0, (t1 - yhat_query) / (t1 - t0), 0.5) + + # Determine which bin to sample from + selected_bin <- ifelse(runif(num_queries) < p_left, bin_idx, pmin(bin_idx + 1, num_bins)) + + # Vectorized sampling from lookup table + sampled_indices <- matrix(sample(1:ncol(lookup_table), num_queries * m, replace = TRUE), nrow = num_queries) + imputed_values <- matrix(lookup_table[cbind(selected_bin, sampled_indices)], nrow = num_queries, ncol = m) + + return(imputed_values) +} + +unquantify <- function(ynum, original_y, quantify = TRUE) { + if (!is.factor(original_y)) return(ynum) + factor_levels <- levels(original_y) + if (!is.numeric(ynum)) stop("ynum must be numeric") + + if (quantify) { + # Get unique numeric values and their corresponding factor levels in original y + unique_ynum <- unique(ynum) + unique_levels <- unique(original_y) + + # Ensure levels are assigned in the same order as original_y + mapping <- setNames(as.character(unique_levels), unique_ynum) + + # Assign factor levels based on the mapping + reconstructed_y <- factor(mapping[as.character(ynum)], levels = factor_levels) + } else { + # Reverse integer encoding (simple conversion) + reconstructed_y <- factor(factor_levels[ynum], levels = factor_levels) + } + + return(unname(reconstructed_y)) +} \ No newline at end of file diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd new file mode 100644 index 000000000..c1a538c0d --- /dev/null +++ b/man/mice.impute.pmmsplit.Rd @@ -0,0 +1,191 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mice.impute.pmmsplit.R +\name{mice.impute.pmmsplit} +\alias{mice.impute.pmmsplit} +\alias{pmmsplit} +\title{Imputation by predictive mean matching} +\usage{ +mice.impute.pmmsplit( + y, + ry, + x, + wy = NULL, + donors = 10L, + matchtype = 1L, + exclude = NULL, + quantify = TRUE, + trim = 1L, + ridge = 1e-05, + num_bins = 50, + ... +) +} +\arguments{ +\item{y}{Vector to be imputed} + +\item{ry}{Logical vector of length \code{length(y)} indicating the +the subset \code{y[ry]} of elements in \code{y} to which the imputation +model is fitted. The \code{ry} generally distinguishes the observed +(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} + +\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for +\code{y}. Matrix \code{x} may have no missing values.} + +\item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value +indicates locations in \code{y} for which imputations are created.} + +\item{donors}{The size of the donor pool among which a draw is made. +The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects +the closest match, but is not recommended. Values between 3L and 10L +provide the best results in most cases (Morris et al, 2015).} + +\item{matchtype}{Type of matching distance. The default choice +(\code{matchtype = 1L}) calculates the distance between +the \emph{predicted} value of \code{yobs} and +the \emph{drawn} values of \code{ymis} (called type-1 matching). +Other choices are \code{matchtype = 0L} +(distance between predicted values) and \code{matchtype = 2L} +(distance between drawn values).} + +\item{exclude}{Dependent values to exclude from the imputation model +and the collection of donor values} + +\item{quantify}{Logical. If \code{TRUE}, factor levels are replaced +by the first canonical variate before fitting the imputation model. +If false, the procedure reverts to the old behaviour and takes the +integer codes (which may lack a sensible interpretation). +Relevant only of \code{y} is a factor.} + +\item{trim}{Scalar integer. Minimum number of observations required in a +category in order to be considered as a potential donor value. +Relevant only of \code{y} is a factor.} + +\item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent +problems with multicollinearity. The default is \code{ridge = 1e-05}, +which means that 0.01 percent of the diagonal is added to the cross-product. +Larger ridges may result in more biased estimates. For highly noisy data +(e.g. many junk variables), set \code{ridge = 1e-06} or even lower to +reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} + +\item{num_bins}{The number of bins used to store the predictive mean matching +model. The default is 50.} + +\item{...}{Other named arguments.} +} +\value{ +Vector with imputed data, same type as \code{y}, and of length +\code{sum(wy)} +} +\description{ +\code{pmmsplit()} is an implementation of pmm that saves the imputation model +and generates imputations from the saved model. +} +\examples{ +# We normally call mice.impute.pmmsplit() from within mice() +# But we may call it directly as follows (not recommended) + +set.seed(53177) +xname <- c("age", "hgt", "wgt") +r <- stats::complete.cases(boys[, xname]) +x <- boys[r, xname] +y <- boys[r, "tv"] +ry <- !is.na(y) +table(ry) + +# percentage of missing data in tv +sum(!ry) / length(ry) + +# Impute missing tv data +yimp <- mice.impute.pmmsplit(y, ry, x) +length(yimp) +hist(yimp, xlab = "Imputed missing tv") + +# Impute all tv data +yimp <- mice.impute.pmmsplit(y, ry, x, wy = rep(TRUE, length(y))) +length(yimp) +hist(yimp, xlab = "Imputed missing and observed tv") +plot(jitter(y), jitter(yimp), + main = "Predictive mean matching on age, height and weight", + xlab = "Observed tv (n = 224)", + ylab = "Imputed tv (n = 224)" +) +abline(0, 1) +cor(y, yimp, use = "pair") + +# Use blots to exclude different values per column +# Create blots object +blots <- make.blots(boys) +# Exclude ml 1 through 5 from tv donor pool +blots$tv$exclude <- c(1:5) +# Exclude 100 random observed heights from tv donor pool +blots$hgt$exclude <- sample(unique(boys$hgt), 100) +imp <- mice(boys, method = "pmmsplit", print = FALSE, blots = blots, seed=123) +blots$hgt$exclude \%in\% unlist(c(imp$imp$hgt)) # MUST be all FALSE +blots$tv$exclude \%in\% unlist(c(imp$imp$tv)) # MUST be all FALSE + +# Factor quantification +xname <- c("age", "hgt", "wgt") +br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] +r <- stats::complete.cases(br[, xname]) +x <- br[r, xname] +y <- factor(br[r, "tv"]) +ry <- !is.na(y) +table(y) + +# impute factor by optimizing canonical correlation y, x +mice.impute.pmmsplit(y, ry, x) + +# only categories with at least 2 cases can be donor +mice.impute.pmmsplit(y, ry, x, trim = 2L) + +# in addition, eliminate category 20 +mice.impute.pmmsplit(y, ry, x, trim = 2L, exclude = 20) + +# to get old behavior: as.integer(y)) +mice.impute.pmmsplit(y, ry, x, quantify = FALSE) +} +\references{ +Little, R.J.A. (1988), Missing data adjustments in large surveys +(with discussion), Journal of Business Economics and Statistics, 6, 287--301. + +Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive +mean matching and local residual draws. BMC Med Res Methodol. ;14:75. + +Van Buuren, S. (2018). +\href{https://stefvanbuuren.name/fimd/sec-pmm.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +Chapman & Hall/CRC. Boca Raton, FL. + +Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate +Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical +Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} +} +\seealso{ +Other univariate imputation functions: +\code{\link{mice.impute.cart}()}, +\code{\link{mice.impute.lasso.logreg}()}, +\code{\link{mice.impute.lasso.norm}()}, +\code{\link{mice.impute.lasso.select.logreg}()}, +\code{\link{mice.impute.lasso.select.norm}()}, +\code{\link{mice.impute.lda}()}, +\code{\link{mice.impute.logreg}()}, +\code{\link{mice.impute.logreg.boot}()}, +\code{\link{mice.impute.mean}()}, +\code{\link{mice.impute.midastouch}()}, +\code{\link{mice.impute.mnar.logreg}()}, +\code{\link{mice.impute.mpmm}()}, +\code{\link{mice.impute.norm}()}, +\code{\link{mice.impute.norm.boot}()}, +\code{\link{mice.impute.norm.nob}()}, +\code{\link{mice.impute.norm.predict}()}, +\code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.polr}()}, +\code{\link{mice.impute.polyreg}()}, +\code{\link{mice.impute.quadratic}()}, +\code{\link{mice.impute.rf}()}, +\code{\link{mice.impute.ri}()} +} +\author{ +Stef van Buuren +} +\concept{univariate imputation functions} +\keyword{datagen} diff --git a/tests/testthat/test-mice.impute.pmmsplit.R b/tests/testthat/test-mice.impute.pmmsplit.R new file mode 100644 index 000000000..6bbd43389 --- /dev/null +++ b/tests/testthat/test-mice.impute.pmmsplit.R @@ -0,0 +1,114 @@ +context("mice.impute.pmmsplit") + +xname <- c("age", "hgt", "wgt") +br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] +r <- stats::complete.cases(br[, xname]) +x <- br[r, xname] +y <- br[r, "tv"] +ry <- !is.na(y) + +wy1 <- !ry +wy2 <- rep(TRUE, length(y)) +wy3 <- rep(FALSE, length(y)) +wy4 <- rep(c(TRUE, FALSE), times = c(1, length(y) - 1)) + +test_that("Returns requested length", { + expect_equal(length(mice.impute.pmmsplit(y, ry, x)), sum(!ry)) + expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy1)), sum(wy1)) + expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy2)), sum(wy2)) + expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy3)), sum(wy3)) + expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy4)), sum(wy4)) +}) + +test_that("Excludes donors", { + expect_false(all(c(15:25) %in% mice.impute.pmmsplit(y, ry, x, exclude = c(15:25)))) +}) + +imp1 <- mice(nhanes, printFlag = FALSE, seed = 123) +imp2 <- mice(nhanes, printFlag = FALSE, seed = 123, exclude = c(-1, 1032)) +test_that("excluding unobserved values does not impact pmmsplit", { + expect_identical(imp1$imp, imp2$imp) +}) + +context("optimal scaling") + +# Factor quantification +y <- factor(br[r, "tv"]) + +# impute factor by optimizing canonical correlation y, x +data1 <- data.frame(y, x) +test_that("cancor proceeds normally", { + expect_silent(imp1 <- mice(data1, method = "pmmsplit", remove.collinear = FALSE, eps = 0, + maxit = 1, m = 1, print = FALSE, seed = 1)) +}) + +# > cca$xcoef[, 2L] +# yf6 yf8 yf10 yf12 yf13 yf15 yf16 yf20 yf25 +# 0.8458 -0.0469 -0.3182 -0.3458 0.0825 0.0799 0.0383 -0.0517 -0.0460 + +# include duplicate x +data2 <- data1 +data2$age2 <- data2$age +data2$age3 <- data2$age +data2$age4 <- data2$age +data2$age5 <- data2$age +data2$age6 <- data2$age +data2$age7 <- data2$age +data2$age8 <- data2$age +data2$age9 <- data2$age +data2$age10 <- data2$age +data2$age11 <- data2$age +data2$age12 <- data2$age +data2$age13 <- data2$age +data2$age14 <- data2$age +data2$age15 <- data2$age +data2$age16 <- data2$age +data2$age17 <- data2$age +data2$age18 <- data2$age +data2$age19 <- data2$age +data2$age20 <- data2$age +data2$age21 <- data2$age +data2$age22 <- data2$age +data2$age23 <- data2$age +data2$age24 <- data2$age +data2$age25 <- data2$age + +# impute factor by optimizing canonical correlation y, x +test_that("cancor proceeds normally with many duplicates", { + expect_warning(imp2 <- mice(data2, method = "pmmsplit", remove.collinear = FALSE, eps = 0, + maxit = 1, m = 1, seed = 1, print = FALSE)) +}) + +# add junk variables +data3 <- data1 +data3$j1 <- rnorm(nrow(data3)) +data3$j2 <- rnorm(nrow(data3)) +data3$j3 <- rnorm(nrow(data3)) +data3$j4 <- rnorm(nrow(data3)) +data3$j5 <- rnorm(nrow(data3)) +data3$j6 <- rnorm(nrow(data3)) +data3$j7 <- rnorm(nrow(data3)) +data3$j8 <- rnorm(nrow(data3)) +data3$j9 <- rnorm(nrow(data3)) +data3$j10 <- rnorm(nrow(data3)) +data3$j11 <- rnorm(nrow(data3)) +data3$j12 <- rnorm(nrow(data3)) +data3$j13 <- rnorm(nrow(data3)) +data3$j14 <- rnorm(nrow(data3)) +data3$j15 <- rnorm(nrow(data3)) +data3$j16 <- rnorm(nrow(data3)) +data3$j17 <- rnorm(nrow(data3)) +data3$j18 <- rnorm(nrow(data3)) +data3$j19 <- rnorm(nrow(data3)) +data3$j20 <- rnorm(nrow(data3)) +data3$j21 <- rnorm(nrow(data3)) +data3$j22 <- rnorm(nrow(data3)) +data3$j23 <- rnorm(nrow(data3)) +data3$j24 <- rnorm(nrow(data3)) +data3$j25 <- rnorm(nrow(data3)) + + +test_that("cancor with many junk variables does not crash", { + expect_warning(imp3 <- mice(data3, method = "pmmsplit", remove.collinear = FALSE, eps = 0, + maxit = 1, m = 1, seed = 1, print = FALSE)) +}) diff --git a/tests/testthat/test-quantify.R b/tests/testthat/test-quantify.R new file mode 100644 index 000000000..a6f1e3df3 --- /dev/null +++ b/tests/testthat/test-quantify.R @@ -0,0 +1,48 @@ + +test_that("quantify() and unquantify() work correctly", { + set.seed(123) + + # Original factor variable + y <- factor(sample(c("A", "B", "C"), 10, replace = TRUE), levels = c("A", "B", "C")) + + # Simulated `x` (covariates) + x <- matrix(rnorm(10 * 3), ncol = 3) + + # Logical `ry` (observed vs missing) + ry <- sample(c(TRUE), 10, replace = TRUE) + + # Quantify the factor (optimal scaling) + ynum_quantified <- mice:::quantify(y, ry, x) + + # Convert to integer encoding + ynum_integer <- as.integer(y) + + # Reverse optimal scaling + y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, y, quantify = TRUE) + + # Reverse integer encoding + y_reconstructed_integer <- mice:::unquantify(ynum_integer, y, quantify = FALSE) + + # Test 1: Levels should remain in the original order + expect_equal(levels(y_reconstructed_quantified), levels(y)) + expect_equal(levels(y_reconstructed_integer), levels(y)) + + # Test 2: Factor reconstruction should match original + expect_equal(y_reconstructed_quantified, y) + expect_equal(y_reconstructed_integer, y) + + # Test 3: Handle missing values correctly + y_with_na <- y + y_with_na[c(2, 5)] <- NA + + ynum_quantified_na <- mice:::quantify(y_with_na, ry, x) + y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, y_with_na, quantify = TRUE) + + expect_true(is.na(y_reconstructed_na[2])) + expect_true(is.na(y_reconstructed_na[5])) + + # Test 4: Unquantify should return original y if y is not a factor + expect_equal(mice:::unquantify(ynum_quantified, as.numeric(y), quantify = TRUE), ynum_quantified) +}) + + From cead19509ae8b2446b063a651b64b6e973be2ac5 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 5 Mar 2025 23:42:26 +0100 Subject: [PATCH 028/147] Add infrastructure to store imputation models --- R/cbind.R | 12 +++ R/initialize.models.R | 16 ++++ R/mice.R | 28 ++++-- R/mice.impute.2l.bin.R | 3 +- R/mice.impute.2l.lmer.R | 4 +- R/mice.impute.pmm.R | 21 +---- R/mice.impute.pmmsplit.R | 170 +++++++++++++++++++++--------------- R/mice.impute.polyreg.R | 17 ++-- R/mice.mids.R | 5 +- R/mids.R | 5 ++ R/rbind.R | 2 + R/sampler.R | 11 ++- man/mice.Rd | 5 ++ man/mice.impute.pmmsplit.Rd | 7 +- man/mids.Rd | 8 ++ 15 files changed, 202 insertions(+), 112 deletions(-) create mode 100644 R/initialize.models.R diff --git a/R/cbind.R b/R/cbind.R index 820540519..1943dde6e 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,6 +97,7 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots + models <- x$models ignore <- x$ignore # seed, lastSeedValue, number of iterations, chainMean and chainVar @@ -125,6 +126,7 @@ cbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, + models = models, ignore = ignore, seed = seed, iteration = iteration, @@ -231,6 +233,15 @@ cbind.mids.mids <- function(x, y, call) { names(post) <- varnames blots <- c(x$blots, y$blots) names(blots) <- blocknames + + # Function to copy all objects from one environment to another + merge_envs <- function(target_env, source_env) { + for (name in ls(source_env, all.names = TRUE)) { + assign(name, get(name, envir = source_env), envir = target_env) + } + return(target_env) + } + models <- merge_envs(x$models, y$models) ignore <- x$ignore # For the elements seed, lastSeedValue and iteration the values @@ -296,6 +307,7 @@ cbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, + models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/initialize.models.R b/R/initialize.models.R new file mode 100644 index 000000000..e4c238621 --- /dev/null +++ b/R/initialize.models.R @@ -0,0 +1,16 @@ +# +## Example usage +# visitSequence <- c("block1", "block2", "block3") # Example block names +# m <- 5 # Number of imputations +# models <- initialize_models(visitSequence, m) +initialize.models <- function(visitSequence, m) { + models <- new.env(parent = emptyenv()) + + # Pre-allocate NULL entries for every (blockname, iteration) pair + for (block in visitSequence) { + for (iter in 1:m) { + assign(paste0(block, "_", iter), NULL, envir = models) + } + } + return(models) +} diff --git a/R/mice.R b/R/mice.R index 8c731efbb..6adc49a9d 100644 --- a/R/mice.R +++ b/R/mice.R @@ -219,6 +219,9 @@ #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function #' called for block \code{blockname}. +#' @param models An environment that can be used to store fitted imputation +#' models. The models are stored in the environment under the name of the +#' block. The models can be used to predict missing values in new data. #' @param post A vector of strings with length \code{ncol(data)} specifying #' expressions as strings. Each string is parsed and #' executed within the \code{sampler()} function to post-process @@ -329,6 +332,7 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, + models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, @@ -430,7 +434,7 @@ mice <- function(data, # check visitSequence, edit predictorMatrix for monotone user.visitSequence <- visitSequence visitSequence <- check.visitSequence(visitSequence, - data = data, where = where, blocks = blocks + data = data, where = where, blocks = blocks ) predictorMatrix <- mice.edit.predictorMatrix( predictorMatrix = predictorMatrix, @@ -463,6 +467,19 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post + # prepare model estimates environment + if (is.null(models)) { + models <- new.env(parent = emptyenv()) + + # Pre-allocate list() entries for every (blockname, iteration) pair + for (i in 1:m) { + for (h in visitSequence) { + assign(paste0(h, "_", i), new.env(), envir = models) + } + } + } + # if (is.null(models)) models <- initialize.models(visitSequence, m) + # initialize imputations nmis <- apply(is.na(data), 2, sum) imp <- initialize.imp( @@ -476,7 +493,7 @@ mice <- function(data, q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, + modeltype, blots, models, post, c(from, to), printFlag, ... ) @@ -499,19 +516,20 @@ mice <- function(data, modeltype = modeltype, post = post, blots = blots, + models = models, ignore = ignore, seed = seed, iteration = q$iteration, lastSeedValue = get(".Random.seed", - envir = globalenv(), mode = "integer", - inherits = FALSE), + envir = globalenv(), mode = "integer", + inherits = FALSE), chainMean = q$chainMean, chainVar = q$chainVar, loggedEvents = loggedEvents) if (!is.null(midsobj$loggedEvents)) { warning("Number of logged events: ", nrow(midsobj$loggedEvents), - call. = FALSE + call. = FALSE ) } return(midsobj) diff --git a/R/mice.impute.2l.bin.R b/R/mice.impute.2l.bin.R index 3b1f62d2d..a061d8e93 100644 --- a/R/mice.impute.2l.bin.R +++ b/R/mice.impute.2l.bin.R @@ -68,8 +68,7 @@ mice.impute.2l.bin <- function(y, ry, x, type, suppressWarnings(fit <- try( lme4::glmer(formula(randmodel), data = data.frame(yobs, xobs), - family = binomial, ... - ), + family = binomial), silent = TRUE )) if (!is.null(attr(fit, "class"))) { diff --git a/R/mice.impute.2l.lmer.R b/R/mice.impute.2l.lmer.R index a1f2f2e5a..e48d39ffc 100644 --- a/R/mice.impute.2l.lmer.R +++ b/R/mice.impute.2l.lmer.R @@ -79,9 +79,7 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... ) suppressWarnings(fit <- try( lme4::lmer(formula(randmodel), - data = data.frame(yobs, xobs), - ... - ), + data = data.frame(yobs, xobs)), silent = TRUE )) if (inherits(fit, "try-error")) { diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index bedece950..1db1e915f 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -181,14 +181,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, x <- cbind(1, as.matrix(x)) # quantify categories for factors - ynum <- y - if (is.factor(y)) { - if (quantify) { - ynum <- quantify(y, ry, x) - } else { - ynum <- as.integer(y) - } - } + ynum <- quantify(y, ry, x, quantify = quantify) # parameter estimation parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) @@ -262,15 +255,3 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, m <- sample(y[d <= ds[donors]], 1) return(m) } - -quantify <- function(y, ry, x) { - # replaces (reduced set of) categories by optimal scaling - yf <- factor(y[ry], exclude = NULL) - yd <- model.matrix(~ 0 + yf) - xd <- x[ry, , drop = FALSE] - cca <- cancor(yd, xd, xcenter = FALSE, ycenter = FALSE) - ynum <- as.integer(y) - ynum[ry] <- scale(as.vector(yd %*% cca$xcoef[, 2L])) - # plot(y[ry], ynum[ry]) - return(ynum) -} diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index 1afb15aee..4e03e13e4 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -4,7 +4,8 @@ #' \code{pmmsplit()} is an implementation of pmm that saves the imputation model #' and generates imputations from the saved model. #' @aliases pmmsplit -#' @param num_bins The number of bins used to store the predictive mean matching +#' @param model Storage for the model estimates +#' @param nbins The number of bins used to store the predictive mean matching #' model. The default is 50. #' @inheritParams mice.impute.pmm #' @return Vector with imputed data, same type as \code{y}, and of length @@ -90,11 +91,11 @@ #' # to get old behavior: as.integer(y)) #' mice.impute.pmmsplit(y, ry, x, quantify = FALSE) #' @export -mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = 10L, +mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, matchtype = 1L, exclude = NULL, quantify = TRUE, trim = 1L, - ridge = 1e-05, - num_bins = 50, ...) { + ridge = 1e-05, nbins = NULL, + model = list(), ...) { if (is.null(wy)) { wy <- !ry } @@ -123,72 +124,82 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = 10L, x <- cbind(1, as.matrix(x)) # quantify categories for factors - ynum <- y - if (is.factor(y)) { - if (quantify) { - ynum <- quantify(y, ry, x) - } else { - ynum <- as.integer(y) - } - } + ynum <- quantify(y, ry, x, quantify = quantify) - # train the imputation model using the data - # parameter estimation + # predicted values for observed part parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) - - if (matchtype %in% c(0L, 1L)) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$coef - } else { - yhatobs <- x[ry, , drop = FALSE] %*% parm$beta - } - yhatobs <- as.vector(yhatobs) - - # save predictive beta, bin_edge, and lookup table - stored <- preprocess_yhat(yhatobs, ynum[ry], k = donors, num_bins = num_bins) - stored$beta <- parm$beta if (matchtype == 0L) { - stored$beta <- parm$coef + beta.mis <- beta.obs <- parm$coef } - - # impute the missing data - if (matchtype == 0L) { - yhatmis <- x[wy, , drop = FALSE] %*% stored$beta - } else { - yhatmis <- x[wy, , drop = FALSE] %*% stored$beta + if (matchtype == 1L) { + beta.obs <- parm$coef + beta.mis <- parm$beta } - - impy <- draw_neighbors_pmm(yhatmis, - bin_edges = stored$bin_edges, - lookup_table = stored$lookup_table, - m = 1) - # convert back to factor + if (matchtype == 2L) { + beta.mis <- beta.obs <- parm$beta + } + yhatobs <- as.vector(x[ry, , drop = FALSE] %*% beta.obs) + + # divide predictions into bins + nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) + donors <- initialize.donors(donors, length(yhatobs)) + prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) + + # save model into environment + model$setup <- list(method = "pmmsplit", + donors = donors, + nbins = nbins, + matchtype = matchtype, + exclude = exclude, + quantify = quantify, + trim = trim, + ridge = ridge) + model$beta.obs <- beta.obs + model$beta.mis <- beta.mis + model$edges <- prep$edges + model$lookup <- prep$lookup + + # from here on, we deal with the missing entries + # linear predictor for missing data + yhatmis <- x[wy, , drop = FALSE] %*% model$beta.mis + impy <- draw.neighbors.pmm(yhatmis, + edges = model$edges, + lookup = model$lookup, + m = 1L) + + # convert back to factor if needed impy <- unquantify(impy, y) - return(impy) - # idx <- matchindex(yhatobs, yhatmis, donors) - # return(y[ry][idx]) } +initialize.nbins <- function(nbins, n, nu) { + if (is.null(nbins)) { + nbins <- round(4 * log(n) + 1.5) + } -preprocess_yhat <- function(yhat, y, k = 10, num_bins = 50) { - stopifnot(length(yhat) == length(y)) - - # Ensure valid k - n <- length(yhat) - k <- max(1, min(k, n)) # Clamp k between 1 and n - - # Determine unique yhat values and adjust num_bins accordingly - unique_yhat <- unique(yhat) - num_unique <- length(unique_yhat) + # max nbin is number of unique yhat values + if (nbins > nu) { + # message("Warning: nbins (", nbins, ") exceeds unique yhat values (", nu, "). Adjusting to ", nu, ".") + nbins <- nu + } + # Ensure at least 2 bins + nbins <- max(2L, nbins) + return(nbins) +} - if (num_bins > num_unique) { - # message("Warning: num_bins (", num_bins, ") exceeds unique yhat values (", num_unique, "). Adjusting to ", num_unique, ".") - num_bins <- num_unique +initialize.donors <- function(donors, n) { + if (is.null(donors)) { + donors <- round(n / 600 + 7) } - num_bins <- max(2, num_bins) # Ensure at least 2 bins + donors <- max(1L, min(donors, n)) + return(donors) +} + +bin.yhat <- function(yhat, y, k = 10, nbins = 25) { + stopifnot(length(yhat) == length(y)) # Compute percentile-based bin edges - bin_edges <- quantile(yhat, probs = seq(0, 1, length.out = num_bins + 1), type = 7, na.rm = TRUE) + edges <- quantile(yhat, probs = seq(0, 1, length.out = nbins + 1), type = 7, na.rm = TRUE) # Sort yhat and y together sort_order <- order(yhat) @@ -196,16 +207,16 @@ preprocess_yhat <- function(yhat, y, k = 10, num_bins = 50) { y_sorted <- y[sort_order] # Initialize lookup table - lookup_table <- matrix(NA_real_, nrow = num_bins, ncol = k) + lookup <- matrix(NA_real_, nrow = nbins, ncol = k) # Assign values to bins - bin_idx <- findInterval(yhat_sorted, vec = bin_edges, all.inside = TRUE) + bin_idx <- findInterval(yhat_sorted, vec = edges, all.inside = TRUE) # Split y_sorted by bins bin_values_list <- split(y_sorted, bin_idx) # Fill lookup table - lookup_table <- t(sapply(seq_len(num_bins), function(b) { + lookup <- t(sapply(seq_len(nbins), function(b) { bin_values <- bin_values_list[[as.character(b)]] if (length(bin_values) > 0) { @@ -217,34 +228,55 @@ preprocess_yhat <- function(yhat, y, k = 10, num_bins = 50) { } })) - return(list(bin_edges = bin_edges, lookup_table = lookup_table)) + return(list(edges = edges, lookup = lookup)) } -draw_neighbors_pmm <- function(yhat_query, bin_edges, lookup_table, m = 1) { +draw.neighbors.pmm <- function(yhat_query, edges, lookup, m = 1) { num_queries <- length(yhat_query) - num_bins <- length(bin_edges) - 1 # Bins are defined by edges[i] and edges[i+1] + nbins <- length(edges) - 1 # Bins are defined by edges[i] and edges[i+1] # Initialize result matrix: rows = number of queries, columns = m draws per query imputed_values <- matrix(NA_real_, nrow = num_queries, ncol = m) # Find the bin for each query value - bin_idx <- findInterval(yhat_query, bin_edges, rightmost.closed = TRUE, all.inside = TRUE) + bin_idx <- findInterval(yhat_query, edges, rightmost.closed = TRUE, all.inside = TRUE) # Compute probability of selecting from left bin (smooth transition) - t0 <- bin_edges[pmax(bin_idx, 1)] - t1 <- bin_edges[pmin(bin_idx + 1, num_bins)] + t0 <- edges[pmax(bin_idx, 1)] + t1 <- edges[pmin(bin_idx + 1, nbins)] p_left <- ifelse(t1 > t0, (t1 - yhat_query) / (t1 - t0), 0.5) # Determine which bin to sample from - selected_bin <- ifelse(runif(num_queries) < p_left, bin_idx, pmin(bin_idx + 1, num_bins)) + selected_bin <- ifelse(runif(num_queries) < p_left, bin_idx, pmin(bin_idx + 1, nbins)) # Vectorized sampling from lookup table - sampled_indices <- matrix(sample(1:ncol(lookup_table), num_queries * m, replace = TRUE), nrow = num_queries) - imputed_values <- matrix(lookup_table[cbind(selected_bin, sampled_indices)], nrow = num_queries, ncol = m) + sampled_indices <- matrix(sample(1:ncol(lookup), num_queries * m, replace = TRUE), nrow = num_queries) + imputed_values <- matrix(lookup[cbind(selected_bin, sampled_indices)], nrow = num_queries, ncol = m) return(imputed_values) } +quantify <- function(y, ry, x, quantify = TRUE) { + if (!is.factor(y)) { + return(y) + } + if (!quantify) { + return(as.integer(y)) + } + + # replace (reduced set of) categories by optimal scaling + yf <- factor(y[ry], exclude = NULL) + yd <- model.matrix(~ 0 + yf) + xd <- x[ry, , drop = FALSE] + cca <- cancor(yd, xd, xcenter = FALSE, ycenter = FALSE) + # NOTE: We must be more careful if imputations from a previous iteration + # need to be processed. The line below is a quick fix. + ynum <- as.integer(y) + # Scale only observed y's, since these are used to predict missing y's + ynum[ry] <- scale(as.vector(yd %*% cca$xcoef[, 2L])) + return(ynum) +} + unquantify <- function(ynum, original_y, quantify = TRUE) { if (!is.factor(original_y)) return(ynum) factor_levels <- levels(original_y) @@ -266,4 +298,4 @@ unquantify <- function(ynum, original_y, quantify = TRUE) { } return(unname(reconstructed_y)) -} \ No newline at end of file +} diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 4d3055f23..8489ee4be 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -82,11 +82,18 @@ mice.impute.polyreg <- function(y, ry, x, wy = NULL, nnet.maxit = 100, return(rep(levels(fy)[cat.has.all.obs], sum(wy))) } - fit <- nnet::multinom(formula(xy), - data = xy[ry, , drop = FALSE], weights = w[ry], - maxit = nnet.maxit, trace = nnet.trace, MaxNWts = nnet.MaxNWts, - ... - ) + # prevent model from dots to be passed to multinom + dots <- list(...) + dots$model <- NULL + + # Call multinom() without `model` in dots + fit <- do.call(nnet::multinom, c( + list(formula(xy), + data = xy[ry, , drop = FALSE], weights = w[ry], + maxit = nnet.maxit, trace = nnet.trace, MaxNWts = nnet.MaxNWts), + dots + )) + post <- predict(fit, xy[wy, , drop = FALSE], type = "probs") if (sum(wy) == 1) { post <- matrix(post, nrow = 1, ncol = length(post)) diff --git a/R/mice.mids.R b/R/mice.mids.R index 75d51c2a9..b36a43326 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -112,8 +112,8 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { q <- sampler( obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, - obj$formulas, obj$modeltype, obj$blots, obj$post, - c(from, to), printFlag, ... + obj$formulas, obj$modeltype, obj$blots, obj$models, + obj$post, c(from, to), printFlag, ... ) imp <- q$imp @@ -167,6 +167,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { modeltype = obj$modeltype, post = obj$post, blots = obj$blots, + models = obj$models, ignore = obj$ignore, seed = obj$seed, iteration = sumIt, diff --git a/R/mids.R b/R/mids.R index 79ee110c4..31c1bc264 100644 --- a/R/mids.R +++ b/R/mids.R @@ -58,6 +58,8 @@ #' with commands for post-processing.} #' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} #' function.} +#' \item{\code{models}:}{The \code{models} list contains imputation model +#' estimates.} #' \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating #' the rows in \code{data} used to build the imputation model. (new in \code{mice 3.12.0})} #' \item{\code{seed}:}{The seed value of the solution.} @@ -135,6 +137,7 @@ #' formulas = list(a = a ~ b, b = b ~ a), #' post = NULL, #' blots = NULL, +#' models = NULL, #' ignore = logical(nrow(data)), #' seed = 123, #' iteration = 1, @@ -159,6 +162,7 @@ mids <- function( modeltype = character(), post = character(), blots = list(), + models = new.env(), ignore = logical(), seed = integer(), iteration = integer(), @@ -186,6 +190,7 @@ mids <- function( modeltype = modeltype, post = post, blots = blots, + models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/rbind.R b/R/rbind.R index f4abc8871..a52a11811 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -46,6 +46,7 @@ rbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots + models <- x$models predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -75,6 +76,7 @@ rbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, + models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/sampler.R b/R/sampler.R index 0a4adca24..349fcf170 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,7 +2,7 @@ # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, + modeltype, blots, models, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] @@ -45,6 +45,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, if (ct == "formula") ff <- formulas[[h]] else ff <- NULL pred <- predictorMatrix[h, ] user <- blots[[h]] + key <- paste0(h, "_", i) # univariate/multivariate logic theMethod <- method[h] @@ -74,12 +75,14 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, + model = models[[key]], yname = j, k = k, ct = ct, user = user, ignore = ignore, ... ) + # update data data[(!r[, j]) & where[, j], j] <- imp[[j]][(!r[, j])[where[, j]], i] @@ -177,8 +180,8 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } -sampler.univ <- function(data, r, where, pred, formula, method, yname, k, - ct = "pred", user, ignore, ...) { +sampler.univ <- function(data, r, where, pred, formula, method, model, + yname, k, ct = "pred", user, ignore, ...) { j <- yname[1L] if (ct == "pred") { @@ -242,7 +245,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, imputes <- data[wy, j] imputes[!cc] <- NA - args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type), user, list(...)) + args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, model = model), user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } diff --git a/man/mice.Rd b/man/mice.Rd index 2db20a230..cbad2f225 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,6 +18,7 @@ mice( formulas, modeltype = NULL, blots = NULL, + models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, @@ -128,6 +129,10 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} +\item{models}{An environment that can be used to store fitted imputation +models. The models are stored in the environment under the name of the +block. The models can be used to predict missing values in new data.} + \item{post}{A vector of strings with length \code{ncol(data)} specifying expressions as strings. Each string is parsed and executed within the \code{sampler()} function to post-process diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd index c1a538c0d..7808d9ef8 100644 --- a/man/mice.impute.pmmsplit.Rd +++ b/man/mice.impute.pmmsplit.Rd @@ -10,13 +10,14 @@ mice.impute.pmmsplit( ry, x, wy = NULL, - donors = 10L, + donors = NULL, matchtype = 1L, exclude = NULL, quantify = TRUE, trim = 1L, ridge = 1e-05, - num_bins = 50, + num_bins = NULL, + model = NULL, ... ) } @@ -70,6 +71,8 @@ reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} \item{num_bins}{The number of bins used to store the predictive mean matching model. The default is 50.} +\item{model}{Storage for the model estimates} + \item{...}{Other named arguments.} } \value{ diff --git a/man/mids.Rd b/man/mids.Rd index ee33b1cc7..20fd8e3f8 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -23,6 +23,7 @@ mids( modeltype = character(), post = character(), blots = list(), + models = new.env(), ignore = logical(), seed = integer(), iteration = integer(), @@ -153,6 +154,10 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} +\item{models}{An environment that can be used to store fitted imputation +models. The models are stored in the environment under the name of the +block. The models can be used to predict missing values in new data.} + \item{ignore}{A logical vector of \code{nrow(data)} elements indicating which rows are ignored when creating the imputation model. The default \code{NULL} includes all rows that have an observed value of the variable @@ -258,6 +263,8 @@ identified by its name, so list names must correspond to block names.} with commands for post-processing.} \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} function.} +\item{\code{models}:}{The \code{models} list contains imputation model +estimates.} \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating the rows in \code{data} used to build the imputation model. (new in \code{mice 3.12.0})} \item{\code{seed}:}{The seed value of the solution.} @@ -343,6 +350,7 @@ imp <- mids( formulas = list(a = a ~ b, b = b ~ a), post = NULL, blots = NULL, + models = NULL, ignore = logical(nrow(data)), seed = 123, iteration = 1, From 925bf0595478f567b94e83b27340ad5f692c78a8 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 6 Mar 2025 14:55:53 +0100 Subject: [PATCH 029/147] Add `operations` argument to mice() function and mids definition. It can take values "estimate" (estimate parameters and generate imputations, classic MICE behavior), "fit"(estimate parameters and store the imputation model without data and without generating imputations) and "fill" (use a previously stored imputation model to generate imputations, without re-estimating parameters) --- R/mice.R | 31 ++++++++++++++++++++++++++++++- R/mice.mids.R | 4 +++- R/mids.R | 3 +++ R/sampler.R | 2 +- man/mice.Rd | 24 ++++++++++++++++++++++++ man/mids.Rd | 16 ++++++++++++++++ 6 files changed, 77 insertions(+), 3 deletions(-) diff --git a/R/mice.R b/R/mice.R index 6adc49a9d..dfc8a7f67 100644 --- a/R/mice.R +++ b/R/mice.R @@ -219,6 +219,19 @@ #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function #' called for block \code{blockname}. +#' @param operations A character vector specifying the operation to perform for +#' each imputation block. The available options are: +#' \describe{ +#' \item{"estimate"}{Estimate parameters and generate imputations +#' (classic MICE behavior). This is the default.} +#' \item{"fit"}{Estimate parameters and store the imputation model +#' without data and without generating imputations.} +#' \item{"fill"}{Use a previously stored imputation model to generate +#' imputations, without re-estimating parameters.} +#' } +#' This argument can be specified as a named vector, where names correspond +#' to variables and values specify the operation for each variable. If a +#' single value is provided, it applies to all blocks. #' @param models An environment that can be used to store fitted imputation #' models. The models are stored in the environment under the name of the #' block. The models can be used to predict missing values in new data. @@ -299,6 +312,15 @@ #' # imputation on mixed data with a different method per column #' mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) #' +#' # Fit model for `bmi`, estimate others +#' operations <- c("bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") +#' fit <- mice(nhanes, method = "pmmsplit", operations = operations) +#' +#' # Fill missing `bmi` values using pre-trained model +#' operations <- c("bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") +#' imp <- mice(nhanes, method = "pmmsplit", operations = operations, +#' models = fit$models) +#' #' \dontrun{ #' # example where we fit the imputation model on the train data #' # and apply the model to impute the test data @@ -332,6 +354,7 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, + operations = "estimate", models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -467,6 +490,11 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post + # check operations + if (length(operations) == 1L) { + operations <- setNames(rep(operations, length(names(data))), names(data)) + } + # prepare model estimates environment if (is.null(models)) { models <- new.env(parent = emptyenv()) @@ -493,7 +521,7 @@ mice <- function(data, q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, models, + modeltype, blots, operations, models, post, c(from, to), printFlag, ... ) @@ -516,6 +544,7 @@ mice <- function(data, modeltype = modeltype, post = post, blots = blots, + operations = operations, models = models, ignore = ignore, seed = seed, diff --git a/R/mice.mids.R b/R/mice.mids.R index b36a43326..6cef4b1a1 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -112,7 +112,8 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { q <- sampler( obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, - obj$formulas, obj$modeltype, obj$blots, obj$models, + obj$formulas, obj$modeltype, obj$blots, + obj$operations, obj$models, obj$post, c(from, to), printFlag, ... ) @@ -167,6 +168,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { modeltype = obj$modeltype, post = obj$post, blots = obj$blots, + operations = obj$operations, models = obj$models, ignore = obj$ignore, seed = obj$seed, diff --git a/R/mids.R b/R/mids.R index 31c1bc264..7069536dc 100644 --- a/R/mids.R +++ b/R/mids.R @@ -58,6 +58,7 @@ #' with commands for post-processing.} #' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} #' function.} +#' \item{\code{operations}:}{A character vector of length \code{length(blocks)}.} #' \item{\code{models}:}{The \code{models} list contains imputation model #' estimates.} #' \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating @@ -162,6 +163,7 @@ mids <- function( modeltype = character(), post = character(), blots = list(), + operations = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -190,6 +192,7 @@ mids <- function( modeltype = modeltype, post = post, blots = blots, + operations = operations, models = models, ignore = ignore, seed = seed, diff --git a/R/sampler.R b/R/sampler.R index 349fcf170..09c77b9f1 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,7 +2,7 @@ # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, models, + modeltype, blots, operations, models, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] diff --git a/man/mice.Rd b/man/mice.Rd index cbad2f225..bbe7ce643 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,6 +18,7 @@ mice( formulas, modeltype = NULL, blots = NULL, + operations = "estimate", models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -129,6 +130,20 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} +\item{operations}{A character vector specifying the operation to perform for +each imputation block. The available options are: +\describe{ +\item{"estimate"}{Estimate parameters and generate imputations +(classic MICE behavior). This is the default.} +\item{"fit"}{Estimate parameters and store the imputation model +without data and without generating imputations.} +\item{"fill"}{Use a previously stored imputation model to generate +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the operation for each variable. If a +single value is provided, it applies to all blocks.} + \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the block. The models can be used to predict missing values in new data.} @@ -395,6 +410,15 @@ complete(imp) # imputation on mixed data with a different method per column mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +# Fit model for `bmi`, estimate others +operations <- c("bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") +fit <- mice(nhanes, method = "pmmsplit", operations = operations) + +# Fill missing `bmi` values using pre-trained model +operations <- c("bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") +imp <- mice(nhanes, method = "pmmsplit", operations = operations, + models = fit$models) + \dontrun{ # example where we fit the imputation model on the train data # and apply the model to impute the test data diff --git a/man/mids.Rd b/man/mids.Rd index 20fd8e3f8..fdd5f9a8a 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -23,6 +23,7 @@ mids( modeltype = character(), post = character(), blots = list(), + operations = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -154,6 +155,20 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} +\item{operations}{A character vector specifying the operation to perform for +each imputation block. The available options are: +\describe{ +\item{"estimate"}{Estimate parameters and generate imputations +(classic MICE behavior). This is the default.} +\item{"fit"}{Estimate parameters and store the imputation model +without data and without generating imputations.} +\item{"fill"}{Use a previously stored imputation model to generate +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the operation for each variable. If a +single value is provided, it applies to all blocks.} + \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the block. The models can be used to predict missing values in new data.} @@ -263,6 +278,7 @@ identified by its name, so list names must correspond to block names.} with commands for post-processing.} \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} function.} +\item{\code{operations}:}{A character vector of length \code{length(blocks)}.} \item{\code{models}:}{The \code{models} list contains imputation model estimates.} \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating From 4bd6225dacb70834de6e6be684c5d43eb6ce1958 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 6 Mar 2025 23:35:37 +0100 Subject: [PATCH 030/147] Add check.operations() and extend models initialization to store 1..m --- R/check.operations.R | 74 +++++++++++++++ R/mice.R | 31 +++---- R/mice.impute.pmmsplit.R | 100 +++++++++++---------- R/sampler.R | 7 +- man/mice.Rd | 10 +-- man/mice.impute.pmm.Rd | 1 + man/mice.impute.pmmsplit.Rd | 11 ++- tests/testthat/test-mice.impute.pmmsplit.R | 2 +- 8 files changed, 162 insertions(+), 74 deletions(-) create mode 100644 R/check.operations.R diff --git a/R/check.operations.R b/R/check.operations.R new file mode 100644 index 000000000..362b385b0 --- /dev/null +++ b/R/check.operations.R @@ -0,0 +1,74 @@ +check.operations <- function(operations, data, models = NULL, blocks = NULL) { + if (is.null(operations)) { + operations <- "estimate" + } + + valid_operations <- c("estimate", "fit", "fill") + + # 1. Default blocks to individual variables if not provided + if (is.null(blocks)) { + blocks <- setNames(as.list(names(data)), names(data)) + } + + # Convert blocks into actual variable names + bv <- unique(unlist(blocks)) + + # 2. Expand operations if it's a single value + if (length(operations) == 1) { + operations <- setNames(rep(operations, length(bv)), bv) + } + + # 3. Check if all names in operations exist in blocks + notFound <- !names(operations) %in% bv + if (any(notFound)) { + stop(paste0( + "The following variables specified in `operations` are not present in `blocks`: ", + paste(names(operations)[notFound], collapse = ", "), ".\n", + "Ensure all specified variables match those in `blocks`." + )) + } + + # 4. Check if all operations are valid + invalid_ops <- setdiff(unique(operations), valid_operations) + if (length(invalid_ops) > 0) { + stop(paste0( + "Invalid operation(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", + "Valid operations are: ", paste(valid_operations, collapse = ", "), ".\n", + "Please correct the `operations` argument." + )) + } + + # 5. Prevent "fill" if models is NULL + if ("fill" %in% operations && is.null(models)) { + stop("The operation 'fill' requires a stored model, but `models` is NULL.\n", + "Please provide a valid `models` object containing trained imputation models.") + } + + # 6. Ensure that all "fill" variables have a trained model in models + if ("fill" %in% operations && !is.null(models)) { + fill_vars <- names(operations[operations == "fill"]) + missing_models <- setdiff(fill_vars, ls(models)) + if (length(missing_models) > 0) { + stop(paste0( + "The following variables specified as 'fill' do not have stored models: ", + paste(missing_models, collapse = ", "), ".\n", + "Ensure these variables were previously fitted before using 'fill'." + )) + } + } + + # 7. Ensure all variables in models exist in blocks + if (!is.null(models)) { + trained_vars <- ls(models) + missing_from_data <- setdiff(trained_vars, bv) # Use block variable names + if (length(missing_from_data) > 0) { + stop(paste0( + "The following variables are present in `models` but missing from `data`: ", + paste(missing_from_data, collapse = ", "), ".\n", + "Ensure that all stored models correspond to variables in the dataset." + )) + } + } + + return(operations) +} diff --git a/R/mice.R b/R/mice.R index dfc8a7f67..dde0e05a6 100644 --- a/R/mice.R +++ b/R/mice.R @@ -310,16 +310,16 @@ #' complete(imp) #' #' # imputation on mixed data with a different method per column -#' mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +#' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) #' #' # Fit model for `bmi`, estimate others #' operations <- c("bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") -#' fit <- mice(nhanes, method = "pmmsplit", operations = operations) +#' imp2 <- mice(nhanes, method = "pmmsplit", operations = operations) #' #' # Fill missing `bmi` values using pre-trained model #' operations <- c("bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") -#' imp <- mice(nhanes, method = "pmmsplit", operations = operations, -#' models = fit$models) +#' imp3 <- mice(nhanes, method = "pmmsplit", operations = operations, +#' models = imp2$models) #' #' \dontrun{ #' # example where we fit the imputation model on the train data @@ -354,7 +354,7 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, - operations = "estimate", + operations = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -490,23 +490,24 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # check operations - if (length(operations) == 1L) { - operations <- setNames(rep(operations, length(names(data))), names(data)) - } + # check operations in current model + operations <- check.operations(operations, data, models, blocks) - # prepare model estimates environment + # Initialize models only for "fit" and "fill" blocks that are missing in models if (is.null(models)) { models <- new.env(parent = emptyenv()) - - # Pre-allocate list() entries for every (blockname, iteration) pair + } + model_vars <- names(operations[operations %in% c("fit", "fill")]) + for (block in model_vars) { + if (!exists(block, envir = models)) { + models[[block]] <- new.env(parent = emptyenv()) + } for (i in 1:m) { - for (h in visitSequence) { - assign(paste0(h, "_", i), new.env(), envir = models) + if (!exists(as.character(i), envir = models[[block]])) { + models[[block]][[as.character(i)]] <- new.env(parent = emptyenv()) } } } - # if (is.null(models)) models <- initialize.models(visitSequence, m) # initialize imputations nmis <- apply(is.na(data), 2, sum) diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index 4e03e13e4..314b2e7a6 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -4,6 +4,7 @@ #' \code{pmmsplit()} is an implementation of pmm that saves the imputation model #' and generates imputations from the saved model. #' @aliases pmmsplit +#' @param operation The operation to be performed. The default is \code{"estimate"}. #' @param model Storage for the model estimates #' @param nbins The number of bins used to store the predictive mean matching #' model. The default is 50. @@ -34,7 +35,7 @@ #' set.seed(53177) #' xname <- c("age", "hgt", "wgt") #' r <- stats::complete.cases(boys[, xname]) -#' x <- boys[r, xname] +#' x <- as.matrix(boys[r, xname]) #' y <- boys[r, "tv"] #' ry <- !is.na(y) #' table(ry) @@ -74,7 +75,7 @@ #' xname <- c("age", "hgt", "wgt") #' br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] #' r <- stats::complete.cases(br[, xname]) -#' x <- br[r, xname] +#' x <- as.matrix(br[r, xname]) #' y <- factor(br[r, "tv"]) #' ry <- !is.na(y) #' table(y) @@ -95,38 +96,44 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, matchtype = 1L, exclude = NULL, quantify = TRUE, trim = 1L, ridge = 1e-05, nbins = NULL, - model = list(), ...) { + operation = "estimate", + model = NULL, ...) { if (is.null(wy)) { wy <- !ry } - # Reformulate the imputation problem such that - # 1. the imputation model disregards records with excluded y-values - # 2. the donor set does not contain excluded y-values - - # Keep sparse categories out of the imputation model - if (is.factor(y)) { - active <- !ry | y %in% (levels(y)[table(y) >= trim]) - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] - } - # Keep excluded values out of the imputation model - if (!is.null(exclude)) { - active <- !ry | !y %in% exclude - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] + # **Only enforce `model` for "fit" and "fill"** + if (operation %in% c("fit", "fill")) { + if (is.null(model)) { + stop(paste("`model` cannot be NULL for operation:", operation)) + } + if (!is.environment(model)) { + stop("`model` must be an environment to store results persistently.") + } } - x <- cbind(1, as.matrix(x)) + # **Handle "fill" Operation: Use Pre-Stored Model Without Re-Training** + if (operation == "fill") { + if (!length(ls(model))) { + stop("No stored model found for 'fill' operation.") + } + + # Compute linear predictor for missing data + yhatmis <- x[wy, , drop = FALSE] %*% model$beta.mis + impy <- draw.neighbors.pmm(yhatmis, + edges = model$edges, + lookup = model$lookup, + m = 1L) - # quantify categories for factors + # Convert back to factor if needed + impy <- unquantify(impy, y) + return(impy) + } + + # **Handle "estimate" and "fit": Train Model** ynum <- quantify(y, ry, x, quantify = quantify) - # predicted values for observed part + # Predicted values for observed part parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) if (matchtype == 0L) { beta.mis <- beta.obs <- parm$coef @@ -140,34 +147,35 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, } yhatobs <- as.vector(x[ry, , drop = FALSE] %*% beta.obs) - # divide predictions into bins + # Divide predictions into bins nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) donors <- initialize.donors(donors, length(yhatobs)) prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) - # save model into environment - model$setup <- list(method = "pmmsplit", - donors = donors, - nbins = nbins, - matchtype = matchtype, - exclude = exclude, - quantify = quantify, - trim = trim, - ridge = ridge) - model$beta.obs <- beta.obs - model$beta.mis <- beta.mis - model$edges <- prep$edges - model$lookup <- prep$lookup - - # from here on, we deal with the missing entries - # linear predictor for missing data - yhatmis <- x[wy, , drop = FALSE] %*% model$beta.mis + # **Store Model for "fit" (skip for "estimate")** + if (operation == "fit") { + model$setup <- list(method = "pmmsplit", + donors = donors, + nbins = nbins, + matchtype = matchtype, + exclude = exclude, + quantify = quantify, + trim = trim, + ridge = ridge) + model$beta.obs <- beta.obs + model$beta.mis <- beta.mis + model$edges <- prep$edges + model$lookup <- prep$lookup + } + + # **Compute Missing Value Imputations** + yhatmis <- x[wy, , drop = FALSE] %*% beta.mis impy <- draw.neighbors.pmm(yhatmis, - edges = model$edges, - lookup = model$lookup, + edges = prep$edges, + lookup = prep$lookup, m = 1L) - # convert back to factor if needed + # Convert back to factor if needed impy <- unquantify(impy, y) return(impy) } diff --git a/R/sampler.R b/R/sampler.R index 09c77b9f1..57531e2d3 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -75,7 +75,8 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, - model = models[[key]], + operation = operations[j], + model = models[[j]][[as.character(i)]], yname = j, k = k, ct = ct, user = user, ignore = ignore, @@ -180,7 +181,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } -sampler.univ <- function(data, r, where, pred, formula, method, model, +sampler.univ <- function(data, r, where, pred, formula, method, operation, model, yname, k, ct = "pred", user, ignore, ...) { j <- yname[1L] @@ -245,7 +246,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, model, imputes <- data[wy, j] imputes[!cc] <- NA - args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, model = model), user, list(...)) + args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, operation = operation, model = model), user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } diff --git a/man/mice.Rd b/man/mice.Rd index bbe7ce643..8516353c7 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,7 +18,7 @@ mice( formulas, modeltype = NULL, blots = NULL, - operations = "estimate", + operations = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -408,16 +408,16 @@ imp$imp$bmi complete(imp) # imputation on mixed data with a different method per column -mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) # Fit model for `bmi`, estimate others operations <- c("bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") -fit <- mice(nhanes, method = "pmmsplit", operations = operations) +imp2 <- mice(nhanes, method = "pmmsplit", operations = operations) # Fill missing `bmi` values using pre-trained model operations <- c("bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") -imp <- mice(nhanes, method = "pmmsplit", operations = operations, - models = fit$models) +imp3 <- mice(nhanes, method = "pmmsplit", operations = operations, + models = imp2$models) \dontrun{ # example where we fit the imputation model on the train data diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index ff0336988..32fb9ac52 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -203,6 +203,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd index 7808d9ef8..fac5f1355 100644 --- a/man/mice.impute.pmmsplit.Rd +++ b/man/mice.impute.pmmsplit.Rd @@ -16,7 +16,8 @@ mice.impute.pmmsplit( quantify = TRUE, trim = 1L, ridge = 1e-05, - num_bins = NULL, + nbins = NULL, + operation = "estimate", model = NULL, ... ) @@ -68,9 +69,11 @@ Larger ridges may result in more biased estimates. For highly noisy data (e.g. many junk variables), set \code{ridge = 1e-06} or even lower to reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} -\item{num_bins}{The number of bins used to store the predictive mean matching +\item{nbins}{The number of bins used to store the predictive mean matching model. The default is 50.} +\item{operation}{The operation to be performed. The default is \code{"estimate"}.} + \item{model}{Storage for the model estimates} \item{...}{Other named arguments.} @@ -90,7 +93,7 @@ and generates imputations from the saved model. set.seed(53177) xname <- c("age", "hgt", "wgt") r <- stats::complete.cases(boys[, xname]) -x <- boys[r, xname] +x <- as.matrix(boys[r, xname]) y <- boys[r, "tv"] ry <- !is.na(y) table(ry) @@ -130,7 +133,7 @@ blots$tv$exclude \%in\% unlist(c(imp$imp$tv)) # MUST be all FALSE xname <- c("age", "hgt", "wgt") br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] r <- stats::complete.cases(br[, xname]) -x <- br[r, xname] +x <- as.matrix(br[r, xname]) y <- factor(br[r, "tv"]) ry <- !is.na(y) table(y) diff --git a/tests/testthat/test-mice.impute.pmmsplit.R b/tests/testthat/test-mice.impute.pmmsplit.R index 6bbd43389..812f69c8c 100644 --- a/tests/testthat/test-mice.impute.pmmsplit.R +++ b/tests/testthat/test-mice.impute.pmmsplit.R @@ -3,7 +3,7 @@ context("mice.impute.pmmsplit") xname <- c("age", "hgt", "wgt") br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] r <- stats::complete.cases(br[, xname]) -x <- br[r, xname] +x <- as.matrix(br[r, xname]) y <- br[r, "tv"] ry <- !is.na(y) From 274df0503c38a03385a877d2dbac2d2c457fa2d4 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 6 Mar 2025 23:36:56 +0100 Subject: [PATCH 031/147] Add the formula for creating the design matrix to each stored model, support operations and models in cbind(), rbind() and filter(), and create stricter tests in check.operations() --- R/cbind.R | 7 +++ R/check.operations.R | 6 ++ R/filter.R | 6 ++ R/mice.R | 21 ++++--- R/rbind.R | 6 ++ R/sampler.R | 77 +++++++++++++++++--------- man/filter.mids.Rd | 2 + man/mice.Rd | 21 ++++--- man/mice.impute.cart.Rd | 1 + man/mice.impute.lasso.logreg.Rd | 1 + man/mice.impute.lasso.norm.Rd | 1 + man/mice.impute.lasso.select.logreg.Rd | 1 + man/mice.impute.lasso.select.norm.Rd | 1 + man/mice.impute.lda.Rd | 1 + man/mice.impute.logreg.Rd | 1 + man/mice.impute.logreg.boot.Rd | 1 + man/mice.impute.mean.Rd | 1 + man/mice.impute.midastouch.Rd | 1 + man/mice.impute.mnar.Rd | 1 + man/mice.impute.mpmm.Rd | 1 + man/mice.impute.norm.Rd | 1 + man/mice.impute.norm.boot.Rd | 1 + man/mice.impute.norm.nob.Rd | 1 + man/mice.impute.norm.predict.Rd | 1 + man/mice.impute.polr.Rd | 1 + man/mice.impute.polyreg.Rd | 1 + man/mice.impute.quadratic.Rd | 1 + man/mice.impute.rf.Rd | 1 + man/mice.impute.ri.Rd | 1 + man/mids.Rd | 4 +- 30 files changed, 130 insertions(+), 41 deletions(-) diff --git a/R/cbind.R b/R/cbind.R index 1943dde6e..879547354 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,6 +97,8 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots + operations <- c(x$operations, "estimate") + names(operations) <- c(names(x$operations), tail(varnames, 1L)) models <- x$models ignore <- x$ignore @@ -126,6 +128,7 @@ cbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, + operations = operations, models = models, ignore = ignore, seed = seed, @@ -233,6 +236,9 @@ cbind.mids.mids <- function(x, y, call) { names(post) <- varnames blots <- c(x$blots, y$blots) names(blots) <- blocknames + operations <- c(x$operations, y$operations) + # FIXME: Assumes combined operation names yields unique names as in colnames(data) + names(operations) <- make.unique(c(names(x$operations), names(y$operations))) # Function to copy all objects from one environment to another merge_envs <- function(target_env, source_env) { @@ -307,6 +313,7 @@ cbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, + operations = operations, models = models, ignore = ignore, seed = seed, diff --git a/R/check.operations.R b/R/check.operations.R index 362b385b0..273af3ba0 100644 --- a/R/check.operations.R +++ b/R/check.operations.R @@ -18,6 +18,12 @@ check.operations <- function(operations, data, models = NULL, blocks = NULL) { operations <- setNames(rep(operations, length(bv)), bv) } + # check length + if (length(operations) != length(bv)) { + stop("The length of `operations` (", length(operations), + ") must match the number of variables in `blocks` (", length(bv),").") + } + # 3. Check if all names in operations exist in blocks notFound <- !names(operations) %in% bv if (any(notFound)) { diff --git a/R/filter.R b/R/filter.R index 9ff67b95c..98198320c 100644 --- a/R/filter.R +++ b/R/filter.R @@ -33,6 +33,8 @@ dplyr::filter #' \code{formulas} \tab Equals \code{.data$formulas}\cr #' \code{post} \tab Equals \code{.data$post}\cr #' \code{blots} \tab Equals \code{.data$blots}\cr +#' \code{operations} \tab Equals \code{.data$operations}\cr +#' \code{models} \tab Equals \code{.data$models}\cr #' \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr #' \code{seed} \tab Equals \code{.data$seed}\cr #' \code{iteration} \tab Equals \code{.data$iteration}\cr @@ -79,6 +81,8 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { formulas <- .data$formulas modeltype <- .data$modeltype blots <- .data$blots + operations <- .data$operations + models <- .data$models post <- .data$post seed <- .data$seed iteration <- .data$iteration @@ -120,6 +124,8 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { modeltype = modeltype, post = post, blots = blots, + operations = operations, + models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/mice.R b/R/mice.R index dde0e05a6..eae301664 100644 --- a/R/mice.R +++ b/R/mice.R @@ -231,7 +231,9 @@ #' } #' This argument can be specified as a named vector, where names correspond #' to variables and values specify the operation for each variable. If a -#' single value is provided, it applies to all blocks. +#' single value is provided, it applies to the variables in all blocks. The +#' length of the vector must match the number of variables present in the +#' blocks. #' @param models An environment that can be used to store fitted imputation #' models. The models are stored in the environment under the name of the #' block. The models can be used to predict missing values in new data. @@ -310,16 +312,21 @@ #' complete(imp) #' #' # imputation on mixed data with a different method per column -#' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +#' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) #' -#' # Fit model for `bmi`, estimate others -#' operations <- c("bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") -#' imp2 <- mice(nhanes, method = "pmmsplit", operations = operations) +#' # Store model for `bmi`, estimate others as usual +#' operations <- c("age" = "estimate", "bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") +#' imp2 <- mice(nhanes, method = "pmmsplit", operations = operations, print = FALSE) +#' +#' # Inspects the stored model for imputation 1 for `bmi` +#' ls(imp2$models$bmi$"1") +#' imp2$models$bmi$"1"$formula +#' imp2$models$bmi$"1"$beta.mis #' #' # Fill missing `bmi` values using pre-trained model -#' operations <- c("bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") +#' operations <- c("age" = "estimate", "bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") #' imp3 <- mice(nhanes, method = "pmmsplit", operations = operations, -#' models = imp2$models) +#' models = imp2$models, print = FALSE) #' #' \dontrun{ #' # example where we fit the imputation model on the train data diff --git a/R/rbind.R b/R/rbind.R index a52a11811..c9098b031 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -46,6 +46,7 @@ rbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots + operations <- x$operations models <- x$models predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -76,6 +77,7 @@ rbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, + operations = operations, models = models, ignore = ignore, seed = seed, @@ -127,6 +129,8 @@ rbind.mids.mids <- function(x, y, call) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots + operations <- x$operations + models <- x$models ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -174,6 +178,8 @@ rbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, + operations = operations, + models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/sampler.R b/R/sampler.R index 57531e2d3..1c35634e6 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -116,13 +116,13 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, )) } else { stop("Cannot call function of type ", ct, - call. = FALSE + call. = FALSE ) } if (is.null(imputes)) { stop("No imputations from ", theMethod, - h, - call. = FALSE + h, + call. = FALSE ) } for (j in names(imputes)) { @@ -138,7 +138,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, wy <- where[, j] ry <- r[, j] imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], - na.action = na.pass + na.action = na.pass ) data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] } @@ -185,27 +185,8 @@ sampler.univ <- function(data, r, where, pred, formula, method, operation, model yname, k, ct = "pred", user, ignore, ...) { j <- yname[1L] - if (ct == "pred") { - vars <- colnames(data)[pred != 0] - xnames <- setdiff(vars, j) - if (length(xnames) > 0L) { - formula <- reformulate(backticks(xnames), response = backticks(j)) - formula <- update(formula, ". ~ . ") - } else { - formula <- as.formula(paste0(j, " ~ 1")) - } - } - - if (ct == "formula") { - # move terms other than j from lhs to rhs - ymove <- setdiff(lhs(formula), j) - formula <- update(formula, paste(j, " ~ . ")) - if (length(ymove) > 0L) { - formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) - } - } - - # get the model matrix + # prepare formula and model matrix + formula <- prepare.formula(formula, data, model, j, ct, pred, operation) x <- obtain.design(data, formula) # expand pred vector to model matrix, remove intercept @@ -246,7 +227,51 @@ sampler.univ <- function(data, r, where, pred, formula, method, operation, model imputes <- data[wy, j] imputes[!cc] <- NA - args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, operation = operation, model = model), user, list(...)) + args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, + operation = operation, model = model), + user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } + + +prepare.formula <- function(formula, data, model, j, ct, pred, operation) { + # prepares the formula for univariate imputation + # saves (for "fit") or retrieves (for "fill") the formula + + # for "fill", use the stored formula instead of recalculating + if (operation == "fill") { + if (!exists("formula", envir = model)) { + stop("Error: No stored formula found in model for 'fill' operation.") + } + formula <- get("formula", envir = model) + return(formula) + } + + if (ct == "pred") { + vars <- colnames(data)[pred != 0] + xnames <- setdiff(vars, j) + if (length(xnames) > 0L) { + formula <- reformulate(backticks(xnames), response = backticks(j)) + formula <- update(formula, ". ~ . ") + } else { + formula <- as.formula(paste0(j, " ~ 1")) + } + } + + if (ct == "formula") { + # move terms other than j from lhs to rhs + ymove <- setdiff(lhs(formula), j) + formula <- update(formula, paste(j, " ~ . ")) + if (length(ymove) > 0L) { + formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) + } + } + + # store formula in `model` only when operation == "fit" + if (operation == "fit") { + assign("formula", formula, envir = model) + } + + return(formula) +} diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index 61e419a03..b44673ecf 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -44,6 +44,8 @@ The function constructs the elements of the filtered \code{mids} object as follo \code{formulas} \tab Equals \code{.data$formulas}\cr \code{post} \tab Equals \code{.data$post}\cr \code{blots} \tab Equals \code{.data$blots}\cr +\code{operations} \tab Equals \code{.data$operations}\cr +\code{models} \tab Equals \code{.data$models}\cr \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr \code{seed} \tab Equals \code{.data$seed}\cr \code{iteration} \tab Equals \code{.data$iteration}\cr diff --git a/man/mice.Rd b/man/mice.Rd index 8516353c7..0d4864db7 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -142,7 +142,9 @@ imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond to variables and values specify the operation for each variable. If a -single value is provided, it applies to all blocks.} +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks.} \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the @@ -408,16 +410,21 @@ imp$imp$bmi complete(imp) # imputation on mixed data with a different method per column -imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) -# Fit model for `bmi`, estimate others -operations <- c("bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") -imp2 <- mice(nhanes, method = "pmmsplit", operations = operations) +# Store model for `bmi`, estimate others as usual +operations <- c("age" = "estimate", "bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") +imp2 <- mice(nhanes, method = "pmmsplit", operations = operations, print = FALSE) + +# Inspects the stored model for imputation 1 for `bmi` +ls(imp2$models$bmi$"1") +imp2$models$bmi$"1"$formula +imp2$models$bmi$"1"$beta.mis # Fill missing `bmi` values using pre-trained model -operations <- c("bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") +operations <- c("age" = "estimate", "bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") imp3 <- mice(nhanes, method = "pmmsplit", operations = operations, - models = imp2$models) + models = imp2$models, print = FALSE) \dontrun{ # example where we fit the imputation model on the train data diff --git a/man/mice.impute.cart.Rd b/man/mice.impute.cart.Rd index 19a1767d4..1c1d0be70 100644 --- a/man/mice.impute.cart.Rd +++ b/man/mice.impute.cart.Rd @@ -87,6 +87,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.logreg.Rd b/man/mice.impute.lasso.logreg.Rd index d102536d9..0c26b9bd3 100644 --- a/man/mice.impute.lasso.logreg.Rd +++ b/man/mice.impute.lasso.logreg.Rd @@ -78,6 +78,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.norm.Rd b/man/mice.impute.lasso.norm.Rd index 6e6fb86e2..69b692e0f 100644 --- a/man/mice.impute.lasso.norm.Rd +++ b/man/mice.impute.lasso.norm.Rd @@ -78,6 +78,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.select.logreg.Rd b/man/mice.impute.lasso.select.logreg.Rd index 027e2a513..056479f53 100644 --- a/man/mice.impute.lasso.select.logreg.Rd +++ b/man/mice.impute.lasso.select.logreg.Rd @@ -86,6 +86,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.select.norm.Rd b/man/mice.impute.lasso.select.norm.Rd index e825a028c..4fefd0009 100644 --- a/man/mice.impute.lasso.select.norm.Rd +++ b/man/mice.impute.lasso.select.norm.Rd @@ -88,6 +88,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lda.Rd b/man/mice.impute.lda.Rd index e46b23505..2a02840b0 100644 --- a/man/mice.impute.lda.Rd +++ b/man/mice.impute.lda.Rd @@ -86,6 +86,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index 8427031d2..87ae4b140 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -82,6 +82,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.logreg.boot.Rd b/man/mice.impute.logreg.boot.Rd index 2076dc202..802619ebb 100644 --- a/man/mice.impute.logreg.boot.Rd +++ b/man/mice.impute.logreg.boot.Rd @@ -62,6 +62,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.mean.Rd b/man/mice.impute.mean.Rd index 3c3435bc4..b44864ea4 100644 --- a/man/mice.impute.mean.Rd +++ b/man/mice.impute.mean.Rd @@ -68,6 +68,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.midastouch.Rd b/man/mice.impute.midastouch.Rd index cfa5c310a..a749f22c2 100644 --- a/man/mice.impute.midastouch.Rd +++ b/man/mice.impute.midastouch.Rd @@ -142,6 +142,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.mnar.Rd b/man/mice.impute.mnar.Rd index 3568786ae..d3ec6154e 100644 --- a/man/mice.impute.mnar.Rd +++ b/man/mice.impute.mnar.Rd @@ -189,6 +189,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.mpmm.Rd b/man/mice.impute.mpmm.Rd index 4d82409bd..761513139 100644 --- a/man/mice.impute.mpmm.Rd +++ b/man/mice.impute.mpmm.Rd @@ -81,6 +81,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.Rd b/man/mice.impute.norm.Rd index a082d1ffc..101817344 100644 --- a/man/mice.impute.norm.Rd +++ b/man/mice.impute.norm.Rd @@ -74,6 +74,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.boot.Rd b/man/mice.impute.norm.boot.Rd index b426a7139..8e4dbb5a5 100644 --- a/man/mice.impute.norm.boot.Rd +++ b/man/mice.impute.norm.boot.Rd @@ -58,6 +58,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.nob.Rd b/man/mice.impute.norm.nob.Rd index 683170dc9..242f1e503 100644 --- a/man/mice.impute.norm.nob.Rd +++ b/man/mice.impute.norm.nob.Rd @@ -80,6 +80,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.predict.Rd b/man/mice.impute.norm.predict.Rd index 86b2f7ecb..92e816079 100644 --- a/man/mice.impute.norm.predict.Rd +++ b/man/mice.impute.norm.predict.Rd @@ -77,6 +77,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.polr.Rd b/man/mice.impute.polr.Rd index 21f17912b..1e5432fef 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -116,6 +116,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, \code{\link{mice.impute.rf}()}, diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index 30cf4f435..87bde974c 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -103,6 +103,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.quadratic}()}, \code{\link{mice.impute.rf}()}, diff --git a/man/mice.impute.quadratic.Rd b/man/mice.impute.quadratic.Rd index b8e7d441b..4912fd9e6 100644 --- a/man/mice.impute.quadratic.Rd +++ b/man/mice.impute.quadratic.Rd @@ -118,6 +118,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.rf}()}, diff --git a/man/mice.impute.rf.Rd b/man/mice.impute.rf.Rd index ee288b634..ecbc0f1af 100644 --- a/man/mice.impute.rf.Rd +++ b/man/mice.impute.rf.Rd @@ -110,6 +110,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.ri.Rd b/man/mice.impute.ri.Rd index 8a5b6ccf8..c397228ad 100644 --- a/man/mice.impute.ri.Rd +++ b/man/mice.impute.ri.Rd @@ -66,6 +66,7 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, +\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mids.Rd b/man/mids.Rd index fdd5f9a8a..f8929c590 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -167,7 +167,9 @@ imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond to variables and values specify the operation for each variable. If a -single value is provided, it applies to all blocks.} +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks.} \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the From cd7895fa2a36a764d1e5161e5ebb27b972149705 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 7 Mar 2025 12:11:27 +0100 Subject: [PATCH 032/147] Define activities with levels "walk", "train" and "run" --- R/cbind.R | 14 +++---- R/check.activities.R | 78 ++++++++++++++++++++++++++++++++++++ R/check.operations.R | 80 ------------------------------------- R/filter.R | 6 +-- R/mice.R | 32 +++++++-------- R/mice.impute.pmmsplit.R | 22 +++++----- R/mice.mids.R | 4 +- R/mids.R | 6 +-- R/rbind.R | 8 ++-- R/sampler.R | 24 +++++------ man/filter.mids.Rd | 2 +- man/mice.Rd | 20 +++++----- man/mice.impute.pmmsplit.Rd | 4 +- man/mids.Rd | 14 +++---- 14 files changed, 156 insertions(+), 158 deletions(-) create mode 100644 R/check.activities.R delete mode 100644 R/check.operations.R diff --git a/R/cbind.R b/R/cbind.R index 879547354..ed4b16509 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,8 +97,8 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots - operations <- c(x$operations, "estimate") - names(operations) <- c(names(x$operations), tail(varnames, 1L)) + activities <- c(x$activities, "walk") + names(activities) <- c(names(x$activities), tail(varnames, 1L)) models <- x$models ignore <- x$ignore @@ -128,7 +128,7 @@ cbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, @@ -236,9 +236,9 @@ cbind.mids.mids <- function(x, y, call) { names(post) <- varnames blots <- c(x$blots, y$blots) names(blots) <- blocknames - operations <- c(x$operations, y$operations) - # FIXME: Assumes combined operation names yields unique names as in colnames(data) - names(operations) <- make.unique(c(names(x$operations), names(y$operations))) + activities <- c(x$activities, y$activities) + # FIXME: Assumes combined activity names yields unique names as in colnames(data) + names(activities) <- make.unique(c(names(x$activities), names(y$activities))) # Function to copy all objects from one environment to another merge_envs <- function(target_env, source_env) { @@ -313,7 +313,7 @@ cbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, diff --git a/R/check.activities.R b/R/check.activities.R new file mode 100644 index 000000000..ed921fae1 --- /dev/null +++ b/R/check.activities.R @@ -0,0 +1,78 @@ +check.activities <- function(activities, data, models = NULL, blocks = NULL) { + if (is.null(activities)) { + activities <- "walk" + } + + valid_activities <- c("walk", "train", "run") + + # 1. Default blocks to individual variables if not provided + if (is.null(blocks)) { + blocks <- setNames(as.list(names(data)), names(data)) + } + + # 2. Expand activities if it's a single value + bv <- unique(unlist(blocks)) + if (length(activities) == 1) { + activities <- setNames(rep(activities, length(bv)), bv) + } + + # 3. Check length + if (length(activities) != length(bv)) { + stop("The length of `activities` (", length(activities), + ") must match the number of variables in `blocks` (", length(bv),").") + } + + # 4. Check if all names in activities exist in blocks + notFound <- !names(activities) %in% bv + if (any(notFound)) { + stop(paste0( + "The following variables specified in `activities` are not present in `blocks`: ", + paste(names(activities)[notFound], collapse = ", "), ".\n", + "Ensure all specified variables match those in `blocks`." + )) + } + + # 5. Check if all activities are valid + invalid_ops <- setdiff(unique(activities), valid_activities) + if (length(invalid_ops) > 0) { + stop(paste0( + "Invalid activity(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", + "Valid activities are: ", paste(valid_activities, collapse = ", "), ".\n", + "Please correct the `activities` argument." + )) + } + + # 6. Prevent "run" if models is NULL + if ("run" %in% activities && is.null(models)) { + stop("The activity 'fill' requires a stored model, but `models` is NULL.\n", + "Please provide a valid `models` object containing trained imputation models.") + } + + # 7. Ensure that all "run" variables have a trained model in models + if ("run" %in% activities && !is.null(models)) { + fill_vars <- names(activities[activities == "run"]) + missing_models <- setdiff(fill_vars, ls(models)) + if (length(missing_models) > 0) { + stop(paste0( + "The following variables specified as 'fill' do not have stored models: ", + paste(missing_models, collapse = ", "), ".\n", + "Ensure these variables were previously fitted before using 'fill'." + )) + } + } + + # 8. Ensure all variables in models exist in blocks + if (!is.null(models)) { + trained_vars <- ls(models) + missing_from_data <- setdiff(trained_vars, bv) # Use block variable names + if (length(missing_from_data) > 0) { + stop(paste0( + "The following variables are present in `models` but missing from `data`: ", + paste(missing_from_data, collapse = ", "), ".\n", + "Ensure that all stored models correspond to variables in the dataset." + )) + } + } + + return(activities) +} diff --git a/R/check.operations.R b/R/check.operations.R deleted file mode 100644 index 273af3ba0..000000000 --- a/R/check.operations.R +++ /dev/null @@ -1,80 +0,0 @@ -check.operations <- function(operations, data, models = NULL, blocks = NULL) { - if (is.null(operations)) { - operations <- "estimate" - } - - valid_operations <- c("estimate", "fit", "fill") - - # 1. Default blocks to individual variables if not provided - if (is.null(blocks)) { - blocks <- setNames(as.list(names(data)), names(data)) - } - - # Convert blocks into actual variable names - bv <- unique(unlist(blocks)) - - # 2. Expand operations if it's a single value - if (length(operations) == 1) { - operations <- setNames(rep(operations, length(bv)), bv) - } - - # check length - if (length(operations) != length(bv)) { - stop("The length of `operations` (", length(operations), - ") must match the number of variables in `blocks` (", length(bv),").") - } - - # 3. Check if all names in operations exist in blocks - notFound <- !names(operations) %in% bv - if (any(notFound)) { - stop(paste0( - "The following variables specified in `operations` are not present in `blocks`: ", - paste(names(operations)[notFound], collapse = ", "), ".\n", - "Ensure all specified variables match those in `blocks`." - )) - } - - # 4. Check if all operations are valid - invalid_ops <- setdiff(unique(operations), valid_operations) - if (length(invalid_ops) > 0) { - stop(paste0( - "Invalid operation(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", - "Valid operations are: ", paste(valid_operations, collapse = ", "), ".\n", - "Please correct the `operations` argument." - )) - } - - # 5. Prevent "fill" if models is NULL - if ("fill" %in% operations && is.null(models)) { - stop("The operation 'fill' requires a stored model, but `models` is NULL.\n", - "Please provide a valid `models` object containing trained imputation models.") - } - - # 6. Ensure that all "fill" variables have a trained model in models - if ("fill" %in% operations && !is.null(models)) { - fill_vars <- names(operations[operations == "fill"]) - missing_models <- setdiff(fill_vars, ls(models)) - if (length(missing_models) > 0) { - stop(paste0( - "The following variables specified as 'fill' do not have stored models: ", - paste(missing_models, collapse = ", "), ".\n", - "Ensure these variables were previously fitted before using 'fill'." - )) - } - } - - # 7. Ensure all variables in models exist in blocks - if (!is.null(models)) { - trained_vars <- ls(models) - missing_from_data <- setdiff(trained_vars, bv) # Use block variable names - if (length(missing_from_data) > 0) { - stop(paste0( - "The following variables are present in `models` but missing from `data`: ", - paste(missing_from_data, collapse = ", "), ".\n", - "Ensure that all stored models correspond to variables in the dataset." - )) - } - } - - return(operations) -} diff --git a/R/filter.R b/R/filter.R index 98198320c..2263093eb 100644 --- a/R/filter.R +++ b/R/filter.R @@ -33,7 +33,7 @@ dplyr::filter #' \code{formulas} \tab Equals \code{.data$formulas}\cr #' \code{post} \tab Equals \code{.data$post}\cr #' \code{blots} \tab Equals \code{.data$blots}\cr -#' \code{operations} \tab Equals \code{.data$operations}\cr +#' \code{activities} \tab Equals \code{.data$activities}\cr #' \code{models} \tab Equals \code{.data$models}\cr #' \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr #' \code{seed} \tab Equals \code{.data$seed}\cr @@ -81,7 +81,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { formulas <- .data$formulas modeltype <- .data$modeltype blots <- .data$blots - operations <- .data$operations + activities <- .data$activities models <- .data$models post <- .data$post seed <- .data$seed @@ -124,7 +124,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, diff --git a/R/mice.R b/R/mice.R index eae301664..7ce380d6c 100644 --- a/R/mice.R +++ b/R/mice.R @@ -219,18 +219,18 @@ #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function #' called for block \code{blockname}. -#' @param operations A character vector specifying the operation to perform for +#' @param activities A character vector specifying the activity to perform for #' each imputation block. The available options are: #' \describe{ -#' \item{"estimate"}{Estimate parameters and generate imputations +#' \item{"walk"}{Estimate parameters and generate imputations #' (classic MICE behavior). This is the default.} -#' \item{"fit"}{Estimate parameters and store the imputation model +#' \item{"train"}{Estimate parameters and store the imputation model #' without data and without generating imputations.} -#' \item{"fill"}{Use a previously stored imputation model to generate +#' \item{"run"}{Use a previously stored imputation model to generate #' imputations, without re-estimating parameters.} #' } #' This argument can be specified as a named vector, where names correspond -#' to variables and values specify the operation for each variable. If a +#' to variables and values specify the activity for each variable. If a #' single value is provided, it applies to the variables in all blocks. The #' length of the vector must match the number of variables present in the #' blocks. @@ -315,8 +315,8 @@ #' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) #' #' # Store model for `bmi`, estimate others as usual -#' operations <- c("age" = "estimate", "bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") -#' imp2 <- mice(nhanes, method = "pmmsplit", operations = operations, print = FALSE) +#' activities <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") +#' imp2 <- mice(nhanes, method = "pmmsplit", activities = activities, print = FALSE) #' #' # Inspects the stored model for imputation 1 for `bmi` #' ls(imp2$models$bmi$"1") @@ -324,8 +324,8 @@ #' imp2$models$bmi$"1"$beta.mis #' #' # Fill missing `bmi` values using pre-trained model -#' operations <- c("age" = "estimate", "bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") -#' imp3 <- mice(nhanes, method = "pmmsplit", operations = operations, +#' activities <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") +#' imp3 <- mice(nhanes, method = "pmmsplit", activities = activities, #' models = imp2$models, print = FALSE) #' #' \dontrun{ @@ -361,7 +361,7 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, - operations = NULL, + activities = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -497,14 +497,14 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # check operations in current model - operations <- check.operations(operations, data, models, blocks) + # check activities in current model + activities <- check.activities(activities, data, models, blocks) - # Initialize models only for "fit" and "fill" blocks that are missing in models + # Initialize models only for "train" and "run" blocks that are missing in models if (is.null(models)) { models <- new.env(parent = emptyenv()) } - model_vars <- names(operations[operations %in% c("fit", "fill")]) + model_vars <- names(activities[activities %in% c("train", "run")]) for (block in model_vars) { if (!exists(block, envir = models)) { models[[block]] <- new.env(parent = emptyenv()) @@ -529,7 +529,7 @@ mice <- function(data, q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, operations, models, + modeltype, blots, activities, models, post, c(from, to), printFlag, ... ) @@ -552,7 +552,7 @@ mice <- function(data, modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index 314b2e7a6..2035f1286 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -4,7 +4,7 @@ #' \code{pmmsplit()} is an implementation of pmm that saves the imputation model #' and generates imputations from the saved model. #' @aliases pmmsplit -#' @param operation The operation to be performed. The default is \code{"estimate"}. +#' @param activity The activity to be performed. The default is \code{"walk"}. #' @param model Storage for the model estimates #' @param nbins The number of bins used to store the predictive mean matching #' model. The default is 50. @@ -96,26 +96,26 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, matchtype = 1L, exclude = NULL, quantify = TRUE, trim = 1L, ridge = 1e-05, nbins = NULL, - operation = "estimate", + activity = "walk", model = NULL, ...) { if (is.null(wy)) { wy <- !ry } - # **Only enforce `model` for "fit" and "fill"** - if (operation %in% c("fit", "fill")) { + # **Only enforce `model` for "train" and "run"** + if (activity %in% c("train", "run")) { if (is.null(model)) { - stop(paste("`model` cannot be NULL for operation:", operation)) + stop(paste("`model` cannot be NULL for activity:", activity)) } if (!is.environment(model)) { stop("`model` must be an environment to store results persistently.") } } - # **Handle "fill" Operation: Use Pre-Stored Model Without Re-Training** - if (operation == "fill") { + # **Handle "run" activity: Use Pre-Stored Model Without Re-Training** + if (activity == "run") { if (!length(ls(model))) { - stop("No stored model found for 'fill' operation.") + stop("No stored model found for 'fill' activity.") } # Compute linear predictor for missing data @@ -130,7 +130,7 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, return(impy) } - # **Handle "estimate" and "fit": Train Model** + # **Handle "walk" and "train": Train Model** ynum <- quantify(y, ry, x, quantify = quantify) # Predicted values for observed part @@ -152,8 +152,8 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, donors <- initialize.donors(donors, length(yhatobs)) prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) - # **Store Model for "fit" (skip for "estimate")** - if (operation == "fit") { + # **Store Model for "train" (skip for "walk")** + if (activity == "train") { model$setup <- list(method = "pmmsplit", donors = donors, nbins = nbins, diff --git a/R/mice.mids.R b/R/mice.mids.R index 6cef4b1a1..1dd04b1ff 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -113,7 +113,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, obj$formulas, obj$modeltype, obj$blots, - obj$operations, obj$models, + obj$activities, obj$models, obj$post, c(from, to), printFlag, ... ) @@ -168,7 +168,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { modeltype = obj$modeltype, post = obj$post, blots = obj$blots, - operations = obj$operations, + activities = obj$activities, models = obj$models, ignore = obj$ignore, seed = obj$seed, diff --git a/R/mids.R b/R/mids.R index 7069536dc..c98db2a57 100644 --- a/R/mids.R +++ b/R/mids.R @@ -58,7 +58,7 @@ #' with commands for post-processing.} #' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} #' function.} -#' \item{\code{operations}:}{A character vector of length \code{length(blocks)}.} +#' \item{\code{activities}:}{A character vector of length \code{length(blocks)}.} #' \item{\code{models}:}{The \code{models} list contains imputation model #' estimates.} #' \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating @@ -163,7 +163,7 @@ mids <- function( modeltype = character(), post = character(), blots = list(), - operations = character(), + activities = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -192,7 +192,7 @@ mids <- function( modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, diff --git a/R/rbind.R b/R/rbind.R index c9098b031..ce0b466f3 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -46,7 +46,7 @@ rbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots - operations <- x$operations + activities <- x$activities models <- x$models predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -77,7 +77,7 @@ rbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, @@ -129,7 +129,7 @@ rbind.mids.mids <- function(x, y, call) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots - operations <- x$operations + activities <- x$activities models <- x$models ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix @@ -178,7 +178,7 @@ rbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, - operations = operations, + activities = activities, models = models, ignore = ignore, seed = seed, diff --git a/R/sampler.R b/R/sampler.R index 1c35634e6..49630d771 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,7 +2,7 @@ # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, operations, models, + modeltype, blots, activities, models, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] @@ -75,7 +75,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, - operation = operations[j], + activity = activities[j], model = models[[j]][[as.character(i)]], yname = j, k = k, ct = ct, @@ -181,12 +181,12 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } -sampler.univ <- function(data, r, where, pred, formula, method, operation, model, +sampler.univ <- function(data, r, where, pred, formula, method, activity, model, yname, k, ct = "pred", user, ignore, ...) { j <- yname[1L] # prepare formula and model matrix - formula <- prepare.formula(formula, data, model, j, ct, pred, operation) + formula <- prepare.formula(formula, data, model, j, ct, pred, activity) x <- obtain.design(data, formula) # expand pred vector to model matrix, remove intercept @@ -228,21 +228,21 @@ sampler.univ <- function(data, r, where, pred, formula, method, operation, model imputes[!cc] <- NA args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, - operation = operation, model = model), + activity = activity, model = model), user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } -prepare.formula <- function(formula, data, model, j, ct, pred, operation) { +prepare.formula <- function(formula, data, model, j, ct, pred, activity) { # prepares the formula for univariate imputation - # saves (for "fit") or retrieves (for "fill") the formula + # saves (for "train") or retrieves (for "run") the formula - # for "fill", use the stored formula instead of recalculating - if (operation == "fill") { + # for "run", use the stored formula instead of recalculating + if (activity == "run") { if (!exists("formula", envir = model)) { - stop("Error: No stored formula found in model for 'fill' operation.") + stop("Error: No stored formula found in model for 'fill' activity.") } formula <- get("formula", envir = model) return(formula) @@ -268,8 +268,8 @@ prepare.formula <- function(formula, data, model, j, ct, pred, operation) { } } - # store formula in `model` only when operation == "fit" - if (operation == "fit") { + # store formula in `model` only when activity == "train" + if (activity == "train") { assign("formula", formula, envir = model) } diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index b44673ecf..7ff9d3618 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -44,7 +44,7 @@ The function constructs the elements of the filtered \code{mids} object as follo \code{formulas} \tab Equals \code{.data$formulas}\cr \code{post} \tab Equals \code{.data$post}\cr \code{blots} \tab Equals \code{.data$blots}\cr -\code{operations} \tab Equals \code{.data$operations}\cr +\code{activities} \tab Equals \code{.data$activities}\cr \code{models} \tab Equals \code{.data$models}\cr \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr \code{seed} \tab Equals \code{.data$seed}\cr diff --git a/man/mice.Rd b/man/mice.Rd index 0d4864db7..e0340dac3 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,7 +18,7 @@ mice( formulas, modeltype = NULL, blots = NULL, - operations = NULL, + activities = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -130,18 +130,18 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} -\item{operations}{A character vector specifying the operation to perform for +\item{activities}{A character vector specifying the activity to perform for each imputation block. The available options are: \describe{ -\item{"estimate"}{Estimate parameters and generate imputations +\item{"walk"}{Estimate parameters and generate imputations (classic MICE behavior). This is the default.} -\item{"fit"}{Estimate parameters and store the imputation model +\item{"train"}{Estimate parameters and store the imputation model without data and without generating imputations.} -\item{"fill"}{Use a previously stored imputation model to generate +\item{"run"}{Use a previously stored imputation model to generate imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the operation for each variable. If a +to variables and values specify the activity for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks.} @@ -413,8 +413,8 @@ complete(imp) imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) # Store model for `bmi`, estimate others as usual -operations <- c("age" = "estimate", "bmi" = "fit", "hyp" = "estimate", "chl" = "estimate") -imp2 <- mice(nhanes, method = "pmmsplit", operations = operations, print = FALSE) +activities <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") +imp2 <- mice(nhanes, method = "pmmsplit", activities = activities, print = FALSE) # Inspects the stored model for imputation 1 for `bmi` ls(imp2$models$bmi$"1") @@ -422,8 +422,8 @@ imp2$models$bmi$"1"$formula imp2$models$bmi$"1"$beta.mis # Fill missing `bmi` values using pre-trained model -operations <- c("age" = "estimate", "bmi" = "fill", "hyp" = "estimate", "chl" = "estimate") -imp3 <- mice(nhanes, method = "pmmsplit", operations = operations, +activities <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") +imp3 <- mice(nhanes, method = "pmmsplit", activities = activities, models = imp2$models, print = FALSE) \dontrun{ diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd index fac5f1355..c4a32fe79 100644 --- a/man/mice.impute.pmmsplit.Rd +++ b/man/mice.impute.pmmsplit.Rd @@ -17,7 +17,7 @@ mice.impute.pmmsplit( trim = 1L, ridge = 1e-05, nbins = NULL, - operation = "estimate", + activity = "walk", model = NULL, ... ) @@ -72,7 +72,7 @@ reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} \item{nbins}{The number of bins used to store the predictive mean matching model. The default is 50.} -\item{operation}{The operation to be performed. The default is \code{"estimate"}.} +\item{activity}{The activity to be performed. The default is \code{"walk"}.} \item{model}{Storage for the model estimates} diff --git a/man/mids.Rd b/man/mids.Rd index f8929c590..cb6e0621f 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -23,7 +23,7 @@ mids( modeltype = character(), post = character(), blots = list(), - operations = character(), + activities = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -155,18 +155,18 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} -\item{operations}{A character vector specifying the operation to perform for +\item{activities}{A character vector specifying the activity to perform for each imputation block. The available options are: \describe{ -\item{"estimate"}{Estimate parameters and generate imputations +\item{"walk"}{Estimate parameters and generate imputations (classic MICE behavior). This is the default.} -\item{"fit"}{Estimate parameters and store the imputation model +\item{"train"}{Estimate parameters and store the imputation model without data and without generating imputations.} -\item{"fill"}{Use a previously stored imputation model to generate +\item{"run"}{Use a previously stored imputation model to generate imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the operation for each variable. If a +to variables and values specify the activity for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks.} @@ -280,7 +280,7 @@ identified by its name, so list names must correspond to block names.} with commands for post-processing.} \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} function.} -\item{\code{operations}:}{A character vector of length \code{length(blocks)}.} +\item{\code{activities}:}{A character vector of length \code{length(blocks)}.} \item{\code{models}:}{The \code{models} list contains imputation model estimates.} \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating From 9a6e75affd23b38bb1ca3a500d85f62b4878fa96 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 7 Mar 2025 14:26:29 +0100 Subject: [PATCH 033/147] Preserve current behaviour to set method == "" for complete variables under "walk" actions --- R/method.R | 33 ++++++++++++++++++++++++++------- R/mice.R | 7 +++---- R/mice.impute.pmmsplit.R | 1 + man/make.method.Rd | 17 +++++++++++++++++ 4 files changed, 47 insertions(+), 11 deletions(-) diff --git a/R/method.R b/R/method.R index 7501db866..b588323d2 100644 --- a/R/method.R +++ b/R/method.R @@ -12,6 +12,7 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), + activities = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { method <- rep("", length(blocks)) names(method) <- names(blocks) @@ -28,30 +29,46 @@ make.method <- function(data, method[j] <- defaultMethod[k] } nimp <- nimp(where, blocks) - method[nimp == 0] <- "" + + # preserve old behaviour that sets method <- "" with walk + names(method) <- names(blocks) + if (!is.null(activities)) { + for (j in names(blocks)) { + vname <- blocks[[j]] + if ("walk" %in% activities[vname] && nimp[j] == 0L) method[j] <- "" + } + } + method } -check.method <- function(method, data, where, blocks, defaultMethod) { +check.method <- function(method, data, where, blocks, activities, defaultMethod) { if (is.null(method)) { return(make.method( data = data, where = where, blocks = blocks, + activities = activities, defaultMethod = defaultMethod )) } nimp <- nimp(where, blocks) - # expand user's imputation method to all visited columns + # expand user's imputation method to all visited blocks # single string supplied by user (implicit assumption of two columns) - if (length(method) == 1) { + if (length(method) == 1L) { if (is.passive(method)) { - stop("Cannot have a passive imputation method for every column.") + stop("Cannot have a passive imputation method for every block.") } method <- rep(method, length(blocks)) - method[nimp == 0] <- "" + names(method) <- names(blocks) + + # preserve old behaviour that sets method <- "" with walk + for (j in names(blocks)) { + vname <- blocks[[j]] + if ("walk" %in% activities[vname] && nimp[j] == 0L) method[j] <- "" + } } # check the length of the argument @@ -117,8 +134,10 @@ check.method <- function(method, data, where, blocks, defaultMethod) { call. = FALSE ) } + # preserve old behaviour that sets method <- "" with walk + if ("walk" %in% activities[vname] && nimp[j] == 0L) method[j] <- "" } - method[nimp == 0] <- "" + unlist(method) } diff --git a/R/mice.R b/R/mice.R index 7ce380d6c..d18ba03d4 100644 --- a/R/mice.R +++ b/R/mice.R @@ -472,9 +472,11 @@ mice <- function(data, user.visitSequence = user.visitSequence, maxit = maxit ) + activities <- check.activities(activities, data, models, blocks) method <- check.method( method = method, data = data, where = where, - blocks = blocks, defaultMethod = defaultMethod + blocks = blocks, activities = activities, + defaultMethod = defaultMethod ) post <- check.post(post, data) blots <- check.blots(blots, data, blocks) @@ -497,9 +499,6 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # check activities in current model - activities <- check.activities(activities, data, models, blocks) - # Initialize models only for "train" and "run" blocks that are missing in models if (is.null(models)) { models <- new.env(parent = emptyenv()) diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index 2035f1286..8e9d5f202 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -155,6 +155,7 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, # **Store Model for "train" (skip for "walk")** if (activity == "train") { model$setup <- list(method = "pmmsplit", + n = length(yhatobs), donors = donors, nbins = nbins, matchtype = matchtype, diff --git a/man/make.method.Rd b/man/make.method.Rd index a4d9a843f..c077cc6c4 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -8,6 +8,7 @@ make.method( data, where = make.where(data), blocks = make.blocks(data), + activities = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr") ) } @@ -37,6 +38,22 @@ matrix are set to \code{FALSE} of variables that are not block members. A variable may appear in multiple blocks. In that case, it is effectively re-imputed each time that it is visited.} +\item{activities}{A character vector specifying the activity to perform for +each imputation block. The available options are: +\describe{ +\item{"walk"}{Estimate parameters and generate imputations +(classic MICE behavior). This is the default.} +\item{"train"}{Estimate parameters and store the imputation model +without data and without generating imputations.} +\item{"run"}{Use a previously stored imputation model to generate +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the activity for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks.} + \item{defaultMethod}{A vector of length 4 containing the default imputation methods for 1) numeric data, 2) factor data with 2 levels, 3) factor data with > 2 unordered levels, and 4) factor data with > 2 From edbbdb9c6072b61b6c0c0b4fdd907984f562521e Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 7 Mar 2025 15:24:45 +0100 Subject: [PATCH 034/147] Add tests for activities --- R/sampler.R | 2 +- tests/testthat/test-activities.R | 24 ++++++++++++++++++++++++ 2 files changed, 25 insertions(+), 1 deletion(-) create mode 100644 tests/testthat/test-activities.R diff --git a/R/sampler.R b/R/sampler.R index 49630d771..8aa64b0a9 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -207,7 +207,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, activity, model, wy <- complete.cases(x) & where[, j] # nothing to impute - if (all(!wy)) { + if (all(!wy) && activity != "train") { return(numeric(0)) } diff --git a/tests/testthat/test-activities.R b/tests/testthat/test-activities.R new file mode 100644 index 000000000..5549916a9 --- /dev/null +++ b/tests/testthat/test-activities.R @@ -0,0 +1,24 @@ +context("activities") + +# We have to test the following cases: + +# - Does train-run setup with a factor variable produce imputations? +# - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? +# - Does train-run setup with a factor variable produce imputations when the factor has more categories during running than training? +# - Does train-run setup with a factor variable produce imputations when only one factor level is present during training? +# - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? + +test_that("activities work with factor with same number of categories", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_false(is.null(imp1$models$bmi$"1"$lookup)) + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) +}) + +test_that("training works on completely observed variables", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_false(is.null(imp1$models$age$"1"$lookup)) + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) +}) + From 640a405947b781e30ef2c0ec64bb2a48ea6c82e3 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 7 Mar 2025 15:30:22 +0100 Subject: [PATCH 035/147] Rename activities --> actions --- R/cbind.R | 14 +++--- R/{check.activities.R => check.actions.R} | 46 +++++++++---------- R/filter.R | 6 +-- R/method.R | 14 +++--- R/mice.R | 24 +++++----- R/mice.impute.pmmsplit.R | 16 +++---- R/mice.mids.R | 4 +- R/mids.R | 6 +-- R/rbind.R | 8 ++-- R/sampler.R | 22 ++++----- man/filter.mids.Rd | 2 +- man/make.method.Rd | 6 +-- man/mice.Rd | 14 +++--- man/mice.impute.pmmsplit.Rd | 4 +- man/mids.Rd | 8 ++-- .../{test-activities.R => test-actions.R} | 4 +- 16 files changed, 99 insertions(+), 99 deletions(-) rename R/{check.activities.R => check.actions.R} (55%) rename tests/testthat/{test-activities.R => test-actions.R} (93%) diff --git a/R/cbind.R b/R/cbind.R index ed4b16509..d0855aede 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,8 +97,8 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots - activities <- c(x$activities, "walk") - names(activities) <- c(names(x$activities), tail(varnames, 1L)) + actions <- c(x$actions, "walk") + names(actions) <- c(names(x$actions), tail(varnames, 1L)) models <- x$models ignore <- x$ignore @@ -128,7 +128,7 @@ cbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, @@ -236,9 +236,9 @@ cbind.mids.mids <- function(x, y, call) { names(post) <- varnames blots <- c(x$blots, y$blots) names(blots) <- blocknames - activities <- c(x$activities, y$activities) - # FIXME: Assumes combined activity names yields unique names as in colnames(data) - names(activities) <- make.unique(c(names(x$activities), names(y$activities))) + actions <- c(x$actions, y$actions) + # FIXME: Assumes combined action names yields unique names as in colnames(data) + names(actions) <- make.unique(c(names(x$actions), names(y$actions))) # Function to copy all objects from one environment to another merge_envs <- function(target_env, source_env) { @@ -313,7 +313,7 @@ cbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, diff --git a/R/check.activities.R b/R/check.actions.R similarity index 55% rename from R/check.activities.R rename to R/check.actions.R index ed921fae1..652f467b5 100644 --- a/R/check.activities.R +++ b/R/check.actions.R @@ -1,56 +1,56 @@ -check.activities <- function(activities, data, models = NULL, blocks = NULL) { - if (is.null(activities)) { - activities <- "walk" +check.actions <- function(actions, data, models = NULL, blocks = NULL) { + if (is.null(actions)) { + actions <- "walk" } - valid_activities <- c("walk", "train", "run") + valid_actions <- c("walk", "train", "run") # 1. Default blocks to individual variables if not provided if (is.null(blocks)) { blocks <- setNames(as.list(names(data)), names(data)) } - # 2. Expand activities if it's a single value + # 2. Expand actions if it's a single value bv <- unique(unlist(blocks)) - if (length(activities) == 1) { - activities <- setNames(rep(activities, length(bv)), bv) + if (length(actions) == 1) { + actions <- setNames(rep(actions, length(bv)), bv) } # 3. Check length - if (length(activities) != length(bv)) { - stop("The length of `activities` (", length(activities), + if (length(actions) != length(bv)) { + stop("The length of `actions` (", length(actions), ") must match the number of variables in `blocks` (", length(bv),").") } - # 4. Check if all names in activities exist in blocks - notFound <- !names(activities) %in% bv + # 4. Check if all names in actions exist in blocks + notFound <- !names(actions) %in% bv if (any(notFound)) { stop(paste0( - "The following variables specified in `activities` are not present in `blocks`: ", - paste(names(activities)[notFound], collapse = ", "), ".\n", + "The following variables specified in `actions` are not present in `blocks`: ", + paste(names(actions)[notFound], collapse = ", "), ".\n", "Ensure all specified variables match those in `blocks`." )) } - # 5. Check if all activities are valid - invalid_ops <- setdiff(unique(activities), valid_activities) + # 5. Check if all actions are valid + invalid_ops <- setdiff(unique(actions), valid_actions) if (length(invalid_ops) > 0) { stop(paste0( - "Invalid activity(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", - "Valid activities are: ", paste(valid_activities, collapse = ", "), ".\n", - "Please correct the `activities` argument." + "Invalid action(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", + "Valid actions are: ", paste(valid_actions, collapse = ", "), ".\n", + "Please correct the `actions` argument." )) } # 6. Prevent "run" if models is NULL - if ("run" %in% activities && is.null(models)) { - stop("The activity 'fill' requires a stored model, but `models` is NULL.\n", + if ("run" %in% actions && is.null(models)) { + stop("The action 'fill' requires a stored model, but `models` is NULL.\n", "Please provide a valid `models` object containing trained imputation models.") } # 7. Ensure that all "run" variables have a trained model in models - if ("run" %in% activities && !is.null(models)) { - fill_vars <- names(activities[activities == "run"]) + if ("run" %in% actions && !is.null(models)) { + fill_vars <- names(actions[actions == "run"]) missing_models <- setdiff(fill_vars, ls(models)) if (length(missing_models) > 0) { stop(paste0( @@ -74,5 +74,5 @@ check.activities <- function(activities, data, models = NULL, blocks = NULL) { } } - return(activities) + return(actions) } diff --git a/R/filter.R b/R/filter.R index 2263093eb..1c95d00fe 100644 --- a/R/filter.R +++ b/R/filter.R @@ -33,7 +33,7 @@ dplyr::filter #' \code{formulas} \tab Equals \code{.data$formulas}\cr #' \code{post} \tab Equals \code{.data$post}\cr #' \code{blots} \tab Equals \code{.data$blots}\cr -#' \code{activities} \tab Equals \code{.data$activities}\cr +#' \code{actions} \tab Equals \code{.data$actions}\cr #' \code{models} \tab Equals \code{.data$models}\cr #' \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr #' \code{seed} \tab Equals \code{.data$seed}\cr @@ -81,7 +81,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { formulas <- .data$formulas modeltype <- .data$modeltype blots <- .data$blots - activities <- .data$activities + actions <- .data$actions models <- .data$models post <- .data$post seed <- .data$seed @@ -124,7 +124,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, diff --git a/R/method.R b/R/method.R index b588323d2..a9d15f0c5 100644 --- a/R/method.R +++ b/R/method.R @@ -12,7 +12,7 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), - activities = NULL, + actions = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { method <- rep("", length(blocks)) names(method) <- names(blocks) @@ -32,10 +32,10 @@ make.method <- function(data, # preserve old behaviour that sets method <- "" with walk names(method) <- names(blocks) - if (!is.null(activities)) { + if (!is.null(actions)) { for (j in names(blocks)) { vname <- blocks[[j]] - if ("walk" %in% activities[vname] && nimp[j] == 0L) method[j] <- "" + if ("walk" %in% actions[vname] && nimp[j] == 0L) method[j] <- "" } } @@ -43,13 +43,13 @@ make.method <- function(data, } -check.method <- function(method, data, where, blocks, activities, defaultMethod) { +check.method <- function(method, data, where, blocks, actions, defaultMethod) { if (is.null(method)) { return(make.method( data = data, where = where, blocks = blocks, - activities = activities, + actions = actions, defaultMethod = defaultMethod )) } @@ -67,7 +67,7 @@ check.method <- function(method, data, where, blocks, activities, defaultMethod) # preserve old behaviour that sets method <- "" with walk for (j in names(blocks)) { vname <- blocks[[j]] - if ("walk" %in% activities[vname] && nimp[j] == 0L) method[j] <- "" + if ("walk" %in% actions[vname] && nimp[j] == 0L) method[j] <- "" } } @@ -135,7 +135,7 @@ check.method <- function(method, data, where, blocks, activities, defaultMethod) ) } # preserve old behaviour that sets method <- "" with walk - if ("walk" %in% activities[vname] && nimp[j] == 0L) method[j] <- "" + if ("walk" %in% actions[vname] && nimp[j] == 0L) method[j] <- "" } unlist(method) diff --git a/R/mice.R b/R/mice.R index d18ba03d4..3a7a10625 100644 --- a/R/mice.R +++ b/R/mice.R @@ -219,7 +219,7 @@ #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function #' called for block \code{blockname}. -#' @param activities A character vector specifying the activity to perform for +#' @param actions A character vector specifying the action to perform for #' each imputation block. The available options are: #' \describe{ #' \item{"walk"}{Estimate parameters and generate imputations @@ -230,7 +230,7 @@ #' imputations, without re-estimating parameters.} #' } #' This argument can be specified as a named vector, where names correspond -#' to variables and values specify the activity for each variable. If a +#' to variables and values specify the action for each variable. If a #' single value is provided, it applies to the variables in all blocks. The #' length of the vector must match the number of variables present in the #' blocks. @@ -315,8 +315,8 @@ #' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) #' #' # Store model for `bmi`, estimate others as usual -#' activities <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") -#' imp2 <- mice(nhanes, method = "pmmsplit", activities = activities, print = FALSE) +#' actions <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") +#' imp2 <- mice(nhanes, method = "pmmsplit", actions = actions, print = FALSE) #' #' # Inspects the stored model for imputation 1 for `bmi` #' ls(imp2$models$bmi$"1") @@ -324,8 +324,8 @@ #' imp2$models$bmi$"1"$beta.mis #' #' # Fill missing `bmi` values using pre-trained model -#' activities <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") -#' imp3 <- mice(nhanes, method = "pmmsplit", activities = activities, +#' actions <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") +#' imp3 <- mice(nhanes, method = "pmmsplit", actions = actions, #' models = imp2$models, print = FALSE) #' #' \dontrun{ @@ -361,7 +361,7 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, - activities = NULL, + actions = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -472,10 +472,10 @@ mice <- function(data, user.visitSequence = user.visitSequence, maxit = maxit ) - activities <- check.activities(activities, data, models, blocks) + actions <- check.actions(actions, data, models, blocks) method <- check.method( method = method, data = data, where = where, - blocks = blocks, activities = activities, + blocks = blocks, actions = actions, defaultMethod = defaultMethod ) post <- check.post(post, data) @@ -503,7 +503,7 @@ mice <- function(data, if (is.null(models)) { models <- new.env(parent = emptyenv()) } - model_vars <- names(activities[activities %in% c("train", "run")]) + model_vars <- names(actions[actions %in% c("train", "run")]) for (block in model_vars) { if (!exists(block, envir = models)) { models[[block]] <- new.env(parent = emptyenv()) @@ -528,7 +528,7 @@ mice <- function(data, q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, activities, models, + modeltype, blots, actions, models, post, c(from, to), printFlag, ... ) @@ -551,7 +551,7 @@ mice <- function(data, modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index 8e9d5f202..f3f3cdb55 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -4,7 +4,7 @@ #' \code{pmmsplit()} is an implementation of pmm that saves the imputation model #' and generates imputations from the saved model. #' @aliases pmmsplit -#' @param activity The activity to be performed. The default is \code{"walk"}. +#' @param action The action to be performed. The default is \code{"walk"}. #' @param model Storage for the model estimates #' @param nbins The number of bins used to store the predictive mean matching #' model. The default is 50. @@ -96,26 +96,26 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, matchtype = 1L, exclude = NULL, quantify = TRUE, trim = 1L, ridge = 1e-05, nbins = NULL, - activity = "walk", + action = "walk", model = NULL, ...) { if (is.null(wy)) { wy <- !ry } # **Only enforce `model` for "train" and "run"** - if (activity %in% c("train", "run")) { + if (action %in% c("train", "run")) { if (is.null(model)) { - stop(paste("`model` cannot be NULL for activity:", activity)) + stop(paste("`model` cannot be NULL for action:", action)) } if (!is.environment(model)) { stop("`model` must be an environment to store results persistently.") } } - # **Handle "run" activity: Use Pre-Stored Model Without Re-Training** - if (activity == "run") { + # **Handle "run" action: Use Pre-Stored Model Without Re-Training** + if (action == "run") { if (!length(ls(model))) { - stop("No stored model found for 'fill' activity.") + stop("No stored model found for 'fill' action.") } # Compute linear predictor for missing data @@ -153,7 +153,7 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) # **Store Model for "train" (skip for "walk")** - if (activity == "train") { + if (action == "train") { model$setup <- list(method = "pmmsplit", n = length(yhatobs), donors = donors, diff --git a/R/mice.mids.R b/R/mice.mids.R index 1dd04b1ff..a3a02a457 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -113,7 +113,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, obj$formulas, obj$modeltype, obj$blots, - obj$activities, obj$models, + obj$actions, obj$models, obj$post, c(from, to), printFlag, ... ) @@ -168,7 +168,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { modeltype = obj$modeltype, post = obj$post, blots = obj$blots, - activities = obj$activities, + actions = obj$actions, models = obj$models, ignore = obj$ignore, seed = obj$seed, diff --git a/R/mids.R b/R/mids.R index c98db2a57..1cdb33918 100644 --- a/R/mids.R +++ b/R/mids.R @@ -58,7 +58,7 @@ #' with commands for post-processing.} #' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} #' function.} -#' \item{\code{activities}:}{A character vector of length \code{length(blocks)}.} +#' \item{\code{actions}:}{A character vector of length \code{length(blocks)}.} #' \item{\code{models}:}{The \code{models} list contains imputation model #' estimates.} #' \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating @@ -163,7 +163,7 @@ mids <- function( modeltype = character(), post = character(), blots = list(), - activities = character(), + actions = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -192,7 +192,7 @@ mids <- function( modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, diff --git a/R/rbind.R b/R/rbind.R index ce0b466f3..2d7419268 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -46,7 +46,7 @@ rbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots - activities <- x$activities + actions <- x$actions models <- x$models predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -77,7 +77,7 @@ rbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, @@ -129,7 +129,7 @@ rbind.mids.mids <- function(x, y, call) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots - activities <- x$activities + actions <- x$actions models <- x$models ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix @@ -178,7 +178,7 @@ rbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, - activities = activities, + actions = actions, models = models, ignore = ignore, seed = seed, diff --git a/R/sampler.R b/R/sampler.R index 8aa64b0a9..8057c020d 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,7 +2,7 @@ # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, activities, models, + modeltype, blots, actions, models, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] @@ -75,7 +75,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, - activity = activities[j], + action = actions[j], model = models[[j]][[as.character(i)]], yname = j, k = k, ct = ct, @@ -181,12 +181,12 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } -sampler.univ <- function(data, r, where, pred, formula, method, activity, model, +sampler.univ <- function(data, r, where, pred, formula, method, action, model, yname, k, ct = "pred", user, ignore, ...) { j <- yname[1L] # prepare formula and model matrix - formula <- prepare.formula(formula, data, model, j, ct, pred, activity) + formula <- prepare.formula(formula, data, model, j, ct, pred, action) x <- obtain.design(data, formula) # expand pred vector to model matrix, remove intercept @@ -207,7 +207,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, activity, model, wy <- complete.cases(x) & where[, j] # nothing to impute - if (all(!wy) && activity != "train") { + if (all(!wy) && action != "train") { return(numeric(0)) } @@ -228,21 +228,21 @@ sampler.univ <- function(data, r, where, pred, formula, method, activity, model, imputes[!cc] <- NA args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, - activity = activity, model = model), + action = action, model = model), user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } -prepare.formula <- function(formula, data, model, j, ct, pred, activity) { +prepare.formula <- function(formula, data, model, j, ct, pred, action) { # prepares the formula for univariate imputation # saves (for "train") or retrieves (for "run") the formula # for "run", use the stored formula instead of recalculating - if (activity == "run") { + if (action == "run") { if (!exists("formula", envir = model)) { - stop("Error: No stored formula found in model for 'fill' activity.") + stop("Error: No stored formula found in model for 'fill' action.") } formula <- get("formula", envir = model) return(formula) @@ -268,8 +268,8 @@ prepare.formula <- function(formula, data, model, j, ct, pred, activity) { } } - # store formula in `model` only when activity == "train" - if (activity == "train") { + # store formula in `model` only when action == "train" + if (action == "train") { assign("formula", formula, envir = model) } diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index 7ff9d3618..232106190 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -44,7 +44,7 @@ The function constructs the elements of the filtered \code{mids} object as follo \code{formulas} \tab Equals \code{.data$formulas}\cr \code{post} \tab Equals \code{.data$post}\cr \code{blots} \tab Equals \code{.data$blots}\cr -\code{activities} \tab Equals \code{.data$activities}\cr +\code{actions} \tab Equals \code{.data$actions}\cr \code{models} \tab Equals \code{.data$models}\cr \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr \code{seed} \tab Equals \code{.data$seed}\cr diff --git a/man/make.method.Rd b/man/make.method.Rd index c077cc6c4..c3bcef7c9 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -8,7 +8,7 @@ make.method( data, where = make.where(data), blocks = make.blocks(data), - activities = NULL, + actions = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr") ) } @@ -38,7 +38,7 @@ matrix are set to \code{FALSE} of variables that are not block members. A variable may appear in multiple blocks. In that case, it is effectively re-imputed each time that it is visited.} -\item{activities}{A character vector specifying the activity to perform for +\item{actions}{A character vector specifying the action to perform for each imputation block. The available options are: \describe{ \item{"walk"}{Estimate parameters and generate imputations @@ -49,7 +49,7 @@ without data and without generating imputations.} imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the activity for each variable. If a +to variables and values specify the action for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks.} diff --git a/man/mice.Rd b/man/mice.Rd index e0340dac3..e13a8b327 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,7 +18,7 @@ mice( formulas, modeltype = NULL, blots = NULL, - activities = NULL, + actions = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -130,7 +130,7 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} -\item{activities}{A character vector specifying the activity to perform for +\item{actions}{A character vector specifying the action to perform for each imputation block. The available options are: \describe{ \item{"walk"}{Estimate parameters and generate imputations @@ -141,7 +141,7 @@ without data and without generating imputations.} imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the activity for each variable. If a +to variables and values specify the action for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks.} @@ -413,8 +413,8 @@ complete(imp) imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) # Store model for `bmi`, estimate others as usual -activities <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") -imp2 <- mice(nhanes, method = "pmmsplit", activities = activities, print = FALSE) +actions <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") +imp2 <- mice(nhanes, method = "pmmsplit", actions = actions, print = FALSE) # Inspects the stored model for imputation 1 for `bmi` ls(imp2$models$bmi$"1") @@ -422,8 +422,8 @@ imp2$models$bmi$"1"$formula imp2$models$bmi$"1"$beta.mis # Fill missing `bmi` values using pre-trained model -activities <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") -imp3 <- mice(nhanes, method = "pmmsplit", activities = activities, +actions <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") +imp3 <- mice(nhanes, method = "pmmsplit", actions = actions, models = imp2$models, print = FALSE) \dontrun{ diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd index c4a32fe79..92f9afa37 100644 --- a/man/mice.impute.pmmsplit.Rd +++ b/man/mice.impute.pmmsplit.Rd @@ -17,7 +17,7 @@ mice.impute.pmmsplit( trim = 1L, ridge = 1e-05, nbins = NULL, - activity = "walk", + action = "walk", model = NULL, ... ) @@ -72,7 +72,7 @@ reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} \item{nbins}{The number of bins used to store the predictive mean matching model. The default is 50.} -\item{activity}{The activity to be performed. The default is \code{"walk"}.} +\item{action}{The action to be performed. The default is \code{"walk"}.} \item{model}{Storage for the model estimates} diff --git a/man/mids.Rd b/man/mids.Rd index cb6e0621f..575bfd3dc 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -23,7 +23,7 @@ mids( modeltype = character(), post = character(), blots = list(), - activities = character(), + actions = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -155,7 +155,7 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} -\item{activities}{A character vector specifying the activity to perform for +\item{actions}{A character vector specifying the action to perform for each imputation block. The available options are: \describe{ \item{"walk"}{Estimate parameters and generate imputations @@ -166,7 +166,7 @@ without data and without generating imputations.} imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the activity for each variable. If a +to variables and values specify the action for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks.} @@ -280,7 +280,7 @@ identified by its name, so list names must correspond to block names.} with commands for post-processing.} \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} function.} -\item{\code{activities}:}{A character vector of length \code{length(blocks)}.} +\item{\code{actions}:}{A character vector of length \code{length(blocks)}.} \item{\code{models}:}{The \code{models} list contains imputation model estimates.} \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating diff --git a/tests/testthat/test-activities.R b/tests/testthat/test-actions.R similarity index 93% rename from tests/testthat/test-activities.R rename to tests/testthat/test-actions.R index 5549916a9..42fe30fd2 100644 --- a/tests/testthat/test-activities.R +++ b/tests/testthat/test-actions.R @@ -1,4 +1,4 @@ -context("activities") +context("actions") # We have to test the following cases: @@ -8,7 +8,7 @@ context("activities") # - Does train-run setup with a factor variable produce imputations when only one factor level is present during training? # - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? -test_that("activities work with factor with same number of categories", { +test_that("actions work with factor with same number of categories", { expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) From 17cf6bb762d15f80b4865688b91ec5fbb4690275 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 10 Mar 2025 09:16:25 +0100 Subject: [PATCH 036/147] Add model$factor and test for quantify() and unquantify() --- R/mice.impute.pmm.R | 32 ++++++++------- R/mice.impute.pmmsplit.R | 70 ++++++++------------------------ R/quantify.R | 34 ++++++++++++++++ tests/testthat/test-actions.R | 8 ++-- tests/testthat/test-activities.R | 37 +++++++++++++++++ tests/testthat/test-quantify.R | 67 ++++++++++++++++++++---------- 6 files changed, 155 insertions(+), 93 deletions(-) create mode 100644 R/quantify.R create mode 100644 tests/testthat/test-activities.R diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 1db1e915f..0d3f6af7c 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -161,27 +161,31 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, # 1. the imputation model disregards records with excluded y-values # 2. the donor set does not contain excluded y-values - # Keep sparse categories out of the imputation model + # Retain categories with ">= trim" observations in the imputation model + # Exclude values from the imputation model if (is.factor(y)) { - active <- !ry | y %in% (levels(y)[table(y) >= trim]) - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] - } - # Keep excluded values out of the imputation model - if (!is.null(exclude)) { + active <- !ry | (y %in% levels(y)[table(y) >= trim] & !y %in% exclude) + if (any(!active)) { + y <- droplevels(y[active]) + ry <- ry[active] + x <- x[active, , drop = FALSE] + wy <- wy[active] + } + } else { active <- !ry | !y %in% exclude - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] + if (any(!active)) { + y <- y[active] + ry <- ry[active] + x <- x[active, , drop = FALSE] + wy <- wy[active] + } } x <- cbind(1, as.matrix(x)) # quantify categories for factors - ynum <- quantify(y, ry, x, quantify = quantify) + q <- quantify(y, ry, x, quantify = quantify) + ynum <- q$ynum # parameter estimation parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index f3f3cdb55..12facdd3f 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -102,7 +102,7 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, wy <- !ry } - # **Only enforce `model` for "train" and "run"** + # Only enforce `model` for "train" and "run" if (action %in% c("train", "run")) { if (is.null(model)) { stop(paste("`model` cannot be NULL for action:", action)) @@ -112,10 +112,10 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, } } - # **Handle "run" action: Use Pre-Stored Model Without Re-Training** + # Handle "run" action: Use pre-stored model without re-training if (action == "run") { if (!length(ls(model))) { - stop("No stored model found for 'fill' action.") + stop("No stored model found for 'run' action.") } # Compute linear predictor for missing data @@ -126,12 +126,17 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, m = 1L) # Convert back to factor if needed - impy <- unquantify(impy, y) + impy <- unquantify(ynum = impy, + quant = model$factor$quant, + labels = model$factor$levels) return(impy) } - # **Handle "walk" and "train": Train Model** - ynum <- quantify(y, ry, x, quantify = quantify) + # Handle "walk" and "train": train model + + # Quantify factor by optimal scaling + f <- quantify(y, ry, x, quantify = quantify) + ynum <- f$ynum # Predicted values for observed part parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) @@ -152,7 +157,7 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, donors <- initialize.donors(donors, length(yhatobs)) prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) - # **Store Model for "train" (skip for "walk")** + # Store model for "train", skip for "walk" if (action == "train") { model$setup <- list(method = "pmmsplit", n = length(yhatobs), @@ -167,9 +172,10 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, model$beta.mis <- beta.mis model$edges <- prep$edges model$lookup <- prep$lookup + model$factor <- list(f$labels, f$quant) } - # **Compute Missing Value Imputations** + # Compute imputations yhatmis <- x[wy, , drop = FALSE] %*% beta.mis impy <- draw.neighbors.pmm(yhatmis, edges = prep$edges, @@ -177,7 +183,9 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, m = 1L) # Convert back to factor if needed - impy <- unquantify(impy, y) + impy <- unquantify(ynum = impy, + quant = f$quant, + labels = f$labels) return(impy) } @@ -264,47 +272,3 @@ draw.neighbors.pmm <- function(yhat_query, edges, lookup, m = 1) { return(imputed_values) } - -quantify <- function(y, ry, x, quantify = TRUE) { - if (!is.factor(y)) { - return(y) - } - if (!quantify) { - return(as.integer(y)) - } - - # replace (reduced set of) categories by optimal scaling - yf <- factor(y[ry], exclude = NULL) - yd <- model.matrix(~ 0 + yf) - xd <- x[ry, , drop = FALSE] - cca <- cancor(yd, xd, xcenter = FALSE, ycenter = FALSE) - # NOTE: We must be more careful if imputations from a previous iteration - # need to be processed. The line below is a quick fix. - ynum <- as.integer(y) - # Scale only observed y's, since these are used to predict missing y's - ynum[ry] <- scale(as.vector(yd %*% cca$xcoef[, 2L])) - return(ynum) -} - -unquantify <- function(ynum, original_y, quantify = TRUE) { - if (!is.factor(original_y)) return(ynum) - factor_levels <- levels(original_y) - if (!is.numeric(ynum)) stop("ynum must be numeric") - - if (quantify) { - # Get unique numeric values and their corresponding factor levels in original y - unique_ynum <- unique(ynum) - unique_levels <- unique(original_y) - - # Ensure levels are assigned in the same order as original_y - mapping <- setNames(as.character(unique_levels), unique_ynum) - - # Assign factor levels based on the mapping - reconstructed_y <- factor(mapping[as.character(ynum)], levels = factor_levels) - } else { - # Reverse integer encoding (simple conversion) - reconstructed_y <- factor(factor_levels[ynum], levels = factor_levels) - } - - return(unname(reconstructed_y)) -} diff --git a/R/quantify.R b/R/quantify.R new file mode 100644 index 000000000..2a561e85d --- /dev/null +++ b/R/quantify.R @@ -0,0 +1,34 @@ +quantify <- function(y, ry, x, quantify = TRUE) { + if (!is.factor(y)) { + return(list(ynum = y, + labels = NULL, + quant = NULL)) + } + if (!quantify) { + ynum <- as.integer(y) + return(list(ynum = ynum, + labels = levels(y), + quant = 1L:length(levels(y)))) + } + + # replace (reduced set of) categories by optimal scaling + yf <- factor(y[ry], exclude = NULL) + yd <- model.matrix(~ 0 + yf) + xd <- cbind(1, x[ry, , drop = FALSE]) + cca <- cancor(y = yd, x = xd, xcenter = FALSE, ycenter = FALSE) + oldlevels <- levels(y) + levels(y) <- as.vector(cca$ycoef[, 2L]) + ynum <- as.numeric(as.character(y)) + return(list(ynum = ynum, + labels = oldlevels, + quant = as.numeric(levels(y)))) +} + +unquantify <- function(ynum = NULL, quant = NULL, labels = NULL) { + if (is.null(labels)) return(ynum) + y <- factor(ynum, levels = quant, labels = labels) + if (anyNA(levels(y))) { + y <- droplevels(y, exclude = NA) + } + return(y) +} diff --git a/tests/testthat/test-actions.R b/tests/testthat/test-actions.R index 42fe30fd2..2dbffa3c7 100644 --- a/tests/testthat/test-actions.R +++ b/tests/testthat/test-actions.R @@ -9,16 +9,16 @@ context("actions") # - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? test_that("actions work with factor with same number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, act = "train", method = "pmmsplit", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) }) test_that("training works on completely observed variables", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, act = "train", method = "pmmsplit", print = FALSE)) expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) }) diff --git a/tests/testthat/test-activities.R b/tests/testthat/test-activities.R new file mode 100644 index 000000000..33a6dfee2 --- /dev/null +++ b/tests/testthat/test-activities.R @@ -0,0 +1,37 @@ +context("actions") + +# We have to test the following cases: + +# - Does train-run setup with a factor variable produce imputations? +# - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? +# - Does train-run setup with a factor variable produce imputations when the factor has more categories during running than training? +# - Does train-run setup with a factor variable produce imputations when only one factor level is present during training? +# - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? + +test_that("actions work with factor with same number of categories", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_false(is.null(imp1$models$bmi$"1"$lookup)) + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) +}) + +test_that("training works on completely observed variables", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_false(is.null(imp1$models$age$"1"$lookup)) + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) +}) + +# make a few missing values in age +nhanes2$age[10:15] <- NA +# remove category 60-99 from age during running +nhanes3 <- nhanes2 +nhanes3[["age"]] <- droplevels(nhanes3[["age"]], exclude = "60-99") + +test_that("training and running works with factor with different number of categories", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) + expect_false(is.null(imp1$models$age$"1"$lookup)) + expect_silent(imp2 <- mice(nhanes3, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) +}) + diff --git a/tests/testthat/test-quantify.R b/tests/testthat/test-quantify.R index a6f1e3df3..572485a86 100644 --- a/tests/testthat/test-quantify.R +++ b/tests/testthat/test-quantify.R @@ -1,27 +1,20 @@ - -test_that("quantify() and unquantify() work correctly", { +test_that("quantify() and unquantify() work correctly for factors", { set.seed(123) - - # Original factor variable y <- factor(sample(c("A", "B", "C"), 10, replace = TRUE), levels = c("A", "B", "C")) - - # Simulated `x` (covariates) x <- matrix(rnorm(10 * 3), ncol = 3) - - # Logical `ry` (observed vs missing) ry <- sample(c(TRUE), 10, replace = TRUE) # Quantify the factor (optimal scaling) - ynum_quantified <- mice:::quantify(y, ry, x) - - # Convert to integer encoding - ynum_integer <- as.integer(y) + f <- mice:::quantify(y, ry, x) + ynum_quantified <- f$ynum + y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_quantified) - # Reverse optimal scaling - y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, y, quantify = TRUE) - - # Reverse integer encoding - y_reconstructed_integer <- mice:::unquantify(ynum_integer, y, quantify = FALSE) + # Integer coding + f <- mice:::quantify(y, ry, x, quantify = FALSE) + ynum_quantified <- f$ynum + y_reconstructed_integer <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_integer) # Test 1: Levels should remain in the original order expect_equal(levels(y_reconstructed_quantified), levels(y)) @@ -31,18 +24,48 @@ test_that("quantify() and unquantify() work correctly", { expect_equal(y_reconstructed_quantified, y) expect_equal(y_reconstructed_integer, y) - # Test 3: Handle missing values correctly + # Handle missing values, with extra level y_with_na <- y y_with_na[c(2, 5)] <- NA + ry[c(2,5)] <- FALSE - ynum_quantified_na <- mice:::quantify(y_with_na, ry, x) - y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, y_with_na, quantify = TRUE) + f <- mice:::quantify(y_with_na, ry, x) + ynum_quantified_na <- f$ynum + y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, quant = f$quant, labels = f$labels) + expect_equal(y_with_na, y_reconstructed_na) expect_true(is.na(y_reconstructed_na[2])) expect_true(is.na(y_reconstructed_na[5])) +}) - # Test 4: Unquantify should return original y if y is not a factor - expect_equal(mice:::unquantify(ynum_quantified, as.numeric(y), quantify = TRUE), ynum_quantified) +test_that("quantify() and unquantify() work correctly for numeric variables", { + set.seed(123) + y <- rnorm(10) + x <- matrix(rnorm(10 * 3), ncol = 3) + ry <- sample(c(TRUE), 10, replace = TRUE) + + # Pass through a numeric variable + f <- mice:::quantify(y, ry, x) + ynum_quantified <- f$ynum + y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_quantified) + + # Pass through, integer coding + f <- mice:::quantify(y, ry, x, quantify = FALSE) + ynum_quantified <- f$ynum + y_reconstructed_integer <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_integer) + + # Handle missing values, with extra level + y_with_na <- y + y_with_na[c(2, 5)] <- NA + ry[c(2,5)] <- FALSE + + f <- mice:::quantify(y_with_na, ry, x) + ynum_quantified_na <- f$ynum + y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, quant = f$quant, labels = f$labels) + expect_equal(y_with_na, y_reconstructed_na) }) + From c08f0102e8cc41ddec65a47aa00ee6f03acee10a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 10 Mar 2025 11:02:09 +0100 Subject: [PATCH 037/147] Create four task types: "generate", "retain", "train" and "apply". --- R/cbind.R | 14 ++-- R/check.actions.R | 78 ------------------- R/check.tasks.R | 78 +++++++++++++++++++ R/filter.R | 6 +- R/method.R | 14 ++-- R/mice.R | 39 +++++----- R/mice.impute.pmmsplit.R | 22 +++--- R/mice.mids.R | 4 +- R/mids.R | 13 ++-- R/rbind.R | 8 +- R/sampler.R | 26 +++---- man/filter.mids.Rd | 2 +- man/make.method.Rd | 19 ++--- man/mice.Rd | 27 +++---- man/mice.impute.pmmsplit.Rd | 4 +- man/mids.Rd | 28 +++---- tests/testthat/test-activities.R | 37 --------- tests/testthat/test-mice.impute.durr.logreg.R | 1 + .../testthat/{test-actions.R => test-tasks.R} | 8 +- 19 files changed, 199 insertions(+), 229 deletions(-) delete mode 100644 R/check.actions.R create mode 100644 R/check.tasks.R delete mode 100644 tests/testthat/test-activities.R rename tests/testthat/{test-actions.R => test-tasks.R} (68%) diff --git a/R/cbind.R b/R/cbind.R index d0855aede..dae0fd0e3 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,8 +97,8 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots - actions <- c(x$actions, "walk") - names(actions) <- c(names(x$actions), tail(varnames, 1L)) + tasks <- c(x$tasks, "walk") + names(tasks) <- c(names(x$tasks), tail(varnames, 1L)) models <- x$models ignore <- x$ignore @@ -128,7 +128,7 @@ cbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, @@ -236,9 +236,9 @@ cbind.mids.mids <- function(x, y, call) { names(post) <- varnames blots <- c(x$blots, y$blots) names(blots) <- blocknames - actions <- c(x$actions, y$actions) - # FIXME: Assumes combined action names yields unique names as in colnames(data) - names(actions) <- make.unique(c(names(x$actions), names(y$actions))) + tasks <- c(x$tasks, y$tasks) + # FIXME: Assumes combined task names yields unique names as in colnames(data) + names(tasks) <- make.unique(c(names(x$tasks), names(y$tasks))) # Function to copy all objects from one environment to another merge_envs <- function(target_env, source_env) { @@ -313,7 +313,7 @@ cbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, diff --git a/R/check.actions.R b/R/check.actions.R deleted file mode 100644 index 652f467b5..000000000 --- a/R/check.actions.R +++ /dev/null @@ -1,78 +0,0 @@ -check.actions <- function(actions, data, models = NULL, blocks = NULL) { - if (is.null(actions)) { - actions <- "walk" - } - - valid_actions <- c("walk", "train", "run") - - # 1. Default blocks to individual variables if not provided - if (is.null(blocks)) { - blocks <- setNames(as.list(names(data)), names(data)) - } - - # 2. Expand actions if it's a single value - bv <- unique(unlist(blocks)) - if (length(actions) == 1) { - actions <- setNames(rep(actions, length(bv)), bv) - } - - # 3. Check length - if (length(actions) != length(bv)) { - stop("The length of `actions` (", length(actions), - ") must match the number of variables in `blocks` (", length(bv),").") - } - - # 4. Check if all names in actions exist in blocks - notFound <- !names(actions) %in% bv - if (any(notFound)) { - stop(paste0( - "The following variables specified in `actions` are not present in `blocks`: ", - paste(names(actions)[notFound], collapse = ", "), ".\n", - "Ensure all specified variables match those in `blocks`." - )) - } - - # 5. Check if all actions are valid - invalid_ops <- setdiff(unique(actions), valid_actions) - if (length(invalid_ops) > 0) { - stop(paste0( - "Invalid action(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", - "Valid actions are: ", paste(valid_actions, collapse = ", "), ".\n", - "Please correct the `actions` argument." - )) - } - - # 6. Prevent "run" if models is NULL - if ("run" %in% actions && is.null(models)) { - stop("The action 'fill' requires a stored model, but `models` is NULL.\n", - "Please provide a valid `models` object containing trained imputation models.") - } - - # 7. Ensure that all "run" variables have a trained model in models - if ("run" %in% actions && !is.null(models)) { - fill_vars <- names(actions[actions == "run"]) - missing_models <- setdiff(fill_vars, ls(models)) - if (length(missing_models) > 0) { - stop(paste0( - "The following variables specified as 'fill' do not have stored models: ", - paste(missing_models, collapse = ", "), ".\n", - "Ensure these variables were previously fitted before using 'fill'." - )) - } - } - - # 8. Ensure all variables in models exist in blocks - if (!is.null(models)) { - trained_vars <- ls(models) - missing_from_data <- setdiff(trained_vars, bv) # Use block variable names - if (length(missing_from_data) > 0) { - stop(paste0( - "The following variables are present in `models` but missing from `data`: ", - paste(missing_from_data, collapse = ", "), ".\n", - "Ensure that all stored models correspond to variables in the dataset." - )) - } - } - - return(actions) -} diff --git a/R/check.tasks.R b/R/check.tasks.R new file mode 100644 index 000000000..51de4faae --- /dev/null +++ b/R/check.tasks.R @@ -0,0 +1,78 @@ +check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { + if (is.null(tasks)) { + tasks <- "generate" + } + + valid_tasks <- c("generate", "retain", "train", "apply") + + # 1. Default blocks to individual variables if not provided + if (is.null(blocks)) { + blocks <- setNames(as.list(names(data)), names(data)) + } + + # 2. Expand tasks if it's a single value + bv <- unique(unlist(blocks)) + if (length(tasks) == 1) { + tasks <- setNames(rep(tasks, length(bv)), bv) + } + + # 3. Check length + if (length(tasks) != length(bv)) { + stop("The length of `tasks` (", length(tasks), + ") must match the number of variables in `blocks` (", length(bv),").") + } + + # 4. Check if all names in tasks exist in blocks + notFound <- !names(tasks) %in% bv + if (any(notFound)) { + stop(paste0( + "The following variables specified in `tasks` are not present in `blocks`: ", + paste(names(tasks)[notFound], collapse = ", "), ".\n", + "Ensure all specified variables match those in `blocks`." + )) + } + + # 5. Check if all tasks are valid + invalid_ops <- setdiff(unique(tasks), valid_tasks) + if (length(invalid_ops) > 0) { + stop(paste0( + "Invalid task(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", + "Valid tasks are: ", paste(valid_tasks, collapse = ", "), ".\n", + "Please correct the `tasks` argument." + )) + } + + # 6. Prevent "apply" if models is NULL + if ("apply" %in% tasks && is.null(models)) { + stop("The task 'apply' requires a stored model, but `models` is NULL.\n", + "Please provide a valid `models` object containing trained imputation models.") + } + + # 7. Ensure that all "apply" variables have a trained model in models + if ("apply" %in% tasks && !is.null(models)) { + fill_vars <- names(tasks[tasks == "apply"]) + missing_models <- setdiff(fill_vars, ls(models)) + if (length(missing_models) > 0) { + stop(paste0( + "The following variables specified as 'apply' do not have stored models: ", + paste(missing_models, collapse = ", "), ".\n", + "Ensure these variables were previously fitted before using 'apply'." + )) + } + } + + # 8. Ensure all variables in models exist in blocks + if (!is.null(models)) { + trained_vars <- ls(models) + missing_from_data <- setdiff(trained_vars, bv) # Use block variable names + if (length(missing_from_data) > 0) { + stop(paste0( + "The following variables are present in `models` but missing from `data`: ", + paste(missing_from_data, collapse = ", "), ".\n", + "Ensure that all stored models correspond to variables in the dataset." + )) + } + } + + return(tasks) +} diff --git a/R/filter.R b/R/filter.R index 1c95d00fe..5a86a5c8f 100644 --- a/R/filter.R +++ b/R/filter.R @@ -33,7 +33,7 @@ dplyr::filter #' \code{formulas} \tab Equals \code{.data$formulas}\cr #' \code{post} \tab Equals \code{.data$post}\cr #' \code{blots} \tab Equals \code{.data$blots}\cr -#' \code{actions} \tab Equals \code{.data$actions}\cr +#' \code{tasks} \tab Equals \code{.data$tasks}\cr #' \code{models} \tab Equals \code{.data$models}\cr #' \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr #' \code{seed} \tab Equals \code{.data$seed}\cr @@ -81,7 +81,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { formulas <- .data$formulas modeltype <- .data$modeltype blots <- .data$blots - actions <- .data$actions + tasks <- .data$tasks models <- .data$models post <- .data$post seed <- .data$seed @@ -124,7 +124,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, diff --git a/R/method.R b/R/method.R index a9d15f0c5..73bf7167e 100644 --- a/R/method.R +++ b/R/method.R @@ -12,7 +12,7 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), - actions = NULL, + tasks = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { method <- rep("", length(blocks)) names(method) <- names(blocks) @@ -32,10 +32,10 @@ make.method <- function(data, # preserve old behaviour that sets method <- "" with walk names(method) <- names(blocks) - if (!is.null(actions)) { + if (!is.null(tasks)) { for (j in names(blocks)) { vname <- blocks[[j]] - if ("walk" %in% actions[vname] && nimp[j] == 0L) method[j] <- "" + if ("walk" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" } } @@ -43,13 +43,13 @@ make.method <- function(data, } -check.method <- function(method, data, where, blocks, actions, defaultMethod) { +check.method <- function(method, data, where, blocks, tasks, defaultMethod) { if (is.null(method)) { return(make.method( data = data, where = where, blocks = blocks, - actions = actions, + tasks = tasks, defaultMethod = defaultMethod )) } @@ -67,7 +67,7 @@ check.method <- function(method, data, where, blocks, actions, defaultMethod) { # preserve old behaviour that sets method <- "" with walk for (j in names(blocks)) { vname <- blocks[[j]] - if ("walk" %in% actions[vname] && nimp[j] == 0L) method[j] <- "" + if ("walk" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" } } @@ -135,7 +135,7 @@ check.method <- function(method, data, where, blocks, actions, defaultMethod) { ) } # preserve old behaviour that sets method <- "" with walk - if ("walk" %in% actions[vname] && nimp[j] == 0L) method[j] <- "" + if ("walk" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" } unlist(method) diff --git a/R/mice.R b/R/mice.R index 3a7a10625..2fc560737 100644 --- a/R/mice.R +++ b/R/mice.R @@ -219,21 +219,22 @@ #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function #' called for block \code{blockname}. -#' @param actions A character vector specifying the action to perform for +#' @param tasks A character vector specifying the task to perform for #' each imputation block. The available options are: #' \describe{ -#' \item{"walk"}{Estimate parameters and generate imputations -#' (classic MICE behavior). This is the default.} -#' \item{"train"}{Estimate parameters and store the imputation model -#' without data and without generating imputations.} -#' \item{"run"}{Use a previously stored imputation model to generate +#' \item{"generate"}{Estimate parameters, generate imputations and store +#' the original data plus imputations (classic MICE behavior).} +#' \item{"retain" }{Estimate parameters, generate imputations and +#' store the original data, the imputations and the imputation model.} +#' \item{"train"}{As retain, but store only the imputation model.} +#' \item{"apply"}{Apply a previously trained imputation model to generate #' imputations, without re-estimating parameters.} #' } #' This argument can be specified as a named vector, where names correspond -#' to variables and values specify the action for each variable. If a +#' to variables and values specify the task for each variable. If a #' single value is provided, it applies to the variables in all blocks. The #' length of the vector must match the number of variables present in the -#' blocks. +#' blocks. The default is \code{"generate"}. #' @param models An environment that can be used to store fitted imputation #' models. The models are stored in the environment under the name of the #' block. The models can be used to predict missing values in new data. @@ -315,8 +316,8 @@ #' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) #' #' # Store model for `bmi`, estimate others as usual -#' actions <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") -#' imp2 <- mice(nhanes, method = "pmmsplit", actions = actions, print = FALSE) +#' tasks <- c("age" = "generate", "bmi" = "retain", "hyp" = "generate", "chl" = "generate") +#' imp2 <- mice(nhanes, method = "pmmsplit", tasks = tasks, print = FALSE) #' #' # Inspects the stored model for imputation 1 for `bmi` #' ls(imp2$models$bmi$"1") @@ -324,8 +325,8 @@ #' imp2$models$bmi$"1"$beta.mis #' #' # Fill missing `bmi` values using pre-trained model -#' actions <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") -#' imp3 <- mice(nhanes, method = "pmmsplit", actions = actions, +#' tasks <- c("age" = "generate", "bmi" = "apply", "hyp" = "generate", "chl" = "generate") +#' imp3 <- mice(nhanes, method = "pmmsplit", tasks = tasks, #' models = imp2$models, print = FALSE) #' #' \dontrun{ @@ -361,7 +362,7 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, - actions = NULL, + tasks = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -472,10 +473,10 @@ mice <- function(data, user.visitSequence = user.visitSequence, maxit = maxit ) - actions <- check.actions(actions, data, models, blocks) + tasks <- check.tasks(tasks, data, models, blocks) method <- check.method( method = method, data = data, where = where, - blocks = blocks, actions = actions, + blocks = blocks, tasks = tasks, defaultMethod = defaultMethod ) post <- check.post(post, data) @@ -499,11 +500,11 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # Initialize models only for "train" and "run" blocks that are missing in models + # Initialize models for "retain", "train" and "apply" blocks that are missing in models if (is.null(models)) { models <- new.env(parent = emptyenv()) } - model_vars <- names(actions[actions %in% c("train", "run")]) + model_vars <- names(tasks[tasks %in% c("retain", "train", "apply")]) for (block in model_vars) { if (!exists(block, envir = models)) { models[[block]] <- new.env(parent = emptyenv()) @@ -528,7 +529,7 @@ mice <- function(data, q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, actions, models, + modeltype, blots, tasks, models, post, c(from, to), printFlag, ... ) @@ -551,7 +552,7 @@ mice <- function(data, modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R index 12facdd3f..4b86398cb 100644 --- a/R/mice.impute.pmmsplit.R +++ b/R/mice.impute.pmmsplit.R @@ -4,7 +4,7 @@ #' \code{pmmsplit()} is an implementation of pmm that saves the imputation model #' and generates imputations from the saved model. #' @aliases pmmsplit -#' @param action The action to be performed. The default is \code{"walk"}. +#' @param task The task to be performed. The default is \code{"generate"}. #' @param model Storage for the model estimates #' @param nbins The number of bins used to store the predictive mean matching #' model. The default is 50. @@ -96,26 +96,26 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, matchtype = 1L, exclude = NULL, quantify = TRUE, trim = 1L, ridge = 1e-05, nbins = NULL, - action = "walk", + task = "generate", model = NULL, ...) { if (is.null(wy)) { wy <- !ry } - # Only enforce `model` for "train" and "run" - if (action %in% c("train", "run")) { + # Only enforce `model` for "retain", "train" and "apply" + if (task %in% c("retain", "train", "apply")) { if (is.null(model)) { - stop(paste("`model` cannot be NULL for action:", action)) + stop(paste("`model` cannot be NULL for task:", task)) } if (!is.environment(model)) { stop("`model` must be an environment to store results persistently.") } } - # Handle "run" action: Use pre-stored model without re-training - if (action == "run") { + # Handle "apply" task: Use pre-stored model without re-training + if (task == "apply") { if (!length(ls(model))) { - stop("No stored model found for 'run' action.") + stop("No stored model found for 'apply' task.") } # Compute linear predictor for missing data @@ -132,7 +132,7 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, return(impy) } - # Handle "walk" and "train": train model + # Estimate model if "generate", "retain" and "train" # Quantify factor by optimal scaling f <- quantify(y, ry, x, quantify = quantify) @@ -157,8 +157,8 @@ mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, donors <- initialize.donors(donors, length(yhatobs)) prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) - # Store model for "train", skip for "walk" - if (action == "train") { + # Store model for "retain" and "train"; skip for "generate" and "apply" + if (task %in% c("retain", "train")) { model$setup <- list(method = "pmmsplit", n = length(yhatobs), donors = donors, diff --git a/R/mice.mids.R b/R/mice.mids.R index a3a02a457..6f9672ce2 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -113,7 +113,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, obj$formulas, obj$modeltype, obj$blots, - obj$actions, obj$models, + obj$tasks, obj$models, obj$post, c(from, to), printFlag, ... ) @@ -168,7 +168,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { modeltype = obj$modeltype, post = obj$post, blots = obj$blots, - actions = obj$actions, + tasks = obj$tasks, models = obj$models, ignore = obj$ignore, seed = obj$seed, diff --git a/R/mids.R b/R/mids.R index 1cdb33918..3bd8c3c5b 100644 --- a/R/mids.R +++ b/R/mids.R @@ -58,7 +58,7 @@ #' with commands for post-processing.} #' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} #' function.} -#' \item{\code{actions}:}{A character vector of length \code{length(blocks)}.} +#' \item{\code{tasks}:}{A character vector of length \code{length(blocks)}.} #' \item{\code{models}:}{The \code{models} list contains imputation model #' estimates.} #' \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating @@ -74,7 +74,7 @@ #' \item{\code{chainVar}:}{An array with similar structure as #' \code{chainMean}, containing the variance of the imputed values.} #' \item{\code{loggedEvents}:}{A \code{data.frame} with five columns -#' containing warnings, corrective actions, and other inside info.} +#' containing warnings, corrective tasks, and other inside info.} #' \item{\code{version}:}{Version number of \code{mice} package that #' created the object.} #' \item{\code{date}:}{Date at which the object was created.} @@ -82,10 +82,10 @@ #' #' @section LoggedEvents: #' The \code{loggedEvents} entry is a matrix with five columns containing a -#' record of automatic removal actions. It is \code{NULL} is no action was +#' record of automatic removal tasks. It is \code{NULL} is no record was #' made. At initialization the program removes constant variables, and #' removes variables to cause collinearity. -#' During iteration, the program does the following actions: +#' During iteration, the program does the following tasks: #' \itemize{ #' \item One or more variables that are linearly dependent are removed #' (for categorical data, a 'variable' corresponds to a dummy variable) @@ -138,6 +138,7 @@ #' formulas = list(a = a ~ b, b = b ~ a), #' post = NULL, #' blots = NULL, +#' tasks = NULL, #' models = NULL, #' ignore = logical(nrow(data)), #' seed = 123, @@ -163,7 +164,7 @@ mids <- function( modeltype = character(), post = character(), blots = list(), - actions = character(), + tasks = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -192,7 +193,7 @@ mids <- function( modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, diff --git a/R/rbind.R b/R/rbind.R index 2d7419268..2247cee48 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -46,7 +46,7 @@ rbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots - actions <- x$actions + tasks <- x$tasks models <- x$models predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -77,7 +77,7 @@ rbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, @@ -129,7 +129,7 @@ rbind.mids.mids <- function(x, y, call) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots - actions <- x$actions + tasks <- x$tasks models <- x$models ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix @@ -178,7 +178,7 @@ rbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, - actions = actions, + tasks = tasks, models = models, ignore = ignore, seed = seed, diff --git a/R/sampler.R b/R/sampler.R index 8057c020d..a8a03e799 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,7 +2,7 @@ # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, actions, models, + modeltype, blots, tasks, models, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] @@ -75,7 +75,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, - action = actions[j], + task = tasks[j], model = models[[j]][[as.character(i)]], yname = j, k = k, ct = ct, @@ -181,12 +181,12 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } -sampler.univ <- function(data, r, where, pred, formula, method, action, model, +sampler.univ <- function(data, r, where, pred, formula, method, task, model, yname, k, ct = "pred", user, ignore, ...) { j <- yname[1L] # prepare formula and model matrix - formula <- prepare.formula(formula, data, model, j, ct, pred, action) + formula <- prepare.formula(formula, data, model, j, ct, pred, task) x <- obtain.design(data, formula) # expand pred vector to model matrix, remove intercept @@ -207,7 +207,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, action, model, wy <- complete.cases(x) & where[, j] # nothing to impute - if (all(!wy) && action != "train") { + if (all(!wy) && !task %in% c("retain", "train")) { return(numeric(0)) } @@ -228,21 +228,21 @@ sampler.univ <- function(data, r, where, pred, formula, method, action, model, imputes[!cc] <- NA args <- c(list(y = y, ry = ry, x = x, wy = wy, type = type, - action = action, model = model), + task = task, model = model), user, list(...)) imputes[cc] <- do.call(f, args = args) imputes } -prepare.formula <- function(formula, data, model, j, ct, pred, action) { +prepare.formula <- function(formula, data, model, j, ct, pred, task) { # prepares the formula for univariate imputation - # saves (for "train") or retrieves (for "run") the formula + # saves (for "retain" and "train") or retrieves (for "apply") the formula - # for "run", use the stored formula instead of recalculating - if (action == "run") { + # for "apply", use the stored formula instead of recalculating + if (task == "apply") { if (!exists("formula", envir = model)) { - stop("Error: No stored formula found in model for 'fill' action.") + stop("Error: No stored formula found in model for 'apply' task.") } formula <- get("formula", envir = model) return(formula) @@ -268,8 +268,8 @@ prepare.formula <- function(formula, data, model, j, ct, pred, action) { } } - # store formula in `model` only when action == "train" - if (action == "train") { + # store formula in `model` only when task is "retain" or "train" + if (task %in% c("retain", "train")) { assign("formula", formula, envir = model) } diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index 232106190..bd5c9256a 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -44,7 +44,7 @@ The function constructs the elements of the filtered \code{mids} object as follo \code{formulas} \tab Equals \code{.data$formulas}\cr \code{post} \tab Equals \code{.data$post}\cr \code{blots} \tab Equals \code{.data$blots}\cr -\code{actions} \tab Equals \code{.data$actions}\cr +\code{tasks} \tab Equals \code{.data$tasks}\cr \code{models} \tab Equals \code{.data$models}\cr \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr \code{seed} \tab Equals \code{.data$seed}\cr diff --git a/man/make.method.Rd b/man/make.method.Rd index c3bcef7c9..18365573a 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -8,7 +8,7 @@ make.method( data, where = make.where(data), blocks = make.blocks(data), - actions = NULL, + tasks = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr") ) } @@ -38,21 +38,22 @@ matrix are set to \code{FALSE} of variables that are not block members. A variable may appear in multiple blocks. In that case, it is effectively re-imputed each time that it is visited.} -\item{actions}{A character vector specifying the action to perform for +\item{tasks}{A character vector specifying the task to perform for each imputation block. The available options are: \describe{ -\item{"walk"}{Estimate parameters and generate imputations -(classic MICE behavior). This is the default.} -\item{"train"}{Estimate parameters and store the imputation model -without data and without generating imputations.} -\item{"run"}{Use a previously stored imputation model to generate +\item{"generate"}{Estimate parameters, generate imputations and store +the original data plus imputations (classic MICE behavior).} +\item{"retain" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"train"}{As retain, but store only the imputation model.} +\item{"apply"}{Apply a previously trained imputation model to generate imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the action for each variable. If a +to variables and values specify the task for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the -blocks.} +blocks. The default is \code{"generate"}.} \item{defaultMethod}{A vector of length 4 containing the default imputation methods for 1) numeric data, 2) factor data with 2 levels, 3) diff --git a/man/mice.Rd b/man/mice.Rd index e13a8b327..9c75cf6db 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,7 +18,7 @@ mice( formulas, modeltype = NULL, blots = NULL, - actions = NULL, + tasks = NULL, models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -130,21 +130,22 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} -\item{actions}{A character vector specifying the action to perform for +\item{tasks}{A character vector specifying the task to perform for each imputation block. The available options are: \describe{ -\item{"walk"}{Estimate parameters and generate imputations -(classic MICE behavior). This is the default.} -\item{"train"}{Estimate parameters and store the imputation model -without data and without generating imputations.} -\item{"run"}{Use a previously stored imputation model to generate +\item{"generate"}{Estimate parameters, generate imputations and store +the original data plus imputations (classic MICE behavior).} +\item{"retain" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"train"}{As retain, but store only the imputation model.} +\item{"apply"}{Apply a previously trained imputation model to generate imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the action for each variable. If a +to variables and values specify the task for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the -blocks.} +blocks. The default is \code{"generate"}.} \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the @@ -413,8 +414,8 @@ complete(imp) imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) # Store model for `bmi`, estimate others as usual -actions <- c("age" = "walk", "bmi" = "train", "hyp" = "walk", "chl" = "walk") -imp2 <- mice(nhanes, method = "pmmsplit", actions = actions, print = FALSE) +tasks <- c("age" = "generate", "bmi" = "retain", "hyp" = "generate", "chl" = "generate") +imp2 <- mice(nhanes, method = "pmmsplit", tasks = tasks, print = FALSE) # Inspects the stored model for imputation 1 for `bmi` ls(imp2$models$bmi$"1") @@ -422,8 +423,8 @@ imp2$models$bmi$"1"$formula imp2$models$bmi$"1"$beta.mis # Fill missing `bmi` values using pre-trained model -actions <- c("age" = "walk", "bmi" = "run", "hyp" = "walk", "chl" = "walk") -imp3 <- mice(nhanes, method = "pmmsplit", actions = actions, +tasks <- c("age" = "generate", "bmi" = "apply", "hyp" = "generate", "chl" = "generate") +imp3 <- mice(nhanes, method = "pmmsplit", tasks = tasks, models = imp2$models, print = FALSE) \dontrun{ diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd index 92f9afa37..acb275571 100644 --- a/man/mice.impute.pmmsplit.Rd +++ b/man/mice.impute.pmmsplit.Rd @@ -17,7 +17,7 @@ mice.impute.pmmsplit( trim = 1L, ridge = 1e-05, nbins = NULL, - action = "walk", + task = "generate", model = NULL, ... ) @@ -72,7 +72,7 @@ reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} \item{nbins}{The number of bins used to store the predictive mean matching model. The default is 50.} -\item{action}{The action to be performed. The default is \code{"walk"}.} +\item{task}{The task to be performed. The default is \code{"generate"}.} \item{model}{Storage for the model estimates} diff --git a/man/mids.Rd b/man/mids.Rd index 575bfd3dc..28adda0b2 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -23,7 +23,7 @@ mids( modeltype = character(), post = character(), blots = list(), - actions = character(), + tasks = character(), models = new.env(), ignore = logical(), seed = integer(), @@ -155,21 +155,22 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} -\item{actions}{A character vector specifying the action to perform for +\item{tasks}{A character vector specifying the task to perform for each imputation block. The available options are: \describe{ -\item{"walk"}{Estimate parameters and generate imputations -(classic MICE behavior). This is the default.} -\item{"train"}{Estimate parameters and store the imputation model -without data and without generating imputations.} -\item{"run"}{Use a previously stored imputation model to generate +\item{"generate"}{Estimate parameters, generate imputations and store +the original data plus imputations (classic MICE behavior).} +\item{"retain" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"train"}{As retain, but store only the imputation model.} +\item{"apply"}{Apply a previously trained imputation model to generate imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond -to variables and values specify the action for each variable. If a +to variables and values specify the task for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the -blocks.} +blocks. The default is \code{"generate"}.} \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the @@ -280,7 +281,7 @@ identified by its name, so list names must correspond to block names.} with commands for post-processing.} \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} function.} -\item{\code{actions}:}{A character vector of length \code{length(blocks)}.} +\item{\code{tasks}:}{A character vector of length \code{length(blocks)}.} \item{\code{models}:}{The \code{models} list contains imputation model estimates.} \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating @@ -296,7 +297,7 @@ Note that observed data are not present in this mean.} \item{\code{chainVar}:}{An array with similar structure as \code{chainMean}, containing the variance of the imputed values.} \item{\code{loggedEvents}:}{A \code{data.frame} with five columns -containing warnings, corrective actions, and other inside info.} +containing warnings, corrective tasks, and other inside info.} \item{\code{version}:}{Version number of \code{mice} package that created the object.} \item{\code{date}:}{Date at which the object was created.} @@ -306,10 +307,10 @@ created the object.} \section{LoggedEvents}{ The \code{loggedEvents} entry is a matrix with five columns containing a -record of automatic removal actions. It is \code{NULL} is no action was +record of automatic removal tasks. It is \code{NULL} is no record was made. At initialization the program removes constant variables, and removes variables to cause collinearity. -During iteration, the program does the following actions: +During iteration, the program does the following tasks: \itemize{ \item One or more variables that are linearly dependent are removed (for categorical data, a 'variable' corresponds to a dummy variable) @@ -368,6 +369,7 @@ imp <- mids( formulas = list(a = a ~ b, b = b ~ a), post = NULL, blots = NULL, + tasks = NULL, models = NULL, ignore = logical(nrow(data)), seed = 123, diff --git a/tests/testthat/test-activities.R b/tests/testthat/test-activities.R deleted file mode 100644 index 33a6dfee2..000000000 --- a/tests/testthat/test-activities.R +++ /dev/null @@ -1,37 +0,0 @@ -context("actions") - -# We have to test the following cases: - -# - Does train-run setup with a factor variable produce imputations? -# - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? -# - Does train-run setup with a factor variable produce imputations when the factor has more categories during running than training? -# - Does train-run setup with a factor variable produce imputations when only one factor level is present during training? -# - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? - -test_that("actions work with factor with same number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) - expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) - expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) -}) - -test_that("training works on completely observed variables", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) - expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) - expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) -}) - -# make a few missing values in age -nhanes2$age[10:15] <- NA -# remove category 60-99 from age during running -nhanes3 <- nhanes2 -nhanes3[["age"]] <- droplevels(nhanes3[["age"]], exclude = "60-99") - -test_that("training and running works with factor with different number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 2, act = "train", method = "pmmsplit", print = FALSE)) - expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes3, m = 2, maxit = 2, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) - expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) -}) - diff --git a/tests/testthat/test-mice.impute.durr.logreg.R b/tests/testthat/test-mice.impute.durr.logreg.R index 271e17b82..adb87cede 100644 --- a/tests/testthat/test-mice.impute.durr.logreg.R +++ b/tests/testthat/test-mice.impute.durr.logreg.R @@ -54,6 +54,7 @@ durr_custom <- mice(X, nfolds = 5, print = FALSE ) +methods <- make.method(X) logreg_default <- mice(X, m = 2, maxit = 2, method = "logreg", print = FALSE diff --git a/tests/testthat/test-actions.R b/tests/testthat/test-tasks.R similarity index 68% rename from tests/testthat/test-actions.R rename to tests/testthat/test-tasks.R index 2dbffa3c7..c1c645a77 100644 --- a/tests/testthat/test-actions.R +++ b/tests/testthat/test-tasks.R @@ -9,16 +9,16 @@ context("actions") # - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? test_that("actions work with factor with same number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, act = "train", method = "pmmsplit", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmmsplit", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmmsplit", models = imp1$models, print = FALSE)) expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) }) test_that("training works on completely observed variables", { - expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, act = "train", method = "pmmsplit", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmmsplit", print = FALSE)) expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, act = "run", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmmsplit", models = imp1$models, print = FALSE)) expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) }) From 25d0c2ad4242bc7c9560d38f2744432b769731ca Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 10 Mar 2025 18:57:46 +0100 Subject: [PATCH 038/147] Remove the experimental pmmsplit() and incorporate all of its functionality into the regular mice.impute.pmm() function. PMM has now support from tasks "generate", "retain", "train" and "apply". --- NAMESPACE | 2 - R/check.tasks.R | 19 ++ R/mice.R | 13 +- R/mice.impute.pmm.R | 326 ++++++++++++++------- R/mice.impute.pmmsplit.R | 274 ----------------- man/mice.Rd | 13 +- man/mice.impute.cart.Rd | 1 - man/mice.impute.lasso.logreg.Rd | 1 - man/mice.impute.lasso.norm.Rd | 1 - man/mice.impute.lasso.select.logreg.Rd | 1 - man/mice.impute.lasso.select.norm.Rd | 1 - man/mice.impute.lda.Rd | 1 - man/mice.impute.logreg.Rd | 1 - man/mice.impute.logreg.boot.Rd | 1 - man/mice.impute.mean.Rd | 1 - man/mice.impute.midastouch.Rd | 1 - man/mice.impute.mnar.Rd | 1 - man/mice.impute.mpmm.Rd | 1 - man/mice.impute.norm.Rd | 1 - man/mice.impute.norm.boot.Rd | 1 - man/mice.impute.norm.nob.Rd | 1 - man/mice.impute.norm.predict.Rd | 1 - man/mice.impute.pmm.Rd | 54 +++- man/mice.impute.pmmsplit.Rd | 197 ------------- man/mice.impute.polr.Rd | 1 - man/mice.impute.polyreg.Rd | 1 - man/mice.impute.quadratic.Rd | 1 - man/mice.impute.rf.Rd | 1 - man/mice.impute.ri.Rd | 1 - man/pmm.match.Rd | 49 ---- tests/testthat/test-mice.impute.pmm.R | 1 + tests/testthat/test-mice.impute.pmmsplit.R | 114 ------- tests/testthat/test-tasks.R | 8 +- 33 files changed, 297 insertions(+), 794 deletions(-) delete mode 100644 R/mice.impute.pmmsplit.R delete mode 100644 man/mice.impute.pmmsplit.Rd delete mode 100644 man/pmm.match.Rd delete mode 100644 tests/testthat/test-mice.impute.pmmsplit.R diff --git a/NAMESPACE b/NAMESPACE index 6b13cca89..46434fbc6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -47,7 +47,6 @@ S3method(with,mids) S3method(xyplot,mads) S3method(xyplot,mids) export(.norm.draw) -export(.pmm.match) export(D1) export(D2) export(D3) @@ -133,7 +132,6 @@ export(mice.impute.norm.predict) export(mice.impute.panImpute) export(mice.impute.passive) export(mice.impute.pmm) -export(mice.impute.pmmsplit) export(mice.impute.polr) export(mice.impute.polyreg) export(mice.impute.quadratic) diff --git a/R/check.tasks.R b/R/check.tasks.R index 51de4faae..4fe1775c8 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -1,4 +1,5 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { + # This function is called during initialization if (is.null(tasks)) { tasks <- "generate" } @@ -76,3 +77,21 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { return(tasks) } + +check.model <- function(model, + task = c("generate", "retain", "train", "apply")) { + # This function is called during iteration + task <- match.arg(task) + if (task %in% c("retain", "train", "apply")) { + if (is.null(model)) { + stop(paste("`model` cannot be NULL for task:", task)) + } + if (!is.environment(model)) { + stop("`model` must be an environment to store results persistently.") + } + } + if (task == "apply" && !length(ls(model))) { + stop("No stored model found for 'apply' task.") + } + return() +} diff --git a/R/mice.R b/R/mice.R index 2fc560737..33853e4ad 100644 --- a/R/mice.R +++ b/R/mice.R @@ -313,11 +313,17 @@ #' complete(imp) #' #' # imputation on mixed data with a different method per column -#' imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) +#' imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) +#' +#' # store all imputation models +#' imp1 <- mice(nhanes, tasks = "retain", print = FALSE) +#' ls(imp1$models$bmi$"1") +#' imp1$models$bmi$"1"$formula +#' imp1$models$bmi$"1"$beta.mis #' #' # Store model for `bmi`, estimate others as usual #' tasks <- c("age" = "generate", "bmi" = "retain", "hyp" = "generate", "chl" = "generate") -#' imp2 <- mice(nhanes, method = "pmmsplit", tasks = tasks, print = FALSE) +#' imp2 <- mice(nhanes, tasks = tasks, print = FALSE) #' #' # Inspects the stored model for imputation 1 for `bmi` #' ls(imp2$models$bmi$"1") @@ -326,8 +332,7 @@ #' #' # Fill missing `bmi` values using pre-trained model #' tasks <- c("age" = "generate", "bmi" = "apply", "hyp" = "generate", "chl" = "generate") -#' imp3 <- mice(nhanes, method = "pmmsplit", tasks = tasks, -#' models = imp2$models, print = FALSE) +#' imp3 <- mice(nhanes, tasks = tasks, models = imp2$models, print = FALSE) #' #' \dontrun{ #' # example where we fit the imputation model on the train data diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 0d3f6af7c..ad3a3fc01 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -8,29 +8,52 @@ #' (\code{TRUE}) and missing values (\code{FALSE}) in \code{y}. #' @param x Numeric design matrix with \code{length(y)} rows with predictors for #' \code{y}. Matrix \code{x} may have no missing values. -#' @param exclude Dependent values to exclude from the imputation model -#' and the collection of donor values -#' @param quantify Logical. If \code{TRUE}, factor levels are replaced -#' by the first canonical variate before fitting the imputation model. -#' If false, the procedure reverts to the old behaviour and takes the -#' integer codes (which may lack a sensible interpretation). -#' Relevant only of \code{y} is a factor. -#' @param trim Scalar integer. Minimum number of observations required in a -#' category in order to be considered as a potential donor value. -#' Relevant only of \code{y} is a factor. #' @param wy Logical vector of length \code{length(y)}. A \code{TRUE} value #' indicates locations in \code{y} for which imputations are created. #' @param donors The size of the donor pool among which a draw is made. #' The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects -#' the closest match, but is not recommended. Values between 3L and 10L -#' provide the best results in most cases (Morris et al, 2015). -#' @param matchtype Type of matching distance. The default choice +#' the closest match, but is not recommended. Values between 5L and 10L +#' provide the best results (Morris et al, 2015). +#' For tasks \code{"retain"} and \code{"train"}, the number of donors +#' is calculated internally based on the number of +#' observations in \code{yobs}. +#' @param matchtype Type of matching distance. The default (recommended) choice #' (\code{matchtype = 1L}) calculates the distance between #' the \emph{predicted} value of \code{yobs} and #' the \emph{drawn} values of \code{ymis} (called type-1 matching). #' Other choices are \code{matchtype = 0L} #' (distance between predicted values) and \code{matchtype = 2L} #' (distance between drawn values). +#' @param quantify Logical. If \code{TRUE}, factor levels are replaced +#' by the first canonical variate before fitting the imputation model. +#' If false, the procedure reverts to the old behaviour and takes the +#' integer codes (which may lack a sensible interpretation). +#' Relevant only of \code{y} is a factor. +#' @param exclude Dependent values to exclude from the imputation model +#' and the collection of donor values +#' @param trim Scalar integer. Minimum number of observations required in a +#' category in order to be considered as a potential donor value. +#' Relevant only of \code{y} is a factor. +#' @param task Character string. The task to be performed. Can +#' be \code{"generate"}, \code{"retain"}, \code{"train"} or \code{"apply"}. +#' The default is \code{"generate"} (classic MICE). See \code{mice()} for +#' details. +#' @param model An environment created by a parent to store the imputation +#' model setup and estimates. The model is stored in the \code{mids} object +#' under tasks \code{"retain"} and \code{"train"}, and is needed as input +#' for task \code{"apply"}. The object \code{model} is not used under +#' task \code{"generate"}. +#' @param nimp Experimental. Number of random imputations per missing values +#' generated from a fitted model under tasks \code{"retain"} and \code{"train"}. +#' The default is 1. The \code{nimp} parameter is different from \code{m}, +#' the number of multiple imputations, because it generates repeated +#' imputations from a single model. The \code{nimp} parameter is useful +#' for large samples to reduce the computational burden, but still awaits +#' support within the mice algorithm. +#' @param nbins The number of bins used to store the predictive mean matching +#' model. Under tasks \code{"retain"} and \code{"train"}, the number of donors +#' is calculated internally based on the number of observations in \code{yobs} +#' and the number of unique predictive values. #' @param ridge The ridge penalty used in \code{.norm.draw()} to prevent #' problems with multicollinearity. The default is \code{ridge = 1e-05}, #' which means that 0.01 percent of the diagonal is added to the cross-product. @@ -39,18 +62,14 @@ #' reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher. #' @param use.matcher Logical. Set \code{use.matcher = TRUE} to specify #' the C function \code{matcher()}, the now deprecated matching function that -#' was default in versions -#' \code{2.22} (June 2014) to \code{3.11.7} (Oct 2020). Since version \code{3.12.0} -#' \code{mice()} uses the much faster \code{matchindex} C function. Use -#' the deprecated \code{matcher} function only for exact reproduction. +#' was default in versions of \code{mice} prior to \code{3.12.0}. #' @param \dots Other named arguments. #' @return Vector with imputed data, same type as \code{y}, and of length #' \code{sum(wy)} -#' @author Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn +#' @author Stef van Buuren #' @details #' Imputation of \code{y} by predictive mean matching, based on #' van Buuren (2012, p. 73). The procedure is as follows: -#' #' \enumerate{ #' \item{Calculate the cross-product matrix \eqn{S=X_{obs}'X_{obs}}.} #' \item{Calculate \eqn{V = (S+{diag}(S)\kappa)^{-1}}, with some small ridge @@ -149,113 +168,198 @@ #' # to get old behavior: as.integer(y)) #' mice.impute.pmm(y, ry, x, quantify = FALSE) #' @export -mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, - matchtype = 1L, exclude = NULL, - quantify = TRUE, trim = 1L, - ridge = 1e-05, use.matcher = FALSE, ...) { - if (is.null(wy)) { - wy <- !ry - } - - # Reformulate the imputation problem such that - # 1. the imputation model disregards records with excluded y-values - # 2. the donor set does not contain excluded y-values +mice.impute.pmm <- function(y, ry, x, wy = NULL, + donors = 5L, matchtype = 1L, quantify = TRUE, + exclude = NULL, trim = 1L, + task = "generate", model = NULL, nimp = 1L, + nbins = NULL, ridge = 1e-05, use.matcher = FALSE, + ...) +{ + check.model(model, task) + if (is.null(wy)) wy <- !ry - # Retain categories with ">= trim" observations in the imputation model - # Exclude values from the imputation model + # Remove excluded values and trim small categories if (is.factor(y)) { - active <- !ry | (y %in% levels(y)[table(y) >= trim] & !y %in% exclude) - if (any(!active)) { - y <- droplevels(y[active]) - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] - } + freq <- table(y) + keep_levels <- names(freq[freq >= trim & !(names(freq) %in% exclude)]) + y <- factor(y, levels = keep_levels) + idx <- !ry | y %in% keep_levels } else { - active <- !ry | !y %in% exclude - if (any(!active)) { - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] - } + idx <- !ry | !(y %in% exclude) + } + if (any(!idx)) { + y <- y[idx] + ry <- ry[idx] + x <- x[idx, , drop = FALSE] + wy <- wy[idx] } + # Add intercept column to x x <- cbind(1, as.matrix(x)) - # quantify categories for factors - q <- quantify(y, ry, x, quantify = quantify) - ynum <- q$ynum + # Task: apply (impute from stored model) + if (task == "apply") { + yhatmis <- x[wy, ] %*% model$beta.mis + return(pmm.impute(yhatmis, model, nimp = nimp, ...)) + } + + # -- Remaining tasks: generate, retain, train -- - # parameter estimation - parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) + # Quantify factor levels + f <- quantify(y, ry, x, quantify = quantify) + ynum <- f$ynum - if (matchtype == 0L) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$coef - yhatmis <- x[wy, , drop = FALSE] %*% parm$coef - } + # Predict missing values using Normal draw + parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) if (matchtype == 1L) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$coef - yhatmis <- x[wy, , drop = FALSE] %*% parm$beta + beta.obs <- parm$coef + beta.mis <- parm$beta + } else if (matchtype == 0L) { + beta.mis <- beta.obs <- parm$coef + } else if (matchtype == 2L) { + beta.mis <- beta.obs <- parm$beta } - if (matchtype == 2L) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$beta - yhatmis <- x[wy, , drop = FALSE] %*% parm$beta + x_ry <- x[ry, , drop = FALSE] + x_wy <- x[wy, , drop = FALSE] + yhatobs <- as.vector(x_ry %*% beta.obs) + yhatmis <- x_wy %*% beta.mis + + # Generate task: Impute values (classic MICE PMM) + if (task == "generate") { + if (use.matcher) { + idx <- matcher(yhatobs, yhatmis, k = donors) + } else { + idx <- matchindex(yhatobs, yhatmis, donors) + } + return(y[ry][idx]) } - if (use.matcher) { - idx <- matcher(yhatobs, yhatmis, k = donors) - } else { - idx <- matchindex(yhatobs, yhatmis, donors) + + # -- Remaining tasks: retain, train -- + + # Divide predictions into bins + nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) + donors <- initialize.donors(donors, length(yhatobs)) + prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) + + # Store the imputation model in models environment + model$setup <- list(method = "pmm", + n = length(yhatobs), + donors = donors, + matchtype = matchtype, + quantify = quantify, + exclude = exclude, + trim = trim, + task = task, + nbins = nbins, + ridge = ridge) + model$beta.obs <- beta.obs + model$beta.mis <- beta.mis + model$edges <- prep$edges + model$lookup <- prep$lookup + model$factor <- list(labels = f$labels, quant = f$quant) + + # Compute imputations from model + return(pmm.impute(yhatmis, model, nimp = nimp, ...)) +} + + + +# --- PMM helpers + +pmm.impute <- function(yhatmis, model, nimp = 1L, ...) { + # Task "apply": Compute imputations without estimating new model + impy <- draw.neighbors.pmm(yhatmis, + edges = model$edges, + lookup = model$lookup, + m = nimp) + # Convert back to factor if needed + impy <- unquantify(ynum = impy, + quant = model$factor$quant, + labels = model$factor$labels) + return(impy) +} + +initialize.nbins <- function(nbins, n, nu) { + if (is.null(nbins)) { + nbins <- round(4 * log(n) + 1.5) } - return(y[ry][idx]) + # max nbin is number of unique yhat values + if (nbins > nu) { + # message("Warning: nbins (", nbins, ") exceeds unique yhat values (", nu, "). Adjusting to ", nu, ".") + nbins <- nu + } + # Ensure at least 2 bins + nbins <- max(2L, nbins) + return(nbins) } -#' Finds an imputed value from matches in the predictive metric (deprecated) -#' -#' This function finds matches among the observed data in the predictive -#' mean metric. It selects the \code{donors} closest matches, randomly -#' samples one of the donors, and returns the observed value of the -#' match. -#' -#' This function is included for backward compatibility. It was -#' used up to \code{mice 2.21}. The current \code{mice.impute.pmm()} -#' function calls the faster \code{C} function \code{matcher} instead of -#' \code{.pmm.match()}. -#' -#' @aliases .pmm.match -#' @param z A scalar containing the predicted value for the current case -#' to be imputed. -#' @param yhat A vector containing the predicted values for all cases with an observed -#' outcome. -#' @param y A vector of \code{length(yhat)} elements containing the observed outcome -#' @param donors The size of the donor pool among which a draw is made. The default is -#' \code{donors = 5}. Setting \code{donors = 1} always selects the closest match. Values -#' between 3 and 10 provide the best results. Note: This setting was changed from -#' 3 to 5 in version 2.19, based on simulation work by Tim Morris (UCL). -#' @param \dots Other parameters (not used). -#' @return A scalar containing the observed value of the selected donor. -#' @author Stef van Buuren -#' @rdname pmm.match -#' @references -#' Schenker N & Taylor JMG (1996) Partially parametric techniques -#' for multiple imputation. \emph{Computational Statistics and Data Analysis}, 22, 425-446. -#' -#' Little RJA (1988) Missing-data adjustments in large surveys (with discussion). -#' \emph{Journal of Business Economics and Statistics}, 6, 287-301. -#' -#' @export -.pmm.match <- function(z, yhat = yhat, y = y, donors = 5, ...) { - d <- abs(yhat - z) - f <- d > 0 - a1 <- ifelse(any(f), min(d[f]), 1) - d <- d + runif(length(d), 0, a1 / 10^10) - if (donors == 1) { - return(y[which.min(d)]) +initialize.donors <- function(donors, n) { + if (is.null(donors)) { + donors <- round(n / 600 + 7) } - donors <- min(donors, length(d)) - donors <- max(donors, 1) - ds <- sort.int(d, partial = donors) - m <- sample(y[d <= ds[donors]], 1) - return(m) + donors <- max(1L, min(donors, n)) + return(donors) } + +bin.yhat <- function(yhat, y, k = 10, nbins = 25) { + stopifnot(length(yhat) == length(y)) + + # Compute percentile-based bin edges + edges <- quantile(yhat, probs = seq(0, 1, length.out = nbins + 1), type = 7, na.rm = TRUE) + + # Sort yhat and y together + sort_order <- order(yhat) + yhat_sorted <- yhat[sort_order] + y_sorted <- y[sort_order] + + # Initialize lookup table + lookup <- matrix(NA_real_, nrow = nbins, ncol = k) + + # Assign values to bins + bin_idx <- findInterval(yhat_sorted, vec = edges, all.inside = TRUE) + + # Split y_sorted by bins + bin_values_list <- split(y_sorted, bin_idx) + + # Fill lookup table + lookup <- t(sapply(seq_len(nbins), function(b) { + bin_values <- bin_values_list[[as.character(b)]] + + if (length(bin_values) > 0) { + # If more than k values are available, randomly sample k + sample(bin_values, size = k, replace = length(bin_values) < k) + } else { + # If bin is empty, sample from entire y_sorted to avoid NA values + sample(y_sorted, size = k, replace = TRUE) + } + })) + + return(list(edges = edges, lookup = lookup)) +} + +draw.neighbors.pmm <- function(yhat_query, edges, lookup, m = 1) { + num_queries <- length(yhat_query) + nbins <- length(edges) - 1 # Bins are defined by edges[i] and edges[i+1] + + # Initialize result matrix: rows = number of queries, columns = m draws per query + imputed_values <- matrix(NA_real_, nrow = num_queries, ncol = m) + + # Find the bin for each query value + bin_idx <- findInterval(yhat_query, edges, rightmost.closed = TRUE, all.inside = TRUE) + + # Compute probability of selecting from left bin (smooth transition) + t0 <- edges[pmax(bin_idx, 1)] + t1 <- edges[pmin(bin_idx + 1, nbins)] + p_left <- ifelse(t1 > t0, (t1 - yhat_query) / (t1 - t0), 0.5) + + # Determine which bin to sample from + selected_bin <- ifelse(runif(num_queries) < p_left, bin_idx, pmin(bin_idx + 1, nbins)) + + # Vectorized sampling from lookup table + sampled_indices <- matrix(sample(1:ncol(lookup), num_queries * m, replace = TRUE), nrow = num_queries) + imputed_values <- matrix(lookup[cbind(selected_bin, sampled_indices)], nrow = num_queries, ncol = m) + + return(imputed_values) +} + diff --git a/R/mice.impute.pmmsplit.R b/R/mice.impute.pmmsplit.R deleted file mode 100644 index 4b86398cb..000000000 --- a/R/mice.impute.pmmsplit.R +++ /dev/null @@ -1,274 +0,0 @@ -#' Imputation by predictive mean matching -#' -#' -#' \code{pmmsplit()} is an implementation of pmm that saves the imputation model -#' and generates imputations from the saved model. -#' @aliases pmmsplit -#' @param task The task to be performed. The default is \code{"generate"}. -#' @param model Storage for the model estimates -#' @param nbins The number of bins used to store the predictive mean matching -#' model. The default is 50. -#' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} -#' @author Stef van Buuren -#' -#' @references Little, R.J.A. (1988), Missing data adjustments in large surveys -#' (with discussion), Journal of Business Economics and Statistics, 6, 287--301. -#' -#' Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive -#' mean matching and local residual draws. BMC Med Res Methodol. ;14:75. -#' -#' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-pmm.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} -#' Chapman & Hall/CRC. Boca Raton, FL. -#' -#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate -#' Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical -#' Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} -#' @family univariate imputation functions -#' @keywords datagen -#' @examples -#' # We normally call mice.impute.pmmsplit() from within mice() -#' # But we may call it directly as follows (not recommended) -#' -#' set.seed(53177) -#' xname <- c("age", "hgt", "wgt") -#' r <- stats::complete.cases(boys[, xname]) -#' x <- as.matrix(boys[r, xname]) -#' y <- boys[r, "tv"] -#' ry <- !is.na(y) -#' table(ry) -#' -#' # percentage of missing data in tv -#' sum(!ry) / length(ry) -#' -#' # Impute missing tv data -#' yimp <- mice.impute.pmmsplit(y, ry, x) -#' length(yimp) -#' hist(yimp, xlab = "Imputed missing tv") -#' -#' # Impute all tv data -#' yimp <- mice.impute.pmmsplit(y, ry, x, wy = rep(TRUE, length(y))) -#' length(yimp) -#' hist(yimp, xlab = "Imputed missing and observed tv") -#' plot(jitter(y), jitter(yimp), -#' main = "Predictive mean matching on age, height and weight", -#' xlab = "Observed tv (n = 224)", -#' ylab = "Imputed tv (n = 224)" -#' ) -#' abline(0, 1) -#' cor(y, yimp, use = "pair") -#' -#' # Use blots to exclude different values per column -#' # Create blots object -#' blots <- make.blots(boys) -#' # Exclude ml 1 through 5 from tv donor pool -#' blots$tv$exclude <- c(1:5) -#' # Exclude 100 random observed heights from tv donor pool -#' blots$hgt$exclude <- sample(unique(boys$hgt), 100) -#' imp <- mice(boys, method = "pmmsplit", print = FALSE, blots = blots, seed=123) -#' blots$hgt$exclude %in% unlist(c(imp$imp$hgt)) # MUST be all FALSE -#' blots$tv$exclude %in% unlist(c(imp$imp$tv)) # MUST be all FALSE -#' -#' # Factor quantification -#' xname <- c("age", "hgt", "wgt") -#' br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] -#' r <- stats::complete.cases(br[, xname]) -#' x <- as.matrix(br[r, xname]) -#' y <- factor(br[r, "tv"]) -#' ry <- !is.na(y) -#' table(y) -#' -#' # impute factor by optimizing canonical correlation y, x -#' mice.impute.pmmsplit(y, ry, x) -#' -#' # only categories with at least 2 cases can be donor -#' mice.impute.pmmsplit(y, ry, x, trim = 2L) -#' -#' # in addition, eliminate category 20 -#' mice.impute.pmmsplit(y, ry, x, trim = 2L, exclude = 20) -#' -#' # to get old behavior: as.integer(y)) -#' mice.impute.pmmsplit(y, ry, x, quantify = FALSE) -#' @export -mice.impute.pmmsplit <- function(y, ry, x, wy = NULL, donors = NULL, - matchtype = 1L, exclude = NULL, - quantify = TRUE, trim = 1L, - ridge = 1e-05, nbins = NULL, - task = "generate", - model = NULL, ...) { - if (is.null(wy)) { - wy <- !ry - } - - # Only enforce `model` for "retain", "train" and "apply" - if (task %in% c("retain", "train", "apply")) { - if (is.null(model)) { - stop(paste("`model` cannot be NULL for task:", task)) - } - if (!is.environment(model)) { - stop("`model` must be an environment to store results persistently.") - } - } - - # Handle "apply" task: Use pre-stored model without re-training - if (task == "apply") { - if (!length(ls(model))) { - stop("No stored model found for 'apply' task.") - } - - # Compute linear predictor for missing data - yhatmis <- x[wy, , drop = FALSE] %*% model$beta.mis - impy <- draw.neighbors.pmm(yhatmis, - edges = model$edges, - lookup = model$lookup, - m = 1L) - - # Convert back to factor if needed - impy <- unquantify(ynum = impy, - quant = model$factor$quant, - labels = model$factor$levels) - return(impy) - } - - # Estimate model if "generate", "retain" and "train" - - # Quantify factor by optimal scaling - f <- quantify(y, ry, x, quantify = quantify) - ynum <- f$ynum - - # Predicted values for observed part - parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) - if (matchtype == 0L) { - beta.mis <- beta.obs <- parm$coef - } - if (matchtype == 1L) { - beta.obs <- parm$coef - beta.mis <- parm$beta - } - if (matchtype == 2L) { - beta.mis <- beta.obs <- parm$beta - } - yhatobs <- as.vector(x[ry, , drop = FALSE] %*% beta.obs) - - # Divide predictions into bins - nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) - donors <- initialize.donors(donors, length(yhatobs)) - prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) - - # Store model for "retain" and "train"; skip for "generate" and "apply" - if (task %in% c("retain", "train")) { - model$setup <- list(method = "pmmsplit", - n = length(yhatobs), - donors = donors, - nbins = nbins, - matchtype = matchtype, - exclude = exclude, - quantify = quantify, - trim = trim, - ridge = ridge) - model$beta.obs <- beta.obs - model$beta.mis <- beta.mis - model$edges <- prep$edges - model$lookup <- prep$lookup - model$factor <- list(f$labels, f$quant) - } - - # Compute imputations - yhatmis <- x[wy, , drop = FALSE] %*% beta.mis - impy <- draw.neighbors.pmm(yhatmis, - edges = prep$edges, - lookup = prep$lookup, - m = 1L) - - # Convert back to factor if needed - impy <- unquantify(ynum = impy, - quant = f$quant, - labels = f$labels) - return(impy) -} - -initialize.nbins <- function(nbins, n, nu) { - if (is.null(nbins)) { - nbins <- round(4 * log(n) + 1.5) - } - - # max nbin is number of unique yhat values - if (nbins > nu) { - # message("Warning: nbins (", nbins, ") exceeds unique yhat values (", nu, "). Adjusting to ", nu, ".") - nbins <- nu - } - # Ensure at least 2 bins - nbins <- max(2L, nbins) - return(nbins) -} - -initialize.donors <- function(donors, n) { - if (is.null(donors)) { - donors <- round(n / 600 + 7) - } - donors <- max(1L, min(donors, n)) - return(donors) -} - -bin.yhat <- function(yhat, y, k = 10, nbins = 25) { - stopifnot(length(yhat) == length(y)) - - # Compute percentile-based bin edges - edges <- quantile(yhat, probs = seq(0, 1, length.out = nbins + 1), type = 7, na.rm = TRUE) - - # Sort yhat and y together - sort_order <- order(yhat) - yhat_sorted <- yhat[sort_order] - y_sorted <- y[sort_order] - - # Initialize lookup table - lookup <- matrix(NA_real_, nrow = nbins, ncol = k) - - # Assign values to bins - bin_idx <- findInterval(yhat_sorted, vec = edges, all.inside = TRUE) - - # Split y_sorted by bins - bin_values_list <- split(y_sorted, bin_idx) - - # Fill lookup table - lookup <- t(sapply(seq_len(nbins), function(b) { - bin_values <- bin_values_list[[as.character(b)]] - - if (length(bin_values) > 0) { - # If more than k values are available, randomly sample k - sample(bin_values, size = k, replace = length(bin_values) < k) - } else { - # If bin is empty, sample from entire y_sorted to avoid NA values - sample(y_sorted, size = k, replace = TRUE) - } - })) - - return(list(edges = edges, lookup = lookup)) -} - -draw.neighbors.pmm <- function(yhat_query, edges, lookup, m = 1) { - num_queries <- length(yhat_query) - nbins <- length(edges) - 1 # Bins are defined by edges[i] and edges[i+1] - - # Initialize result matrix: rows = number of queries, columns = m draws per query - imputed_values <- matrix(NA_real_, nrow = num_queries, ncol = m) - - # Find the bin for each query value - bin_idx <- findInterval(yhat_query, edges, rightmost.closed = TRUE, all.inside = TRUE) - - # Compute probability of selecting from left bin (smooth transition) - t0 <- edges[pmax(bin_idx, 1)] - t1 <- edges[pmin(bin_idx + 1, nbins)] - p_left <- ifelse(t1 > t0, (t1 - yhat_query) / (t1 - t0), 0.5) - - # Determine which bin to sample from - selected_bin <- ifelse(runif(num_queries) < p_left, bin_idx, pmin(bin_idx + 1, nbins)) - - # Vectorized sampling from lookup table - sampled_indices <- matrix(sample(1:ncol(lookup), num_queries * m, replace = TRUE), nrow = num_queries) - imputed_values <- matrix(lookup[cbind(selected_bin, sampled_indices)], nrow = num_queries, ncol = m) - - return(imputed_values) -} diff --git a/man/mice.Rd b/man/mice.Rd index 9c75cf6db..5dadffbd9 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -411,11 +411,17 @@ imp$imp$bmi complete(imp) # imputation on mixed data with a different method per column -imp1 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) +imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) + +# store all imputation models +imp1 <- mice(nhanes, tasks = "retain", print = FALSE) +ls(imp1$models$bmi$"1") +imp1$models$bmi$"1"$formula +imp1$models$bmi$"1"$beta.mis # Store model for `bmi`, estimate others as usual tasks <- c("age" = "generate", "bmi" = "retain", "hyp" = "generate", "chl" = "generate") -imp2 <- mice(nhanes, method = "pmmsplit", tasks = tasks, print = FALSE) +imp2 <- mice(nhanes, tasks = tasks, print = FALSE) # Inspects the stored model for imputation 1 for `bmi` ls(imp2$models$bmi$"1") @@ -424,8 +430,7 @@ imp2$models$bmi$"1"$beta.mis # Fill missing `bmi` values using pre-trained model tasks <- c("age" = "generate", "bmi" = "apply", "hyp" = "generate", "chl" = "generate") -imp3 <- mice(nhanes, method = "pmmsplit", tasks = tasks, - models = imp2$models, print = FALSE) +imp3 <- mice(nhanes, tasks = tasks, models = imp2$models, print = FALSE) \dontrun{ # example where we fit the imputation model on the train data diff --git a/man/mice.impute.cart.Rd b/man/mice.impute.cart.Rd index 1c1d0be70..19a1767d4 100644 --- a/man/mice.impute.cart.Rd +++ b/man/mice.impute.cart.Rd @@ -87,7 +87,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.logreg.Rd b/man/mice.impute.lasso.logreg.Rd index 0c26b9bd3..d102536d9 100644 --- a/man/mice.impute.lasso.logreg.Rd +++ b/man/mice.impute.lasso.logreg.Rd @@ -78,7 +78,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.norm.Rd b/man/mice.impute.lasso.norm.Rd index 69b692e0f..6e6fb86e2 100644 --- a/man/mice.impute.lasso.norm.Rd +++ b/man/mice.impute.lasso.norm.Rd @@ -78,7 +78,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.select.logreg.Rd b/man/mice.impute.lasso.select.logreg.Rd index 056479f53..027e2a513 100644 --- a/man/mice.impute.lasso.select.logreg.Rd +++ b/man/mice.impute.lasso.select.logreg.Rd @@ -86,7 +86,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lasso.select.norm.Rd b/man/mice.impute.lasso.select.norm.Rd index 4fefd0009..e825a028c 100644 --- a/man/mice.impute.lasso.select.norm.Rd +++ b/man/mice.impute.lasso.select.norm.Rd @@ -88,7 +88,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.lda.Rd b/man/mice.impute.lda.Rd index 2a02840b0..e46b23505 100644 --- a/man/mice.impute.lda.Rd +++ b/man/mice.impute.lda.Rd @@ -86,7 +86,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index 87ae4b140..8427031d2 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -82,7 +82,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.logreg.boot.Rd b/man/mice.impute.logreg.boot.Rd index 802619ebb..2076dc202 100644 --- a/man/mice.impute.logreg.boot.Rd +++ b/man/mice.impute.logreg.boot.Rd @@ -62,7 +62,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.mean.Rd b/man/mice.impute.mean.Rd index b44864ea4..3c3435bc4 100644 --- a/man/mice.impute.mean.Rd +++ b/man/mice.impute.mean.Rd @@ -68,7 +68,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.midastouch.Rd b/man/mice.impute.midastouch.Rd index a749f22c2..cfa5c310a 100644 --- a/man/mice.impute.midastouch.Rd +++ b/man/mice.impute.midastouch.Rd @@ -142,7 +142,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.mnar.Rd b/man/mice.impute.mnar.Rd index d3ec6154e..3568786ae 100644 --- a/man/mice.impute.mnar.Rd +++ b/man/mice.impute.mnar.Rd @@ -189,7 +189,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.mpmm.Rd b/man/mice.impute.mpmm.Rd index 761513139..4d82409bd 100644 --- a/man/mice.impute.mpmm.Rd +++ b/man/mice.impute.mpmm.Rd @@ -81,7 +81,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.Rd b/man/mice.impute.norm.Rd index 101817344..a082d1ffc 100644 --- a/man/mice.impute.norm.Rd +++ b/man/mice.impute.norm.Rd @@ -74,7 +74,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.boot.Rd b/man/mice.impute.norm.boot.Rd index 8e4dbb5a5..b426a7139 100644 --- a/man/mice.impute.norm.boot.Rd +++ b/man/mice.impute.norm.boot.Rd @@ -58,7 +58,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.nob.Rd b/man/mice.impute.norm.nob.Rd index 242f1e503..683170dc9 100644 --- a/man/mice.impute.norm.nob.Rd +++ b/man/mice.impute.norm.nob.Rd @@ -80,7 +80,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.norm.predict.Rd b/man/mice.impute.norm.predict.Rd index 92e816079..86b2f7ecb 100644 --- a/man/mice.impute.norm.predict.Rd +++ b/man/mice.impute.norm.predict.Rd @@ -77,7 +77,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 32fb9ac52..78ce4ddfa 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -12,9 +12,13 @@ mice.impute.pmm( wy = NULL, donors = 5L, matchtype = 1L, - exclude = NULL, quantify = TRUE, + exclude = NULL, trim = 1L, + task = "generate", + model = NULL, + nimp = 1L, + nbins = NULL, ridge = 1e-05, use.matcher = FALSE, ... @@ -36,10 +40,13 @@ indicates locations in \code{y} for which imputations are created.} \item{donors}{The size of the donor pool among which a draw is made. The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects -the closest match, but is not recommended. Values between 3L and 10L -provide the best results in most cases (Morris et al, 2015).} +the closest match, but is not recommended. Values between 5L and 10L +provide the best results (Morris et al, 2015). +For tasks \code{"retain"} and \code{"train"}, the number of donors +is calculated internally based on the number of +observations in \code{yobs}.} -\item{matchtype}{Type of matching distance. The default choice +\item{matchtype}{Type of matching distance. The default (recommended) choice (\code{matchtype = 1L}) calculates the distance between the \emph{predicted} value of \code{yobs} and the \emph{drawn} values of \code{ymis} (called type-1 matching). @@ -47,19 +54,43 @@ Other choices are \code{matchtype = 0L} (distance between predicted values) and \code{matchtype = 2L} (distance between drawn values).} -\item{exclude}{Dependent values to exclude from the imputation model -and the collection of donor values} - \item{quantify}{Logical. If \code{TRUE}, factor levels are replaced by the first canonical variate before fitting the imputation model. If false, the procedure reverts to the old behaviour and takes the integer codes (which may lack a sensible interpretation). Relevant only of \code{y} is a factor.} +\item{exclude}{Dependent values to exclude from the imputation model +and the collection of donor values} + \item{trim}{Scalar integer. Minimum number of observations required in a category in order to be considered as a potential donor value. Relevant only of \code{y} is a factor.} +\item{task}{Character string. The task to be performed. Can +be \code{"generate"}, \code{"retain"}, \code{"train"} or \code{"apply"}. +The default is \code{"generate"} (classic MICE). See \code{mice()} for +details.} + +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"retain"} and \code{"train"}, and is needed as input +for task \code{"apply"}. The object \code{model} is not used under +task \code{"generate"}.} + +\item{nimp}{Experimental. Number of random imputations per missing values +generated from a fitted model under tasks \code{"retain"} and \code{"train"}. +The default is 1. The \code{nimp} parameter is different from \code{m}, +the number of multiple imputations, because it generates repeated +imputations from a single model. The \code{nimp} parameter is useful +for large samples to reduce the computational burden, but still awaits +support within the mice algorithm.} + +\item{nbins}{The number of bins used to store the predictive mean matching +model. Under tasks \code{"retain"} and \code{"train"}, the number of donors +is calculated internally based on the number of observations in \code{yobs} +and the number of unique predictive values.} + \item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent problems with multicollinearity. The default is \code{ridge = 1e-05}, which means that 0.01 percent of the diagonal is added to the cross-product. @@ -69,10 +100,7 @@ reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} \item{use.matcher}{Logical. Set \code{use.matcher = TRUE} to specify the C function \code{matcher()}, the now deprecated matching function that -was default in versions -\code{2.22} (June 2014) to \code{3.11.7} (Oct 2020). Since version \code{3.12.0} -\code{mice()} uses the much faster \code{matchindex} C function. Use -the deprecated \code{matcher} function only for exact reproduction.} +was default in versions of \code{mice} prior to \code{3.12.0}.} \item{\dots}{Other named arguments.} } @@ -86,7 +114,6 @@ Imputation by predictive mean matching \details{ Imputation of \code{y} by predictive mean matching, based on van Buuren (2012, p. 73). The procedure is as follows: - \enumerate{ \item{Calculate the cross-product matrix \eqn{S=X_{obs}'X_{obs}}.} \item{Calculate \eqn{V = (S+{diag}(S)\kappa)^{-1}}, with some small ridge @@ -203,7 +230,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, @@ -211,7 +237,7 @@ Other univariate imputation functions: \code{\link{mice.impute.ri}()} } \author{ -Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn +Stef van Buuren } \concept{univariate imputation functions} \keyword{datagen} diff --git a/man/mice.impute.pmmsplit.Rd b/man/mice.impute.pmmsplit.Rd deleted file mode 100644 index acb275571..000000000 --- a/man/mice.impute.pmmsplit.Rd +++ /dev/null @@ -1,197 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/mice.impute.pmmsplit.R -\name{mice.impute.pmmsplit} -\alias{mice.impute.pmmsplit} -\alias{pmmsplit} -\title{Imputation by predictive mean matching} -\usage{ -mice.impute.pmmsplit( - y, - ry, - x, - wy = NULL, - donors = NULL, - matchtype = 1L, - exclude = NULL, - quantify = TRUE, - trim = 1L, - ridge = 1e-05, - nbins = NULL, - task = "generate", - model = NULL, - ... -) -} -\arguments{ -\item{y}{Vector to be imputed} - -\item{ry}{Logical vector of length \code{length(y)} indicating the -the subset \code{y[ry]} of elements in \code{y} to which the imputation -model is fitted. The \code{ry} generally distinguishes the observed -(\code{TRUE}) and missing values (\code{FALSE}) in \code{y}.} - -\item{x}{Numeric design matrix with \code{length(y)} rows with predictors for -\code{y}. Matrix \code{x} may have no missing values.} - -\item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value -indicates locations in \code{y} for which imputations are created.} - -\item{donors}{The size of the donor pool among which a draw is made. -The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects -the closest match, but is not recommended. Values between 3L and 10L -provide the best results in most cases (Morris et al, 2015).} - -\item{matchtype}{Type of matching distance. The default choice -(\code{matchtype = 1L}) calculates the distance between -the \emph{predicted} value of \code{yobs} and -the \emph{drawn} values of \code{ymis} (called type-1 matching). -Other choices are \code{matchtype = 0L} -(distance between predicted values) and \code{matchtype = 2L} -(distance between drawn values).} - -\item{exclude}{Dependent values to exclude from the imputation model -and the collection of donor values} - -\item{quantify}{Logical. If \code{TRUE}, factor levels are replaced -by the first canonical variate before fitting the imputation model. -If false, the procedure reverts to the old behaviour and takes the -integer codes (which may lack a sensible interpretation). -Relevant only of \code{y} is a factor.} - -\item{trim}{Scalar integer. Minimum number of observations required in a -category in order to be considered as a potential donor value. -Relevant only of \code{y} is a factor.} - -\item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent -problems with multicollinearity. The default is \code{ridge = 1e-05}, -which means that 0.01 percent of the diagonal is added to the cross-product. -Larger ridges may result in more biased estimates. For highly noisy data -(e.g. many junk variables), set \code{ridge = 1e-06} or even lower to -reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} - -\item{nbins}{The number of bins used to store the predictive mean matching -model. The default is 50.} - -\item{task}{The task to be performed. The default is \code{"generate"}.} - -\item{model}{Storage for the model estimates} - -\item{...}{Other named arguments.} -} -\value{ -Vector with imputed data, same type as \code{y}, and of length -\code{sum(wy)} -} -\description{ -\code{pmmsplit()} is an implementation of pmm that saves the imputation model -and generates imputations from the saved model. -} -\examples{ -# We normally call mice.impute.pmmsplit() from within mice() -# But we may call it directly as follows (not recommended) - -set.seed(53177) -xname <- c("age", "hgt", "wgt") -r <- stats::complete.cases(boys[, xname]) -x <- as.matrix(boys[r, xname]) -y <- boys[r, "tv"] -ry <- !is.na(y) -table(ry) - -# percentage of missing data in tv -sum(!ry) / length(ry) - -# Impute missing tv data -yimp <- mice.impute.pmmsplit(y, ry, x) -length(yimp) -hist(yimp, xlab = "Imputed missing tv") - -# Impute all tv data -yimp <- mice.impute.pmmsplit(y, ry, x, wy = rep(TRUE, length(y))) -length(yimp) -hist(yimp, xlab = "Imputed missing and observed tv") -plot(jitter(y), jitter(yimp), - main = "Predictive mean matching on age, height and weight", - xlab = "Observed tv (n = 224)", - ylab = "Imputed tv (n = 224)" -) -abline(0, 1) -cor(y, yimp, use = "pair") - -# Use blots to exclude different values per column -# Create blots object -blots <- make.blots(boys) -# Exclude ml 1 through 5 from tv donor pool -blots$tv$exclude <- c(1:5) -# Exclude 100 random observed heights from tv donor pool -blots$hgt$exclude <- sample(unique(boys$hgt), 100) -imp <- mice(boys, method = "pmmsplit", print = FALSE, blots = blots, seed=123) -blots$hgt$exclude \%in\% unlist(c(imp$imp$hgt)) # MUST be all FALSE -blots$tv$exclude \%in\% unlist(c(imp$imp$tv)) # MUST be all FALSE - -# Factor quantification -xname <- c("age", "hgt", "wgt") -br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] -r <- stats::complete.cases(br[, xname]) -x <- as.matrix(br[r, xname]) -y <- factor(br[r, "tv"]) -ry <- !is.na(y) -table(y) - -# impute factor by optimizing canonical correlation y, x -mice.impute.pmmsplit(y, ry, x) - -# only categories with at least 2 cases can be donor -mice.impute.pmmsplit(y, ry, x, trim = 2L) - -# in addition, eliminate category 20 -mice.impute.pmmsplit(y, ry, x, trim = 2L, exclude = 20) - -# to get old behavior: as.integer(y)) -mice.impute.pmmsplit(y, ry, x, quantify = FALSE) -} -\references{ -Little, R.J.A. (1988), Missing data adjustments in large surveys -(with discussion), Journal of Business Economics and Statistics, 6, 287--301. - -Morris TP, White IR, Royston P (2015). Tuning multiple imputation by predictive -mean matching and local residual draws. BMC Med Res Methodol. ;14:75. - -Van Buuren, S. (2018). -\href{https://stefvanbuuren.name/fimd/sec-pmm.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} -Chapman & Hall/CRC. Boca Raton, FL. - -Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate -Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical -Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} -} -\seealso{ -Other univariate imputation functions: -\code{\link{mice.impute.cart}()}, -\code{\link{mice.impute.lasso.logreg}()}, -\code{\link{mice.impute.lasso.norm}()}, -\code{\link{mice.impute.lasso.select.logreg}()}, -\code{\link{mice.impute.lasso.select.norm}()}, -\code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg}()}, -\code{\link{mice.impute.logreg.boot}()}, -\code{\link{mice.impute.mean}()}, -\code{\link{mice.impute.midastouch}()}, -\code{\link{mice.impute.mnar.logreg}()}, -\code{\link{mice.impute.mpmm}()}, -\code{\link{mice.impute.norm}()}, -\code{\link{mice.impute.norm.boot}()}, -\code{\link{mice.impute.norm.nob}()}, -\code{\link{mice.impute.norm.predict}()}, -\code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.polr}()}, -\code{\link{mice.impute.polyreg}()}, -\code{\link{mice.impute.quadratic}()}, -\code{\link{mice.impute.rf}()}, -\code{\link{mice.impute.ri}()} -} -\author{ -Stef van Buuren -} -\concept{univariate imputation functions} -\keyword{datagen} diff --git a/man/mice.impute.polr.Rd b/man/mice.impute.polr.Rd index 1e5432fef..21f17912b 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -116,7 +116,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, \code{\link{mice.impute.rf}()}, diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index 87bde974c..30cf4f435 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -103,7 +103,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.quadratic}()}, \code{\link{mice.impute.rf}()}, diff --git a/man/mice.impute.quadratic.Rd b/man/mice.impute.quadratic.Rd index 4912fd9e6..b8e7d441b 100644 --- a/man/mice.impute.quadratic.Rd +++ b/man/mice.impute.quadratic.Rd @@ -118,7 +118,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.rf}()}, diff --git a/man/mice.impute.rf.Rd b/man/mice.impute.rf.Rd index ecbc0f1af..ee288b634 100644 --- a/man/mice.impute.rf.Rd +++ b/man/mice.impute.rf.Rd @@ -110,7 +110,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.ri.Rd b/man/mice.impute.ri.Rd index c397228ad..8a5b6ccf8 100644 --- a/man/mice.impute.ri.Rd +++ b/man/mice.impute.ri.Rd @@ -66,7 +66,6 @@ Other univariate imputation functions: \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, \code{\link{mice.impute.pmm}()}, -\code{\link{mice.impute.pmmsplit}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/pmm.match.Rd b/man/pmm.match.Rd deleted file mode 100644 index d4404115f..000000000 --- a/man/pmm.match.Rd +++ /dev/null @@ -1,49 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/mice.impute.pmm.R -\name{.pmm.match} -\alias{.pmm.match} -\title{Finds an imputed value from matches in the predictive metric (deprecated)} -\usage{ -.pmm.match(z, yhat = yhat, y = y, donors = 5, ...) -} -\arguments{ -\item{z}{A scalar containing the predicted value for the current case -to be imputed.} - -\item{yhat}{A vector containing the predicted values for all cases with an observed -outcome.} - -\item{y}{A vector of \code{length(yhat)} elements containing the observed outcome} - -\item{donors}{The size of the donor pool among which a draw is made. The default is -\code{donors = 5}. Setting \code{donors = 1} always selects the closest match. Values -between 3 and 10 provide the best results. Note: This setting was changed from -3 to 5 in version 2.19, based on simulation work by Tim Morris (UCL).} - -\item{\dots}{Other parameters (not used).} -} -\value{ -A scalar containing the observed value of the selected donor. -} -\description{ -This function finds matches among the observed data in the predictive -mean metric. It selects the \code{donors} closest matches, randomly -samples one of the donors, and returns the observed value of the -match. -} -\details{ -This function is included for backward compatibility. It was -used up to \code{mice 2.21}. The current \code{mice.impute.pmm()} -function calls the faster \code{C} function \code{matcher} instead of -\code{.pmm.match()}. -} -\references{ -Schenker N & Taylor JMG (1996) Partially parametric techniques -for multiple imputation. \emph{Computational Statistics and Data Analysis}, 22, 425-446. - -Little RJA (1988) Missing-data adjustments in large surveys (with discussion). -\emph{Journal of Business Economics and Statistics}, 6, 287-301. -} -\author{ -Stef van Buuren -} diff --git a/tests/testthat/test-mice.impute.pmm.R b/tests/testthat/test-mice.impute.pmm.R index 33fbb74c6..ec7355db6 100644 --- a/tests/testthat/test-mice.impute.pmm.R +++ b/tests/testthat/test-mice.impute.pmm.R @@ -112,3 +112,4 @@ test_that("cancor with many junk variables does not crash", { expect_warning(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, maxit = 1, m = 1, seed = 1, print = FALSE)) }) + diff --git a/tests/testthat/test-mice.impute.pmmsplit.R b/tests/testthat/test-mice.impute.pmmsplit.R deleted file mode 100644 index 812f69c8c..000000000 --- a/tests/testthat/test-mice.impute.pmmsplit.R +++ /dev/null @@ -1,114 +0,0 @@ -context("mice.impute.pmmsplit") - -xname <- c("age", "hgt", "wgt") -br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ] -r <- stats::complete.cases(br[, xname]) -x <- as.matrix(br[r, xname]) -y <- br[r, "tv"] -ry <- !is.na(y) - -wy1 <- !ry -wy2 <- rep(TRUE, length(y)) -wy3 <- rep(FALSE, length(y)) -wy4 <- rep(c(TRUE, FALSE), times = c(1, length(y) - 1)) - -test_that("Returns requested length", { - expect_equal(length(mice.impute.pmmsplit(y, ry, x)), sum(!ry)) - expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy1)), sum(wy1)) - expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy2)), sum(wy2)) - expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy3)), sum(wy3)) - expect_equal(length(mice.impute.pmmsplit(y, ry, x, wy = wy4)), sum(wy4)) -}) - -test_that("Excludes donors", { - expect_false(all(c(15:25) %in% mice.impute.pmmsplit(y, ry, x, exclude = c(15:25)))) -}) - -imp1 <- mice(nhanes, printFlag = FALSE, seed = 123) -imp2 <- mice(nhanes, printFlag = FALSE, seed = 123, exclude = c(-1, 1032)) -test_that("excluding unobserved values does not impact pmmsplit", { - expect_identical(imp1$imp, imp2$imp) -}) - -context("optimal scaling") - -# Factor quantification -y <- factor(br[r, "tv"]) - -# impute factor by optimizing canonical correlation y, x -data1 <- data.frame(y, x) -test_that("cancor proceeds normally", { - expect_silent(imp1 <- mice(data1, method = "pmmsplit", remove.collinear = FALSE, eps = 0, - maxit = 1, m = 1, print = FALSE, seed = 1)) -}) - -# > cca$xcoef[, 2L] -# yf6 yf8 yf10 yf12 yf13 yf15 yf16 yf20 yf25 -# 0.8458 -0.0469 -0.3182 -0.3458 0.0825 0.0799 0.0383 -0.0517 -0.0460 - -# include duplicate x -data2 <- data1 -data2$age2 <- data2$age -data2$age3 <- data2$age -data2$age4 <- data2$age -data2$age5 <- data2$age -data2$age6 <- data2$age -data2$age7 <- data2$age -data2$age8 <- data2$age -data2$age9 <- data2$age -data2$age10 <- data2$age -data2$age11 <- data2$age -data2$age12 <- data2$age -data2$age13 <- data2$age -data2$age14 <- data2$age -data2$age15 <- data2$age -data2$age16 <- data2$age -data2$age17 <- data2$age -data2$age18 <- data2$age -data2$age19 <- data2$age -data2$age20 <- data2$age -data2$age21 <- data2$age -data2$age22 <- data2$age -data2$age23 <- data2$age -data2$age24 <- data2$age -data2$age25 <- data2$age - -# impute factor by optimizing canonical correlation y, x -test_that("cancor proceeds normally with many duplicates", { - expect_warning(imp2 <- mice(data2, method = "pmmsplit", remove.collinear = FALSE, eps = 0, - maxit = 1, m = 1, seed = 1, print = FALSE)) -}) - -# add junk variables -data3 <- data1 -data3$j1 <- rnorm(nrow(data3)) -data3$j2 <- rnorm(nrow(data3)) -data3$j3 <- rnorm(nrow(data3)) -data3$j4 <- rnorm(nrow(data3)) -data3$j5 <- rnorm(nrow(data3)) -data3$j6 <- rnorm(nrow(data3)) -data3$j7 <- rnorm(nrow(data3)) -data3$j8 <- rnorm(nrow(data3)) -data3$j9 <- rnorm(nrow(data3)) -data3$j10 <- rnorm(nrow(data3)) -data3$j11 <- rnorm(nrow(data3)) -data3$j12 <- rnorm(nrow(data3)) -data3$j13 <- rnorm(nrow(data3)) -data3$j14 <- rnorm(nrow(data3)) -data3$j15 <- rnorm(nrow(data3)) -data3$j16 <- rnorm(nrow(data3)) -data3$j17 <- rnorm(nrow(data3)) -data3$j18 <- rnorm(nrow(data3)) -data3$j19 <- rnorm(nrow(data3)) -data3$j20 <- rnorm(nrow(data3)) -data3$j21 <- rnorm(nrow(data3)) -data3$j22 <- rnorm(nrow(data3)) -data3$j23 <- rnorm(nrow(data3)) -data3$j24 <- rnorm(nrow(data3)) -data3$j25 <- rnorm(nrow(data3)) - - -test_that("cancor with many junk variables does not crash", { - expect_warning(imp3 <- mice(data3, method = "pmmsplit", remove.collinear = FALSE, eps = 0, - maxit = 1, m = 1, seed = 1, print = FALSE)) -}) diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index c1c645a77..cde8e76bd 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -9,16 +9,16 @@ context("actions") # - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? test_that("actions work with factor with same number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmmsplit", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) }) test_that("training works on completely observed variables", { - expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmmsplit", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmmsplit", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) }) From 0c9e3b102b91e20e8c65bc4e4208c62055c33434 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 12:29:29 +0100 Subject: [PATCH 039/147] Define store as retain if tasks are non-uniform --- R/mice.R | 1 + 1 file changed, 1 insertion(+) diff --git a/R/mice.R b/R/mice.R index 33853e4ad..d3262627b 100644 --- a/R/mice.R +++ b/R/mice.R @@ -479,6 +479,7 @@ mice <- function(data, maxit = maxit ) tasks <- check.tasks(tasks, data, models, blocks) + store <- ifelse(length(unique(tasks)) == 1L, tasks[1L], "retain") method <- check.method( method = method, data = data, where = where, blocks = blocks, tasks = tasks, From 5c1271a0b3b3dc784e54648c3866e92588a05473 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 13:53:19 +0100 Subject: [PATCH 040/147] Add a 'store' field to the mids object --- R/mice.R | 3 ++- R/mids.R | 9 ++++++--- man/mids.Rd | 6 +++++- 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/R/mice.R b/R/mice.R index d3262627b..3e8951181 100644 --- a/R/mice.R +++ b/R/mice.R @@ -568,7 +568,8 @@ mice <- function(data, inherits = FALSE), chainMean = q$chainMean, chainVar = q$chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents, + store = store) if (!is.null(midsobj$loggedEvents)) { warning("Number of logged events: ", nrow(midsobj$loggedEvents), diff --git a/R/mids.R b/R/mids.R index 3bd8c3c5b..37e923f5e 100644 --- a/R/mids.R +++ b/R/mids.R @@ -24,6 +24,7 @@ #' @param loggedEvents Calculated field #' @param version Calculated field #' @param date Calculated field +#' @param store Calculated field #' @return #' \code{mids()} returns a \code{mids} object. #' @@ -78,6 +79,7 @@ #' \item{\code{version}:}{Version number of \code{mice} package that #' created the object.} #' \item{\code{date}:}{Date at which the object was created.} +#' \item{\code{store}:}{A string, indicating the type of mids object.} #' } #' #' @section LoggedEvents: @@ -177,7 +179,8 @@ mids <- function( chainVar = list(), loggedEvents = data.frame(), version = packageVersion("mice"), - date = Sys.Date()) { + date = Sys.Date(), + store = "generate") { obj <- list( data = data, imp = imp, @@ -203,8 +206,8 @@ mids <- function( chainVar = chainVar, loggedEvents = loggedEvents, version = packageVersion("mice"), - date = Sys.Date() - ) + date = Sys.Date(), + store = store) class(obj) <- "mids" return(obj) } diff --git a/man/mids.Rd b/man/mids.Rd index 28adda0b2..8010470da 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -34,7 +34,8 @@ mids( chainVar = list(), loggedEvents = data.frame(), version = packageVersion("mice"), - date = Sys.Date() + date = Sys.Date(), + store = "generate" ) \method{plot}{mids}( @@ -205,6 +206,8 @@ generator alone.} \item{date}{Calculated field} +\item{store}{Calculated field} + \item{x}{An object of class \code{mids}} \item{y}{A formula that specifies which variables, stream and iterations are plotted. @@ -301,6 +304,7 @@ containing warnings, corrective tasks, and other inside info.} \item{\code{version}:}{Version number of \code{mice} package that created the object.} \item{\code{date}:}{Date at which the object was created.} +\item{\code{store}:}{A string, indicating the type of mids object.} } } From d2bdff77224239fa46e412397b422088d4855d04 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 14:00:47 +0100 Subject: [PATCH 041/147] Update cbind() --- R/cbind.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/cbind.R b/R/cbind.R index dae0fd0e3..ae4bc7341 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,7 +97,7 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots - tasks <- c(x$tasks, "walk") + tasks <- c(x$tasks, "generate") names(tasks) <- c(names(x$tasks), tail(varnames, 1L)) models <- x$models ignore <- x$ignore From 830db0fbab461da0fc56c4e4961547d35cf4f483 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 15:45:44 +0100 Subject: [PATCH 042/147] Preserve old behavior of setting method <- "" if there is nothing to impute --- R/method.R | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/R/method.R b/R/method.R index 5f24c9778..55de00d5e 100644 --- a/R/method.R +++ b/R/method.R @@ -30,19 +30,18 @@ make.method <- function(data, } nimp <- nimp(where, blocks) - # preserve old behaviour that sets method <- "" with walk + # preserve old behaviour that sets method <- "" with generate names(method) <- names(blocks) if (!is.null(tasks)) { for (j in names(blocks)) { vname <- blocks[[j]] - if ("walk" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" + if (nimp[j] == 0L && "generate" == tasks[vname]) method[j] <- "" } } method } - check.method <- function(method, data, where, blocks, tasks, defaultMethod) { if (is.null(method)) { return(make.method( @@ -64,10 +63,10 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { method <- rep(method, length(blocks)) names(method) <- names(blocks) - # preserve old behaviour that sets method <- "" with walk + # preserve old behaviour that sets method <- "" with generate for (j in names(blocks)) { vname <- blocks[[j]] - if ("walk" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" + if (nimp[j] == 0L && "generate" == tasks[vname]) method[j] <- "" } } @@ -136,8 +135,8 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { call. = FALSE ) } - # preserve old behaviour that sets method <- "" with walk - if ("walk" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" + # preserve old behaviour that sets method <- "" with generate + # if ("generate" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" } unlist(method) From 04e2383b95c08476703a1ec34984d133081fe8af Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 15:46:51 +0100 Subject: [PATCH 043/147] Extend the mids object with the store field, and create four different versions of the mids object --- R/mids.R | 112 +++++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 85 insertions(+), 27 deletions(-) diff --git a/R/mids.R b/R/mids.R index 37e923f5e..f9929b76e 100644 --- a/R/mids.R +++ b/R/mids.R @@ -181,35 +181,93 @@ mids <- function( version = packageVersion("mice"), date = Sys.Date(), store = "generate") { - obj <- list( - data = data, - imp = imp, - m = m, - where = where, - blocks = blocks, - call = call, - nmis = nmis, - method = method, - predictorMatrix = predictorMatrix, - visitSequence = visitSequence, - formulas = formulas, - modeltype = modeltype, - post = post, - blots = blots, - tasks = tasks, - models = models, - ignore = ignore, - seed = seed, - iteration = iteration, - lastSeedValue = lastSeedValue, - chainMean = chainMean, - chainVar = chainVar, - loggedEvents = loggedEvents, - version = packageVersion("mice"), - date = Sys.Date(), - store = store) + + if (store == "generate") { + obj <- list( + data = data, + imp = imp, + m = m, + where = where, + blocks = blocks, + call = call, + nmis = nmis, + method = method, + predictorMatrix = predictorMatrix, + visitSequence = visitSequence, + formulas = formulas, + modeltype = modeltype, + post = post, + blots = blots, + tasks = tasks, + models = models, + ignore = ignore, + seed = seed, + iteration = iteration, + lastSeedValue = lastSeedValue, + chainMean = chainMean, + chainVar = chainVar, + loggedEvents = loggedEvents, + version = packageVersion("mice"), + date = Sys.Date(), + store = store) + } else if (store == "retain") { + obj <- list( + data = data, + imp = imp, + m = m, + where = where, + blocks = blocks, + call = call, + nmis = nmis, + method = method, + predictorMatrix = predictorMatrix, + visitSequence = visitSequence, + formulas = formulas, + modeltype = modeltype, + post = post, + blots = blots, + tasks = tasks, + models = models, + ignore = ignore, + seed = seed, + iteration = iteration, + lastSeedValue = lastSeedValue, + chainMean = chainMean, + chainVar = chainVar, + loggedEvents = loggedEvents, + version = packageVersion("mice"), + date = Sys.Date(), + store = store) + } else if (store == "train") { + obj <- list( + m = m, + blocks = blocks, + method = method, + blots = blots, + visitSequence = visitSequence, + iteration = iteration, + lastSeedValue = lastSeedValue, + tasks = tasks, + models = models, + store = store, + version = packageVersion("mice"), + date = Sys.Date(), + call = call) + } else if (store == "apply") { + obj <- list( + store = store, + version = packageVersion("mice"), + date = Sys.Date(), + call = call, + imp = imp) + } else { + stop("store must be one of 'generate', 'retain', 'train', or 'apply'") + } + class(obj) <- "mids" return(obj) + + } #' Plot the trace lines of the MICE algorithm From 6fb8e955bf283f3f9036cb74b1ce515e87144f94 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 15:47:41 +0100 Subject: [PATCH 044/147] Add an error if the user specifies higher m in apply than m in training model --- R/mice.R | 8 ++++++-- tests/testthat/test-tasks.R | 11 +++++------ 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/R/mice.R b/R/mice.R index 3e8951181..7bfa8abb8 100644 --- a/R/mice.R +++ b/R/mice.R @@ -510,8 +510,12 @@ mice <- function(data, if (is.null(models)) { models <- new.env(parent = emptyenv()) } - model_vars <- names(tasks[tasks %in% c("retain", "train", "apply")]) - for (block in model_vars) { + model.vars <- names(tasks[tasks %in% c("retain", "train", "apply")]) + m.train <- length(models[[model.vars[1L]]]) + if (any(tasks %in% "apply") && m > m.train) { + stop("Number of imputations (", m, ") is greater than training model (", m.train, ").") + } + for (block in model.vars) { if (!exists(block, envir = models)) { models[[block]] <- new.env(parent = emptyenv()) } diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index cde8e76bd..b19bf2d8b 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -1,4 +1,4 @@ -context("actions") +context("tasks") # We have to test the following cases: @@ -8,17 +8,16 @@ context("actions") # - Does train-run setup with a factor variable produce imputations when only one factor level is present during training? # - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? -test_that("actions work with factor with same number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmm", print = FALSE)) +test_that("tasks work with factor with same number of categories", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) - expect_identical(imp1$models$bmi$"1"$lookup, imp2$models$bmi$"1"$lookup) + expect_error(imp2 <- mice(nhanes2, m = 3, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE), "Number of imputations") + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) }) test_that("training works on completely observed variables", { expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$age$"1"$lookup)) expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) - expect_identical(imp1$models$age$"1"$lookup, imp2$models$age$"1"$lookup) }) From 9346811e78b94896728ac88d89f1799b3df912cd Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 11 Mar 2025 19:54:47 +0100 Subject: [PATCH 045/147] Do not remove variables when task == "apply" --- R/complete.R | 5 +++++ R/mids.R | 10 +++++----- R/sampler.R | 12 +++++++----- tests/testthat/test-tasks.R | 1 + 4 files changed, 18 insertions(+), 10 deletions(-) diff --git a/R/complete.R b/R/complete.R index 2c1cfe242..6c3b245fc 100644 --- a/R/complete.R +++ b/R/complete.R @@ -84,6 +84,10 @@ complete.mids <- function(data, action = 1L, include = FALSE, mild = FALSE, order = c("last", "first"), ...) { if (!is.mids(data)) stop("'data' not of class 'mids'") + if (data$store == "train") { + stop(paste("Cannot complete training object.\n", + "Use task 'generate' or 'retain' to estimate imputation model.")) + } order <- match.arg(order) m <- as.integer(data$m) @@ -166,3 +170,4 @@ single.complete <- function(data, where, imp, ell) { } data } + diff --git a/R/mids.R b/R/mids.R index f9929b76e..eb2cb5bcb 100644 --- a/R/mids.R +++ b/R/mids.R @@ -255,19 +255,19 @@ mids <- function( call = call) } else if (store == "apply") { obj <- list( + data = data, + imp = imp, + m = m, + where = where, store = store, version = packageVersion("mice"), date = Sys.Date(), - call = call, - imp = imp) + call = call) } else { stop("store must be one of 'generate', 'retain', 'train', or 'apply'") } - class(obj) <- "mids" return(obj) - - } #' Plot the trace lines of the MICE algorithm diff --git a/R/sampler.R b/R/sampler.R index a8a03e799..29280046b 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -215,11 +215,13 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, if (k == 1L) check.df(x, y, ry) # remove linear dependencies - keep <- remove.lindep(x, y, ry, ...) - x <- x[, keep, drop = FALSE] - type <- type[keep] - if (ncol(x) != length(type)) { - stop("Internal error: length(type) != number of predictors") + if (task != "apply") { + keep <- remove.lindep(x, y, ry, ...) + x <- x[, keep, drop = FALSE] + type <- type[keep] + if (ncol(x) != length(type)) { + stop("Internal error: length(type) != number of predictors") + } } # here we go diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index b19bf2d8b..051f7311e 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -13,6 +13,7 @@ test_that("tasks work with factor with same number of categories", { expect_false(is.null(imp1$models$bmi$"1"$lookup)) expect_error(imp2 <- mice(nhanes2, m = 3, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE), "Number of imputations") expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2[1,], m = 2, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) }) test_that("training works on completely observed variables", { From 51a4f917c73fdd415a7fcbf0f4edb90a3efe6e06 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 12 Mar 2025 09:36:18 +0100 Subject: [PATCH 046/147] Rename tasks to "impute", "train" and "fill", and introduce a 'compact' argument to mice() --- R/cbind.R | 2 +- R/check.tasks.R | 30 +++++++++++++++--------------- R/complete.R | 6 +++--- R/method.R | 10 ++++------ R/mice.R | 35 ++++++++++++++++++++++------------- R/mice.impute.pmm.R | 30 +++++++++++++++--------------- R/mids.R | 12 ++++++------ R/sampler.R | 16 ++++++++-------- man/make.method.Rd | 12 ++++++------ man/mice.Rd | 26 +++++++++++++++++--------- man/mice.impute.pmm.Rd | 18 +++++++++--------- man/mids.Rd | 14 +++++++------- tests/testthat/test-tasks.R | 8 ++++---- 13 files changed, 117 insertions(+), 102 deletions(-) diff --git a/R/cbind.R b/R/cbind.R index ae4bc7341..c41b7f3e0 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,7 +97,7 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots - tasks <- c(x$tasks, "generate") + tasks <- c(x$tasks, "impute") names(tasks) <- c(names(x$tasks), tail(varnames, 1L)) models <- x$models ignore <- x$ignore diff --git a/R/check.tasks.R b/R/check.tasks.R index 4fe1775c8..23f97d2df 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -1,10 +1,10 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { # This function is called during initialization if (is.null(tasks)) { - tasks <- "generate" + tasks <- "impute" } - valid_tasks <- c("generate", "retain", "train", "apply") + valid_tasks <- c("impute", "train", "fill") # 1. Default blocks to individual variables if not provided if (is.null(blocks)) { @@ -43,21 +43,21 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { )) } - # 6. Prevent "apply" if models is NULL - if ("apply" %in% tasks && is.null(models)) { - stop("The task 'apply' requires a stored model, but `models` is NULL.\n", - "Please provide a valid `models` object containing trained imputation models.") + # 6. Prevent "fill" if models is NULL + if ("fill" %in% tasks && is.null(models)) { + stop("The task 'fill' requires a stored model, but `models` is NULL.\n", + "Please provide a valid `models` object with a trained imputation model.") } - # 7. Ensure that all "apply" variables have a trained model in models - if ("apply" %in% tasks && !is.null(models)) { - fill_vars <- names(tasks[tasks == "apply"]) + # 7. Ensure that all "fill" variables have a trained model in models + if ("fill" %in% tasks && !is.null(models)) { + fill_vars <- names(tasks[tasks == "fill"]) missing_models <- setdiff(fill_vars, ls(models)) if (length(missing_models) > 0) { stop(paste0( - "The following variables specified as 'apply' do not have stored models: ", + "The following variables specified as 'fill' do not have stored models: ", paste(missing_models, collapse = ", "), ".\n", - "Ensure these variables were previously fitted before using 'apply'." + "Ensure these variables were previously fitted before using 'fill'." )) } } @@ -79,10 +79,10 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { } check.model <- function(model, - task = c("generate", "retain", "train", "apply")) { + task = c("impute", "train", "fill")) { # This function is called during iteration task <- match.arg(task) - if (task %in% c("retain", "train", "apply")) { + if (task %in% c("train", "fill")) { if (is.null(model)) { stop(paste("`model` cannot be NULL for task:", task)) } @@ -90,8 +90,8 @@ check.model <- function(model, stop("`model` must be an environment to store results persistently.") } } - if (task == "apply" && !length(ls(model))) { - stop("No stored model found for 'apply' task.") + if (task == "fill" && !length(ls(model))) { + stop("No stored model found for 'fill' task.") } return() } diff --git a/R/complete.R b/R/complete.R index 6c3b245fc..d51922dea 100644 --- a/R/complete.R +++ b/R/complete.R @@ -84,9 +84,9 @@ complete.mids <- function(data, action = 1L, include = FALSE, mild = FALSE, order = c("last", "first"), ...) { if (!is.mids(data)) stop("'data' not of class 'mids'") - if (data$store == "train") { - stop(paste("Cannot complete training object.\n", - "Use task 'generate' or 'retain' to estimate imputation model.")) + if (data$store == "train_compact") { + stop(paste("Cannot complete compact training object.\n", + "Set 'compact = FALSE' to preserve training data and imputations.")) } order <- match.arg(order) diff --git a/R/method.R b/R/method.R index 55de00d5e..e4eba3615 100644 --- a/R/method.R +++ b/R/method.R @@ -30,12 +30,12 @@ make.method <- function(data, } nimp <- nimp(where, blocks) - # preserve old behaviour that sets method <- "" with generate + # preserve old behaviour that sets method <- "" with impute task names(method) <- names(blocks) if (!is.null(tasks)) { for (j in names(blocks)) { vname <- blocks[[j]] - if (nimp[j] == 0L && "generate" == tasks[vname]) method[j] <- "" + if (nimp[j] == 0L && "impute" == tasks[vname]) method[j] <- "" } } @@ -63,10 +63,10 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { method <- rep(method, length(blocks)) names(method) <- names(blocks) - # preserve old behaviour that sets method <- "" with generate + # preserve old behaviour that sets method <- "" with impute task for (j in names(blocks)) { vname <- blocks[[j]] - if (nimp[j] == 0L && "generate" == tasks[vname]) method[j] <- "" + if (nimp[j] == 0L && "impute" == tasks[vname]) method[j] <- "" } } @@ -135,8 +135,6 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { call. = FALSE ) } - # preserve old behaviour that sets method <- "" with generate - # if ("generate" %in% tasks[vname] && nimp[j] == 0L) method[j] <- "" } unlist(method) diff --git a/R/mice.R b/R/mice.R index 7bfa8abb8..784e8955c 100644 --- a/R/mice.R +++ b/R/mice.R @@ -222,19 +222,19 @@ #' @param tasks A character vector specifying the task to perform for #' each imputation block. The available options are: #' \describe{ -#' \item{"generate"}{Estimate parameters, generate imputations and store -#' the original data plus imputations (classic MICE behavior).} -#' \item{"retain" }{Estimate parameters, generate imputations and +#' \item{"impute"}{Estimate parameters, generates imputations and store +#' the original data plus imputations, but not the imputation model +#' (classic MICE behavior).} +#' \item{"train" }{Estimate parameters, generate imputations and #' store the original data, the imputations and the imputation model.} -#' \item{"train"}{As retain, but store only the imputation model.} -#' \item{"apply"}{Apply a previously trained imputation model to generate +#' \item{"fill"}{Apply a previously trained imputation model to fill #' imputations, without re-estimating parameters.} #' } #' This argument can be specified as a named vector, where names correspond #' to variables and values specify the task for each variable. If a #' single value is provided, it applies to the variables in all blocks. The #' length of the vector must match the number of variables present in the -#' blocks. The default is \code{"generate"}. +#' blocks. The default is \code{"impute"}. #' @param models An environment that can be used to store fitted imputation #' models. The models are stored in the environment under the name of the #' block. The models can be used to predict missing values in new data. @@ -264,6 +264,12 @@ #' are created by a simple random draw from the data. Note that specification of #' \code{data.init} will start all \code{m} Gibbs sampling streams from the same #' imputation. +#' @param compact A logical value indicating whether the resulting \code{mids} +#' object should be stored in compact form. Only relevant if \code{tasks = 'train'}. +#' If \code{isTRUE(compact)}, training data, imputations and other data-specific +#' elements are removed from the resulting \code{mids} object. The +#' \code{store} element of the will be changed from \code{"train"} to +#' \code{"train.compact"}. The default is \code{compact = FALSE}. #' @param \dots Named arguments that are passed down to the univariate imputation #' functions. #' @@ -316,13 +322,13 @@ #' imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) #' #' # store all imputation models -#' imp1 <- mice(nhanes, tasks = "retain", print = FALSE) +#' imp1 <- mice(nhanes, tasks = "train", print = FALSE) #' ls(imp1$models$bmi$"1") #' imp1$models$bmi$"1"$formula #' imp1$models$bmi$"1"$beta.mis #' #' # Store model for `bmi`, estimate others as usual -#' tasks <- c("age" = "generate", "bmi" = "retain", "hyp" = "generate", "chl" = "generate") +#' tasks <- c("age" = "impute", "bmi" = "train", "hyp" = "impute", "chl" = "impute") #' imp2 <- mice(nhanes, tasks = tasks, print = FALSE) #' #' # Inspects the stored model for imputation 1 for `bmi` @@ -331,7 +337,7 @@ #' imp2$models$bmi$"1"$beta.mis #' #' # Fill missing `bmi` values using pre-trained model -#' tasks <- c("age" = "generate", "bmi" = "apply", "hyp" = "generate", "chl" = "generate") +#' tasks <- c("age" = "impute", "bmi" = "fill", "hyp" = "impute", "chl" = "impute") #' imp3 <- mice(nhanes, tasks = tasks, models = imp2$models, print = FALSE) #' #' \dontrun{ @@ -375,6 +381,7 @@ mice <- function(data, printFlag = TRUE, seed = NA, data.init = NULL, + compact = FALSE, ...) { call <- match.call() check.deprecated(...) @@ -479,7 +486,9 @@ mice <- function(data, maxit = maxit ) tasks <- check.tasks(tasks, data, models, blocks) - store <- ifelse(length(unique(tasks)) == 1L, tasks[1L], "retain") + store <- ifelse(length(unique(tasks)) == 1L, tasks[1L], "train") + if (compact && store == "train") store <- "train_compact" + method <- check.method( method = method, data = data, where = where, blocks = blocks, tasks = tasks, @@ -506,13 +515,13 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # Initialize models for "retain", "train" and "apply" blocks that are missing in models + # Initialize models for "train" and "fill" blocks that are missing in models if (is.null(models)) { models <- new.env(parent = emptyenv()) } - model.vars <- names(tasks[tasks %in% c("retain", "train", "apply")]) + model.vars <- names(tasks[tasks %in% c("train", "fill")]) m.train <- length(models[[model.vars[1L]]]) - if (any(tasks %in% "apply") && m > m.train) { + if (any(tasks %in% "fill") && m > m.train) { stop("Number of imputations (", m, ") is greater than training model (", m.train, ").") } for (block in model.vars) { diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index ad3a3fc01..5cc9c8e77 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -14,7 +14,7 @@ #' The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects #' the closest match, but is not recommended. Values between 5L and 10L #' provide the best results (Morris et al, 2015). -#' For tasks \code{"retain"} and \code{"train"}, the number of donors +#' For task \code{"train"}, the number of donors #' is calculated internally based on the number of #' observations in \code{yobs}. #' @param matchtype Type of matching distance. The default (recommended) choice @@ -35,23 +35,23 @@ #' category in order to be considered as a potential donor value. #' Relevant only of \code{y} is a factor. #' @param task Character string. The task to be performed. Can -#' be \code{"generate"}, \code{"retain"}, \code{"train"} or \code{"apply"}. -#' The default is \code{"generate"} (classic MICE). See \code{mice()} for +#' be \code{"impute"}, \code{"train"} or \code{"fill"}. +#' The default is \code{"impute"} (classic MICE). See \code{mice()} for #' details. #' @param model An environment created by a parent to store the imputation #' model setup and estimates. The model is stored in the \code{mids} object -#' under tasks \code{"retain"} and \code{"train"}, and is needed as input -#' for task \code{"apply"}. The object \code{model} is not used under -#' task \code{"generate"}. +#' under tasks \code{"train"}, and is needed as input +#' for task \code{"fill"}. The object \code{model} is not used under +#' task \code{"impute"}. #' @param nimp Experimental. Number of random imputations per missing values -#' generated from a fitted model under tasks \code{"retain"} and \code{"train"}. +#' generated from a fitted model under task \code{"train"}. #' The default is 1. The \code{nimp} parameter is different from \code{m}, #' the number of multiple imputations, because it generates repeated #' imputations from a single model. The \code{nimp} parameter is useful #' for large samples to reduce the computational burden, but still awaits #' support within the mice algorithm. #' @param nbins The number of bins used to store the predictive mean matching -#' model. Under tasks \code{"retain"} and \code{"train"}, the number of donors +#' model. Under task \code{"train"}, the number of donors #' is calculated internally based on the number of observations in \code{yobs} #' and the number of unique predictive values. #' @param ridge The ridge penalty used in \code{.norm.draw()} to prevent @@ -171,7 +171,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, matchtype = 1L, quantify = TRUE, exclude = NULL, trim = 1L, - task = "generate", model = NULL, nimp = 1L, + task = "impute", model = NULL, nimp = 1L, nbins = NULL, ridge = 1e-05, use.matcher = FALSE, ...) { @@ -198,12 +198,12 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) # Task: apply (impute from stored model) - if (task == "apply") { + if (task == "fill") { yhatmis <- x[wy, ] %*% model$beta.mis return(pmm.impute(yhatmis, model, nimp = nimp, ...)) } - # -- Remaining tasks: generate, retain, train -- + # -- Remaining tasks: impute, train -- # Quantify factor levels f <- quantify(y, ry, x, quantify = quantify) @@ -224,8 +224,8 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, yhatobs <- as.vector(x_ry %*% beta.obs) yhatmis <- x_wy %*% beta.mis - # Generate task: Impute values (classic MICE PMM) - if (task == "generate") { + # Impute task: Impute values (classic MICE PMM) + if (task == "impute") { if (use.matcher) { idx <- matcher(yhatobs, yhatmis, k = donors) } else { @@ -234,7 +234,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, return(y[ry][idx]) } - # -- Remaining tasks: retain, train -- + # -- Remaining task: train -- # Divide predictions into bins nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) @@ -267,7 +267,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # --- PMM helpers pmm.impute <- function(yhatmis, model, nimp = 1L, ...) { - # Task "apply": Compute imputations without estimating new model + # Task "fill": Compute imputations without estimating new model impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, lookup = model$lookup, diff --git a/R/mids.R b/R/mids.R index eb2cb5bcb..5f9ee2791 100644 --- a/R/mids.R +++ b/R/mids.R @@ -180,9 +180,9 @@ mids <- function( loggedEvents = data.frame(), version = packageVersion("mice"), date = Sys.Date(), - store = "generate") { + store = "impute") { - if (store == "generate") { + if (store == "impute") { obj <- list( data = data, imp = imp, @@ -210,7 +210,7 @@ mids <- function( version = packageVersion("mice"), date = Sys.Date(), store = store) - } else if (store == "retain") { + } else if (store == "train") { obj <- list( data = data, imp = imp, @@ -238,7 +238,7 @@ mids <- function( version = packageVersion("mice"), date = Sys.Date(), store = store) - } else if (store == "train") { + } else if (store == "train_compact") { obj <- list( m = m, blocks = blocks, @@ -253,7 +253,7 @@ mids <- function( version = packageVersion("mice"), date = Sys.Date(), call = call) - } else if (store == "apply") { + } else if (store == "fill") { obj <- list( data = data, imp = imp, @@ -264,7 +264,7 @@ mids <- function( date = Sys.Date(), call = call) } else { - stop("store must be one of 'generate', 'retain', 'train', or 'apply'") + stop("store must be one of 'impute', 'train', 'train_compact', or 'fill'") } class(obj) <- "mids" return(obj) diff --git a/R/sampler.R b/R/sampler.R index 29280046b..38de0603e 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -207,7 +207,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, wy <- complete.cases(x) & where[, j] # nothing to impute - if (all(!wy) && !task %in% c("retain", "train")) { + if (all(!wy) && task != "train") { return(numeric(0)) } @@ -215,7 +215,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, if (k == 1L) check.df(x, y, ry) # remove linear dependencies - if (task != "apply") { + if (task != "fill") { keep <- remove.lindep(x, y, ry, ...) x <- x[, keep, drop = FALSE] type <- type[keep] @@ -239,12 +239,12 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, prepare.formula <- function(formula, data, model, j, ct, pred, task) { # prepares the formula for univariate imputation - # saves (for "retain" and "train") or retrieves (for "apply") the formula + # saves (for "train") or retrieves (for "fill") the formula - # for "apply", use the stored formula instead of recalculating - if (task == "apply") { + # for "fill", use the stored formula instead of recalculating + if (task == "fill") { if (!exists("formula", envir = model)) { - stop("Error: No stored formula found in model for 'apply' task.") + stop("Error: No stored formula found in model for 'fill' task.") } formula <- get("formula", envir = model) return(formula) @@ -270,8 +270,8 @@ prepare.formula <- function(formula, data, model, j, ct, pred, task) { } } - # store formula in `model` only when task is "retain" or "train" - if (task %in% c("retain", "train")) { + # store formula in `model` only when task is "train" + if (task == "train") { assign("formula", formula, envir = model) } diff --git a/man/make.method.Rd b/man/make.method.Rd index 18365573a..d54caa628 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -41,19 +41,19 @@ effectively re-imputed each time that it is visited.} \item{tasks}{A character vector specifying the task to perform for each imputation block. The available options are: \describe{ -\item{"generate"}{Estimate parameters, generate imputations and store -the original data plus imputations (classic MICE behavior).} -\item{"retain" }{Estimate parameters, generate imputations and +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and store the original data, the imputations and the imputation model.} -\item{"train"}{As retain, but store only the imputation model.} -\item{"apply"}{Apply a previously trained imputation model to generate +\item{"fill"}{Apply a previously trained imputation model to fill imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond to variables and values specify the task for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the -blocks. The default is \code{"generate"}.} +blocks. The default is \code{"impute"}.} \item{defaultMethod}{A vector of length 4 containing the default imputation methods for 1) numeric data, 2) factor data with 2 levels, 3) diff --git a/man/mice.Rd b/man/mice.Rd index 5dadffbd9..8244fc342 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -26,6 +26,7 @@ mice( printFlag = TRUE, seed = NA, data.init = NULL, + compact = FALSE, ... ) } @@ -133,19 +134,19 @@ called for block \code{blockname}.} \item{tasks}{A character vector specifying the task to perform for each imputation block. The available options are: \describe{ -\item{"generate"}{Estimate parameters, generate imputations and store -the original data plus imputations (classic MICE behavior).} -\item{"retain" }{Estimate parameters, generate imputations and +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and store the original data, the imputations and the imputation model.} -\item{"train"}{As retain, but store only the imputation model.} -\item{"apply"}{Apply a previously trained imputation model to generate +\item{"fill"}{Apply a previously trained imputation model to fill imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond to variables and values specify the task for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the -blocks. The default is \code{"generate"}.} +blocks. The default is \code{"impute"}.} \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the @@ -183,6 +184,13 @@ are created by a simple random draw from the data. Note that specification of \code{data.init} will start all \code{m} Gibbs sampling streams from the same imputation.} +\item{compact}{A logical value indicating whether the resulting should be +stored in compact form. Only relevant if \code{tasks = 'train'}. +If \code{isTRUE(compact)}, training data, imputations and other data-specific +elements are removed from the resulting \code{mids} object. The +\code{store} element of the will be changed from \code{"train"} to +\code{"train.compact"}. The default is \code{compact = FALSE}.} + \item{\dots}{Named arguments that are passed down to the univariate imputation functions.} } @@ -414,13 +422,13 @@ complete(imp) imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) # store all imputation models -imp1 <- mice(nhanes, tasks = "retain", print = FALSE) +imp1 <- mice(nhanes, tasks = "train", print = FALSE) ls(imp1$models$bmi$"1") imp1$models$bmi$"1"$formula imp1$models$bmi$"1"$beta.mis # Store model for `bmi`, estimate others as usual -tasks <- c("age" = "generate", "bmi" = "retain", "hyp" = "generate", "chl" = "generate") +tasks <- c("age" = "impute", "bmi" = "train", "hyp" = "impute", "chl" = "impute") imp2 <- mice(nhanes, tasks = tasks, print = FALSE) # Inspects the stored model for imputation 1 for `bmi` @@ -429,7 +437,7 @@ imp2$models$bmi$"1"$formula imp2$models$bmi$"1"$beta.mis # Fill missing `bmi` values using pre-trained model -tasks <- c("age" = "generate", "bmi" = "apply", "hyp" = "generate", "chl" = "generate") +tasks <- c("age" = "impute", "bmi" = "fill", "hyp" = "impute", "chl" = "impute") imp3 <- mice(nhanes, tasks = tasks, models = imp2$models, print = FALSE) \dontrun{ diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 78ce4ddfa..92574979c 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -15,7 +15,7 @@ mice.impute.pmm( quantify = TRUE, exclude = NULL, trim = 1L, - task = "generate", + task = "impute", model = NULL, nimp = 1L, nbins = NULL, @@ -42,7 +42,7 @@ indicates locations in \code{y} for which imputations are created.} The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects the closest match, but is not recommended. Values between 5L and 10L provide the best results (Morris et al, 2015). -For tasks \code{"retain"} and \code{"train"}, the number of donors +For task \code{"train"}, the number of donors is calculated internally based on the number of observations in \code{yobs}.} @@ -68,18 +68,18 @@ category in order to be considered as a potential donor value. Relevant only of \code{y} is a factor.} \item{task}{Character string. The task to be performed. Can -be \code{"generate"}, \code{"retain"}, \code{"train"} or \code{"apply"}. -The default is \code{"generate"} (classic MICE). See \code{mice()} for +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for details.} \item{model}{An environment created by a parent to store the imputation model setup and estimates. The model is stored in the \code{mids} object -under tasks \code{"retain"} and \code{"train"}, and is needed as input -for task \code{"apply"}. The object \code{model} is not used under -task \code{"generate"}.} +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} \item{nimp}{Experimental. Number of random imputations per missing values -generated from a fitted model under tasks \code{"retain"} and \code{"train"}. +generated from a fitted model under task \code{"train"}. The default is 1. The \code{nimp} parameter is different from \code{m}, the number of multiple imputations, because it generates repeated imputations from a single model. The \code{nimp} parameter is useful @@ -87,7 +87,7 @@ for large samples to reduce the computational burden, but still awaits support within the mice algorithm.} \item{nbins}{The number of bins used to store the predictive mean matching -model. Under tasks \code{"retain"} and \code{"train"}, the number of donors +model. Under task \code{"train"}, the number of donors is calculated internally based on the number of observations in \code{yobs} and the number of unique predictive values.} diff --git a/man/mids.Rd b/man/mids.Rd index 8010470da..3fe6e7c37 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -35,7 +35,7 @@ mids( loggedEvents = data.frame(), version = packageVersion("mice"), date = Sys.Date(), - store = "generate" + store = "impute" ) \method{plot}{mids}( @@ -159,19 +159,19 @@ called for block \code{blockname}.} \item{tasks}{A character vector specifying the task to perform for each imputation block. The available options are: \describe{ -\item{"generate"}{Estimate parameters, generate imputations and store -the original data plus imputations (classic MICE behavior).} -\item{"retain" }{Estimate parameters, generate imputations and +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and store the original data, the imputations and the imputation model.} -\item{"train"}{As retain, but store only the imputation model.} -\item{"apply"}{Apply a previously trained imputation model to generate +\item{"fill"}{Apply a previously trained imputation model to fill imputations, without re-estimating parameters.} } This argument can be specified as a named vector, where names correspond to variables and values specify the task for each variable. If a single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the -blocks. The default is \code{"generate"}.} +blocks. The default is \code{"impute"}.} \item{models}{An environment that can be used to store fitted imputation models. The models are stored in the environment under the name of the diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 051f7311e..1d12c68eb 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -11,14 +11,14 @@ context("tasks") test_that("tasks work with factor with same number of categories", { expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_error(imp2 <- mice(nhanes2, m = 3, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE), "Number of imputations") - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) - expect_silent(imp2 <- mice(nhanes2[1,], m = 2, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) + expect_error(imp2 <- mice(nhanes2, m = 3, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE), "Number of imputations") + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2[1,], m = 2, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE)) }) test_that("training works on completely observed variables", { expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "apply", methode = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE)) }) From 51698fa1a21d67c9728d6f363efc9d97fecc7085 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 12 Mar 2025 14:59:20 +0100 Subject: [PATCH 047/147] Bypass data editing for fill task --- R/edit.setup.R | 59 +++++++++++++++++++++++++++----------------------- R/mice.R | 24 ++++++++++---------- man/mice.Rd | 4 ++-- 3 files changed, 47 insertions(+), 40 deletions(-) diff --git a/R/edit.setup.R b/R/edit.setup.R index f63999563..1db7cf81d 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -1,9 +1,9 @@ -mice.edit.setup <- function(data, setup, - allow.na = FALSE, - remove.constant = TRUE, - remove.collinear = TRUE, - remove_collinear = TRUE, - ...) { +mice.edit.setup <- function(data, setup, tasks, + allow.na = FALSE, + remove.constant = TRUE, + remove.collinear = TRUE, + remove_collinear = TRUE, + ...) { # legacy handling if (!remove_collinear) remove.collinear <- FALSE @@ -18,7 +18,7 @@ mice.edit.setup <- function(data, setup, # FIXME: this function is not yet adapted to blocks if (ncol(pred) != nrow(pred) || length(meth) != nrow(pred) || - ncol(data) != nrow(pred)) { + ncol(data) != nrow(pred)) { return(setup) } @@ -28,28 +28,31 @@ mice.edit.setup <- function(data, setup, for (j in seq_len(ncol(data))) { if (!is.passive(meth[j])) { d.j <- data[, j] - v <- if (is.character(d.j)) NA else var(as.numeric(d.j), na.rm = TRUE) - constant <- if (allow.na) { - if (is.na(v)) FALSE else v < 1000 * .Machine$double.eps - } else { - is.na(v) || v < 1000 * .Machine$double.eps - } - didlog <- FALSE - if (constant && any(pred[, j] != 0) && remove.constant) { - out <- varnames[j] - pred[, j] <- 0 - updateLog(out = out, meth = "constant") - didlog <- TRUE - } - if (constant && meth[j] != "" && remove.constant) { - out <- varnames[j] - pred[j, ] <- 0 - if (!didlog) { + task <- unname(tasks[varnames[j]]) + if (task != "fill") { + v <- if (is.character(d.j)) NA else var(as.numeric(d.j), na.rm = TRUE) + constant <- if (allow.na) { + if (is.na(v)) FALSE else v < 1000 * .Machine$double.eps + } else { + is.na(v) || v < 1000 * .Machine$double.eps + } + didlog <- FALSE + if (constant && any(pred[, j] != 0) && remove.constant) { + out <- varnames[j] + pred[, j] <- 0 updateLog(out = out, meth = "constant") + didlog <- TRUE + } + if (constant && meth[j] != "" && remove.constant) { + out <- varnames[j] + pred[j, ] <- 0 + if (!didlog) { + updateLog(out = out, meth = "constant") + } + meth[j] <- "" + vis <- vis[vis != j] + post[j] <- "" } - meth[j] <- "" - vis <- vis[vis != j] - post[j] <- "" } } } @@ -61,6 +64,8 @@ mice.edit.setup <- function(data, setup, } else { droplist <- NULL } + # do not drop variables with task "fill" + droplist <- setdiff(droplist, names(tasks[tasks == "fill"])) if (length(droplist) > 0) { for (k in seq_along(droplist)) { j <- which(varnames %in% droplist[k]) diff --git a/R/mice.R b/R/mice.R index 784e8955c..fddd74b8d 100644 --- a/R/mice.R +++ b/R/mice.R @@ -503,17 +503,19 @@ mice <- function(data, loggedEvents <- data.frame(it = 0, im = 0, dep = "", meth = "", out = "") # edit imputation setup - setup <- list( - method = method, - predictorMatrix = predictorMatrix, - visitSequence = visitSequence, - post = post - ) - setup <- mice.edit.setup(data, setup, ...) - method <- setup$method - predictorMatrix <- setup$predictorMatrix - visitSequence <- setup$visitSequence - post <- setup$post + # if (any(tasks != "fill")) { + setup <- list( + method = method, + predictorMatrix = predictorMatrix, + visitSequence = visitSequence, + post = post + ) + setup <- mice.edit.setup(data, setup, tasks, ...) + method <- setup$method + predictorMatrix <- setup$predictorMatrix + visitSequence <- setup$visitSequence + post <- setup$post + # } # Initialize models for "train" and "fill" blocks that are missing in models if (is.null(models)) { diff --git a/man/mice.Rd b/man/mice.Rd index 8244fc342..793e97833 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -184,8 +184,8 @@ are created by a simple random draw from the data. Note that specification of \code{data.init} will start all \code{m} Gibbs sampling streams from the same imputation.} -\item{compact}{A logical value indicating whether the resulting should be -stored in compact form. Only relevant if \code{tasks = 'train'}. +\item{compact}{A logical value indicating whether the resulting \code{mids} +object should be stored in compact form. Only relevant if \code{tasks = 'train'}. If \code{isTRUE(compact)}, training data, imputations and other data-specific elements are removed from the resulting \code{mids} object. The \code{store} element of the will be changed from \code{"train"} to From 9dc98a7fd7f09d60f644979e6078b6d4eda460ad Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 12 Mar 2025 14:59:59 +0100 Subject: [PATCH 048/147] Optimize code flow in mice.impute.pmm() --- R/check.tasks.R | 3 +- R/mice.impute.pmm.R | 121 +++++++++++++++++------------------- tests/testthat/test-tasks.R | 17 ++--- 3 files changed, 68 insertions(+), 73 deletions(-) diff --git a/R/check.tasks.R b/R/check.tasks.R index 23f97d2df..b12f6fa17 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -78,8 +78,7 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { return(tasks) } -check.model <- function(model, - task = c("impute", "train", "fill")) { +check.model <- function(model, task = c("impute", "train", "fill")) { # This function is called during iteration task <- match.arg(task) if (task %in% c("train", "fill")) { diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 5cc9c8e77..afd2cc5b2 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -197,19 +197,27 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # Add intercept column to x x <- cbind(1, as.matrix(x)) - # Task: apply (impute from stored model) + # >>> Fill task: fill from stored model if (task == "fill") { + # This catches only length differences, not names + if (ncol(x) != nrow(model$beta.mis)) { + stop(paste("Model mismatch:\n", + "Predictors: ", names(x), "\n", + "Estimates: ", rownames(model$beta.mis))) + } yhatmis <- x[wy, ] %*% model$beta.mis - return(pmm.impute(yhatmis, model, nimp = nimp, ...)) + impy <- draw.neighbors.pmm(yhatmis, + edges = model$edges, + lookup = model$lookup, + nimp = nimp) + return(impy) } - # -- Remaining tasks: impute, train -- - # Quantify factor levels f <- quantify(y, ry, x, quantify = quantify) ynum <- f$ynum - # Predict missing values using Normal draw + # Predict ynum on observed data with linear model parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) if (matchtype == 1L) { beta.obs <- parm$coef @@ -224,7 +232,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, yhatobs <- as.vector(x_ry %*% beta.obs) yhatmis <- x_wy %*% beta.mis - # Impute task: Impute values (classic MICE PMM) + # >>> Impute task: Impute values (classic MICE PMM) if (task == "impute") { if (use.matcher) { idx <- matcher(yhatobs, yhatmis, k = donors) @@ -234,12 +242,12 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, return(y[ry][idx]) } - # -- Remaining task: train -- - - # Divide predictions into bins + # >>> Train task: Store model in environment nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) donors <- initialize.donors(donors, length(yhatobs)) - prep <- bin.yhat(yhatobs, ynum[ry], k = donors, nbins = nbins) + edges <- quantile(yhatobs, probs = seq(0, 1, length.out = nbins + 1L), + type = 7L, na.rm = TRUE) + lookup <- bin.yhat(yhatobs, ynum[ry], k = donors, edges = edges) # Store the imputation model in models environment model$setup <- list(method = "pmm", @@ -254,42 +262,30 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, ridge = ridge) model$beta.obs <- beta.obs model$beta.mis <- beta.mis - model$edges <- prep$edges - model$lookup <- prep$lookup + model$edges <- edges + model$lookup <- matrix((unquantify(lookup, f$quant, levels(y))), + nrow = nbins) model$factor <- list(labels = f$labels, quant = f$quant) # Compute imputations from model - return(pmm.impute(yhatmis, model, nimp = nimp, ...)) -} - - - -# --- PMM helpers - -pmm.impute <- function(yhatmis, model, nimp = 1L, ...) { - # Task "fill": Compute imputations without estimating new model impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, lookup = model$lookup, - m = nimp) - # Convert back to factor if needed - impy <- unquantify(ynum = impy, - quant = model$factor$quant, - labels = model$factor$labels) + nimp = nimp) return(impy) } +# --- PMM helpers + initialize.nbins <- function(nbins, n, nu) { if (is.null(nbins)) { nbins <- round(4 * log(n) + 1.5) } - # max nbin is number of unique yhat values + # max nbin is number of unique yhat values, but ensure at least 2 bins if (nbins > nu) { - # message("Warning: nbins (", nbins, ") exceeds unique yhat values (", nu, "). Adjusting to ", nu, ".") nbins <- nu } - # Ensure at least 2 bins nbins <- max(2L, nbins) return(nbins) } @@ -302,64 +298,61 @@ initialize.donors <- function(donors, n) { return(donors) } -bin.yhat <- function(yhat, y, k = 10, nbins = 25) { +bin.yhat <- function(yhat, y, k, edges) { stopifnot(length(yhat) == length(y)) - # Compute percentile-based bin edges - edges <- quantile(yhat, probs = seq(0, 1, length.out = nbins + 1), type = 7, na.rm = TRUE) - # Sort yhat and y together sort_order <- order(yhat) yhat_sorted <- yhat[sort_order] y_sorted <- y[sort_order] - # Initialize lookup table - lookup <- matrix(NA_real_, nrow = nbins, ncol = k) - # Assign values to bins - bin_idx <- findInterval(yhat_sorted, vec = edges, all.inside = TRUE) + bin <- findInterval(yhat_sorted, vec = edges, all.inside = TRUE) # Split y_sorted by bins - bin_values_list <- split(y_sorted, bin_idx) - - # Fill lookup table + values_list <- split(y_sorted, bin) + + # Fill lookup table with k potential donor values per bin + # If bin is empty, sample from entire y_sorted to avoid NA values + # If only one value is available, repeat it + # Watch out for odd sample(x) behavior when length(x) == 1 + # Sample with replacement if we fewer than k bin values + nbins <- length(edges) - 1L lookup <- t(sapply(seq_len(nbins), function(b) { - bin_values <- bin_values_list[[as.character(b)]] - - if (length(bin_values) > 0) { - # If more than k values are available, randomly sample k - sample(bin_values, size = k, replace = length(bin_values) < k) - } else { - # If bin is empty, sample from entire y_sorted to avoid NA values + values <- values_list[[as.character(b)]] + if (length(values) == 0L) { sample(y_sorted, size = k, replace = TRUE) - } - })) + } else if (length(values) == 1L) { + rep(values, k) + } else { + sample(values, size = k, replace = length(values) < k) + }})) - return(list(edges = edges, lookup = lookup)) + return(lookup) } -draw.neighbors.pmm <- function(yhat_query, edges, lookup, m = 1) { - num_queries <- length(yhat_query) - nbins <- length(edges) - 1 # Bins are defined by edges[i] and edges[i+1] +draw.neighbors.pmm <- function(yhat, edges, lookup, nimp = 1L) { + # Bins are defined by edges[i] and edges[i+1] + n <- length(yhat) + nbins <- length(edges) - 1L - # Initialize result matrix: rows = number of queries, columns = m draws per query - imputed_values <- matrix(NA_real_, nrow = num_queries, ncol = m) + # Result matrix: rows = number of queries, columns = nimp draws per query + imputed_values <- matrix(NA_real_, nrow = n, ncol = nimp) # Find the bin for each query value - bin_idx <- findInterval(yhat_query, edges, rightmost.closed = TRUE, all.inside = TRUE) + bin <- findInterval(yhat, edges, rightmost.closed = TRUE, all.inside = TRUE) # Compute probability of selecting from left bin (smooth transition) - t0 <- edges[pmax(bin_idx, 1)] - t1 <- edges[pmin(bin_idx + 1, nbins)] - p_left <- ifelse(t1 > t0, (t1 - yhat_query) / (t1 - t0), 0.5) + t0 <- edges[pmax(bin, 1L)] + t1 <- edges[pmin(bin + 1L, nbins)] + p_left <- ifelse(t1 > t0, (t1 - yhat) / (t1 - t0), 0.5) # Determine which bin to sample from - selected_bin <- ifelse(runif(num_queries) < p_left, bin_idx, pmin(bin_idx + 1, nbins)) + selected_bin <- ifelse(runif(n) < p_left, bin, pmin(bin + 1L, nbins)) # Vectorized sampling from lookup table - sampled_indices <- matrix(sample(1:ncol(lookup), num_queries * m, replace = TRUE), nrow = num_queries) - imputed_values <- matrix(lookup[cbind(selected_bin, sampled_indices)], nrow = num_queries, ncol = m) - - return(imputed_values) + indices <- matrix(sample(1L:ncol(lookup), n * nimp, replace = TRUE), nrow = n) + impy <- matrix(lookup[cbind(selected_bin, indices)], nrow = n, ncol = nimp) + return(impy) } diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 1d12c68eb..322dfaf5b 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -9,16 +9,19 @@ context("tasks") # - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? test_that("tasks work with factor with same number of categories", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_error(imp2 <- mice(nhanes2, m = 3, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE), "Number of imputations") - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE)) - expect_silent(imp2 <- mice(nhanes2[1,], m = 2, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE)) + expect_error(imp2 <- mice(nhanes2, m = 4, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Number of imputations") + expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2[1,], m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) }) -test_that("training works on completely observed variables", { - expect_silent(imp1 <- mice(nhanes2, m = 1, maxit = 1, task = "train", method = "pmm", print = FALSE)) +test_that("fully synthetic datasets can be created from completely observed variables", { + dataset <- complete(mice(nhanes2, m = 1, maxit = 1, method = "pmm", print = FALSE)) + expect_silent(imp1 <- mice(dataset, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$age$"1"$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 1, maxit = 1, task = "fill", methode = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(dataset, where = make.where(dataset, "all"), m = 2, maxit = 3, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + synt1 <- complete(imp2, 1) + synt2 <- complete(imp2, 2) }) From edd0abb2064dc1da1494c3d012af10a736e48307 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 12 Mar 2025 15:59:28 +0100 Subject: [PATCH 049/147] Inform the user about a mismatch between model and data under task == "fill" --- R/mice.impute.pmm.R | 15 ++++++++++----- tests/testthat/test-tasks.R | 22 ++++++++++++++++------ 2 files changed, 26 insertions(+), 11 deletions(-) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index afd2cc5b2..0aca7984d 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -199,11 +199,16 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # >>> Fill task: fill from stored model if (task == "fill") { - # This catches only length differences, not names - if (ncol(x) != nrow(model$beta.mis)) { - stop(paste("Model mismatch:\n", - "Predictors: ", names(x), "\n", - "Estimates: ", rownames(model$beta.mis))) + formula <- model$formula + if (!length(formula)) { + stop("No model stored in environment") + } + mnames <- rownames(model$beta.mis) + dnames <- colnames(x) + if (ncol(x) != nrow(model$beta.mis) || any(mnames != dnames)) { + stop(paste("Model-Data mismatch: ", deparse(formula), "\n", + " Model:", paste(mnames, collapse = " "), "\n", + " Data: ", paste(dnames, collapse = " "), "\n")) } yhatmis <- x[wy, ] %*% model$beta.mis impy <- draw.neighbors.pmm(yhatmis, diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 322dfaf5b..9d491714a 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -2,18 +2,16 @@ context("tasks") # We have to test the following cases: -# - Does train-run setup with a factor variable produce imputations? -# - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? +# - Does train-run setup with a factor variable produce imputations? YES +# - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? YES # - Does train-run setup with a factor variable produce imputations when the factor has more categories during running than training? -# - Does train-run setup with a factor variable produce imputations when only one factor level is present during training? -# - Does train-run setup with a factor variable produce imputations when only one factor level is present during running? -test_that("tasks work with factor with same number of categories", { +test_that("tasks work with factor with the same categories", { expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) expect_error(imp2 <- mice(nhanes2, m = 4, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Number of imputations") expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) - expect_silent(imp2 <- mice(nhanes2[1,], m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) }) test_that("fully synthetic datasets can be created from completely observed variables", { @@ -25,3 +23,15 @@ test_that("fully synthetic datasets can be created from completely observed vari synt2 <- complete(imp2, 2) }) +test_that("the procedure informs the user about a mismatch between model and data", { + expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, task = "train", method = "pmm", print = FALSE)) + newdata <- nhanes2 + newdata$age <- factor(newdata$age, levels = c(levels(newdata$age), "not_a_level")) + newdata$age[1] <- "not_a_level" + newdata$age[2] <- NA + expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") + newdata <- nhanes2 + levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" + expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") +}) + From 31c248cc1ba6037e94fa695ef7cf3a8df5341a0a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 12 Mar 2025 16:07:17 +0100 Subject: [PATCH 050/147] improve example --- R/mice.impute.pmm.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 0aca7984d..aaf339245 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -165,7 +165,7 @@ #' # in addition, eliminate category 20 #' mice.impute.pmm(y, ry, x, trim = 2L, exclude = 20) #' -#' # to get old behavior: as.integer(y)) +#' # to get old behavior (before mice v3.16.4): as.integer(y)) #' mice.impute.pmm(y, ry, x, quantify = FALSE) #' @export mice.impute.pmm <- function(y, ry, x, wy = NULL, From fa41deb1a12839b81da5998cf379b791ab1bf4a7 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 12 Mar 2025 16:18:10 +0100 Subject: [PATCH 051/147] Create validation function check.model.data.match() --- R/mice.impute.pmm.R | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index aaf339245..ee5a50e06 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -198,18 +198,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) # >>> Fill task: fill from stored model - if (task == "fill") { - formula <- model$formula - if (!length(formula)) { - stop("No model stored in environment") - } - mnames <- rownames(model$beta.mis) - dnames <- colnames(x) - if (ncol(x) != nrow(model$beta.mis) || any(mnames != dnames)) { - stop(paste("Model-Data mismatch: ", deparse(formula), "\n", - " Model:", paste(mnames, collapse = " "), "\n", - " Data: ", paste(dnames, collapse = " "), "\n")) - } + if (task == "fill" && check.model.data.match(model, x)) { yhatmis <- x[wy, ] %*% model$beta.mis impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, @@ -361,3 +350,17 @@ draw.neighbors.pmm <- function(yhat, edges, lookup, nimp = 1L) { return(impy) } +check.model.data.match <- function(model, x) { + formula <- model$formula + if (!length(formula)) { + stop("No model stored in environment") + } + mnames <- rownames(model$beta.mis) + dnames <- colnames(x) + if (ncol(x) != nrow(model$beta.mis) || any(mnames != dnames)) { + stop(paste("Model-Data mismatch: ", deparse(formula), "\n", + " Model:", paste(mnames, collapse = " "), "\n", + " Data: ", paste(dnames, collapse = " "), "\n")) + } + return(TRUE) +} From d14db784d597da6772d8bdc0a6a378944be1cdde Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 08:51:14 +0100 Subject: [PATCH 052/147] Add helper make.tasks() --- NAMESPACE | 1 + R/tasks.R | 26 ++++++++++++++++++++++ _pkgdown.yml | 1 + man/make.tasks.Rd | 55 +++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 83 insertions(+) create mode 100644 R/tasks.R create mode 100644 man/make.tasks.Rd diff --git a/NAMESPACE b/NAMESPACE index 46434fbc6..d2458beaa 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -96,6 +96,7 @@ export(make.method) export(make.modeltype) export(make.post) export(make.predictorMatrix) +export(make.tasks) export(make.visitSequence) export(make.where) export(matchindex) diff --git a/R/tasks.R b/R/tasks.R new file mode 100644 index 000000000..7ef7699c4 --- /dev/null +++ b/R/tasks.R @@ -0,0 +1,26 @@ +#' Creates a \code{tasks} argument +#' +#' This helper function creates a valid \code{tasks} vector. The +#' \code{tasks} vector is an argument to the \code{mice} function that +#' specifies the task for each column in the data. +#' @inheritParams mice +#' @return Character vector of \code{ncol(data)} elements +#' @seealso \code{\link{mice}} +#' @examples +#' make.tasks(nhanes2) +#' @export +make.tasks <- function(data, + tasks = "generate", + blocks = make.blocks(data)) { + bv <- unique(unlist(blocks)) + if (length(tasks) == 1L) { + tasks <- setNames(rep(tasks, length(bv)), bv) + } else { + if (length(tasks) != length(bv)) { + stop("length(tasks) does not match variables to be imputed", call. = FALSE) + } + names(tasks) <- bv + } + + return(tasks) +} diff --git a/_pkgdown.yml b/_pkgdown.yml index 785505ad7..588ebbe44 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -47,6 +47,7 @@ reference: - make.modeltype - make.post - make.predictorMatrix + - make.tasks - make.visitSequence - make.where - construct.blocks diff --git a/man/make.tasks.Rd b/man/make.tasks.Rd new file mode 100644 index 000000000..dd484fd90 --- /dev/null +++ b/man/make.tasks.Rd @@ -0,0 +1,55 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/tasks.R +\name{make.tasks} +\alias{make.tasks} +\title{Creates a \code{tasks} argument} +\usage{ +make.tasks(data, tasks = "generate", blocks = make.blocks(data)) +} +\arguments{ +\item{data}{A data frame or a matrix containing the incomplete data. Missing +values are coded as \code{NA}.} + +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + +\item{blocks}{List of vectors with variable names per block. List elements +may be named to identify blocks. Variables within a block are +imputed by a multivariate imputation method +(see \code{method} argument). By default each variable is placed +into its own block, which is effectively +fully conditional specification (FCS) by univariate models +(variable-by-variable imputation). Only variables whose names appear in +\code{blocks} are imputed. The relevant columns in the \code{where} +matrix are set to \code{FALSE} of variables that are not block members. +A variable may appear in multiple blocks. In that case, it is +effectively re-imputed each time that it is visited.} +} +\value{ +Character vector of \code{ncol(data)} elements +} +\description{ +This helper function creates a valid \code{tasks} vector. The +\code{tasks} vector is an argument to the \code{mice} function that +specifies the task for each column in the data. +} +\examples{ +make.tasks(nhanes2) +} +\seealso{ +\code{\link{mice}} +} From 00fd95a5c94258dbf68e016f7267d3c90c1e005d Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 08:53:43 +0100 Subject: [PATCH 053/147] Add check for whether a method support tasks train and fill --- R/method.R | 12 ++++++++++++ R/mice.R | 24 +++++++++++------------- man/mice.impute.pmm.Rd | 2 +- 3 files changed, 24 insertions(+), 14 deletions(-) diff --git a/R/method.R b/R/method.R index e4eba3615..642b0fc1c 100644 --- a/R/method.R +++ b/R/method.R @@ -39,6 +39,18 @@ make.method <- function(data, } } + # check whether methods support train and fill tasks + for (j in names(blocks)) { + vname <- blocks[[j]] + for (yname in vname) { + mj <- method[j] + task <- tasks[yname] + if (!is.null(task) && task %in% c("train", "fill") && !mj %in% c("pmm")) { + stop(paste("Method", mj, "does not support train and fill tasks. Use method = 'pmm' instead.")) + } + } + } + method } diff --git a/R/mice.R b/R/mice.R index fddd74b8d..a1a4e5c56 100644 --- a/R/mice.R +++ b/R/mice.R @@ -503,19 +503,17 @@ mice <- function(data, loggedEvents <- data.frame(it = 0, im = 0, dep = "", meth = "", out = "") # edit imputation setup - # if (any(tasks != "fill")) { - setup <- list( - method = method, - predictorMatrix = predictorMatrix, - visitSequence = visitSequence, - post = post - ) - setup <- mice.edit.setup(data, setup, tasks, ...) - method <- setup$method - predictorMatrix <- setup$predictorMatrix - visitSequence <- setup$visitSequence - post <- setup$post - # } + setup <- list( + method = method, + predictorMatrix = predictorMatrix, + visitSequence = visitSequence, + post = post + ) + setup <- mice.edit.setup(data, setup, tasks, ...) + method <- setup$method + predictorMatrix <- setup$predictorMatrix + visitSequence <- setup$visitSequence + post <- setup$post # Initialize models for "train" and "fill" blocks that are missing in models if (is.null(models)) { diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 92574979c..1212d4d74 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -194,7 +194,7 @@ mice.impute.pmm(y, ry, x, trim = 2L) # in addition, eliminate category 20 mice.impute.pmm(y, ry, x, trim = 2L, exclude = 20) -# to get old behavior: as.integer(y)) +# to get old behavior (before mice v3.16.4): as.integer(y)) mice.impute.pmm(y, ry, x, quantify = FALSE) } \references{ From fd507830a6f8033c7680bad2d3af286d823e1e2b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 08:54:41 +0100 Subject: [PATCH 054/147] Add infrastructure to an .Rmd article document --- .Rbuildignore | 1 + DESCRIPTION | 1 + vignettes/articles/.gitignore | 2 ++ 3 files changed, 4 insertions(+) create mode 100644 vignettes/articles/.gitignore diff --git a/.Rbuildignore b/.Rbuildignore index 05ea8e9df..61c4cbba2 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -21,3 +21,4 @@ vignettes/ ^LICENSE\.md$ ^\.github$ ^CRAN-SUBMISSION$ +^vignettes/articles$ diff --git a/DESCRIPTION b/DESCRIPTION index 051e6f189..7970f1cdc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -101,3 +101,4 @@ LinkingTo: cpp11, Rcpp License: GPL (>= 2) RoxygenNote: 7.3.2 Roxygen: list(markdown = TRUE) +Config/Needs/website: rmarkdown diff --git a/vignettes/articles/.gitignore b/vignettes/articles/.gitignore new file mode 100644 index 000000000..097b24163 --- /dev/null +++ b/vignettes/articles/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R From 91eba4cb0ece272692bfd362e686a7a685121181 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 11:24:32 +0100 Subject: [PATCH 055/147] Add support for store in cbind() and rbind() --- R/cbind.R | 17 ++++++++++++++++- R/mids.R | 1 - R/rbind.R | 7 +++++++ 3 files changed, 23 insertions(+), 2 deletions(-) diff --git a/R/cbind.R b/R/cbind.R index c41b7f3e0..6542d7558 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -100,6 +100,7 @@ cbind.mids <- function(x, y = NULL, ...) { tasks <- c(x$tasks, "impute") names(tasks) <- c(names(x$tasks), tail(varnames, 1L)) models <- x$models + store <- x$store ignore <- x$ignore # seed, lastSeedValue, number of iterations, chainMean and chainVar @@ -130,6 +131,7 @@ cbind.mids <- function(x, y = NULL, ...) { blots = blots, tasks = tasks, models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, @@ -247,7 +249,19 @@ cbind.mids.mids <- function(x, y, call) { } return(target_env) } - models <- merge_envs(x$models, y$models) + if (!is.null(x$models) && !is.null(y$models)) { + models <- merge_envs(x$models, y$models) + } else if (!is.null(x$models)) { + models <- x$models + } else if (!is.null(y$models)) { + models <- y$models + } else { + models <- NULL + } + store <- x$store + if (y$store != store) { + store <- "train" + } ignore <- x$ignore # For the elements seed, lastSeedValue and iteration the values @@ -315,6 +329,7 @@ cbind.mids.mids <- function(x, y, call) { blots = blots, tasks = tasks, models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/mids.R b/R/mids.R index 5f9ee2791..3d8196510 100644 --- a/R/mids.R +++ b/R/mids.R @@ -199,7 +199,6 @@ mids <- function( post = post, blots = blots, tasks = tasks, - models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/rbind.R b/R/rbind.R index 2247cee48..4a10f7f73 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -48,6 +48,7 @@ rbind.mids <- function(x, y = NULL, ...) { blots <- x$blots tasks <- x$tasks models <- x$models + store <- x$store predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -79,6 +80,7 @@ rbind.mids <- function(x, y = NULL, ...) { blots = blots, tasks = tasks, models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, @@ -131,6 +133,10 @@ rbind.mids.mids <- function(x, y, call) { blots <- x$blots tasks <- x$tasks models <- x$models + store <- x$store + if (y$store != store) { + store <- "train" + } ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -180,6 +186,7 @@ rbind.mids.mids <- function(x, y, call) { blots = blots, tasks = tasks, models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, From a651ba864ed45a9412bfacfdcad3c20ec32de0bc Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 14:53:28 +0100 Subject: [PATCH 056/147] Recycle trained models if user wants to fill >m models --- R/mice.R | 23 ++++++++++++----------- R/sampler.R | 5 ++++- tests/testthat/test-tasks.R | 7 +++---- 3 files changed, 19 insertions(+), 16 deletions(-) diff --git a/R/mice.R b/R/mice.R index a1a4e5c56..f37cefe69 100644 --- a/R/mice.R +++ b/R/mice.R @@ -520,17 +520,18 @@ mice <- function(data, models <- new.env(parent = emptyenv()) } model.vars <- names(tasks[tasks %in% c("train", "fill")]) - m.train <- length(models[[model.vars[1L]]]) - if (any(tasks %in% "fill") && m > m.train) { - stop("Number of imputations (", m, ") is greater than training model (", m.train, ").") - } - for (block in model.vars) { - if (!exists(block, envir = models)) { - models[[block]] <- new.env(parent = emptyenv()) - } - for (i in 1:m) { - if (!exists(as.character(i), envir = models[[block]])) { - models[[block]][[as.character(i)]] <- new.env(parent = emptyenv()) + # if (any(tasks %in% "fill") && m > m.train) { + # stop("Number of imputations (", m, ") is greater than training model (", m.train, ").") + # } + for (varname in model.vars) { + if (tasks[varname] == "train") { + if (!exists(varname, envir = models)) { + models[[varname]] <- new.env(parent = emptyenv()) + } + for (i in 1:m) { + if (!exists(as.character(i), envir = models[[varname]])) { + models[[varname]][[as.character(i)]] <- new.env(parent = emptyenv()) + } } } } diff --git a/R/sampler.R b/R/sampler.R index 38de0603e..f259a8be3 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -70,13 +70,16 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, # (repeated) univariate imputation - pred method if (univ) { for (j in b) { + # if m outruns m.train, recycle m.train + m.train <- length(models[[j]]) + mod <- (i - 1L) %% m.train + 1L imp[[j]][, i] <- sampler.univ( data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, task = tasks[j], - model = models[[j]][[as.character(i)]], + model = models[[j]][[as.character(mod)]], yname = j, k = k, ct = ct, user = user, ignore = ignore, diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 9d491714a..9443e9554 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -6,11 +6,10 @@ context("tasks") # - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? YES # - Does train-run setup with a factor variable produce imputations when the factor has more categories during running than training? -test_that("tasks work with factor with the same categories", { - expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, task = "train", method = "pmm", print = FALSE)) +test_that("m filling recycles training models", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi$"1"$lookup)) - expect_error(imp2 <- mice(nhanes2, m = 4, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Number of imputations") - expect_silent(imp2 <- mice(nhanes2, m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 4, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) }) From 958c22b3df84431325442d2e1df7550fde08e69a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 15:33:32 +0100 Subject: [PATCH 057/147] Organize the elementary level function arguments more consistently. First the global arguments, then the local argument using their sequence of use --- R/mice.impute.pmm.R | 10 ++--- man/mice.impute.pmm.Rd | 94 +++++++++++++++++++++--------------------- 2 files changed, 52 insertions(+), 52 deletions(-) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index ee5a50e06..4a7b67609 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -169,11 +169,11 @@ #' mice.impute.pmm(y, ry, x, quantify = FALSE) #' @export mice.impute.pmm <- function(y, ry, x, wy = NULL, - donors = 5L, matchtype = 1L, quantify = TRUE, - exclude = NULL, trim = 1L, - task = "impute", model = NULL, nimp = 1L, - nbins = NULL, ridge = 1e-05, use.matcher = FALSE, - ...) + task = "impute", model = NULL, + exclude = NULL, trim = 1L, quantify = TRUE, + ridge = 1e-05, matchtype = 1L, + donors = 5L, nbins = NULL, use.matcher = FALSE, + nimp = 1L, ...) { check.model(model, task) if (is.null(wy)) wy <- !ry diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 1212d4d74..6254e55e2 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -10,17 +10,17 @@ mice.impute.pmm( ry, x, wy = NULL, - donors = 5L, - matchtype = 1L, - quantify = TRUE, - exclude = NULL, - trim = 1L, task = "impute", model = NULL, - nimp = 1L, - nbins = NULL, + exclude = NULL, + trim = 1L, + quantify = TRUE, ridge = 1e-05, + matchtype = 1L, + donors = 5L, + nbins = NULL, use.matcher = FALSE, + nimp = 1L, ... ) } @@ -38,35 +38,6 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} -\item{donors}{The size of the donor pool among which a draw is made. -The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects -the closest match, but is not recommended. Values between 5L and 10L -provide the best results (Morris et al, 2015). -For task \code{"train"}, the number of donors -is calculated internally based on the number of -observations in \code{yobs}.} - -\item{matchtype}{Type of matching distance. The default (recommended) choice -(\code{matchtype = 1L}) calculates the distance between -the \emph{predicted} value of \code{yobs} and -the \emph{drawn} values of \code{ymis} (called type-1 matching). -Other choices are \code{matchtype = 0L} -(distance between predicted values) and \code{matchtype = 2L} -(distance between drawn values).} - -\item{quantify}{Logical. If \code{TRUE}, factor levels are replaced -by the first canonical variate before fitting the imputation model. -If false, the procedure reverts to the old behaviour and takes the -integer codes (which may lack a sensible interpretation). -Relevant only of \code{y} is a factor.} - -\item{exclude}{Dependent values to exclude from the imputation model -and the collection of donor values} - -\item{trim}{Scalar integer. Minimum number of observations required in a -category in order to be considered as a potential donor value. -Relevant only of \code{y} is a factor.} - \item{task}{Character string. The task to be performed. Can be \code{"impute"}, \code{"train"} or \code{"fill"}. The default is \code{"impute"} (classic MICE). See \code{mice()} for @@ -78,18 +49,18 @@ under tasks \code{"train"}, and is needed as input for task \code{"fill"}. The object \code{model} is not used under task \code{"impute"}.} -\item{nimp}{Experimental. Number of random imputations per missing values -generated from a fitted model under task \code{"train"}. -The default is 1. The \code{nimp} parameter is different from \code{m}, -the number of multiple imputations, because it generates repeated -imputations from a single model. The \code{nimp} parameter is useful -for large samples to reduce the computational burden, but still awaits -support within the mice algorithm.} +\item{exclude}{Dependent values to exclude from the imputation model +and the collection of donor values} -\item{nbins}{The number of bins used to store the predictive mean matching -model. Under task \code{"train"}, the number of donors -is calculated internally based on the number of observations in \code{yobs} -and the number of unique predictive values.} +\item{trim}{Scalar integer. Minimum number of observations required in a +category in order to be considered as a potential donor value. +Relevant only of \code{y} is a factor.} + +\item{quantify}{Logical. If \code{TRUE}, factor levels are replaced +by the first canonical variate before fitting the imputation model. +If false, the procedure reverts to the old behaviour and takes the +integer codes (which may lack a sensible interpretation). +Relevant only of \code{y} is a factor.} \item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent problems with multicollinearity. The default is \code{ridge = 1e-05}, @@ -98,10 +69,39 @@ Larger ridges may result in more biased estimates. For highly noisy data (e.g. many junk variables), set \code{ridge = 1e-06} or even lower to reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} +\item{matchtype}{Type of matching distance. The default (recommended) choice +(\code{matchtype = 1L}) calculates the distance between +the \emph{predicted} value of \code{yobs} and +the \emph{drawn} values of \code{ymis} (called type-1 matching). +Other choices are \code{matchtype = 0L} +(distance between predicted values) and \code{matchtype = 2L} +(distance between drawn values).} + +\item{donors}{The size of the donor pool among which a draw is made. +The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects +the closest match, but is not recommended. Values between 5L and 10L +provide the best results (Morris et al, 2015). +For task \code{"train"}, the number of donors +is calculated internally based on the number of +observations in \code{yobs}.} + +\item{nbins}{The number of bins used to store the predictive mean matching +model. Under task \code{"train"}, the number of donors +is calculated internally based on the number of observations in \code{yobs} +and the number of unique predictive values.} + \item{use.matcher}{Logical. Set \code{use.matcher = TRUE} to specify the C function \code{matcher()}, the now deprecated matching function that was default in versions of \code{mice} prior to \code{3.12.0}.} +\item{nimp}{Experimental. Number of random imputations per missing values +generated from a fitted model under task \code{"train"}. +The default is 1. The \code{nimp} parameter is different from \code{m}, +the number of multiple imputations, because it generates repeated +imputations from a single model. The \code{nimp} parameter is useful +for large samples to reduce the computational burden, but still awaits +support within the mice algorithm.} + \item{\dots}{Other named arguments.} } \value{ From cd7dd3bb7b27dc22dd1c64da6308e9f43e8ce317 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 16:06:31 +0100 Subject: [PATCH 058/147] Collect check.model.***() function in their own file --- R/check.model.R | 27 +++++++++++++++++++++++++++ R/check.tasks.R | 17 ----------------- R/mice.impute.pmm.R | 16 +--------------- 3 files changed, 28 insertions(+), 32 deletions(-) create mode 100644 R/check.model.R diff --git a/R/check.model.R b/R/check.model.R new file mode 100644 index 000000000..ff52a86aa --- /dev/null +++ b/R/check.model.R @@ -0,0 +1,27 @@ +check.model.exists <- function(model, task) { + if (task == "impute") { + return() + } + if (is.null(model) || !is.environment(model)) { + stop("`model` must be an environment to store results persistently.") + } + if (task == "fill" && !length(ls(model))) { + stop("No stored model found for 'fill' task.") + } + return() +} + +check.model.data.match <- function(model, x) { + formula <- model$formula + if (!length(formula)) { + stop("No model stored in environment") + } + mnames <- rownames(model$beta.mis) + dnames <- colnames(x) + if (ncol(x) != nrow(model$beta.mis) || any(mnames != dnames)) { + stop(paste("Model-Data mismatch: ", deparse(formula), "\n", + " Model:", paste(mnames, collapse = " "), "\n", + " Data: ", paste(dnames, collapse = " "), "\n")) + } + return(TRUE) +} diff --git a/R/check.tasks.R b/R/check.tasks.R index b12f6fa17..f71350688 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -77,20 +77,3 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { return(tasks) } - -check.model <- function(model, task = c("impute", "train", "fill")) { - # This function is called during iteration - task <- match.arg(task) - if (task %in% c("train", "fill")) { - if (is.null(model)) { - stop(paste("`model` cannot be NULL for task:", task)) - } - if (!is.environment(model)) { - stop("`model` must be an environment to store results persistently.") - } - } - if (task == "fill" && !length(ls(model))) { - stop("No stored model found for 'fill' task.") - } - return() -} diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 4a7b67609..b9cf9ef3b 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -175,7 +175,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, nbins = NULL, use.matcher = FALSE, nimp = 1L, ...) { - check.model(model, task) + check.model.exists(model, task) if (is.null(wy)) wy <- !ry # Remove excluded values and trim small categories @@ -350,17 +350,3 @@ draw.neighbors.pmm <- function(yhat, edges, lookup, nimp = 1L) { return(impy) } -check.model.data.match <- function(model, x) { - formula <- model$formula - if (!length(formula)) { - stop("No model stored in environment") - } - mnames <- rownames(model$beta.mis) - dnames <- colnames(x) - if (ncol(x) != nrow(model$beta.mis) || any(mnames != dnames)) { - stop(paste("Model-Data mismatch: ", deparse(formula), "\n", - " Model:", paste(mnames, collapse = " "), "\n", - " Data: ", paste(dnames, collapse = " "), "\n")) - } - return(TRUE) -} From 91ae872f07ec78f6cf4007ac5699ecc9b4c15a4e Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 17:17:57 +0100 Subject: [PATCH 059/147] Add a check on the match of the stored and requested methods --- R/check.model.R | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/R/check.model.R b/R/check.model.R index ff52a86aa..939e73c69 100644 --- a/R/check.model.R +++ b/R/check.model.R @@ -5,13 +5,10 @@ check.model.exists <- function(model, task) { if (is.null(model) || !is.environment(model)) { stop("`model` must be an environment to store results persistently.") } - if (task == "fill" && !length(ls(model))) { - stop("No stored model found for 'fill' task.") - } return() } -check.model.data.match <- function(model, x) { +check.model.match <- function(model, x, method) { formula <- model$formula if (!length(formula)) { stop("No model stored in environment") @@ -23,5 +20,12 @@ check.model.data.match <- function(model, x) { " Model:", paste(mnames, collapse = " "), "\n", " Data: ", paste(dnames, collapse = " "), "\n")) } + + mmeth <- model$setup$method + if (length(mmeth) && mmeth != method) { + stop(paste("Model-Method mismatch: ", deparse(formula), "\n", + " Model: ", mmeth, "\n", + " Method: ", method, "\n")) + } return(TRUE) } From ce8a237cdcb01f55aeae66afe5f8db723d0ebc13 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 17:19:03 +0100 Subject: [PATCH 060/147] Change nimp --> mlocal to distinguish it from other uses of the name 'nimp' --- R/mice.impute.pmm.R | 27 ++++++++++++++------------- man/mice.impute.pmm.Rd | 8 ++++---- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index b9cf9ef3b..5c7aec9e4 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -43,11 +43,11 @@ #' under tasks \code{"train"}, and is needed as input #' for task \code{"fill"}. The object \code{model} is not used under #' task \code{"impute"}. -#' @param nimp Experimental. Number of random imputations per missing values +#' @param mlocal Experimental. Number of random imputations per missing values #' generated from a fitted model under task \code{"train"}. -#' The default is 1. The \code{nimp} parameter is different from \code{m}, +#' The default is 1. The \code{mlocal} parameter is different from \code{m}, #' the number of multiple imputations, because it generates repeated -#' imputations from a single model. The \code{nimp} parameter is useful +#' imputations from a single model. The \code{mlocal} parameter is useful #' for large samples to reduce the computational burden, but still awaits #' support within the mice algorithm. #' @param nbins The number of bins used to store the predictive mean matching @@ -173,9 +173,10 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, exclude = NULL, trim = 1L, quantify = TRUE, ridge = 1e-05, matchtype = 1L, donors = 5L, nbins = NULL, use.matcher = FALSE, - nimp = 1L, ...) + mlocal = 1L, ...) { check.model.exists(model, task) + method <- "pmm" if (is.null(wy)) wy <- !ry # Remove excluded values and trim small categories @@ -198,12 +199,12 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) # >>> Fill task: fill from stored model - if (task == "fill" && check.model.data.match(model, x)) { + if (task == "fill" && check.model.match(model, x, method)) { yhatmis <- x[wy, ] %*% model$beta.mis impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, lookup = model$lookup, - nimp = nimp) + mlocal = mlocal) return(impy) } @@ -244,7 +245,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, lookup <- bin.yhat(yhatobs, ynum[ry], k = donors, edges = edges) # Store the imputation model in models environment - model$setup <- list(method = "pmm", + model$setup <- list(method = method, n = length(yhatobs), donors = donors, matchtype = matchtype, @@ -265,7 +266,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, lookup = model$lookup, - nimp = nimp) + mlocal = mlocal) return(impy) } @@ -325,13 +326,13 @@ bin.yhat <- function(yhat, y, k, edges) { return(lookup) } -draw.neighbors.pmm <- function(yhat, edges, lookup, nimp = 1L) { +draw.neighbors.pmm <- function(yhat, edges, lookup, mlocal = 1L) { # Bins are defined by edges[i] and edges[i+1] n <- length(yhat) nbins <- length(edges) - 1L - # Result matrix: rows = number of queries, columns = nimp draws per query - imputed_values <- matrix(NA_real_, nrow = n, ncol = nimp) + # Result matrix: rows = number of queries, columns = mlocal draws per query + imputed_values <- matrix(NA_real_, nrow = n, ncol = mlocal) # Find the bin for each query value bin <- findInterval(yhat, edges, rightmost.closed = TRUE, all.inside = TRUE) @@ -345,8 +346,8 @@ draw.neighbors.pmm <- function(yhat, edges, lookup, nimp = 1L) { selected_bin <- ifelse(runif(n) < p_left, bin, pmin(bin + 1L, nbins)) # Vectorized sampling from lookup table - indices <- matrix(sample(1L:ncol(lookup), n * nimp, replace = TRUE), nrow = n) - impy <- matrix(lookup[cbind(selected_bin, indices)], nrow = n, ncol = nimp) + indices <- matrix(sample(1L:ncol(lookup), n * mlocal, replace = TRUE), nrow = n) + impy <- matrix(lookup[cbind(selected_bin, indices)], nrow = n, ncol = mlocal) return(impy) } diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 6254e55e2..46480a7a6 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -20,7 +20,7 @@ mice.impute.pmm( donors = 5L, nbins = NULL, use.matcher = FALSE, - nimp = 1L, + mlocal = 1L, ... ) } @@ -94,11 +94,11 @@ and the number of unique predictive values.} the C function \code{matcher()}, the now deprecated matching function that was default in versions of \code{mice} prior to \code{3.12.0}.} -\item{nimp}{Experimental. Number of random imputations per missing values +\item{mlocal}{Experimental. Number of random imputations per missing values generated from a fitted model under task \code{"train"}. -The default is 1. The \code{nimp} parameter is different from \code{m}, +The default is 1. The \code{mlocal} parameter is different from \code{m}, the number of multiple imputations, because it generates repeated -imputations from a single model. The \code{nimp} parameter is useful +imputations from a single model. The \code{mlocal} parameter is useful for large samples to reduce the computational burden, but still awaits support within the mice algorithm.} From b9b44ea7c6df2f8d6613f991b6f2195bac5690d6 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 17:20:03 +0100 Subject: [PATCH 061/147] Add support for train and fill to mice.impute.norm() --- R/mice.impute.norm.R | 27 ++++++++++++++++++++++++--- man/mice.impute.norm.Rd | 29 ++++++++++++++++++++++++++++- 2 files changed, 52 insertions(+), 4 deletions(-) diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 664e1d4e5..5cbc6edc8 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -34,11 +34,32 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.norm <- function(y, ry, x, wy = NULL, ...) { +mice.impute.norm <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + ridge = 1e-05, + ...) { + check.model.exists(model, task) + method <- "norm" if (is.null(wy)) wy <- !ry x <- cbind(1, as.matrix(x)) - parm <- .norm.draw(y, ry, x, ...) - x[wy, ] %*% parm$beta + rnorm(sum(wy)) * parm$sigma + + if (task == "fill" && check.model.match(model, x, method)) { + return(x[wy, ] %*% model$beta.mis + rnorm(sum(wy)) * model$sigma) + } + + parm <- .norm.draw(y, ry, x, ridge = ridge, ...) + + if (task == "train") { + model$setup <- list(method = method, + n = sum(ry), + task = task, + ridge = ridge) + model$beta.obs <- parm$coef + model$beta.mis <- parm$beta + model$sigma <- parm$sigma + } + + return(x[wy, ] %*% parm$beta + rnorm(sum(wy)) * parm$sigma) } diff --git a/man/mice.impute.norm.Rd b/man/mice.impute.norm.Rd index a082d1ffc..a814b7adf 100644 --- a/man/mice.impute.norm.Rd +++ b/man/mice.impute.norm.Rd @@ -5,7 +5,16 @@ \alias{norm} \title{Imputation by Bayesian linear regression} \usage{ -mice.impute.norm(y, ry, x, wy = NULL, ...) +mice.impute.norm( + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + ridge = 1e-05, + ... +) } \arguments{ \item{y}{Vector to be imputed} @@ -21,6 +30,24 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} + +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} + +\item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent +problems with multicollinearity. The default is \code{ridge = 1e-05}, +which means that 0.01 percent of the diagonal is added to the cross-product. +Larger ridges may result in more biased estimates. For highly noisy data +(e.g. many junk variables), set \code{ridge = 1e-06} or even lower to +reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} + \item{...}{Other named arguments.} } \value{ From d3c263f64cba774b356922fa1103fc32f3bdbc75 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 13 Mar 2025 18:27:05 +0100 Subject: [PATCH 062/147] Add support for train and fill to mice.impute.logreg() --- R/method.R | 5 +++-- R/mice.impute.logreg.R | 40 +++++++++++++++++++++++++++++---------- man/mice.impute.logreg.Rd | 13 ++++++++++++- 3 files changed, 45 insertions(+), 13 deletions(-) diff --git a/R/method.R b/R/method.R index 642b0fc1c..252254cfa 100644 --- a/R/method.R +++ b/R/method.R @@ -39,14 +39,15 @@ make.method <- function(data, } } + support <- c("pmm", "norm", "logreg") # check whether methods support train and fill tasks for (j in names(blocks)) { vname <- blocks[[j]] for (yname in vname) { mj <- method[j] task <- tasks[yname] - if (!is.null(task) && task %in% c("train", "fill") && !mj %in% c("pmm")) { - stop(paste("Method", mj, "does not support train and fill tasks. Use method = 'pmm' instead.")) + if (!is.null(task) && task %in% c("train", "fill") && !mj %in% support) { + stop(paste("Method", mj, "lacks support for train and fill tasks.")) } } } diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index fe1a7782e..656546b24 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -43,7 +43,11 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.logreg <- function(y, ry, x, wy = NULL, ...) { +mice.impute.logreg <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + ...) { + check.model.exists(model, task) + method <- "logreg" if (is.null(wy)) wy <- !ry # augment data in order to evade perfect prediction @@ -54,31 +58,47 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, ...) { wy <- aug$wy w <- aug$w - # fit model x <- cbind(1, as.matrix(x)) + + if (task == "fill" && check.model.match(model, x, method)) { + lp <- x[wy, , drop = FALSE] %*% model$beta.mis + return(logreg.draw(lp, levels(y))) + } + expr <- expression(glm.fit( x = x[ry, , drop = FALSE], y = y[ry], family = quasibinomial(link = logit), - weights = w[ry] - )) + weights = w[ry])) fit <- eval(expr) fit.sum <- summary.glm(fit) beta <- coef(fit) rv <- t(chol(sym(fit.sum$cov.unscaled))) beta.star <- beta + rv %*% rnorm(ncol(rv)) - # draw imputations - p <- 1 / (1 + exp(-(x[wy, , drop = FALSE] %*% beta.star))) + if (task == "train") { + model$setup <- list(method = method, + n = sum(ry), + task = task) + model$beta.obs <- as.matrix(beta) + model$beta.mis <- beta.star + model$factor <- list(labels = levels(y), quant = c(0, 1)) + } + + lp <- x[wy, , drop = FALSE] %*% beta.star + return(logreg.draw(lp, levels(y))) +} + +logreg.draw <- function(lp, levels) { + p <- 1 / (1 + exp(-lp)) vec <- (runif(nrow(p)) <= p) vec[vec] <- 1 - if (is.factor(y)) { - vec <- factor(vec, c(0, 1), levels(y)) + if (!is.null(levels)) { + vec <- factor(vec, c(0, 1), levels) } - vec + return(vec) } - #' Imputation by logistic regression using the bootstrap #' #' Imputes univariate missing data using logistic regression diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index 8427031d2..284e2dbf7 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -4,7 +4,7 @@ \alias{mice.impute.logreg} \title{Imputation by logistic regression} \usage{ -mice.impute.logreg(y, ry, x, wy = NULL, ...) +mice.impute.logreg(y, ry, x, wy = NULL, task = "impute", model = NULL, ...) } \arguments{ \item{y}{Vector to be imputed} @@ -20,6 +20,17 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} + +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} + \item{...}{Other named arguments.} } \value{ From b87cf31470a13ab9514f41a88ecfd5381a71a642 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 14 Mar 2025 13:07:14 +0100 Subject: [PATCH 063/147] Add support for train and fill to mice.impute.polyreg() --- R/method.R | 2 +- R/mice.impute.logreg.R | 4 +- R/mice.impute.polyreg.R | 115 ++++++++++++++++++++++++++----------- man/mice.impute.polyreg.Rd | 43 +++++++++++--- 4 files changed, 120 insertions(+), 44 deletions(-) diff --git a/R/method.R b/R/method.R index 252254cfa..de259aca6 100644 --- a/R/method.R +++ b/R/method.R @@ -39,7 +39,7 @@ make.method <- function(data, } } - support <- c("pmm", "norm", "logreg") + support <- c("pmm", "norm", "logreg", "polyreg") # check whether methods support train and fill tasks for (j in names(blocks)) { vname <- blocks[[j]] diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index 656546b24..ccd08ae1d 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -166,7 +166,7 @@ augment <- function(y, ry, x, wy, maxcat = 50) { # This function will prevent augmented data beyond the min and # the max of the data # Input: - # x: numeric data.frame (n rows) + # x: numeric matrix (n rows) # y: factor or numeric vector (lengt n) # ry: logical vector (length n) # Output: @@ -205,7 +205,7 @@ augment <- function(y, ry, x, wy, maxcat = 50) { e <- rep(rep(icod, each = 2), p) dimnames(d) <- list(paste0("AUG", seq_len(nrow(d))), dimnames(x)[[2]]) - xa <- rbind.data.frame(x, d) + xa <- rbind(x, d) # beware, concatenation of factors ya <- if (is.factor(y)) as.factor(levels(y)[c(y, e)]) else c(y, e) diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 8489ee4be..e7cd4d151 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -1,12 +1,21 @@ #' Imputation of unordered data by polytomous regression #' #' Imputes missing data in a categorical variable using polytomous regression +#' for unordered factors. #' #' @aliases mice.impute.polyreg #' @inheritParams mice.impute.pmm -#' @param nnet.maxit Tuning parameter for \code{nnet()}. -#' @param nnet.trace Tuning parameter for \code{nnet()}. -#' @param nnet.MaxNWts Tuning parameter for \code{nnet()}. +#' @param maxit Tuning parameter for \code{nnet()}. +#' @param MaxNWts Tuning parameter for \code{nnet()}. Internally, the procedure +#' computes the number of weights needed for the multinomial model as +#' 100 + \code{ncol(x)} times \code{length(levels(y)) - 1L)}. +#' Use \code{MaxNWts} to override this default if you get the +#' “too many weights” error. +#' @param nnet.maxit Legacy parameter. +#' @param nnet.MaxNWts Legacy parameter. +#' @param reltol Convergence parameter for \code{nnet()}. +#' @param warmstart Logical. If \code{TRUE}, the estimation process +#' uses weights from the previous iteration as warm starts. #' @return Vector with imputed data, same type as \code{y}, and of length #' \code{sum(wy)} #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 @@ -15,9 +24,6 @@ #' variables by the Bayesian polytomous regression model. See J.P.L. Brand #' (1999), Chapter 4, Appendix B. #' -#' By default, unordered factors with more than two levels are imputed by -#' \code{mice.impute.polyreg()}. -#' #' The method consists of the following steps: #' \enumerate{ #' \item Fit categorical response as a multinomial model @@ -51,14 +57,20 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.polyreg <- function(y, ry, x, wy = NULL, nnet.maxit = 100, - nnet.trace = FALSE, nnet.MaxNWts = 1500, ...) { +mice.impute.polyreg <- function( + y, ry, x, wy = NULL, + task = "impute", model = NULL, + nnet.maxit = NULL, nnet.MaxNWts = NULL, + maxit = NULL, MaxNWts = NULL, reltol = NULL, + warmstart = FALSE, ...) { + + check.model.exists(model, task) + method <- "polyreg" if (is.null(wy)) { wy <- !ry } - # augment data to evade issues with perfect prediction - x <- as.matrix(x) + # Augment data to evade issues with perfect prediction aug <- augment(y, ry, x, wy) x <- aug$x y <- aug$y @@ -66,43 +78,82 @@ mice.impute.polyreg <- function(y, ry, x, wy = NULL, nnet.maxit = 100, wy <- aug$wy w <- aug$w - fy <- as.factor(y) - nc <- length(levels(fy)) - un <- rep(runif(sum(wy)), each = nc) - - xy <- cbind.data.frame(y = y, x = x) - - if (ncol(x) == 0L) { - xy <- data.frame(xy, int = 1) + if (task == "fill") { + x <- cbind(`(Intercept)` = 1, x[wy, , drop = FALSE]) + check.model.match(model, x, method) + return(polyreg.draw(x = x, + beta = model$beta.mis, + levels = levels(y))) } - # escape with same impute if the dependent does not vary + # Escape estimation with same impute if the dependent does not vary cat.has.all.obs <- table(y[ry]) == sum(ry) if (any(cat.has.all.obs)) { - return(rep(levels(fy)[cat.has.all.obs], sum(wy))) + return(rep(levels(y)[cat.has.all.obs], sum(wy))) } - # prevent model from dots to be passed to multinom + # Set hyper-parameters + if (!missing(nnet.maxit)) maxit <- nnet.maxit + if (!missing(nnet.MaxNWts)) MaxNWts <- nnet.MaxNWts + MaxNWts_needed <- 100L + as.integer(ncol(x) * (length(levels(y)) - 1)) dots <- list(...) - dots$model <- NULL + dots$maxit <- ifelse(is.null(maxit), 100L, maxit) + dots$MaxNWts <- ifelse(is.null(MaxNWts), MaxNWts_needed, MaxNWts) + dots$reltol <- ifelse(is.null(reltol), 0.0001, reltol) + if (warmstart && task == "train" && !is.null(model$wts)) { + dots$Wts <- model$wts + } - # Call multinom() without `model` in dots + # Estimate model + xy <- cbind.data.frame(y, x) fit <- do.call(nnet::multinom, c( list(formula(xy), data = xy[ry, , drop = FALSE], weights = w[ry], - maxit = nnet.maxit, trace = nnet.trace, MaxNWts = nnet.MaxNWts), + model = FALSE, trace = FALSE), dots )) - post <- predict(fit, xy[wy, , drop = FALSE], type = "probs") - if (sum(wy) == 1) { - post <- matrix(post, nrow = 1, ncol = length(post)) + # Make names consistent + x <- cbind(`(Intercept)` = 1, x[wy, , drop = FALSE]) + beta <- coef(fit) + if (is.vector(beta)) { + beta <- matrix(beta, ncol = 1L) + } else { + beta <- t(beta) } - if (is.vector(post)) { - post <- matrix(c(1 - post, post), ncol = 2) + rownames(beta) <- gsub("`", "", rownames(beta)) + + # Save for future use + if (task == "train") { + model$setup <- list(method = method, + n = sum(ry), + task = task, + maxit = dots$maxit, + MaxNWts = dots$MaxNWts, + reltol = dots$reltol, + warmstart = warmstart) + model$result <- list(nWts = length(fit$wts), + value = fit$value, + convergence = fit$convergence) + model$beta.mis <- beta + if (warmstart) model$wts <- fit$wts + model$factor <- list(labels = levels(y), quant = NULL) } - draws <- un > apply(post, 1, cumsum) - idx <- 1 + apply(draws, 2, sum) - levels(fy)[idx] + # Draw imputations + return(polyreg.draw(x = x, + beta = beta, + levels = levels(y))) +} + +polyreg.draw <- function(x, beta, levels) { + if (nrow(x) == 0L) return(character(0)) + lp <- x %*% beta + p <- exp(lp) / rowSums(exp(lp) + 1) + post <- cbind(1 - rowSums(p), p) + un <- rep(runif(nrow(x)), each = length(levels)) + draws <- un > apply(post, 1L, cumsum) + idx <- 1L + apply(draws, 2L, sum) + return(levels[idx]) } + diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index 30cf4f435..bf4f97b8a 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -9,9 +9,14 @@ mice.impute.polyreg( ry, x, wy = NULL, - nnet.maxit = 100, - nnet.trace = FALSE, - nnet.MaxNWts = 1500, + task = "impute", + model = NULL, + nnet.maxit = NULL, + nnet.MaxNWts = NULL, + maxit = NULL, + MaxNWts = NULL, + reltol = NULL, + warmstart = FALSE, ... ) } @@ -29,11 +34,33 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} -\item{nnet.maxit}{Tuning parameter for \code{nnet()}.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} -\item{nnet.trace}{Tuning parameter for \code{nnet()}.} +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} -\item{nnet.MaxNWts}{Tuning parameter for \code{nnet()}.} +\item{nnet.maxit}{Legacy parameter.} + +\item{nnet.MaxNWts}{Legacy parameter.} + +\item{maxit}{Tuning parameter for \code{nnet()}.} + +\item{MaxNWts}{Tuning parameter for \code{nnet()}. Internally, the procedure +computes the number of weights needed for the multinomial model as +100 + \code{ncol(x)} times \code{length(levels(y)) - 1L)}. +Use \code{MaxNWts} to override this default if you get the +“too many weights” error.} + +\item{reltol}{Convergence parameter for \code{nnet()}.} + +\item{warmstart}{Logical. If \code{TRUE}, the estimation process +uses weights from the previous iteration as warm starts.} \item{...}{Other named arguments.} } @@ -43,15 +70,13 @@ Vector with imputed data, same type as \code{y}, and of length } \description{ Imputes missing data in a categorical variable using polytomous regression +for unordered factors. } \details{ The function \code{mice.impute.polyreg()} imputes categorical response variables by the Bayesian polytomous regression model. See J.P.L. Brand (1999), Chapter 4, Appendix B. -By default, unordered factors with more than two levels are imputed by -\code{mice.impute.polyreg()}. - The method consists of the following steps: \enumerate{ \item Fit categorical response as a multinomial model From d39508c276c222efffaf18fa14678c498a3bbba8 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 16 Mar 2025 17:32:45 +0100 Subject: [PATCH 064/147] Where possible, store beta.obs and beta.mis as vectors --- R/check.model.R | 5 +++-- R/mice.impute.logreg.R | 4 ++-- R/mice.impute.norm.R | 4 ++-- R/mice.impute.pmm.R | 8 ++++---- R/mice.impute.polyreg.R | 6 ++++-- 5 files changed, 15 insertions(+), 12 deletions(-) diff --git a/R/check.model.R b/R/check.model.R index 939e73c69..c970fa80a 100644 --- a/R/check.model.R +++ b/R/check.model.R @@ -13,9 +13,10 @@ check.model.match <- function(model, x, method) { if (!length(formula)) { stop("No model stored in environment") } - mnames <- rownames(model$beta.mis) + mnames <- names(model$beta.mis) + if (is.matrix(model$beta.mis)) mnames <- rownames(model$beta.mis) dnames <- colnames(x) - if (ncol(x) != nrow(model$beta.mis) || any(mnames != dnames)) { + if (ncol(x) != length(mnames) || any(mnames != dnames)) { stop(paste("Model-Data mismatch: ", deparse(formula), "\n", " Model:", paste(mnames, collapse = " "), "\n", " Data: ", paste(dnames, collapse = " "), "\n")) diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index ccd08ae1d..bd9ec2b53 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -80,8 +80,8 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, model$setup <- list(method = method, n = sum(ry), task = task) - model$beta.obs <- as.matrix(beta) - model$beta.mis <- beta.star + model$beta.obs <- drop(beta) + model$beta.mis <- drop(beta.star) model$factor <- list(labels = levels(y), quant = c(0, 1)) } diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 5cbc6edc8..726bda170 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -54,8 +54,8 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, n = sum(ry), task = task, ridge = ridge) - model$beta.obs <- parm$coef - model$beta.mis <- parm$beta + model$beta.obs <- drop(parm$coef) + model$beta.mis <- drop(parm$beta) model$sigma <- parm$sigma } diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 5c7aec9e4..0ff50ea26 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -215,12 +215,12 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # Predict ynum on observed data with linear model parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) if (matchtype == 1L) { - beta.obs <- parm$coef - beta.mis <- parm$beta + beta.obs <- drop(parm$coef) + beta.mis <- drop(parm$beta) } else if (matchtype == 0L) { - beta.mis <- beta.obs <- parm$coef + beta.mis <- beta.obs <- drop(parm$coef) } else if (matchtype == 2L) { - beta.mis <- beta.obs <- parm$beta + beta.mis <- beta.obs <- drop(parm$beta) } x_ry <- x[ry, , drop = FALSE] x_wy <- x[wy, , drop = FALSE] diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index e7cd4d151..ff0fa4cf8 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -79,7 +79,8 @@ mice.impute.polyreg <- function( w <- aug$w if (task == "fill") { - x <- cbind(`(Intercept)` = 1, x[wy, , drop = FALSE]) + x <- x[wy, , drop = FALSE] + x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) check.model.match(model, x, method) return(polyreg.draw(x = x, beta = model$beta.mis, @@ -114,7 +115,8 @@ mice.impute.polyreg <- function( )) # Make names consistent - x <- cbind(`(Intercept)` = 1, x[wy, , drop = FALSE]) + x <- x[wy, , drop = FALSE] + x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) beta <- coef(fit) if (is.vector(beta)) { beta <- matrix(beta, ncol = 1L) From 0a83f4b66df4e2b2fbe2c1ed12e6168987978a08 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 16 Mar 2025 17:34:16 +0100 Subject: [PATCH 065/147] Add support for train and fill for mice.impute.polr() --- NAMESPACE | 1 + R/imports.R | 2 +- R/method.R | 2 +- R/mice.impute.polr.R | 169 +++++++++++++++++++++++++++------------- man/mice.impute.polr.Rd | 71 +++++++++++------ 5 files changed, 164 insertions(+), 81 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index d2458beaa..1f7cc149e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -239,6 +239,7 @@ importFrom(stats,na.omit) importFrom(stats,na.pass) importFrom(stats,pchisq) importFrom(stats,pf) +importFrom(stats,plogis) importFrom(stats,predict) importFrom(stats,pt) importFrom(stats,qt) diff --git a/R/imports.R b/R/imports.R index 4e600f17e..bbece5460 100644 --- a/R/imports.R +++ b/R/imports.R @@ -15,7 +15,7 @@ #' formula gaussian getCall #' glm is.empty.model lm lm.fit #' median model.frame model.matrix -#' na.exclude na.omit na.pass +#' na.exclude na.omit na.pass plogis #' pf predict pt qt quantile quasibinomial #' rbinom rchisq reformulate rgamma rnorm runif #' sd setNames summary.glm terms update var vcov diff --git a/R/method.R b/R/method.R index de259aca6..8995595a7 100644 --- a/R/method.R +++ b/R/method.R @@ -39,7 +39,7 @@ make.method <- function(data, } } - support <- c("pmm", "norm", "logreg", "polyreg") + support <- c("pmm", "norm", "logreg", "polr", "polyreg") # check whether methods support train and fill tasks for (j in names(blocks)) { vname <- blocks[[j]] diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index ef354f18d..2d260cf86 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -1,36 +1,33 @@ -#' Imputation of ordered data by polytomous regression +#' Imputation of categorical data by the ordered logistic (polr) model +#' +#' The function \code{mice.impute.polr()} imputes missing data in an +#' ordinal categorical variable using the proportional odds logistic +#' regression model (`polr`). This model, also known as the cumulative +#' link model, estimates cumulative probabilities using a set of +#' threshold parameters. The method assumes that the effect of predictor +#' variables is the same across all category transitions (proportional odds +#' assumption). #' -#' Imputes missing data in a categorical variable using polytomous regression #' @aliases mice.impute.polr #' @inheritParams mice.impute.pmm -#' @param nnet.maxit Tuning parameter for \code{nnet()}. -#' @param nnet.trace Tuning parameter for \code{nnet()}. -#' @param nnet.MaxNWts Tuning parameter for \code{nnet()}. -#' @param polr.to.loggedEvents A logical indicating whether each fallback +#' @inheritParams mice.impute.polyreg +#' @param polr.to.loggedEvents A logical indicating whether each fall-back #' to the \code{multinom()} function should be written to \code{loggedEvents}. #' The default is \code{FALSE}. #' @return Vector with imputed data, same type as \code{y}, and of length #' \code{sum(wy)} #' @details -#' The function \code{mice.impute.polr()} imputes for ordered categorical response -#' variables by the proportional odds logistic regression (polr) model. The -#' function repeatedly applies logistic regression on the successive splits. The -#' model is also known as the cumulative link model. -#' -#' By default, ordered factors with more than two levels are imputed by -#' \code{mice.impute.polr}. -#' #' The algorithm of \code{mice.impute.polr} uses the function \code{polr()} from #' the \code{MASS} package. #' #' In order to avoid bias due to perfect prediction, the algorithm augment the #' data according to the method of White, Daniel and Royston (2010). #' -#' The call to \code{polr} might fail, usually because the data are very sparse. +#' Calls to \code{polr} might fail if the data are very sparse. #' In that case, \code{multinom} is tried as a fallback. #' If the local flag \code{polr.to.loggedEvents} is set to TRUE, -#' a record is written -#' to the \code{loggedEvents} component of the \code{\link{mids}} object. +#' a record is written to the \code{loggedEvents} component of +#' the \code{\link{mids}} object. #' Use \code{mice(data, polr.to.loggedEvents = TRUE)} to set the flag. #' #' @note @@ -40,7 +37,7 @@ #' for \code{polr} in these versions were in fact handled by \code{multinom}. #' See \url{https://github.com/amices/mice/issues/206} for details. #' -#' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 +#' @author Stef van Buuren #' @seealso \code{\link{mice}}, \code{\link[nnet]{multinom}}, #' \code{\link[MASS]{polr}} #' @references @@ -49,10 +46,6 @@ #' Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical #' Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} #' -#' Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of -#' multiple imputation strategies for the statistical analysis of incomplete -#' data sets.} Dissertation. Rotterdam: Erasmus University. -#' #' White, I.R., Daniel, R. Royston, P. (2010). Avoiding bias due to perfect #' prediction in multiple imputation of incomplete categorical variables. #' \emph{Computational Statistics and Data Analysis}, 54, 2267-2275. @@ -62,52 +55,120 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.polr <- function(y, ry, x, wy = NULL, nnet.maxit = 100, - nnet.trace = FALSE, nnet.MaxNWts = 1500, - polr.to.loggedEvents = FALSE, ...) { - if (is.null(wy)) wy <- !ry +mice.impute.polr <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + nnet.maxit = NULL, nnet.MaxNWts = NULL, + maxit = NULL, MaxNWts = NULL, reltol = NULL, + warmstart = FALSE, polr.to.loggedEvents = FALSE, + ...) { + check.model.exists(model, task) + method <- "polr" + if (is.null(wy)) { + wy <- !ry + } - # augment data to evade issues with perfect prediction - x <- as.matrix(x) + # Augment data to evade issues with perfect prediction aug <- augment(y, ry, x, wy) x <- aug$x y <- aug$y ry <- aug$ry wy <- aug$wy w <- aug$w - xy <- cbind.data.frame(y = y, x = x) - ## polr may fail on sparse data. We revert to multinom in such cases. - fit <- try( - suppressWarnings(MASS::polr(formula(xy), - data = xy[ry, , drop = FALSE], - weights = w[ry], - control = list(...) - )), - silent = TRUE + if (task == "fill" && check.model.match(model, x, method)) { + impy <- polr.draw(x = x[wy, , drop = FALSE], + beta = model$beta.mis, + zeta = model$zeta.mis, + levels = model$factor$labels) + return(impy) + } + + # Escape estimation with same impute if the dependent does not vary + cat.has.all.obs <- table(y[ry]) == sum(ry) + if (any(cat.has.all.obs)) { + return(rep(levels(y)[cat.has.all.obs], sum(wy))) + } + + # Set hyper-parameters + dots <- list( + model = FALSE, + method = "logistic", + control = list( + trace = 0L, + maxit = ifelse(is.null(maxit), 100L, maxit), + reltol = ifelse(is.null(reltol), 0.0001, reltol)) ) + if (warmstart && task == "train" && + !is.null(model$beta.mis) && !is.null(model$zeta.mis)) { + dots$start <- c(model$beta.mis, model$zeta.mis) + } + + # Estimate ordered logistic (polr) model with polr + # Fall back to multinom if polr fails + xy <- cbind.data.frame(y, x) + execute <- "polr" + fun <- MASS::polr + args <- c(list(formula = formula(xy), + data = xy[ry, , drop = FALSE]), + dots) + fit <- try(suppressWarnings(do.call(fun, args)), silent = TRUE) if (inherits(fit, "try-error")) { if (polr.to.loggedEvents) { updateLog(out = "polr falls back to multinom", frame = 6) } - fit <- nnet::multinom(formula(xy), - data = xy[ry, , drop = FALSE], - weights = w[ry], - maxit = nnet.maxit, trace = nnet.trace, - MaxNWts = nnet.MaxNWts, ... - ) + execute <- "multinom" + impy <- mice.impute.polyreg( + y = y, ry = ry, x = x, wy = wy, + task = task, model = model, + nnet.maxit = nnet.maxit, nnet.MaxNWts = nnet.MaxNWts, + maxit = maxit, MaxNWts = MaxNWts, reltol = reltol, + warmstart = warmstart, ...) } - post <- predict(fit, xy[wy, , drop = FALSE], type = "probs") - if (sum(wy) == 1) { - post <- matrix(post, nrow = 1, ncol = length(post)) + + # Save for future use + if (task == "train" && execute == "polr") { + model$setup <- list(method = method, + n = sum(ry), + task = task, + maxit = dots$maxit, + reltol = dots$reltol, + warmstart = warmstart) + model$result <- list(value = fit$value, + convergence = fit$convergence) + model$beta.mis <- coef(fit) + model$zeta.mis <- fit$zeta + model$factor <- list(labels = levels(y), quant = NULL) } - fy <- as.factor(y) - nc <- length(levels(fy)) - un <- rep(runif(sum(wy)), each = nc) - if (is.vector(post)) { - post <- matrix(c(1 - post, post), ncol = 2) + + # Draw imputations + if (execute == "polr") { + impy <- polr.draw(x = x[wy, , drop = FALSE], + beta = coef(fit), + zeta = fit$zeta, + levels = levels(y)) } - draws <- un > apply(post, 1, cumsum) - idx <- 1 + apply(draws, 2, sum) - levels(fy)[idx] + + return(impy) +} + +polr.draw <- function(x, beta, zeta, levels) { + if (nrow(x) == 0L) return(character(0)) + eta <- x %*% beta + cumpr <- plogis(matrix(zeta, nrow(x), length(zeta), byrow = TRUE) - as.vector(eta)) + post <- t(apply(cumpr, 1L, function(x) diff(c(0, x, 1)))) + un <- rep(runif(nrow(x)), each = length(levels)) + draws <- un > apply(post, 1L, cumsum) + idx <- 1L + apply(draws, 2L, sum) + return(levels[idx]) } + +safe_call <- function(fun, args) { + result <- try(suppressWarnings(do.call(fun, args)), silent = TRUE) + return(result) +} +safe_polr <- function(formula, data, ...) { + args <- list(formula = formula, data = data, ...) + safe_call(MASS::polr, args) +} + + diff --git a/man/mice.impute.polr.Rd b/man/mice.impute.polr.Rd index 21f17912b..40322b9fd 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -2,16 +2,21 @@ % Please edit documentation in R/mice.impute.polr.R \name{mice.impute.polr} \alias{mice.impute.polr} -\title{Imputation of ordered data by polytomous regression} +\title{Imputation of categorical data by the ordered logistic (polr) model} \usage{ mice.impute.polr( y, ry, x, wy = NULL, - nnet.maxit = 100, - nnet.trace = FALSE, - nnet.MaxNWts = 1500, + task = "impute", + model = NULL, + nnet.maxit = NULL, + nnet.MaxNWts = NULL, + maxit = NULL, + MaxNWts = NULL, + reltol = NULL, + warmstart = FALSE, polr.to.loggedEvents = FALSE, ... ) @@ -30,13 +35,35 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} -\item{nnet.maxit}{Tuning parameter for \code{nnet()}.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} -\item{nnet.trace}{Tuning parameter for \code{nnet()}.} +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} -\item{nnet.MaxNWts}{Tuning parameter for \code{nnet()}.} +\item{nnet.maxit}{Legacy parameter.} -\item{polr.to.loggedEvents}{A logical indicating whether each fallback +\item{nnet.MaxNWts}{Legacy parameter.} + +\item{maxit}{Tuning parameter for \code{nnet()}.} + +\item{MaxNWts}{Tuning parameter for \code{nnet()}. Internally, the procedure +computes the number of weights needed for the multinomial model as +100 + \code{ncol(x)} times \code{length(levels(y)) - 1L)}. +Use \code{MaxNWts} to override this default if you get the +“too many weights” error.} + +\item{reltol}{Convergence parameter for \code{nnet()}.} + +\item{warmstart}{Logical. If \code{TRUE}, the estimation process +uses weights from the previous iteration as warm starts.} + +\item{polr.to.loggedEvents}{A logical indicating whether each fall-back to the \code{multinom()} function should be written to \code{loggedEvents}. The default is \code{FALSE}.} @@ -47,28 +74,26 @@ Vector with imputed data, same type as \code{y}, and of length \code{sum(wy)} } \description{ -Imputes missing data in a categorical variable using polytomous regression +The function \code{mice.impute.polr()} imputes missing data in an +ordinal categorical variable using the proportional odds logistic +regression model (\code{polr}). This model, also known as the cumulative +link model, estimates cumulative probabilities using a set of +threshold parameters. The method assumes that the effect of predictor +variables is the same across all category transitions (proportional odds +assumption). } \details{ -The function \code{mice.impute.polr()} imputes for ordered categorical response -variables by the proportional odds logistic regression (polr) model. The -function repeatedly applies logistic regression on the successive splits. The -model is also known as the cumulative link model. - -By default, ordered factors with more than two levels are imputed by -\code{mice.impute.polr}. - The algorithm of \code{mice.impute.polr} uses the function \code{polr()} from the \code{MASS} package. In order to avoid bias due to perfect prediction, the algorithm augment the data according to the method of White, Daniel and Royston (2010). -The call to \code{polr} might fail, usually because the data are very sparse. +Calls to \code{polr} might fail if the data are very sparse. In that case, \code{multinom} is tried as a fallback. If the local flag \code{polr.to.loggedEvents} is set to TRUE, -a record is written -to the \code{loggedEvents} component of the \code{\link{mids}} object. +a record is written to the \code{loggedEvents} component of +the \code{\link{mids}} object. Use \code{mice(data, polr.to.loggedEvents = TRUE)} to set the flag. } \note{ @@ -83,10 +108,6 @@ Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} -Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of -multiple imputation strategies for the statistical analysis of incomplete -data sets.} Dissertation. Rotterdam: Erasmus University. - White, I.R., Daniel, R. Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. \emph{Computational Statistics and Data Analysis}, 54, 2267-2275. @@ -122,7 +143,7 @@ Other univariate imputation functions: \code{\link{mice.impute.ri}()} } \author{ -Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 +Stef van Buuren } \concept{univariate imputation functions} \keyword{datagen} From 215618bdf0aad6e3de4d3546c5f8a947f9361cfc Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 17 Mar 2025 15:42:34 +0100 Subject: [PATCH 066/147] In the mids-object, store models as a list (instead of an enviroment) for better user experience --- R/initialize.models.R | 16 --- R/mice.R | 42 +++----- R/mids.R | 4 +- R/models.R | 186 +++++++++++++++++++++++++++++++++++ man/export.models.env.Rd | 42 ++++++++ man/import.models.env.Rd | 51 ++++++++++ man/initialize.models.env.Rd | 40 ++++++++ man/mice.Rd | 16 ++- man/mids.Rd | 8 +- tests/testthat/test-models.R | 9 ++ tests/testthat/test-tasks.R | 4 +- 11 files changed, 355 insertions(+), 63 deletions(-) delete mode 100644 R/initialize.models.R create mode 100644 R/models.R create mode 100644 man/export.models.env.Rd create mode 100644 man/import.models.env.Rd create mode 100644 man/initialize.models.env.Rd create mode 100644 tests/testthat/test-models.R diff --git a/R/initialize.models.R b/R/initialize.models.R deleted file mode 100644 index e4c238621..000000000 --- a/R/initialize.models.R +++ /dev/null @@ -1,16 +0,0 @@ -# -## Example usage -# visitSequence <- c("block1", "block2", "block3") # Example block names -# m <- 5 # Number of imputations -# models <- initialize_models(visitSequence, m) -initialize.models <- function(visitSequence, m) { - models <- new.env(parent = emptyenv()) - - # Pre-allocate NULL entries for every (blockname, iteration) pair - for (block in visitSequence) { - for (iter in 1:m) { - assign(paste0(block, "_", iter), NULL, envir = models) - } - } - return(models) -} diff --git a/R/mice.R b/R/mice.R index f37cefe69..19fd2c1dc 100644 --- a/R/mice.R +++ b/R/mice.R @@ -235,9 +235,11 @@ #' single value is provided, it applies to the variables in all blocks. The #' length of the vector must match the number of variables present in the #' blocks. The default is \code{"impute"}. -#' @param models An environment that can be used to store fitted imputation -#' models. The models are stored in the environment under the name of the -#' block. The models can be used to predict missing values in new data. +#' @param models A list that stores fitted imputation models. The models +#' can be used to impute missing values in new data. \code{models} is +#' only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +#' to fill in missing values in new data, where \code{trained} is the +#' \code{mids} object returned by \code{mice(..., tasks = 'train')}. #' @param post A vector of strings with length \code{ncol(data)} specifying #' expressions as strings. Each string is parsed and #' executed within the \code{sampler()} function to post-process @@ -323,18 +325,14 @@ #' #' # store all imputation models #' imp1 <- mice(nhanes, tasks = "train", print = FALSE) -#' ls(imp1$models$bmi$"1") -#' imp1$models$bmi$"1"$formula -#' imp1$models$bmi$"1"$beta.mis +#' imp1$models$bmi[[1]] #' #' # Store model for `bmi`, estimate others as usual #' tasks <- c("age" = "impute", "bmi" = "train", "hyp" = "impute", "chl" = "impute") #' imp2 <- mice(nhanes, tasks = tasks, print = FALSE) #' #' # Inspects the stored model for imputation 1 for `bmi` -#' ls(imp2$models$bmi$"1") -#' imp2$models$bmi$"1"$formula -#' imp2$models$bmi$"1"$beta.mis +#' imp2$models$bmi[[1]] #' #' # Fill missing `bmi` values using pre-trained model #' tasks <- c("age" = "impute", "bmi" = "fill", "hyp" = "impute", "chl" = "impute") @@ -516,36 +514,18 @@ mice <- function(data, post <- setup$post # Initialize models for "train" and "fill" blocks that are missing in models - if (is.null(models)) { - models <- new.env(parent = emptyenv()) - } - model.vars <- names(tasks[tasks %in% c("train", "fill")]) - # if (any(tasks %in% "fill") && m > m.train) { - # stop("Number of imputations (", m, ") is greater than training model (", m.train, ").") - # } - for (varname in model.vars) { - if (tasks[varname] == "train") { - if (!exists(varname, envir = models)) { - models[[varname]] <- new.env(parent = emptyenv()) - } - for (i in 1:m) { - if (!exists(as.character(i), envir = models[[varname]])) { - models[[varname]][[as.character(i)]] <- new.env(parent = emptyenv()) - } - } - } - } + models <- initialize.models.env(models, tasks, m) # initialize imputations - nmis <- apply(is.na(data), 2, sum) + nmis <- apply(is.na(data), 2L, sum) imp <- initialize.imp( data, m, ignore, where, blocks, visitSequence, method, nmis, data.init ) # and iterate... - from <- 1 - to <- from + maxit - 1 + from <- 1L + to <- from + maxit - 1L q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, diff --git a/R/mids.R b/R/mids.R index 3d8196510..f638adcc8 100644 --- a/R/mids.R +++ b/R/mids.R @@ -226,7 +226,7 @@ mids <- function( post = post, blots = blots, tasks = tasks, - models = models, + models = export.models.env(models), ignore = ignore, seed = seed, iteration = iteration, @@ -247,7 +247,7 @@ mids <- function( iteration = iteration, lastSeedValue = lastSeedValue, tasks = tasks, - models = models, + models = export.models.env(models), store = store, version = packageVersion("mice"), date = Sys.Date(), diff --git a/R/models.R b/R/models.R new file mode 100644 index 000000000..74c4a178d --- /dev/null +++ b/R/models.R @@ -0,0 +1,186 @@ +#' Initialize Models Environment +#' +#' Creates an environment structure for models based on specified task types. +#' It ensures that a nested environment is created for each variable in `tasks` +#' that is labeled as `"train"`, with sub-environments for each iteration +#' from `1` to `m`. +#' +#' @param models An existing environment to store models. If `NULL`, a +#' new environment is created. +#' @param tasks A named character vector where names are variable names and +#' values specify task types. Only variables labeled `"train"` will have +#' nested environments created. +#' @param m An integer specifying the number of nested sub-environments to +#' create under each `"train"` variable. +#' +#' @return An environment containing model environments structured as: +#' \itemize{ +#' \item \code{models$varname} - An environment for each `"train"` variable. +#' \item \code{models$varname$i} - Nested environments for each iteration from `1` to `m`. +#' } +#' @examples +#' tasks <- c(a = "train", b = "fill", c = "train", d = "other") +#' m <- 3 +#' models_env <- mice:::initialize.models.env(tasks = tasks, m = m) +#' ls(models_env) # Lists "a" and "c" (only "train" tasks) +#' ls(models_env$a) # Lists "1", "2", "3" +#' ls(models_env$c) # Lists "1", "2", "3" +initialize.models.env <- function(models = NULL, tasks, m) { + + # Import models into environment from a model list object + if (is.list(models)) { + models <- import.models.env(models) + return(models) + } + + # Ensure `models` is an environment + if (is.null(models)) { + models <- new.env(parent = emptyenv()) + } + + # Identify variables that require models (i.e., "train" or "fill" tasks) + model.vars <- names(tasks[tasks %in% c("train", "fill")]) + + for (varname in model.vars) { + if (tasks[varname] == "train") { + # Create an environment for the variable if it doesn't exist + if (!exists(varname, envir = models)) { + models[[varname]] <- new.env(parent = emptyenv()) + } + + # Create nested environments for `1:m` + for (i in seq_len(m)) { + if (!exists(as.character(i), envir = models[[varname]])) { + models[[varname]][[as.character(i)]] <- new.env(parent = emptyenv()) + } + } + } + } + + return(models) +} + +#' Convert Nested Environments to a List of m-Lists +#' +#' Recursively converts a three-level environment structure into a user-friendly +#' list where the imputation level is stored as a vector of length `m`. +#' +#' @param env The root environment containing task environments. +#' @param m The number of imputations (assumes all tasks have the same `m`). +#' @return A named list where: +#' \itemize{ +#' \item Each element corresponds to a task (e.g., "train" variables). +#' \item Each task is stored as a vector of `m` lists (one per imputation iteration). +#' \item Each list contains the objects stored for that imputation. +#' } +#' @examples +#' # Create a nested environment structure +#' models_env <- new.env() +#' models_env$a <- new.env() +#' models_env$a$`1` <- new.env() +#' models_env$a$`1`$model <- "Model A1" +#' models_env$a$`2` <- new.env() +#' models_env$a$`2`$model <- "Model A2" +#' models_env$b <- new.env() +#' models_env$b$`1` <- new.env() +#' models_env$b$`1`$model <- "Model B1" +#' +#' # Convert to a list +#' models_list <- mice:::export.models.env(models_env, m = 2) +#' print(models_list) +#' +export.models.env <- function(env, m = NULL) { + if (!is.environment(env)) stop("Input must be an environment") + + env_to_list <- function(env) { + obj_list <- as.list(env, all.names = TRUE) + for (name in names(obj_list)) { + if (is.environment(obj_list[[name]])) { + obj_list[[name]] <- env_to_list(obj_list[[name]]) + } + } + return(obj_list) + } + + # Convert first-level environment into a list + models_list <- env_to_list(env) + m <- ifelse(is.null(m), max(sapply(models_list, length)), m) + + # Restructure each task's models into a vector of m lists + for (varname in names(models_list)) { + task_models <- models_list[[varname]] + + # Initialize an empty list of length m + imputation_list <- vector("list", m) + + # Fill in models from the extracted task_models + for (i in seq_len(m)) { + iter_name <- as.character(i) + imputation_list[[i]] <- + if (iter_name %in% names(task_models)) { + task_models[[iter_name]] + } else { + list() + } + } + + # Replace with the m-length vector of lists + models_list[[varname]] <- imputation_list + } + + return(models_list) +} + +#' Convert a List of m-Lists Back to a Nested Environment +#' +#' Converts a structured list back into a nested environment where: +#' - The first level contains task names (e.g., "train" variables). +#' - The second level contains iteration indices (`1:m`). +#' - The third level contains stored objects within each iteration. +#' +#' @param models_list A list where: +#' - Each element corresponds to a task (e.g., "train" variables). +#' - Each task contains a vector of `m` lists (one per imputation iteration). +#' - Each list contains the stored objects for that iteration. +#' @return A nested environment structured as: +#' - `models_env$varname` (An environment for each task). +#' - `models_env$varname$i` (Nested environments for each iteration). +#' - Objects within each iteration are stored inside their respective environments. +#' +#' @examples +#' # Example list structure +#' models_list <- list( +#' a = list( +#' list(model = "Model A1"), +#' list(model = "Model A2") +#' ), +#' b = list( +#' list(model = "Model B1"), +#' list() # Empty list for missing iteration +#' ) +#' ) +#' +#' # Convert list to environment +#' models_env <- mice:::import.models.env(models_list) +#' print(ls(models_env)) # Should list "a" and "b" +#' print(ls(models_env$a)) # Should list "1" and "2" +#' print(models_env$a$`1`$model) # Should be "Model A1" +import.models.env <- function(models_list) { + if (!is.list(models_list)) stop("Input must be a list") + + models_env <- new.env(parent = emptyenv()) + + for (varname in names(models_list)) { + models_env[[varname]] <- new.env(parent = emptyenv()) # Create first-level environment + + for (i in seq_along(models_list[[varname]])) { + iteration_data <- models_list[[varname]][[i]] + + if (length(iteration_data) > 0) { # Only create non-empty environments + models_env[[varname]][[as.character(i)]] <- list2env(iteration_data, parent = emptyenv()) + } + } + } + + return(models_env) +} diff --git a/man/export.models.env.Rd b/man/export.models.env.Rd new file mode 100644 index 000000000..409226833 --- /dev/null +++ b/man/export.models.env.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{export.models.env} +\alias{export.models.env} +\title{Convert Nested Environments to a List of m-Lists} +\usage{ +export.models.env(env, m = NULL) +} +\arguments{ +\item{env}{The root environment containing task environments.} + +\item{m}{The number of imputations (assumes all tasks have the same \code{m}).} +} +\value{ +A named list where: +\itemize{ +\item Each element corresponds to a task (e.g., "train" variables). +\item Each task is stored as a vector of \code{m} lists (one per imputation iteration). +\item Each list contains the objects stored for that imputation. +} +} +\description{ +Recursively converts a three-level environment structure into a user-friendly +list where the imputation level is stored as a vector of length \code{m}. +} +\examples{ +# Create a nested environment structure +models_env <- new.env() +models_env$a <- new.env() +models_env$a$`1` <- new.env() +models_env$a$`1`$model <- "Model A1" +models_env$a$`2` <- new.env() +models_env$a$`2`$model <- "Model A2" +models_env$b <- new.env() +models_env$b$`1` <- new.env() +models_env$b$`1`$model <- "Model B1" + +# Convert to a list +models_list <- mice:::export.models.env(models_env, m = 2) +print(models_list) + +} diff --git a/man/import.models.env.Rd b/man/import.models.env.Rd new file mode 100644 index 000000000..109686605 --- /dev/null +++ b/man/import.models.env.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{import.models.env} +\alias{import.models.env} +\title{Convert a List of m-Lists Back to a Nested Environment} +\usage{ +import.models.env(models_list) +} +\arguments{ +\item{models_list}{A list where: +\itemize{ +\item Each element corresponds to a task (e.g., "train" variables). +\item Each task contains a vector of \code{m} lists (one per imputation iteration). +\item Each list contains the stored objects for that iteration. +}} +} +\value{ +A nested environment structured as: +\itemize{ +\item \code{models_env$varname} (An environment for each task). +\item \code{models_env$varname$i} (Nested environments for each iteration). +\item Objects within each iteration are stored inside their respective environments. +} +} +\description{ +Converts a structured list back into a nested environment where: +\itemize{ +\item The first level contains task names (e.g., "train" variables). +\item The second level contains iteration indices (\code{1:m}). +\item The third level contains stored objects within each iteration. +} +} +\examples{ +# Example list structure +models_list <- list( + a = list( + list(model = "Model A1"), + list(model = "Model A2") + ), + b = list( + list(model = "Model B1"), + list() # Empty list for missing iteration + ) +) + +# Convert list to environment +models_env <- mice:::import.models.env(models_list) +print(ls(models_env)) # Should list "a" and "b" +print(ls(models_env$a)) # Should list "1" and "2" +print(models_env$a$`1`$model) # Should be "Model A1" +} diff --git a/man/initialize.models.env.Rd b/man/initialize.models.env.Rd new file mode 100644 index 000000000..2efff4d52 --- /dev/null +++ b/man/initialize.models.env.Rd @@ -0,0 +1,40 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{initialize.models.env} +\alias{initialize.models.env} +\title{Initialize Models Environment} +\usage{ +initialize.models.env(models = NULL, tasks, m) +} +\arguments{ +\item{models}{An existing environment to store models. If \code{NULL}, a +new environment is created.} + +\item{tasks}{A named character vector where names are variable names and +values specify task types. Only variables labeled \code{"train"} will have +nested environments created.} + +\item{m}{An integer specifying the number of nested sub-environments to +create under each \code{"train"} variable.} +} +\value{ +An environment containing model environments structured as: +\itemize{ +\item \code{models$varname} - An environment for each \code{"train"} variable. +\item \code{models$varname$i} - Nested environments for each iteration from \code{1} to \code{m}. +} +} +\description{ +Creates an environment structure for models based on specified task types. +It ensures that a nested environment is created for each variable in \code{tasks} +that is labeled as \code{"train"}, with sub-environments for each iteration +from \code{1} to \code{m}. +} +\examples{ +tasks <- c(a = "train", b = "fill", c = "train", d = "other") +m <- 3 +models_env <- mice:::initialize.models.env(tasks = tasks, m = m) +ls(models_env) # Lists "a" and "c" (only "train" tasks) +ls(models_env$a) # Lists "1", "2", "3" +ls(models_env$c) # Lists "1", "2", "3" +} diff --git a/man/mice.Rd b/man/mice.Rd index 793e97833..0ea098e69 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -148,9 +148,11 @@ single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks. The default is \code{"impute"}.} -\item{models}{An environment that can be used to store fitted imputation -models. The models are stored in the environment under the name of the -block. The models can be used to predict missing values in new data.} +\item{models}{A list that stores fitted imputation models. The models +can be used to impute missing values in new data. \code{models} is +only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +to fill in missing values in new data, where \code{trained} is the +\code{mids} object returned by \code{mice(..., tasks = 'train')}.} \item{post}{A vector of strings with length \code{ncol(data)} specifying expressions as strings. Each string is parsed and @@ -423,18 +425,14 @@ imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE # store all imputation models imp1 <- mice(nhanes, tasks = "train", print = FALSE) -ls(imp1$models$bmi$"1") -imp1$models$bmi$"1"$formula -imp1$models$bmi$"1"$beta.mis +imp1$models$bmi[[1]] # Store model for `bmi`, estimate others as usual tasks <- c("age" = "impute", "bmi" = "train", "hyp" = "impute", "chl" = "impute") imp2 <- mice(nhanes, tasks = tasks, print = FALSE) # Inspects the stored model for imputation 1 for `bmi` -ls(imp2$models$bmi$"1") -imp2$models$bmi$"1"$formula -imp2$models$bmi$"1"$beta.mis +imp2$models$bmi[[1]] # Fill missing `bmi` values using pre-trained model tasks <- c("age" = "impute", "bmi" = "fill", "hyp" = "impute", "chl" = "impute") diff --git a/man/mids.Rd b/man/mids.Rd index 3fe6e7c37..b0fe71748 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -173,9 +173,11 @@ single value is provided, it applies to the variables in all blocks. The length of the vector must match the number of variables present in the blocks. The default is \code{"impute"}.} -\item{models}{An environment that can be used to store fitted imputation -models. The models are stored in the environment under the name of the -block. The models can be used to predict missing values in new data.} +\item{models}{A list that stores fitted imputation models. The models +can be used to impute missing values in new data. \code{models} is +only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +to fill in missing values in new data, where \code{trained} is the +\code{mids} object returned by \code{mice(..., tasks = 'train')}.} \item{ignore}{A logical vector of \code{nrow(data)} elements indicating which rows are ignored when creating the imputation model. The default diff --git a/tests/testthat/test-models.R b/tests/testthat/test-models.R new file mode 100644 index 000000000..1317f975e --- /dev/null +++ b/tests/testthat/test-models.R @@ -0,0 +1,9 @@ +context("models") + +trained <- mice(nhanes2, m = 2, maxit = 1, print = FALSE, tasks = "train", seed = 1) +models_env <- import.models.env(trained$models) +models_list <- export.models.env(models_env) + +test_that("list and environment representation have equal size", { + expect_identical(object.size(trained$models), object.size(models_list)) +}) diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 9443e9554..810d53246 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -8,7 +8,7 @@ context("tasks") test_that("m filling recycles training models", { expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) - expect_false(is.null(imp1$models$bmi$"1"$lookup)) + expect_false(is.null(imp1$models$bmi[[1]]$lookup)) expect_silent(imp2 <- mice(nhanes2, m = 4, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) }) @@ -16,7 +16,7 @@ test_that("m filling recycles training models", { test_that("fully synthetic datasets can be created from completely observed variables", { dataset <- complete(mice(nhanes2, m = 1, maxit = 1, method = "pmm", print = FALSE)) expect_silent(imp1 <- mice(dataset, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) - expect_false(is.null(imp1$models$age$"1"$lookup)) + expect_false(is.null(imp1$models$age[[1]]$lookup)) expect_silent(imp2 <- mice(dataset, where = make.where(dataset, "all"), m = 2, maxit = 3, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) synt1 <- complete(imp2, 1) synt2 <- complete(imp2, 2) From ae3733c563840523db6152a7242428ab370f7b39 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 17 Mar 2025 17:18:48 +0100 Subject: [PATCH 067/147] Store formula in models as string --- R/sampler.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/sampler.R b/R/sampler.R index f259a8be3..21fddfc28 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -250,7 +250,7 @@ prepare.formula <- function(formula, data, model, j, ct, pred, task) { stop("Error: No stored formula found in model for 'fill' task.") } formula <- get("formula", envir = model) - return(formula) + return(as.formula(formula)) } if (ct == "pred") { @@ -275,7 +275,7 @@ prepare.formula <- function(formula, data, model, j, ct, pred, task) { # store formula in `model` only when task is "train" if (task == "train") { - assign("formula", formula, envir = model) + assign("formula", deparse(formula), envir = model) } return(formula) From a64e9c91d9e5026bb64d7fe93794b20b96c3b363 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 17 Mar 2025 17:28:47 +0100 Subject: [PATCH 068/147] Add xnames = colnames(x) to models output --- R/mice.impute.logreg.R | 1 + R/mice.impute.norm.R | 1 + R/mice.impute.pmm.R | 1 + R/mice.impute.polr.R | 3 ++- R/mice.impute.polyreg.R | 1 + 5 files changed, 6 insertions(+), 1 deletion(-) diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index bd9ec2b53..95535e317 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -83,6 +83,7 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, model$beta.obs <- drop(beta) model$beta.mis <- drop(beta.star) model$factor <- list(labels = levels(y), quant = c(0, 1)) + model$xnames <- colnames(x) } lp <- x[wy, , drop = FALSE] %*% beta.star diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 726bda170..7cc85ff9c 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -57,6 +57,7 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, model$beta.obs <- drop(parm$coef) model$beta.mis <- drop(parm$beta) model$sigma <- parm$sigma + model$xnames <- colnames(x) } return(x[wy, ] %*% parm$beta + rnorm(sum(wy)) * parm$sigma) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 0ff50ea26..0ccd7ebe3 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -261,6 +261,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, model$lookup <- matrix((unquantify(lookup, f$quant, levels(y))), nrow = nbins) model$factor <- list(labels = f$labels, quant = f$quant) + model$xnames <- colnames(x) # Compute imputations from model impy <- draw.neighbors.pmm(yhatmis, diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index 2d260cf86..52f875ed6 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -135,9 +135,10 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, warmstart = warmstart) model$result <- list(value = fit$value, convergence = fit$convergence) - model$beta.mis <- coef(fit) + model$beta.mis <- setNames(coef(fit), colnames(x)) model$zeta.mis <- fit$zeta model$factor <- list(labels = levels(y), quant = NULL) + model$xnames <- colnames(x) } # Draw imputations diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index ff0fa4cf8..631f9fc9e 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -140,6 +140,7 @@ mice.impute.polyreg <- function( model$beta.mis <- beta if (warmstart) model$wts <- fit$wts model$factor <- list(labels = levels(y), quant = NULL) + model$xnames <- colnames(x) } # Draw imputations From 4da2efaa65fbfcaecc61532cf99687162d9190a7 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 18 Mar 2025 07:58:42 +0100 Subject: [PATCH 069/147] Perform check on method before check on data in check.model.match() --- R/check.model.R | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/R/check.model.R b/R/check.model.R index c970fa80a..37367a1a0 100644 --- a/R/check.model.R +++ b/R/check.model.R @@ -13,14 +13,6 @@ check.model.match <- function(model, x, method) { if (!length(formula)) { stop("No model stored in environment") } - mnames <- names(model$beta.mis) - if (is.matrix(model$beta.mis)) mnames <- rownames(model$beta.mis) - dnames <- colnames(x) - if (ncol(x) != length(mnames) || any(mnames != dnames)) { - stop(paste("Model-Data mismatch: ", deparse(formula), "\n", - " Model:", paste(mnames, collapse = " "), "\n", - " Data: ", paste(dnames, collapse = " "), "\n")) - } mmeth <- model$setup$method if (length(mmeth) && mmeth != method) { @@ -28,5 +20,15 @@ check.model.match <- function(model, x, method) { " Model: ", mmeth, "\n", " Method: ", method, "\n")) } + + xnames <- model$xnames + # if (is.matrix(model$beta.mis)) mnames <- rownames(model$beta.mis) + dnames <- colnames(x) + if (ncol(x) != length(xnames) || any(xnames != dnames)) { + stop(paste("Model-Data mismatch: ", deparse(formula), "\n", + " Model:", paste(xnames, collapse = " "), "\n", + " Data: ", paste(dnames, collapse = " "), "\n")) + } + return(TRUE) } From 4b403a44f747fd6d75ccf971639dc88ade572616 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 18 Mar 2025 15:18:21 +0100 Subject: [PATCH 070/147] Initialize a model environment for each non-empty model that is not imported --- R/mice.R | 3 +- R/models.R | 25 +++++++--------- man/initialize.models.env.Rd | 56 +++++++++++++++++++++++++++--------- 3 files changed, 55 insertions(+), 29 deletions(-) diff --git a/R/mice.R b/R/mice.R index 19fd2c1dc..e466ed807 100644 --- a/R/mice.R +++ b/R/mice.R @@ -514,7 +514,8 @@ mice <- function(data, post <- setup$post # Initialize models for "train" and "fill" blocks that are missing in models - models <- initialize.models.env(models, tasks, m) + models <- initialize.models.env(models, tasks, method, blocks, m) + method <- overwrite.method(method, blocks, tasks, models) # initialize imputations nmis <- apply(is.na(data), 2L, sum) diff --git a/R/models.R b/R/models.R index 74c4a178d..bdd3724ba 100644 --- a/R/models.R +++ b/R/models.R @@ -5,11 +5,7 @@ #' that is labeled as `"train"`, with sub-environments for each iteration #' from `1` to `m`. #' -#' @param models An existing environment to store models. If `NULL`, a -#' new environment is created. -#' @param tasks A named character vector where names are variable names and -#' values specify task types. Only variables labeled `"train"` will have -#' nested environments created. +#' @inheritParams mice #' @param m An integer specifying the number of nested sub-environments to #' create under each `"train"` variable. #' @@ -18,19 +14,11 @@ #' \item \code{models$varname} - An environment for each `"train"` variable. #' \item \code{models$varname$i} - Nested environments for each iteration from `1` to `m`. #' } -#' @examples -#' tasks <- c(a = "train", b = "fill", c = "train", d = "other") -#' m <- 3 -#' models_env <- mice:::initialize.models.env(tasks = tasks, m = m) -#' ls(models_env) # Lists "a" and "c" (only "train" tasks) -#' ls(models_env$a) # Lists "1", "2", "3" -#' ls(models_env$c) # Lists "1", "2", "3" -initialize.models.env <- function(models = NULL, tasks, m) { +initialize.models.env <- function(models = NULL, tasks, method, blocks, m) { # Import models into environment from a model list object if (is.list(models)) { models <- import.models.env(models) - return(models) } # Ensure `models` is an environment @@ -39,7 +27,16 @@ initialize.models.env <- function(models = NULL, tasks, m) { } # Identify variables that require models (i.e., "train" or "fill" tasks) + imported.models <- names(models) model.vars <- names(tasks[tasks %in% c("train", "fill")]) + empty.methods <- character(0L) + for (h in names(blocks)) { + varnames <- blocks[[h]] + if (method[h] == "") { + empty.methods <- c(empty.methods, varnames) + } + } + model.vars <- setdiff(model.vars, c(imported.models, empty.methods)) for (varname in model.vars) { if (tasks[varname] == "train") { diff --git a/man/initialize.models.env.Rd b/man/initialize.models.env.Rd index 2efff4d52..6a8f6e457 100644 --- a/man/initialize.models.env.Rd +++ b/man/initialize.models.env.Rd @@ -4,15 +4,51 @@ \alias{initialize.models.env} \title{Initialize Models Environment} \usage{ -initialize.models.env(models = NULL, tasks, m) +initialize.models.env(models = NULL, tasks, method, blocks, m) } \arguments{ -\item{models}{An existing environment to store models. If \code{NULL}, a -new environment is created.} +\item{models}{A list that stores fitted imputation models. The models +can be used to impute missing values in new data. \code{models} is +only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +to fill in missing values in new data, where \code{trained} is the +\code{mids} object returned by \code{mice(..., tasks = 'train')}.} -\item{tasks}{A named character vector where names are variable names and -values specify task types. Only variables labeled \code{"train"} will have -nested environments created.} +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + +\item{method}{Can be either a single string, or a vector of strings with +length \code{length(blocks)}, specifying the imputation method to be +used for each column in data. If specified as a single string, the same +method will be used for all blocks. The default imputation method (when no +argument is specified) depends on the measurement level of the target column, +as regulated by the \code{defaultMethod} argument. Columns that need +not be imputed have the empty method \code{""}. See details.} + +\item{blocks}{List of vectors with variable names per block. List elements +may be named to identify blocks. Variables within a block are +imputed by a multivariate imputation method +(see \code{method} argument). By default each variable is placed +into its own block, which is effectively +fully conditional specification (FCS) by univariate models +(variable-by-variable imputation). Only variables whose names appear in +\code{blocks} are imputed. The relevant columns in the \code{where} +matrix are set to \code{FALSE} of variables that are not block members. +A variable may appear in multiple blocks. In that case, it is +effectively re-imputed each time that it is visited.} \item{m}{An integer specifying the number of nested sub-environments to create under each \code{"train"} variable.} @@ -30,11 +66,3 @@ It ensures that a nested environment is created for each variable in \code{tasks that is labeled as \code{"train"}, with sub-environments for each iteration from \code{1} to \code{m}. } -\examples{ -tasks <- c(a = "train", b = "fill", c = "train", d = "other") -m <- 3 -models_env <- mice:::initialize.models.env(tasks = tasks, m = m) -ls(models_env) # Lists "a" and "c" (only "train" tasks) -ls(models_env$a) # Lists "1", "2", "3" -ls(models_env$c) # Lists "1", "2", "3" -} From 71d6c0ae9725255b62ab069ea82b3842492bd61f Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 18 Mar 2025 15:22:22 +0100 Subject: [PATCH 071/147] Overwrite the user-specified method by the stored method for variables that are filled --- R/method.R | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/R/method.R b/R/method.R index 8995595a7..958505a25 100644 --- a/R/method.R +++ b/R/method.R @@ -153,6 +153,23 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { unlist(method) } +overwrite.method <- function(method, blocks, tasks, models) { + # for fill tasks, overwrite method with stored model method + if (length(models) == 0L) { + return(method) + } + for (h in names(method)) { + for (varname in blocks[[h]]) { + if (tasks[varname] %in% c("fill")) { + newmethod <- models[[varname]]$`1`$setup$method + if (is.null(newmethod)) next + method[h] <- newmethod + } + } + } + return(method) +} + # assign methods based on type, # use method 1 if there is no single method within the block From c770c5b4d533d183a5164582f744df7aa6862047 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 18 Mar 2025 21:44:48 +0100 Subject: [PATCH 072/147] Change default "generate" --> "impute" --- R/tasks.R | 2 +- man/make.tasks.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/tasks.R b/R/tasks.R index 7ef7699c4..cecf02626 100644 --- a/R/tasks.R +++ b/R/tasks.R @@ -10,7 +10,7 @@ #' make.tasks(nhanes2) #' @export make.tasks <- function(data, - tasks = "generate", + tasks = "impute", blocks = make.blocks(data)) { bv <- unique(unlist(blocks)) if (length(tasks) == 1L) { diff --git a/man/make.tasks.Rd b/man/make.tasks.Rd index dd484fd90..4a8163d99 100644 --- a/man/make.tasks.Rd +++ b/man/make.tasks.Rd @@ -4,7 +4,7 @@ \alias{make.tasks} \title{Creates a \code{tasks} argument} \usage{ -make.tasks(data, tasks = "generate", blocks = make.blocks(data)) +make.tasks(data, tasks = "impute", blocks = make.blocks(data)) } \arguments{ \item{data}{A data frame or a matrix containing the incomplete data. Missing From ab24bf823d852481abd0cacae6e68dc7b8ec5e14 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 18 Mar 2025 21:50:35 +0100 Subject: [PATCH 073/147] Rename beta.obs --> beta.hat and beta.mis --> beta.dot --- R/check.model.R | 2 +- R/mice.impute.logreg.R | 6 +++--- R/mice.impute.norm.R | 6 +++--- R/mice.impute.pmm.R | 18 +++++++++--------- R/mice.impute.polr.R | 8 ++++---- R/mice.impute.polyreg.R | 4 ++-- 6 files changed, 22 insertions(+), 22 deletions(-) diff --git a/R/check.model.R b/R/check.model.R index 37367a1a0..d8f129bd8 100644 --- a/R/check.model.R +++ b/R/check.model.R @@ -22,7 +22,7 @@ check.model.match <- function(model, x, method) { } xnames <- model$xnames - # if (is.matrix(model$beta.mis)) mnames <- rownames(model$beta.mis) + # if (is.matrix(model$beta.dot)) mnames <- rownames(model$beta.dot) dnames <- colnames(x) if (ncol(x) != length(xnames) || any(xnames != dnames)) { stop(paste("Model-Data mismatch: ", deparse(formula), "\n", diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index 95535e317..e9a6d9819 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -61,7 +61,7 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) if (task == "fill" && check.model.match(model, x, method)) { - lp <- x[wy, , drop = FALSE] %*% model$beta.mis + lp <- x[wy, , drop = FALSE] %*% model$beta.dot return(logreg.draw(lp, levels(y))) } @@ -80,8 +80,8 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, model$setup <- list(method = method, n = sum(ry), task = task) - model$beta.obs <- drop(beta) - model$beta.mis <- drop(beta.star) + model$beta.hat <- drop(beta) + model$beta.dot <- drop(beta.star) model$factor <- list(labels = levels(y), quant = c(0, 1)) model$xnames <- colnames(x) } diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 7cc85ff9c..2f1871928 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -44,7 +44,7 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) if (task == "fill" && check.model.match(model, x, method)) { - return(x[wy, ] %*% model$beta.mis + rnorm(sum(wy)) * model$sigma) + return(x[wy, ] %*% model$beta.dot + rnorm(sum(wy)) * model$sigma) } parm <- .norm.draw(y, ry, x, ridge = ridge, ...) @@ -54,8 +54,8 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, n = sum(ry), task = task, ridge = ridge) - model$beta.obs <- drop(parm$coef) - model$beta.mis <- drop(parm$beta) + model$beta.hat <- drop(parm$coef) + model$beta.dot <- drop(parm$beta) model$sigma <- parm$sigma model$xnames <- colnames(x) } diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 0ccd7ebe3..566254f0a 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -200,7 +200,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # >>> Fill task: fill from stored model if (task == "fill" && check.model.match(model, x, method)) { - yhatmis <- x[wy, ] %*% model$beta.mis + yhatmis <- x[wy, ] %*% model$beta.dot impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, lookup = model$lookup, @@ -215,17 +215,17 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # Predict ynum on observed data with linear model parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) if (matchtype == 1L) { - beta.obs <- drop(parm$coef) - beta.mis <- drop(parm$beta) + beta.hat <- drop(parm$coef) + beta.dot <- drop(parm$beta) } else if (matchtype == 0L) { - beta.mis <- beta.obs <- drop(parm$coef) + beta.dot <- beta.hat <- drop(parm$coef) } else if (matchtype == 2L) { - beta.mis <- beta.obs <- drop(parm$beta) + beta.dot <- beta.hat <- drop(parm$beta) } x_ry <- x[ry, , drop = FALSE] x_wy <- x[wy, , drop = FALSE] - yhatobs <- as.vector(x_ry %*% beta.obs) - yhatmis <- x_wy %*% beta.mis + yhatobs <- as.vector(x_ry %*% beta.hat) + yhatmis <- x_wy %*% beta.dot # >>> Impute task: Impute values (classic MICE PMM) if (task == "impute") { @@ -255,8 +255,8 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, task = task, nbins = nbins, ridge = ridge) - model$beta.obs <- beta.obs - model$beta.mis <- beta.mis + model$beta.hat <- beta.hat + model$beta.dot <- beta.dot model$edges <- edges model$lookup <- matrix((unquantify(lookup, f$quant, levels(y))), nrow = nbins) diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index 52f875ed6..509667a0f 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -77,7 +77,7 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, if (task == "fill" && check.model.match(model, x, method)) { impy <- polr.draw(x = x[wy, , drop = FALSE], - beta = model$beta.mis, + beta = model$beta.dot, zeta = model$zeta.mis, levels = model$factor$labels) return(impy) @@ -99,8 +99,8 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, reltol = ifelse(is.null(reltol), 0.0001, reltol)) ) if (warmstart && task == "train" && - !is.null(model$beta.mis) && !is.null(model$zeta.mis)) { - dots$start <- c(model$beta.mis, model$zeta.mis) + !is.null(model$beta.dot) && !is.null(model$zeta.mis)) { + dots$start <- c(model$beta.dot, model$zeta.mis) } # Estimate ordered logistic (polr) model with polr @@ -135,7 +135,7 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, warmstart = warmstart) model$result <- list(value = fit$value, convergence = fit$convergence) - model$beta.mis <- setNames(coef(fit), colnames(x)) + model$beta.dot <- setNames(coef(fit), colnames(x)) model$zeta.mis <- fit$zeta model$factor <- list(labels = levels(y), quant = NULL) model$xnames <- colnames(x) diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 631f9fc9e..4e754460a 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -83,7 +83,7 @@ mice.impute.polyreg <- function( x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) check.model.match(model, x, method) return(polyreg.draw(x = x, - beta = model$beta.mis, + beta = model$beta.dot, levels = levels(y))) } @@ -137,7 +137,7 @@ mice.impute.polyreg <- function( model$result <- list(nWts = length(fit$wts), value = fit$value, convergence = fit$convergence) - model$beta.mis <- beta + model$beta.dot <- beta if (warmstart) model$wts <- fit$wts model$factor <- list(labels = levels(y), quant = NULL) model$xnames <- colnames(x) From 3df626dfbb40646e1cadd2930614028ea22bf908 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 18 Mar 2025 22:08:59 +0100 Subject: [PATCH 074/147] Rename sigma --> sigma.dot, add sigma.hat --- R/mice.impute.norm.R | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 2f1871928..04c47acf7 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -44,7 +44,7 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) if (task == "fill" && check.model.match(model, x, method)) { - return(x[wy, ] %*% model$beta.dot + rnorm(sum(wy)) * model$sigma) + return(x[wy, ] %*% model$beta.dot + rnorm(sum(wy)) * model$sigma.dot) } parm <- .norm.draw(y, ry, x, ridge = ridge, ...) @@ -56,7 +56,8 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, ridge = ridge) model$beta.hat <- drop(parm$coef) model$beta.dot <- drop(parm$beta) - model$sigma <- parm$sigma + model$sigma.hat <- parm$sigma.hat + model$sigma.dot <- parm$sigma model$xnames <- colnames(x) } @@ -95,10 +96,12 @@ norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { ###' @export .norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { p <- estimice(x[ry, , drop = FALSE], y[ry], ...) - sigma.star <- sqrt(sum((p$r)^2) / rchisq(1, p$df)) + ssq <- sum((p$r)^2) + sigma.hat <- sqrt(ssq / p$df) + sigma.star <- sqrt(ssq / rchisq(1, p$df)) beta.star <- p$c + (t(chol(sym(p$v))) %*% rnorm(ncol(x))) * sigma.star - parm <- list(p$c, beta.star, sigma.star, p$ls.meth) - names(parm) <- c("coef", "beta", "sigma", "estimation") + parm <- list(p$c, beta.star, sigma.star, p$ls.meth, sigma.hat) + names(parm) <- c("coef", "beta", "sigma", "estimation", "sigma.hat") if (any(is.na(parm$coef)) & rank.adjust) { parm$coef[is.na(parm$coef)] <- 0 parm$beta[is.na(parm$beta)] <- 0 From b1ccdb6b067de1e4ddbcd7295b22c589dd0ea8c6 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 21 Mar 2025 08:01:19 +0100 Subject: [PATCH 075/147] Remove confusing symbol --- R/mice.impute.norm.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 04c47acf7..e0a34db0a 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -92,8 +92,8 @@ norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { return(.norm.draw(y, ry, x, rank.adjust = TRUE, ...)) } -###' @rdname norm.draw -###' @export +#' @rdname norm.draw +#' @export .norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { p <- estimice(x[ry, , drop = FALSE], y[ry], ...) ssq <- sum((p$r)^2) From b67c6a14da4426f96f372aea479f83034462363a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 21 Mar 2025 08:02:41 +0100 Subject: [PATCH 076/147] Declare internal functions --- R/models.R | 4 +++- man/export.models.env.Rd | 2 +- man/import.models.env.Rd | 1 + man/initialize.models.env.Rd | 1 + 4 files changed, 6 insertions(+), 2 deletions(-) diff --git a/R/models.R b/R/models.R index bdd3724ba..44a73c96c 100644 --- a/R/models.R +++ b/R/models.R @@ -14,6 +14,7 @@ #' \item \code{models$varname} - An environment for each `"train"` variable. #' \item \code{models$varname$i} - Nested environments for each iteration from `1` to `m`. #' } +#' @keywords internal initialize.models.env <- function(models = NULL, tasks, method, blocks, m) { # Import models into environment from a model list object @@ -85,7 +86,7 @@ initialize.models.env <- function(models = NULL, tasks, method, blocks, m) { #' # Convert to a list #' models_list <- mice:::export.models.env(models_env, m = 2) #' print(models_list) -#' +#' @keywords internal export.models.env <- function(env, m = NULL) { if (!is.environment(env)) stop("Input must be an environment") @@ -162,6 +163,7 @@ export.models.env <- function(env, m = NULL) { #' print(ls(models_env)) # Should list "a" and "b" #' print(ls(models_env$a)) # Should list "1" and "2" #' print(models_env$a$`1`$model) # Should be "Model A1" +#' @keywords internal import.models.env <- function(models_list) { if (!is.list(models_list)) stop("Input must be a list") diff --git a/man/export.models.env.Rd b/man/export.models.env.Rd index 409226833..f422ff05b 100644 --- a/man/export.models.env.Rd +++ b/man/export.models.env.Rd @@ -38,5 +38,5 @@ models_env$b$`1`$model <- "Model B1" # Convert to a list models_list <- mice:::export.models.env(models_env, m = 2) print(models_list) - } +\keyword{internal} diff --git a/man/import.models.env.Rd b/man/import.models.env.Rd index 109686605..72d35a54e 100644 --- a/man/import.models.env.Rd +++ b/man/import.models.env.Rd @@ -49,3 +49,4 @@ print(ls(models_env)) # Should list "a" and "b" print(ls(models_env$a)) # Should list "1" and "2" print(models_env$a$`1`$model) # Should be "Model A1" } +\keyword{internal} diff --git a/man/initialize.models.env.Rd b/man/initialize.models.env.Rd index 6a8f6e457..44a5ebaf4 100644 --- a/man/initialize.models.env.Rd +++ b/man/initialize.models.env.Rd @@ -66,3 +66,4 @@ It ensures that a nested environment is created for each variable in \code{tasks that is labeled as \code{"train"}, with sub-environments for each iteration from \code{1} to \code{m}. } +\keyword{internal} From caac12ec16231bbf041f293ea3f738ac77b2c7fd Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 21 Mar 2025 08:05:10 +0100 Subject: [PATCH 077/147] Add vignette imputation_model.qmd --- .Rbuildignore | 5 +- .gitignore | 2 + vignettes/.gitignore | 1 + vignettes/imputation_model.qmd | 522 ++++ .../libs/quarto-diagram/mermaid-init.js | 275 +++ .../libs/quarto-diagram/mermaid.css | 13 + .../libs/quarto-diagram/mermaid.min.js | 2186 +++++++++++++++++ vignettes/references.bib | 109 + 8 files changed, 3110 insertions(+), 3 deletions(-) create mode 100644 vignettes/.gitignore create mode 100644 vignettes/imputation_model.qmd create mode 100644 vignettes/imputation_model_files/libs/quarto-diagram/mermaid-init.js create mode 100644 vignettes/imputation_model_files/libs/quarto-diagram/mermaid.css create mode 100644 vignettes/imputation_model_files/libs/quarto-diagram/mermaid.min.js create mode 100644 vignettes/references.bib diff --git a/.Rbuildignore b/.Rbuildignore index 61c4cbba2..486e80d98 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -11,7 +11,7 @@ cran-comments.Rmd changes_mice* ^data-raw$ .png -^script$ +^script/ ^CODE_OF_CONDUCT* ^mice\.Rproj$ vignettes/ @@ -20,5 +20,4 @@ vignettes/ ^pkgdown$ ^LICENSE\.md$ ^\.github$ -^CRAN-SUBMISSION$ -^vignettes/articles$ +^CRAN-SUBMISSION$ \ No newline at end of file diff --git a/.gitignore b/.gitignore index 788f95055..9ab84fc54 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ inst/doc *_cache script docs + +/.quarto/ diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 000000000..075b2542a --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/vignettes/imputation_model.qmd b/vignettes/imputation_model.qmd new file mode 100644 index 000000000..1b0e8bb24 --- /dev/null +++ b/vignettes/imputation_model.qmd @@ -0,0 +1,522 @@ +--- +title: "Imputation Models in MICE" +author: "Stef van Buuren" +date: "`r Sys.Date()`" +format: + html: + theme: sandstone + highlight-style: github + number-sections: true + embed-resources: true + toc: true + toc-depth: 3 +bibliography: references.bib +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +## Example + +Suppose you created a risk prediction model for a cohort of patients. The dataset contained missing values, which you imputed using MICE. Now, you want to implement the risk prediction model in clinical practice to assist decision-making for new patients. Since missing values are expected in the new patient data, how should you proceed? + +Clearly, you **cannot retrain the imputation model on the new patients**. The number of new patients may be too small to support proper training, and a new imputation model would likely produce different missing value estimates. These differences raise concerns about the consistency and comparability of the risk prediction model. + +A better approach is to apply the **same imputation model** trained on the original cohort to fill in missing values for new patients. Previously, doing so required manual workarounds that were inefficient and difficult to implement. This vignette introduces a new functionality that lets you **store the imputation model and apply it seamlessly to new patients**, ensuring consistency and reproducibility. + +## Installation + +The new functionality is **currently experimental** and available in the `"tasks"` branch of the `mice` repository. To install this branch from GitHub, use: + +```{r eval=FALSE} +remotes::install_github("amices/mice", ref = "imputation_models") +``` + +## MICE Architecture + +The MICE algorithm follows a **two-level modular architecture**. At the first level, `mice()`, the core function in the `mice` package [@vanbuuren2011; @vanbuuren2018], **orchestrates the imputation process** by managing data preprocessing, variable selection, and iterative imputation steps. At the second level, **MICE applies elementary imputation methods**—such as normal imputation and PMM—to generate missing values based on model-specific assumptions. + +Since storing and reusing imputation models requires both managing the overall imputation process and adjusting the underlying imputation methods, modifications were made at both levels. + +We begin by exploring the **first level** of the MICE architecture, where the new `tasks` and `models` arguments modify the imputation workflow. + +Next, we examine the **second level**, where elementary imputation functions—such as `mice.impute.norm()` and `mice.impute.pmm()`—have been extended to support `tasks` and `models`. + +## New `tasks` and `model` Arguments + +The `mice()` function orchestrates the imputation process by **managing the imputation model and generating imputed values**. It first validates the input data and user-specified settings. It then initializes the imputation model, iterates elementary imputation methods, and returns a `mids` object containing the original dataset, model specifications, and imputed values. + +The `mids` object is central to the multiple imputation workflow, supporting **pooling, diagnostics, and visualization**. + +To enhance imputation flexibility, `mice()` introduces two new arguments: `tasks` and `models`, which we detail in the following sections. + +### Tasks + +The `tasks` argument in `mice()` is a character vector specifying the task to perform for each variable during imputation. The available options are: + +- `tasks = "impute"`: Estimates parameters, generates imputations, and stores the original data along with imputations—but not the imputation model. This corresponds to **classic MICE behavior** and is the default setting. +- `tasks = "train"`: Estimates parameters, generates imputations, and stores the original data, imputations, and imputation model. `tasks = "train"` saves the imputation model along with the data, allowing for both inspection and reuse. +- `tasks = "fill"`: Applies a stored imputation model to new data, generating imputations without re-estimating parameters. + +All `tasks` options produce `mids` objects, which can be used for **pooling, diagnostics, and visualization** as in standard MICE workflows. + +Next, we show practical examples of how to use the `tasks` argument. + +#### Example: `tasks = "impute"` + +Setting `tasks = "impute"` returns a `mids` object identical to the classic MICE implementation: it contains imputed values but **does not store the imputation model**. The following code generates a `mids` object using the built-in `nhanes` dataset: + +```{r} +library(mice, warn.conflicts = FALSE) +imputed <- mice(nhanes, tasks = "impute", seed = 1, print = FALSE) +class(imputed) +``` + +As in classic MICE, specifying `tasks = "impute"` applies this setting to all variables by expanding to: + +```{r} +imputed$tasks +``` + +`tasks = "impute"` behaves exactly like the classic `mice()` function, returning a `mids` object with imputed values only—without storing the imputation model. + + +#### Example: `tasks = "train"` + +To also store the imputation model, use `tasks = "train"` instead of `tasks = "impute"`: + +```{r} +trained <- mice(nhanes, tasks = "train", seed = 1, print = FALSE) +class(trained) +``` + +This time, the `mids` object contains both the imputed values and the imputation model. To confirm the difference, inspect the `store` component: + +```{r} +trained$store +``` + +The new models component in the `mids` object stores the imputation model for each variable and each imputation. A `mids` object with `store == "train"` can later be used to generate imputations for new data. + +Before applying a stored model, let’s highlight two key differences between `store == "impute"` and `store == "train"`: + +- `store == "train"`: The `mids` object includes the trained imputation model in the `models` component. +- `store == "impute"`: No model is stored. + +Depending on the imputation method, the stored models may differ slightly. For parametric methods, storing the model requires saving formulas, estimated coefficients, and metadata such as factor levels. For non-parametric methods like PMM, storing the model involves saving observed values instead of estimated parameters. + +#### Example: `tasks = "fill"` + +Training creates a **transferable representation** of the imputation model, allowing it to be reused on new data **without altering the original model or re-estimating parameters**. To apply a stored model, use `tasks = "fill"` in `mice()`, along with the `models` argument to specify the trained model. + +The following code demonstrates how to use a trained model to fill missing values in new data: + +```{r} +newdata <- data.frame(age = c(2, 1), bmi = c(NA, NA), chl = c(NA, 190), hyp = c(NA, 1)) +filled <- mice(newdata, tasks = "fill", models = trained$models, seed = 1, print = FALSE) +filled$store +filled$data +filled$imp +complete(filled, 2) +``` + +Note the use of both `tasks = "fill"` (to apply the stored model) and `models = trained$models` (to provide the trained imputation model). + +- `filled$store` will be set to `"fill"` if all missing values are imputed using the stored model. +- `filled$data` holds the new dataset with missing values, while `filled$imp` stores the imputations. + +The `complete()` function can be used to extract the multiply-imputed datasets. The example displays the imputed values for the second imputation. + +### Compact Representation of the Imputation Model + +The `compact` argument controls the storage of the trained model. When `compact = TRUE`, the imputation model is stored in a **minimized form**, without retaining the training data used to estimate the model. This form retains all necessary parameters for generating imputed values while excluding training data, making it suitable for **exchange, distribution, and production**. + +```{r} +# Train a compact imputation model (no training data stored) +trained_compact <- mice(nhanes, tasks = "train", compact = TRUE, seed = 1, print = FALSE) +trained_compact$store + +# No training data is stored in the compact model +trained_compact$data +trained_compact$imp + +# But we can still use the compact model to fill missing values in new data +filled_compact <- mice(newdata, tasks = "fill", models = trained_compact$models, seed = 1, print = FALSE) +filled_compact$store +complete(filled_compact, 2) +``` + +Since compact models do not store training data, the `data` and `imp` components remain empty. + +```{r} +# Attempting to complete a compact model without training data will return an error +try(complete(trained_compact)) +``` + +Running `complete()` requires stored imputations to reconstruct datasets, which are unavailable in compact models. As a result, `complete()` does not work when `store == "train_compact"`. + +### Comparison of `tasks = "train"`, `"train_compact"`, and `"fill"` + +| Feature | `tasks = "train"` | `tasks = "train", compact = TRUE` | `tasks = "fill"` | +|--------------------------|------------------|----------------------------------|------------------| +| **Stores imputations?** | ✅ Yes | ❌ No | ✅ Yes (on new data) | +| **Stores imputation model?** | ✅ Yes | ✅ Yes | ❌ No | +| **Stores training data?** | ✅ Yes | ❌ No | ❌ No | +| **Can generate new imputations?** | ✅ Yes | ✅ Yes | ✅ Yes | +| **Requires `models` argument?** | ❌ No | ❌ No | ✅ Yes (to specify trained model) | +| **`complete()` works?** | ✅ Yes | ❌ No | ✅ Yes | +| **Typical use case** | Storing full imputation model and training data | Creating a lightweight imputation model for sharing or production | Applying a trained imputation model to new data | + +#### Explanation of Key Differences +- **`tasks = "train"`**: Stores **everything**, including the original dataset, the imputation model, and all imputations. +- **`tasks = "train", compact = TRUE`**: Stores **only the imputation model**, making it **lightweight** but preventing the use of `complete()`. +- **`tasks = "fill"`**: Uses a previously stored model to generate **new imputations** but does **not** store the model itself. + +### Train and Fill Subsets of Variables + +Imputation models can be trained on a **subset** of variables rather than the entire dataset. This allows missing values in those variables to be filled using the trained model, while other variables are imputed using standard MICE methods. + +The following fragment specifies a vector `tasks = c("bmi", "hyp")` to train the imputation model **only for `bmi` and `hyp`**: + +```{r} +tasks <- make.tasks(nhanes) +tasks[c("bmi", "hyp")] <- "train" +trained_subset <- mice(nhanes, tasks = tasks, seed = 1, print = FALSE) +names(trained_subset$models) +``` + +Now apply the subset model to fill missing values in `newdata`: + +```{r} +tasks[c("bmi", "hyp")] <- c("fill", "fill") +filled_subset <- mice(newdata, tasks = tasks, models = trained_subset$models, seed = 1, print = FALSE) +complete(filled_subset) +``` + +Row 2 was successfully imputed using the trained model for `bmi` and `hyp`. However, imputation fails for row 1 because `mice()` cannot build an imputation model for `chl` (which was not part of the trained subset). + +`mice()` checks each variable for constant or collinear values before building an imputation model. In this case, `chl` is constant (only one value exists), so `mice()` fails to construct an imputation model, leading to missing values propagating to `bmi` and `hyp`. However, variables explicitly set to `"fill"` (`bmi` and `hyp`) do not require a model to be built, so they bypass this check. + +There are two ways to address this issue: + +- Include `chl` in the training subset, effectively training models for all variables. +- Append additional records to `newdata` so that `mice()` can generate an on-the-fly imputation model for `chl`. This approach is shown below: + +```{r} +newdata_append <- rbind(newdata, nhanes[11:20, ]) +filled_subset <- mice(newdata_append, tasks = tasks, models = trained_subset$models, seed = 1, print = FALSE) +complete(filled_subset, 2)[1:2, ] +``` + +Here, 10 records from `nhanes` are appended to `newdata`. This allows `mice()` to train an imputation model for `chl` on the extra data, while still applying the trained model for `bmi` and `hyp`. + +Final observations: + +- When a combination of training and filling is used, the resulting mids object is stored with `store = "train"`. +- Appendix A contains the full specification of `mids` components for store modes `"impute"`, `"train"`, and `"fill"`. + +## Elementary Imputation Functions + +This section explains how `mice.impute.norm()`, `mice.impute.pmm()`, and other `mice.impute.*()` functions have been **adapted to support `tasks` and `models`**, allowing for reusable imputation models and enhanced flexibility in workflows. + +`mice.impute.*()` functions generate imputed values for **individual variables** (univariate imputation) or **groups of variables** (multivariate imputation). `mice()` automatically calls `mice.impute.*()` functions to handle variable-wise and block-wise imputation. + +This section is primarily relevant for developers who wish to extend the `mice` package with new imputation methods. + +### Normal Imputation + +The `mice.impute.norm()` function is responsible for the following tasks: + +1. Verify whether an imputation model exists before proceeding. +2. For `task = "fill"`: Retrieve the stored imputation model, generate imputed values, and return them. +3. Estimate imputation model parameters. +4. For `task = "train"`: Store the imputation model for later use. +5. Generate and return imputed values. + +#### Example: `mice.impute.norm()` + +Let us examine how these actions are implemented in `mice.impute.norm()` for normal imputation: + +```{r} +mice::mice.impute.norm +``` + +The function argument list introduces two new arguments: + +- `model`: An environment that contains the imputation model. +- `task`: Specifies the task to perform (`"impute"`, `"train"`, or `"fill"`). + +The function body clearly separates different tasks: + +1. Check model availability: `check.model.exists(model, task)` +2. Fill missing values `(task = "fill")`: Retrieve the stored model and generate imputations. +3. Estimate parameters: `.norm.draw(y, ry, x, ridge = ridge, ...)` estimates imputation parameters based on the available data. +4. Train and store model `(task = "train")`: Save the trained imputation model. +5. Return imputed values: `return(x[wy, ], ...)`. + +All `mice.impute.*()` functions follow this pattern. + +Model storage and retrieval: The `model` object is an environment that stores imputation parameters. During training, each iteration updates the imputation model by overwriting previous estimates with new ones. After the final iteration, the model is stored as a list in the `models` component of the `mids` object. When `mice()` imports a trained model, it reconstructs this list back into a nested environment—one for each variable and each imputation. + +### Predictive Mean Matching (PMM) + +The `mice.impute.pmm()` function performs the same core tasks as `mice.impute.norm()`. However, PMM is a **semi-parametric imputation method** [@little1988] that requires additional steps to ensure imputations remain consistent with the standard algorithm while enabling imputations without access to the original training dataset. + +To store the imputation model, we use **percentile binning**, which divides the linear predictor into equally sized bins and assigns donor values to each bin. + +#### Steps in `mice.impute.pmm()` + +1. Estimate the imputation model parameters. +2. Calculate the linear predictor for all cases. +3. For each case with a missing outcome: + - Identify the $k$ closest observed cases based on the linear predictor. + - Randomly select one of these $k$ donor values as the imputed value. + +While parameter estimation follows a similar approach to `mice.impute.norm()`, PMM requires an efficient way to store donor values. Instead of storing the full training dataset, we create **equally sized bins** based on the linear predictor. At each bin threshold, we store the $k$ closest donor values. + +When imputing a new case, the function: + +1. Identifies the bin it belongs to by locating its left and right bin thresholds. +2. Draws a donor value from the $k$ closest observed values within the left or right bin. +3. Weighs donor selection based on the case's **relative distance** to the bin thresholds. + +This approach is **memory-efficient**, handles **gaps in the linear predictor**, and allows for **fast and accurate imputations** based on the choice of bins ($t$) and donors ($k$). + +#### Model Storage for PMM + +The `mice.impute.pmm()` function stores the following components in the `model` object: + +- `edges`: A vector of length $t + 1$ defining the bin thresholds. +- `lookup`: A matrix of size $t \times k$ containing donor values for each bin. + +Appendix B describes four internal helper functions: + +- `initialize.nbins()`: Sets the number of bins $t$. +- `initialize.donors()`: Sets the number of donors $k$. +- `bin.yhat()`: Divides the linear predictor into bins using `edges`. +- `draw.neighbors.pmm()`: Selects imputed values from the `lookup` table. + +During training, all four functions are used. However, filling only needs `draw.neighbors.pmm()`. + +#### Experimental Aspects of $t$ and $k$ + +The methods for determining $t$ and $k$ in `initialize.nbins()` and `initialize.donors()` are **preliminary and based on limited simulations**. + +The default values for $t$ (typically 15–30 bins) and $k$ (typically 5–15 donors) were selected based on a small simulation study that varied only **sample size**. The study assumed an imputation model with approximately 10% explained variance. However, because optimal values for $t$ and $k$ likely depend on prediction error, these defaults may not be suitable for other models. Although some work is available in adaptive tuning [@schenker1996], optimizing $t$ and $k$ for different levels of prediction error remains an open research question. + +### Structure of the `models` Component + +When `tasks = "train"`, the `mice()` function stores the imputation model in the `models` component of the `mids` object. The `models` component is a list containing the **setup and estimates** of the imputation model for each variable and each repeated imputation. Since there are `m` imputations and `ncol(data)` variables, there can be **up to `m * ncol(data)` imputation models** in total. + +The `models` component is organized **by variable and imputation number**, allowing each variable's imputation process to be stored separately across imputations. This structure ensures that stored models can be reused for generating imputations on new data. + +The following diagram visualizes the hierarchical structure of the `models` component: + +```{mermaid} +graph TD; + A[mids] --> B[models] + B --> C[bmi] + B --> D[hyp] + B --> E[chl] + C --> F[Imputation 1] + C --> G[Imputation 2] + D --> H[Imputation 1] + D --> I[Imputation 2] + E --> J[Imputation 1] + E --> K[Imputation 2] +``` + +The names of the parts of the first imputation model for `bmi` are: + +```{r models-levels} +names(trained$models$bmi[[1]]) +``` + +In general, the names and types of stored objects depend on the imputation method. The `setup` object for the first imputation model for `bmi` can be accessed as follows: + +```{r} +unlist(trained$models$bmi[[1]]$setup) +``` + +It contains settings specific to the imputation model. We can retrieve the `formula` used in the model as: + +```{r} +trained$models$bmi[[1]]$formula +``` + +which shows that `bmi` is imputed using a linear combination of `age`, `hyp`, and `chl`. We obtain the least squares estimates for the regression weights by: + +```{r} +trained$models$bmi[[1]]$beta.hat +``` + +However, the actual imputation model does not use these weights directly; instead, it draws randomly from a distribution that accounts for parameter uncertainty [@rubin1987]. The drawn values can be accessed as: + +```{r} +trained$models$bmi[[1]]$beta.dot +``` + +For small samples or highly collinear predictors, `beta.hat` and `beta.dot` can differ substantially—a phenomenon sometimes called ‘bouncing betas’, where parameter estimates fluctuate across imputations. To ensure "proper imputation", MICE uses beta draws (`beta.dot`) rather than fixed estimates (`beta.hat`). + +### Additional Components for PMM + +For PMM, two additional objects are stored in `models`: + +- `edges`: A vector of length $t + 1$ containing the bin thresholds $\theta_j$. +- `lookup`: A $t × k$ matrix storing the donor values for each bin. + +The bin thresholds in `edges` are located on: + +```{r} +trained$models$bmi[[1]]$edges +``` + +The `lookup` object is a matrix, where each row represents a bin threshold $\theta_j$ that segments the linear predictor. Observations with $\hat y$ values falling outside the bin range ($\hat y \leq \theta_1$ or $\hat y > \theta_t$) are assigned to the first or last bin, respectively. The `lookup` table for the first imputation model for `bmi` can be accessed as: + +```{r} +trained$models$bmi[[1]]$lookup +``` + +The `lookup` object is used in `draw.neighbors.pmm()` to select donor values for imputations. + +### Sharing the `models` List Component + +The `models` list component in `mice()` allows users to store trained imputation models and apply them to new datasets, ensuring consistent missing data handling without re-estimating parameters. + +Beyond reusability, `models` enhances **transparency** into MICE’s mechanics, serving as a diagnostic tool for refining imputation strategies. By inspecting stored models, users can evaluate imputation decisions, identify patterns, and adjust settings accordingly. + +Additionally, `models` encapsulates all essential elements needed to **standardize and share imputation workflows** across datasets. When combined with a structured codebook, users can create **reproducible imputation modules**, enabling them to share, apply, and publish standardized missing data solutions in different studies and applications. + +## Methodological Considerations + +### Number of Imputations `m` Used in `"train"` and `"fill"` + +Since we did not specify $m_\text{train}$ (the number of imputations for training) and $m_\text{fill}$ (the number of imputations for filling), both steps default to `m = 5`. Although the user can set both independently, we **recommend $m_\text{train} = m_\text{fill}$** to ensure consistency. + +If $m_\text{train} < m_\text{fill}$, the `mice()` function will **automatically recycle** trained models to generate additional imputations, without warning the user. When recycling, the imputations may exhibit too little variability, particularly in small samples, potentially leading to **underestimated downstream variability**. + +If $m_\text{train} > m_\text{fill}$, the `mice()` function will discard the extra imputations. In general, increasing $m_\text{fill}$ reduces Monte Carlo error and improves the stability of the between-imputation variance estimate. + +Despite its statistical drawbacks, many users prefer $m_\text{fill} = 1$ for its simplicity, as working with a single dataset is often more convenient. However, as @dempster1983 [p. 8] caution: + +>>> Imputation is seductive because it can lull the user into the pleasurable state of believing that the data are complete after all. + +A single completed dataset may be a **convenient fiction**, useful for various purposes such as obtaining population estimates, developing an imputation model, or estimating the most likely version of the hidden data values. However, a single imputed dataset fails to account for uncertainty inherent to the missing data, leading to **biased estimates and overconfident inferences** in downstream decision making. + +### Full Fill vs. Partial Fill + +The simplest workflow, known as **full fill**, trains the imputation model on all columns in the dataset. A full fill is useful when data is split by rows (**horizontal partitioning**) or when standardized imputations are needed across datasets with the same structure. In such cases, a model can be trained on one dataset partition and then applied to new datasets containing different records with the same type of variables. A variation on this approach is to train the model on a subset of rows to generate imputations for the remaining rows to save computation time. Another use case occurs when some variables included in the trained model are missing in a new dataset, and the goal is to impute only those missing variables. Since the model does not need to be retrained, imputations for new data can be generated almost instantly. + +However, when data is split into different subsets of columns (**vertical partitioning**), the trained model may not cover all variables. In these cases, **partial fill** can be used, where supported variables are imputed along with additional imputations for variables absent from the original model. + +A common example occurs in **longitudinal studies**, where data is collected at multiple time points. A model trained on data from an earlier time point can be used to impute missing values before extending the model to include variables collected at later time points—without requiring retraining on the full dataset. Another example is the **integration of data from different sources** without directly merging them. Suppose source **A** contains variables $X$ and $Y$, while source **B** contains $X$ and $Z$. Instead of combining these datasets into a single table, an imputation model can be sequentially extended under the assumption of conditional independence $Y \perp Z \mid X$. + +The process proceeds as follows: + +1. Train a model on source **B** to impute $Z$ from $X$. +2. Use this model to impute $Z$ in source **A**. +3. Store the final model for $X, Y$ and $Z$ for future use. + +This approach removes the need to merge sources **A** and **B** into a single dataset. Analysts can share trained models instead of exchanging raw data, thereby improving efficiency, adaptability, and data privacy while still utilizing all available information. + +A key assumption in this approach is **conditional independence** $Y \perp Z \mid X$, meaning that once we account for $X$, there is no remaining association between $Y$ and $Z$. In practice, this assumption may not always hold. If an additional data source **C** containing both $Y$ and $Z$ is available, models trained on source **C** can be incorporated to capture the direct relationship between $Y$ and $Z$, improving the accuracy of imputations. + +### Store + +The `store` component of a `mids` object is automatically determined based on the `tasks` argument. It specifies which components are saved in the imputation model. + +If `tasks` is not specified (or set to `"impute"`), the default behavior is: + +```{r store-impute} +imputed <- mice(nhanes, seed = 1, print = FALSE) +imputed$store +imputed$tasks +imputed$models +``` + +Since `tasks = "impute"` does not train models, the models component remains NULL. The possible values of store are: + +- `impute`: When all tasks are `"impute"`, the `mids` object mimics the classic `mids` structure and does not store models. +- `train`: If one or more tasks include `"train"`, the `mids` object stores the models component for the subset of trained variables. +- `fill`: If all tasks are `"fill"`, the `mids` object does not store models. The setting `store = "fill"` assumes a fully trained model for all variables. + +Note: If additional variables exist in the new data, `mice()` will silently impute them using the trained model. This may produce unintended imputations if the new variables were not part of the training set. The resulting `mids` object will have `store = "train"`. + +Additionally, there is a special fourth `store` value: + +- `train_compact`: This value is assigned when `store = "train"` and `compact = TRUE`. The `train_compact` mode stores a minimized version of the imputation model without training data, making it suitable for production and sharing. + +## Conclusion + +This vignette introduced new functionality in `mice()` that enables storing and reusing imputation models, enhancing reproducibility, efficiency, and interoperability. By utilizing the `tasks` and `models` arguments, users can now train imputation models, apply them to new data, and share models across studies. The flexibility of **full fill** and **partial fill** workflows further supports diverse data structures, including **longitudinal data** and **multi-source datasets**. These enhancements streamline missing data workflows, reduce redundancy, and improve **data privacy** by allowing models to be shared instead of raw datasets. Future developments will refine model selection and optimization for various imputation scenarios. + +## References {.unnumbered} + +::: {#refs} +::: + +## APPENDIX A: Components of the `mids` object {.unnumbered} + +The following table summarizes the components saved by `mice()` for different `store` values. The `store` value is set to `"impute"`, `"train"`, or `"fill"` based on the `tasks` argument. `mice()` return `store == "impute"` by default, and `store == "fill"` if all variables are filled. In all other cases, the `store == "train"`. + +The table lists the components of the `mids` object for each `store` value. + +| Name | Description |I/O| Data Type |Impute|Train|Fill | +|---------------|----------------------------------------------|---|-------------------|-----|-----|-----| +| `predictorMatrix` | Specifies predictor set | I | `matrix` | YES | YES | NO | +| `formulas` | Formulae for imputation models | I | `list` | YES | YES | NO | +| `modeltype` | Form of imputation model | I | `character` | YES | YES | NO | +| `post` | Commands for post-processing | I | `character vector`| YES | YES | NO | +| `ignore` | Logical vector for ignored rows | I | `logical vector` | YES | YES | NO | +| `seed` | Seed value for reproducibility | I | `integer` | YES | YES | NO | +| `nmis` | Count of missing values per variable | O | `numeric vector` | YES | YES | NO | +| `chainMean` | Mean of imputed values | O | `array` | YES | YES | NO | +| `chainVar` | Variance of imputed values | O | `array` | YES | YES | NO | +| `loggedEvents` | Warnings and corrective tasks | O | `data.frame` | YES | YES | NO | +| `blocks` | Blocks of variables for imputation | I | `list` | YES | YES | NO | +| `method` | Imputation method per block | I | `character vector`| YES | YES | NO | +| `blots` | Extra arguments per block | I | `list` | YES | YES | NO | +| `visitSequence` | Order of block visits | I | `character vector`| YES | YES | NO | +| `iteration` | Last iteration number | O | `integer` | YES | YES | NO | +| `lastSeedValue` | Random number generator state | O | `integer` | YES | YES | NO | +| `tasks` | Specifies the imputation tasks | I | `character vector`| YES | YES | NO | +| `models` | Stores imputation model estimates | O | `list` | NO | YES | NO | +| `data` | Data to be imputed | I | `data.frame` | YES | YES | YES | +| `where` | Specifies where imputations occur | I | `matrix` | YES | YES | YES | +| `imp` | List of imputations per variable | O | `list` | YES | YES | YES | +| `m` | Number of imputations | I | `integer` | YES | YES | YES | +| `store` | Storage set | O | `character` | YES | YES | YES | +| `version` | Version number of `mice` package | O | `character` | YES | YES | YES | +| `date` | Date when the object was created | O | `character` | YES | YES | YES | +| `call` | Call that created the object | O | `call` | YES | YES | YES | + +The `mids` object `store` can be minimized from `"train"` to `"train_compact"` by setting `compact = TRUE`. The minimal representation `"train_compact"` saves the following components: `blocks`, `method`, `blots`, `visitSequence`, `iteration`, `lastSeedValue`, `tasks`, `models`, `m`, `store`, `version`, `date` and `call`. This feature is particularly useful if training data cannot be shared, or for production applications where memory efficiency is critical. The minimized model retains only the essential information required for generating imputed values, reducing memory usage while maintaining the core functionality of the imputation model. Note that some downstream functions, like `complete()` or `with()`, cannot support `store = "train_compact"`. + +## APPENDIX B: Computational details of binning in PMM {.unnumbered} + +### Initialization of the Number of Bins (`initialize.nbins`) {.unnumbered} + +The function `initialize.nbins()` determines an appropriate number of bins (`nbins`) to be used in PMM based on the sample size (`n`) and the number of unique values (`nu`) in the predicted or observed data. + +The default computation of `nbins` uses the relation `nbins = round(4 * log(n) + 1.5)`. This formula suggests that the number of bins grows logarithmically with the sample size (`n`), ensuring a reasonable bin width without excessive granularity. The coefficients (`4` and `1.5`) are chosen to scale the number of bins appropriately across different `n`. Since `nbins` represents a discretization of the data, it cannot exceed the number of unique values (`nu`) in the predicted or observed variable. If `nbins > nu`, the function sets `nbins = nu` to ensure that each unique value has its own bin. This adjustment is necessary to prevent empty bins and ensure that each unique value is represented in the binning process. The function enforces a lower bound of 2 bins to prevent degenerate cases where binning would be ineffective. + +For large samples of highly-correlated continuous data, the user can increase the set `nbins = 100` or higher to improve precision. + +### Initialization of the Number of Donors (`initialize.donors()`) {.unnumbered} + +The `initialize.donors()` function determines the number of donors `donors` for imputation based on the sample size (`n`). If `donors` is `NULL`, it is computed using `round(n / 600 + 7)`, ensuring a gradual increase as `n` grows. The computed value is then constrained using `max(1L, min(donors, n))`, which ensures at least one donor while preventing the donor count from exceeding `n`. + +Setting `donors = 1` (not recommended) selects the closest donor. Thus, different cases that are in the same bin will obtain the same imputed value from the left or right edge. Setting `donors = n` (not recommended) effectively samples from the marginal distribution, and weakens the relations between the data. The literature suggests values between 5 and 10 donors [@morris2014]. Because of binning, the optimal number of donors may actually need to be higher than 5 or 10 in order to reduce repetition of donors from the same bin. + +### Binning of the Linear Predictor (`bin.yhat()`) {.unnumbered} + +The `bin.yhat()` function divides the linear predictor into bins using the thresholds `edges`. It first sorts the linear predictor `yhat` and determines the bin index for each value based on the thresholds. The function uses the `findInterval()` function to assign each value to the corresponding bin. The bin index is calculated as `bin = findInterval(yhat, edges, left.open = TRUE)`. The `left.open = TRUE` argument ensures that values equal to the threshold are assigned to the left bin, consistent with the binning process. The function returns the bin index for each value. + +### Drawing Imputations (`draw.neighbors.pmm()`) {.unnumbered} + +The `draw.neighbors.pmm` function selects imputed values using predefined bins. Given predicted values (`yhat`), bin edges (`edges`), and a lookup table (`lookup`), the function assigns each `yhat` value to a bin using `findInterval`. If `yhat` is lower than the first bin or higher than the last bin, it selects the first or last bin. +To ensure smooth transitions between bins, the function calculates a probability weight based on the distance between `yhat` and the bin edges. Using a Bernoulli distribution, it probabilistically selects the left or right bin. Once the bin is selected, the function samples `mlocal` observed `y` values from the corresponding row in the lookup table. 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svg.getAttribute("width"); + if (width === null) { + throw new Error("Couldn't find SVG width"); + } + const numWidth = Number(width.slice(0, -2)); + + if (numWidth > 650) { + changed = true; + svg.setAttribute("width", "100%"); + svg.removeAttribute("height"); + } + }, + + // NB: there's effectively a copy of this function + // in `core/svg.ts`. + // if you change something here, you must keep it consistent there as well. + fixupAlignment(svg, align) { + let style = svg.getAttribute("style") || ""; + + switch (align) { + case "left": + style = `${style}; display: block; margin: auto auto auto 0`; + break; + case "right": + style = `${style}; display: block; margin: auto 0 auto auto`; + break; + case "center": + style = `${style}; display: block; margin: auto auto auto auto`; + break; + } + svg.setAttribute("style", style); + }, + + resolveOptions(svgEl) { + return svgEl.parentElement.parentElement.parentElement.parentElement + .dataset; + }, + + // NB: there's effectively a copy of this function + // in our mermaid runtime in `core/svg.ts`. + // if you change something here, you must keep it consistent there as well. + resolveSize(svgEl) { + const inInches = (size) => { + if (size.endsWith("in")) { + return Number(size.slice(0, -2)); + } + if (size.endsWith("pt") || size.endsWith("px")) { + // assume 96 dpi for now + return Number(size.slice(0, -2)) / 96; + } + return Number(size); + }; + + // these are figWidth and figHeight on purpose, + // because data attributes are translated to camelCase by the DOM API + const kFigWidth = "figWidth", + kFigHeight = "figHeight"; + const options = this.resolveOptions(svgEl); + let width = svgEl.getAttribute("width"); + let height = svgEl.getAttribute("height"); + const getViewBox = () => { + const vb = svgEl.attributes.getNamedItem("viewBox").value; // do it the roundabout way so that viewBox isn't dropped by deno_dom and text/html + if (!vb) return undefined; + const lst = vb.trim().split(" ").map(Number); + if (lst.length !== 4) return undefined; + if (lst.some(isNaN)) return undefined; + return lst; + }; + if (!width || !height) { + // attempt to resolve figure dimensions via viewBox + const viewBox = getViewBox(); + if (viewBox !== undefined) { + const [_mx, _my, vbWidth, vbHeight] = viewBox; + width = `${vbWidth}px`; + height = `${vbHeight}px`; + } else { + throw new Error( + "Mermaid generated an SVG without a viewbox attribute. Without knowing the diagram dimensions, quarto cannot convert it to a PNG" + ); + } + } + + let svgWidthInInches, svgHeightInInches; + + if ( + (width.slice(0, -2) === "pt" && height.slice(0, -2) === "pt") || + (width.slice(0, -2) === "px" && height.slice(0, -2) === "px") || + (!isNaN(Number(width)) && !isNaN(Number(height))) + ) { + // we assume 96 dpi which is generally what seems to be used. + svgWidthInInches = Number(width.slice(0, -2)) / 96; + svgHeightInInches = Number(height.slice(0, -2)) / 96; + } + const viewBox = getViewBox(); + if (viewBox !== undefined) { + // assume width and height come from viewbox. + const [_mx, _my, vbWidth, vbHeight] = viewBox; + svgWidthInInches = vbWidth / 96; + svgHeightInInches = vbHeight / 96; + } else { + throw new Error( + "Internal Error: Couldn't resolve width and height of SVG" + ); + } + const svgWidthOverHeight = svgWidthInInches / svgHeightInInches; + let widthInInches, heightInInches; + + if (options[kFigWidth] && options[kFigHeight]) { + // both were prescribed, so just go with them + widthInInches = inInches(String(options[kFigWidth])); + heightInInches = inInches(String(options[kFigHeight])); + } else if (options[kFigWidth]) { + // we were only given width, use that and adjust height based on aspect ratio; + widthInInches = inInches(String(options[kFigWidth])); + heightInInches = widthInInches / svgWidthOverHeight; + } else if (options[kFigHeight]) { + // we were only given height, use that and adjust width based on aspect ratio; + heightInInches = inInches(String(options[kFigHeight])); + widthInInches = heightInInches * svgWidthOverHeight; + } else { + // we were not given either, use svg's prescribed height + heightInInches = svgHeightInInches; + widthInInches = svgWidthInInches; + } + + return { + widthInInches, + heightInInches, + widthInPoints: Math.round(widthInInches * 96), + heightInPoints: Math.round(heightInInches * 96), + explicitWidth: options?.[kFigWidth] !== undefined, + explicitHeight: options?.[kFigHeight] !== undefined, + }; + }, + + postProcess(svg) { + const options = this.resolveOptions(svg); + if ( + options.responsive && + options["figWidth"] === undefined && + options["figHeight"] === undefined + ) { + this.makeResponsive(svg); + } else { + this.setSvgSize(svg); + } + if (options["reveal"]) { + this.fixupAlignment(svg, options["figAlign"] || "center"); + } + + // forward align attributes to the correct parent dif + // so that the svg figure is aligned correctly + const div = svg.parentElement.parentElement.parentElement; + const align = div.parentElement.parentElement.dataset.layoutAlign; + if (align) { + div.classList.remove("quarto-figure-left"); + div.classList.remove("quarto-figure-center"); + div.classList.remove("quarto-figure-right"); + div.classList.add(`quarto-figure-${align}`); + } + }, +}; + +// deno-lint-ignore no-window-prefix +window.addEventListener( + "load", + async function () { + let i = 0; + // we need pre because of whitespace preservation + for (const el of Array.from(document.querySelectorAll("pre.mermaid-js"))) { + //   doesn't appear to be treated as whitespace by mermaid + // so we replace it with a space. + const text = el.textContent.replaceAll(" ", " "); + const { svg: output } = await mermaid.mermaidAPI.render( + `mermaid-${++i}`, + text, + el + ); + el.innerHTML = output; + if (el.dataset.label) { + // patch mermaid's emitted style + const svg = el.firstChild; + const style = svg.querySelector("style"); + style.innerHTML = style.innerHTML.replaceAll( + `#${svg.id}`, + `#${el.dataset.label}-mermaid` + ); + svg.id = el.dataset.label + "-mermaid"; + delete el.dataset.label; + } + + const svg = el.querySelector("svg"); + const parent = el.parentElement; + parent.removeChild(el); + parent.appendChild(svg); + svg.classList.add("mermaid-js"); + } + for (const svgEl of Array.from( + document.querySelectorAll("svg.mermaid-js") + )) { + _quartoMermaid.postProcess(svgEl); + } + }, + false +); diff --git 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rimaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330});for(let 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strict";al();up();j1();JC=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Oe(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 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e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},hp=o(t=>{let e=new JC;return e.calculate(t),e},"getThemeVariables")});var e7,Pz,Bz=R(()=>{"use strict";al();j1();up();e7=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Et("#cde498",10),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.primaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.actorBorder=Dt(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Dt(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Dt(this.tertiaryColor,40);for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Pz=o(t=>{let e=new e7;return e.calculate(t),e},"getThemeVariables")});var t7,Fz,zz=R(()=>{"use strict";al();up();j1();t7=class{static{o(this,"Theme")}constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=Et(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, 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e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Fz=o(t=>{let e=new t7;return e.calculate(t),e},"getThemeVariables")});var Co,Kb=R(()=>{"use strict";Mz();Oz();jb();Bz();zz();Co={base:{getThemeVariables:Nz},dark:{getThemeVariables:Iz},default:{getThemeVariables:hp},forest:{getThemeVariables:Pz},neutral:{getThemeVariables:Fz}}});var tu,Gz=R(()=>{"use strict";tu={flowchart:{useMaxWidth:!0,titleTopMargin:25,subGraphTitleMargin:{top:0,bottom:0},diagramPadding:8,htmlLabels:!0,nodeSpacing:50,rankSpacing:50,curve:"basis",padding:15,defaultRenderer:"dagre-wrapper",wrappingWidth:200},sequence:{useMaxWidth:!0,hideUnusedParticipants:!1,activationWidth:10,diagramMarginX:50,diagramMarginY:10,actorMargin:50,width:150,height:65,boxMargin:10,boxTextMargin:5,noteMargin:10,messageMargin:35,messageAlign:"center",mirrorActors:!0,forceMenus:!1,bottomMarginAdj:1,rightAngles:!1,showSequenceNumbers:!1,actorFontSize:14,actorFontFamily:'"Open Sans", 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r=="function"||!r.unexpandable:fh.hasOwnProperty(e)&&!fh[e].primitive}},SG=/^[₊₋₌₍₎₀₁₂₃₄₅₆₇₈₉ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓᵦᵧᵨᵩᵪ]/,c4=Object.freeze({"\u208A":"+","\u208B":"-","\u208C":"=","\u208D":"(","\u208E":")","\u2080":"0","\u2081":"1","\u2082":"2","\u2083":"3","\u2084":"4","\u2085":"5","\u2086":"6","\u2087":"7","\u2088":"8","\u2089":"9","\u2090":"a","\u2091":"e","\u2095":"h","\u1D62":"i","\u2C7C":"j","\u2096":"k","\u2097":"l","\u2098":"m","\u2099":"n","\u2092":"o","\u209A":"p","\u1D63":"r","\u209B":"s","\u209C":"t","\u1D64":"u","\u1D65":"v","\u2093":"x","\u1D66":"\u03B2","\u1D67":"\u03B3","\u1D68":"\u03C1","\u1D69":"\u03D5","\u1D6A":"\u03C7","\u207A":"+","\u207B":"-","\u207C":"=","\u207D":"(","\u207E":")","\u2070":"0","\xB9":"1","\xB2":"2","\xB3":"3","\u2074":"4","\u2075":"5","\u2076":"6","\u2077":"7","\u2078":"8","\u2079":"9","\u1D2C":"A","\u1D2E":"B","\u1D30":"D","\u1D31":"E","\u1D33":"G","\u1D34":"H","\u1D35":"I","\u1D36":"J","\u1D37":"K","\u1D38":"L","\u1D39":"M","\u1D3A":"N","\u1D3C":"O","\u1D3E":"P","\u1D3F":"R","\u1D40":"T","\u1D41":"U","\u2C7D":"V","\u1D42":"W","\u1D43":"a","\u1D47":"b","\u1D9C":"c","\u1D48":"d","\u1D49":"e","\u1DA0":"f","\u1D4D":"g",\u02B0:"h","\u2071":"i",\u02B2:"j","\u1D4F":"k",\u02E1:"l","\u1D50":"m",\u207F:"n","\u1D52":"o","\u1D56":"p",\u02B3:"r",\u02E2:"s","\u1D57":"t","\u1D58":"u","\u1D5B":"v",\u02B7:"w",\u02E3:"x",\u02B8:"y","\u1DBB":"z","\u1D5D":"\u03B2","\u1D5E":"\u03B3","\u1D5F":"\u03B4","\u1D60":"\u03D5","\u1D61":"\u03C7","\u1DBF":"\u03B8"}),x7={"\u0301":{text:"\\'",math:"\\acute"},"\u0300":{text:"\\`",math:"\\grave"},"\u0308":{text:'\\"',math:"\\ddot"},"\u0303":{text:"\\~",math:"\\tilde"},"\u0304":{text:"\\=",math:"\\bar"},"\u0306":{text:"\\u",math:"\\breve"},"\u030C":{text:"\\v",math:"\\check"},"\u0302":{text:"\\^",math:"\\hat"},"\u0307":{text:"\\.",math:"\\dot"},"\u030A":{text:"\\r",math:"\\mathring"},"\u030B":{text:"\\H"},"\u0327":{text:"\\c"}},AG={\u00E1:"a\u0301",\u00E0:"a\u0300",\u00E4:"a\u0308",\u01DF:"a\u0308\u0304",\u00E3:"a\u0303",\u0101:"a\u0304",\u0103:"a\u0306",\u1EAF:"a\u0306\u0301",\u1EB1:"a\u0306\u0300",\u1EB5:"a\u0306\u0303",\u01CE:"a\u030C",\u00E2:"a\u0302",\u1EA5:"a\u0302\u0301",\u1EA7:"a\u0302\u0300",\u1EAB:"a\u0302\u0303",\u0227:"a\u0307",\u01E1:"a\u0307\u0304",\u00E5:"a\u030A",\u01FB:"a\u030A\u0301",\u1E03:"b\u0307",\u0107:"c\u0301",\u1E09:"c\u0327\u0301",\u010D:"c\u030C",\u0109:"c\u0302",\u010B:"c\u0307",\u00E7:"c\u0327",\u010F:"d\u030C",\u1E0B:"d\u0307",\u1E11:"d\u0327",\u00E9:"e\u0301",\u00E8:"e\u0300",\u00EB:"e\u0308",\u1EBD:"e\u0303",\u0113:"e\u0304",\u1E17:"e\u0304\u0301",\u1E15:"e\u0304\u0300",\u0115:"e\u0306",\u1E1D:"e\u0327\u0306",\u011B:"e\u030C",\u00EA:"e\u0302",\u1EBF:"e\u0302\u0301",\u1EC1:"e\u0302\u0300",\u1EC5:"e\u0302\u0303",\u0117:"e\u0307",\u0229:"e\u0327",\u1E1F:"f\u0307",\u01F5:"g\u0301",\u1E21:"g\u0304",\u011F:"g\u0306",\u01E7:"g\u030C",\u011D:"g\u0302",\u0121:"g\u0307",\u0123:"g\u0327",\u1E27:"h\u0308",\u021F:"h\u030C",\u0125:"h\u0302",\u1E23:"h\u0307",\u1E29:"h\u0327",\u00ED:"i\u0301",\u00EC:"i\u0300",\u00EF:"i\u0308",\u1E2F:"i\u0308\u0301",\u0129:"i\u0303",\u012B:"i\u0304",\u012D:"i\u0306",\u01D0:"i\u030C",\u00EE:"i\u0302",\u01F0:"j\u030C",\u0135:"j\u0302",\u1E31:"k\u0301",\u01E9:"k\u030C",\u0137:"k\u0327",\u013A:"l\u0301",\u013E:"l\u030C",\u013C:"l\u0327",\u1E3F:"m\u0301",\u1E41:"m\u0307",\u0144:"n\u0301",\u01F9:"n\u0300",\u00F1:"n\u0303",\u0148:"n\u030C",\u1E45:"n\u0307",\u0146:"n\u0327",\u00F3:"o\u0301",\u00F2:"o\u0300",\u00F6:"o\u0308",\u022B:"o\u0308\u0304",\u00F5:"o\u0303",\u1E4D:"o\u0303\u0301",\u1E4F:"o\u0303\u0308",\u022D:"o\u0303\u0304",\u014D:"o\u0304",\u1E53:"o\u0304\u0301",\u1E51:"o\u0304\u0300",\u014F:"o\u0306",\u01D2:"o\u030C",\u00F4:"o\u0302",\u1ED1:"o\u0302\u0301",\u1ED3:"o\u0302\u0300",\u1ED7:"o\u0302\u0303",\u022F:"o\u0307",\u0231:"o\u0307\u0304",\u0151:"o\u030B",\u1E55:"p\u0301",\u1E57:"p\u0307",\u0155:"r\u0301",\u0159:"r\u030C",\u1E59:"r\u0307",\u0157:"r\u0327",\u015B:"s\u0301",\u1E65:"s\u0301\u0307",\u0161:"s\u030C",\u1E67:"s\u030C\u0307",\u015D:"s\u0302",\u1E61:"s\u0307",\u015F:"s\u0327",\u1E97:"t\u0308",\u0165:"t\u030C",\u1E6B:"t\u0307",\u0163:"t\u0327",\u00FA:"u\u0301",\u00F9:"u\u0300",\u00FC:"u\u0308",\u01D8:"u\u0308\u0301",\u01DC:"u\u0308\u0300",\u01D6:"u\u0308\u0304",\u01DA:"u\u0308\u030C",\u0169:"u\u0303",\u1E79:"u\u0303\u0301",\u016B:"u\u0304",\u1E7B:"u\u0304\u0308",\u016D:"u\u0306",\u01D4:"u\u030C",\u00FB:"u\u0302",\u016F:"u\u030A",\u0171:"u\u030B",\u1E7D:"v\u0303",\u1E83:"w\u0301",\u1E81:"w\u0300",\u1E85:"w\u0308",\u0175:"w\u0302",\u1E87:"w\u0307",\u1E98:"w\u030A",\u1E8D:"x\u0308",\u1E8B:"x\u0307",\u00FD:"y\u0301",\u1EF3:"y\u0300",\u00FF:"y\u0308",\u1EF9:"y\u0303",\u0233:"y\u0304",\u0177:"y\u0302",\u1E8F:"y\u0307",\u1E99:"y\u030A",\u017A:"z\u0301",\u017E:"z\u030C",\u1E91:"z\u0302",\u017C:"z\u0307",\u00C1:"A\u0301",\u00C0:"A\u0300",\u00C4:"A\u0308",\u01DE:"A\u0308\u0304",\u00C3:"A\u0303",\u0100:"A\u0304",\u0102:"A\u0306",\u1EAE:"A\u0306\u0301",\u1EB0:"A\u0306\u0300",\u1EB4:"A\u0306\u0303",\u01CD:"A\u03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this.expect("}"),this.nextToken=r,n}parseExpression(e,r){for(var n=[];;){this.mode==="math"&&this.consumeSpaces();var i=this.fetch();if(t.endOfExpression.indexOf(i.text)!==-1||r&&i.text===r||e&&fh[i.text]&&fh[i.text].infix)break;var a=this.parseAtom(r);if(a){if(a.type==="internal")continue}else break;n.push(a)}return this.mode==="text"&&this.formLigatures(n),this.handleInfixNodes(n)}handleInfixNodes(e){for(var r=-1,n,i=0;i=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+r[0]+'" used in math mode',e);var l=wn[this.mode][r].group,u=Xs.range(e),h;if(hxe.hasOwnProperty(l)){var f=l;h={type:"atom",mode:this.mode,family:f,loc:u,text:r}}else h={type:l,mode:this.mode,loc:u,text:r};s=h}else if(r.charCodeAt(0)>=128)this.settings.strict&&(LG(r.charCodeAt(0))?this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+r[0]+'" used in math mode',e):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode 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+`:"
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2:if(Hs=this.productions_[us[1]][1],sh.$=Ot[Ot.length-Hs],sh._$={first_line:pe[pe.length-(Hs||1)].first_line,last_line:pe[pe.length-1].last_line,first_column:pe[pe.length-(Hs||1)].first_column,last_column:pe[pe.length-1].last_column},ah&&(sh._$.range=[pe[pe.length-(Hs||1)].range[0],pe[pe.length-1].range[1]]),Wl=this.performAction.apply(sh,[be,Xc,Ir,ko.yy,us[1],Ot,pe].concat(O1)),typeof Wl<"u")return Wl;Hs&&($t=$t.slice(0,-1*Hs*2),Ot=Ot.slice(0,-1*Hs),pe=pe.slice(0,-1*Hs)),$t.push(this.productions_[us[1]][0]),Ot.push(sh.$),pe.push(sh._$),B1=ur[$t[$t.length-2]][$t[$t.length-1]],$t.push(B1);break;case 3:return!0}}return!0},"parse")},_a=function(){var qi={EOF:1,parseError:o(function(At,$t){if(this.yy.parser)this.yy.parser.parseError(At,$t);else throw new Error(At)},"parseError"),setInput:o(function(ht,At){return this.yy=At||this.yy||{},this._input=ht,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var ht=this._input[0];this.yytext+=ht,this.yyleng++,this.offset++,this.match+=ht,this.matched+=ht;var At=ht.match(/(?:\r\n?|\n).*/g);return At?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),ht},"input"),unput:o(function(ht){var At=ht.length,$t=ht.split(/(?:\r\n?|\n)/g);this._input=ht+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-At),this.offset-=At;var rt=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),$t.length-1&&(this.yylineno-=$t.length-1);var Ot=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:$t?($t.length===rt.length?this.yylloc.first_column:0)+rt[rt.length-$t.length].length-$t[0].length:this.yylloc.first_column-At},this.options.ranges&&(this.yylloc.range=[Ot[0],Ot[0]+this.yyleng-At]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). +`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(ht){this.unput(this.match.slice(ht))},"less"),pastInput:o(function(){var ht=this.matched.substr(0,this.matched.length-this.match.length);return(ht.length>20?"...":"")+ht.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var ht=this.match;return ht.length<20&&(ht+=this._input.substr(0,20-ht.length)),(ht.substr(0,20)+(ht.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var ht=this.pastInput(),At=new Array(ht.length+1).join("-");return ht+this.upcomingInput()+` +`+At+"^"},"showPosition"),test_match:o(function(ht,At){var $t,rt,Ot;if(this.options.backtrack_lexer&&(Ot={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Ot.yylloc.range=this.yylloc.range.slice(0))),rt=ht[0].match(/(?:\r\n?|\n).*/g),rt&&(this.yylineno+=rt.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:rt?rt[rt.length-1].length-rt[rt.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+ht[0].length},this.yytext+=ht[0],this.match+=ht[0],this.matches=ht,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(ht[0].length),this.matched+=ht[0],$t=this.performAction.call(this,this.yy,this,At,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),$t)return $t;if(this._backtrack){for(var pe in Ot)this[pe]=Ot[pe];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var ht,At,$t,rt;this._more||(this.yytext="",this.match="");for(var Ot=this._currentRules(),pe=0;peAt[0].length)){if(At=$t,rt=pe,this.options.backtrack_lexer){if(ht=this.test_match($t,Ot[pe]),ht!==!1)return ht;if(this._backtrack){At=!1;continue}else return!1}else if(!this.options.flex)break}return At?(ht=this.test_match(At,Ot[rt]),ht!==!1?ht:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text. +`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var At=this.next();return At||this.lex()},"lex"),begin:o(function(At){this.conditionStack.push(At)},"begin"),popState:o(function(){var At=this.conditionStack.length-1;return At>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(At){return At=this.conditionStack.length-1-Math.abs(At||0),At>=0?this.conditionStack[At]:"INITIAL"},"topState"),pushState:o(function(At){this.begin(At)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(At,$t,rt,Ot){var pe=Ot;switch(rt){case 0:return this.begin("acc_title"),34;break;case 1:return this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),36;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:this.begin("callbackname");break;case 8:this.popState();break;case 9:this.popState(),this.begin("callbackargs");break;case 10:return 92;case 11:this.popState();break;case 12:return 93;case 13:return"MD_STR";case 14:this.popState();break;case 15:this.begin("md_string");break;case 16:return"STR";case 17:this.popState();break;case 18:this.pushState("string");break;case 19:return 81;case 20:return 99;case 21:return 82;case 22:return 101;case 23:return 83;case 24:return 84;case 25:return 94;case 26:this.begin("click");break;case 27:this.popState();break;case 28:return 85;case 29:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 30:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 31:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 32:return 27;case 33:return 32;case 34:return 95;case 35:return 95;case 36:return 95;case 37:return 95;case 38:return this.popState(),13;break;case 39:return this.popState(),14;break;case 40:return this.popState(),14;break;case 41:return this.popState(),14;break;case 42:return this.popState(),14;break;case 43:return this.popState(),14;break;case 44:return this.popState(),14;break;case 45:return this.popState(),14;break;case 46:return this.popState(),14;break;case 47:return this.popState(),14;break;case 48:return this.popState(),14;break;case 49:return 118;case 50:return 119;case 51:return 120;case 52:return 121;case 53:return 102;case 54:return 108;case 55:return 44;case 56:return 58;case 57:return 42;case 58:return 8;case 59:return 103;case 60:return 112;case 61:return this.popState(),75;break;case 62:return this.pushState("edgeText"),73;break;case 63:return 116;case 64:return this.popState(),75;break;case 65:return this.pushState("thickEdgeText"),73;break;case 66:return 116;case 67:return this.popState(),75;break;case 68:return this.pushState("dottedEdgeText"),73;break;case 69:return 116;case 70:return 75;case 71:return this.popState(),51;break;case 72:return"TEXT";case 73:return this.pushState("ellipseText"),50;break;case 74:return this.popState(),53;break;case 75:return this.pushState("text"),52;break;case 76:return this.popState(),55;break;case 77:return this.pushState("text"),54;break;case 78:return 56;case 79:return this.pushState("text"),65;break;case 80:return this.popState(),62;break;case 81:return this.pushState("text"),61;break;case 82:return this.popState(),47;break;case 83:return this.pushState("text"),46;break;case 84:return this.popState(),67;break;case 85:return this.popState(),69;break;case 86:return 114;case 87:return this.pushState("trapText"),66;break;case 88:return this.pushState("trapText"),68;break;case 89:return 115;case 90:return 65;case 91:return 87;case 92:return"SEP";case 93:return 86;case 94:return 112;case 95:return 108;case 96:return 42;case 97:return 106;case 98:return 111;case 99:return 113;case 100:return this.popState(),60;break;case 101:return this.pushState("text"),60;break;case 102:return this.popState(),49;break;case 103:return this.pushState("text"),48;break;case 104:return this.popState(),31;break;case 105:return this.pushState("text"),29;break;case 106:return this.popState(),64;break;case 107:return this.pushState("text"),63;break;case 108:return"TEXT";case 109:return"QUOTE";case 110:return 9;case 111:return 10;case 112:return 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qi}();Ha.lexer=_a;function To(){this.yy={}}return o(To,"Parser"),To.prototype=Ha,Ha.Parser=To,new To}();vD.parser=vD;Xre=vD});var kNe,ENe,Kre,Qre=R(()=>{"use strict";al();kNe=o((t,e)=>{let r=X1,n=r(t,"r"),i=r(t,"g"),a=r(t,"b");return Ws(n,i,a,e)},"fade"),ENe=o(t=>`.label { + font-family: ${t.fontFamily}; + color: ${t.nodeTextColor||t.textColor}; + } + .cluster-label text { + fill: ${t.titleColor}; + } + .cluster-label span { + color: ${t.titleColor}; + } + .cluster-label span p { + background-color: transparent; + } + + .label text,span { + fill: ${t.nodeTextColor||t.textColor}; + color: ${t.nodeTextColor||t.textColor}; + } + + .node rect, + .node circle, + .node ellipse, + .node polygon, + .node path { + fill: ${t.mainBkg}; + stroke: ${t.nodeBorder}; + stroke-width: 1px; + } + .rough-node .label text , .node .label text { + text-anchor: middle; + } + // .flowchart-label .text-outer-tspan { + // text-anchor: middle; + // } + // .flowchart-label .text-inner-tspan { + // text-anchor: start; + // } + + .node .katex path { + fill: #000; + stroke: #000; + stroke-width: 1px; + } + + .node .label { + text-align: center; + } + .node.clickable { + cursor: pointer; + } + + .arrowheadPath { + fill: ${t.arrowheadColor}; + } + + .edgePath .path { + stroke: ${t.lineColor}; + stroke-width: 2.0px; + } + + .flowchart-link { + stroke: ${t.lineColor}; + fill: none; + } + + .edgeLabel { + background-color: ${t.edgeLabelBackground}; + p { + background-color: ${t.edgeLabelBackground}; + } + rect { + opacity: 0.5; + background-color: ${t.edgeLabelBackground}; + fill: ${t.edgeLabelBackground}; + } + text-align: center; + } + + /* For html labels only */ + .labelBkg { + background-color: ${kNe(t.edgeLabelBackground,.5)}; + // background-color: + } + + .cluster rect { + fill: ${t.clusterBkg}; + stroke: ${t.clusterBorder}; + stroke-width: 1px; + } + + .cluster text { + fill: ${t.titleColor}; + } + + .cluster span { + color: ${t.titleColor}; + } + /* .cluster div { + color: ${t.titleColor}; + } */ + + div.mermaidTooltip { + position: absolute; + text-align: center; + max-width: 200px; + padding: 2px; + font-family: ${t.fontFamily}; + font-size: 12px; + background: ${t.tertiaryColor}; + border: 1px solid ${t.border2}; + border-radius: 2px; + pointer-events: none; + z-index: 100; + } + + .flowchartTitleText { + text-anchor: middle; + font-size: 18px; + fill: ${t.textColor}; + } +`,"getStyles"),Kre=ENe});var cT={};hr(cT,{diagram:()=>CNe});var CNe,uT=R(()=>{"use strict";_t();f9();qre();jre();Qre();CNe={parser:Xre,db:A5,renderer:Wre,styles:Kre,init:o(t=>{t.flowchart||(t.flowchart={}),t.layout&&iS({layout:t.layout}),t.flowchart.arrowMarkerAbsolute=t.arrowMarkerAbsolute,iS({flowchart:{arrowMarkerAbsolute:t.arrowMarkerAbsolute}}),A5.clear(),A5.setGen("gen-2")},"init")}});var xD,rne,nne=R(()=>{"use strict";xD=function(){var t=o(function(A,L,M,N){for(M=M||{},N=A.length;N--;M[A[N]]=L);return 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24:this.$={attributeType:C[D-2],attributeName:C[D-1],attributeKeyTypeList:C[D]};break;case 25:this.$={attributeType:C[D-2],attributeName:C[D-1],attributeComment:C[D]};break;case 26:this.$={attributeType:C[D-3],attributeName:C[D-2],attributeKeyTypeList:C[D-1],attributeComment:C[D]};break;case 27:case 28:case 31:this.$=C[D];break;case 30:C[D-2].push(C[D]),this.$=C[D-2];break;case 32:this.$=C[D].replace(/"/g,"");break;case 33:this.$={cardA:C[D],relType:C[D-1],cardB:C[D-2]};break;case 34:this.$=k.Cardinality.ZERO_OR_ONE;break;case 35:this.$=k.Cardinality.ZERO_OR_MORE;break;case 36:this.$=k.Cardinality.ONE_OR_MORE;break;case 37:this.$=k.Cardinality.ONLY_ONE;break;case 38:this.$=k.Cardinality.MD_PARENT;break;case 39:this.$=k.Identification.NON_IDENTIFYING;break;case 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N=new Error(L);throw N.hash=M,N}},"parseError"),parse:o(function(L){var M=this,N=[0],k=[],I=[null],C=[],O=this.table,D="",P=0,F=0,B=0,$=2,z=1,Y=C.slice.call(arguments,1),Q=Object.create(this.lexer),X={yy:{}};for(var ie in this.yy)Object.prototype.hasOwnProperty.call(this.yy,ie)&&(X.yy[ie]=this.yy[ie]);Q.setInput(L,X.yy),X.yy.lexer=Q,X.yy.parser=this,typeof Q.yylloc>"u"&&(Q.yylloc={});var j=Q.yylloc;C.push(j);var J=Q.options&&Q.options.ranges;typeof X.yy.parseError=="function"?this.parseError=X.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Z(Pe){N.length=N.length-2*Pe,I.length=I.length-Pe,C.length=C.length-Pe}o(Z,"popStack");function H(){var Pe;return Pe=k.pop()||Q.lex()||z,typeof Pe!="number"&&(Pe instanceof Array&&(k=Pe,Pe=k.pop()),Pe=M.symbols_[Pe]||Pe),Pe}o(H,"lex");for(var q,K,se,ce,ue,te,De={},oe,ke,Ie,Se;;){if(se=N[N.length-1],this.defaultActions[se]?ce=this.defaultActions[se]:((q===null||typeof q>"u")&&(q=H()),ce=O[se]&&O[se][q]),typeof 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this.yy=M||this.yy||{},this._input=L,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var L=this._input[0];this.yytext+=L,this.yyleng++,this.offset++,this.match+=L,this.matched+=L;var M=L.match(/(?:\r\n?|\n).*/g);return M?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),L},"input"),unput:o(function(L){var M=L.length,N=L.split(/(?:\r\n?|\n)/g);this._input=L+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-M),this.offset-=M;var k=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),N.length-1&&(this.yylineno-=N.length-1);var I=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:N?(N.length===k.length?this.yylloc.first_column:0)+k[k.length-N.length].length-N[0].length:this.yylloc.first_column-M},this.options.ranges&&(this.yylloc.range=[I[0],I[0]+this.yyleng-M]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). +`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(L){this.unput(this.match.slice(L))},"less"),pastInput:o(function(){var L=this.matched.substr(0,this.matched.length-this.match.length);return(L.length>20?"...":"")+L.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var L=this.match;return L.length<20&&(L+=this._input.substr(0,20-L.length)),(L.substr(0,20)+(L.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var L=this.pastInput(),M=new Array(L.length+1).join("-");return L+this.upcomingInput()+` +`+M+"^"},"showPosition"),test_match:o(function(L,M){var N,k,I;if(this.options.backtrack_lexer&&(I={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(I.yylloc.range=this.yylloc.range.slice(0))),k=L[0].match(/(?:\r\n?|\n).*/g),k&&(this.yylineno+=k.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:k?k[k.length-1].length-k[k.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+L[0].length},this.yytext+=L[0],this.match+=L[0],this.matches=L,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(L[0].length),this.matched+=L[0],N=this.performAction.call(this,this.yy,this,M,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),N)return N;if(this._backtrack){for(var C in I)this[C]=I[C];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var L,M,N,k;this._more||(this.yytext="",this.match="");for(var I=this._currentRules(),C=0;CM[0].length)){if(M=N,k=C,this.options.backtrack_lexer){if(L=this.test_match(N,I[C]),L!==!1)return L;if(this._backtrack){M=!1;continue}else return!1}else if(!this.options.flex)break}return M?(L=this.test_match(M,I[k]),L!==!1?L:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),24;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:return 10;case 8:break;case 9:return 8;case 10:return 28;case 11:return 48;case 12:return 4;case 13:return this.begin("block"),15;break;case 14:return 36;case 15:break;case 16:return 37;case 17:return 34;case 18:return 34;case 19:return 38;case 20:break;case 21:return this.popState(),17;break;case 22:return N.yytext[0];case 23:return 18;case 24:return 19;case 25:return 41;case 26:return 43;case 27:return 43;case 28:return 43;case 29:return 41;case 30:return 41;case 31:return 42;case 32:return 42;case 33:return 42;case 34:return 42;case 35:return 42;case 36:return 43;case 37:return 42;case 38:return 43;case 39:return 44;case 40:return 44;case 41:return 44;case 42:return 44;case 43:return 41;case 44:return 42;case 45:return 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regexp-to-ast library. + This will disable the lexer's first char optimizations. + For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#UNICODE_OPTIMIZE`);else{let T=die(w.PATTERN,e.ensureOptimizations);Qt(T)&&(v=!1),Ee(T,E=>{hN(b,E,y[S])})}else e.ensureOptimizations&&Wm(`${n2} TokenType: <${w.name}> is using a custom token pattern without providing parameter. + This will disable the lexer's first char optimizations. + For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#CUSTOM_OPTIMIZE`),v=!1;return b},[])}),{emptyGroups:g,patternIdxToConfig:y,charCodeToPatternIdxToConfig:x,hasCustom:i,canBeOptimized:v}}function vie(t,e){let r=[],n=tIe(t);r=r.concat(n.errors);let i=rIe(n.valid),a=i.valid;return r=r.concat(i.errors),r=r.concat(eIe(a)),r=r.concat(uIe(a)),r=r.concat(hIe(a,e)),r=r.concat(fIe(a)),r}function eIe(t){let e=[],r=$r(t,n=>zo(n[a0]));return 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->`+i.name+`<- static 'PATTERN' cannot contain end of input anchor '$' + See chevrotain.io/docs/guide/resolving_lexer_errors.html#ANCHORS for details.`,type:Gn.EOI_ANCHOR_FOUND,tokenTypes:[i]}))}function aIe(t){let e=$r(t,n=>n.PATTERN.test(""));return qe(e,n=>({message:"Token Type: ->"+n.name+"<- static 'PATTERN' must not match an empty string",type:Gn.EMPTY_MATCH_PATTERN,tokenTypes:[n]}))}function oIe(t){class e extends _c{static{o(this,"StartAnchorFinder")}constructor(){super(...arguments),this.found=!1}visitStartAnchor(a){this.found=!0}}let r=$r(t,i=>{let a=i.PATTERN;try{let s=jm(a),l=new e;return l.visit(s),l.found}catch{return sIe.test(a.source)}});return qe(r,i=>({message:`Unexpected RegExp Anchor Error: + Token Type: ->`+i.name+`<- static 'PATTERN' cannot contain start of input anchor '^' + See https://chevrotain.io/docs/guide/resolving_lexer_errors.html#ANCHORS for details.`,type:Gn.SOI_ANCHOR_FOUND,tokenTypes:[i]}))}function lIe(t){let e=$r(t,n=>{let i=n[a0];return i 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details.`,type:Gn.NO_LINE_BREAKS_FLAGS}),n}function wie(t){let e={},r=Dr(t);return Ee(r,n=>{let i=t[n];if(wt(i))e[n]=[];else throw Error("non exhaustive match")}),e}function Tie(t){let e=t.PATTERN;if(zo(e))return!1;if(wi(e))return!0;if(Xe(e,"exec"))return!0;if(di(e))return!1;throw Error("non exhaustive match")}function mIe(t){return di(t)&&t.length===1?t.charCodeAt(0):!1}function Eie(t,e){if(Xe(t,"LINE_BREAKS"))return!1;if(zo(t.PATTERN)){try{zT(e,t.PATTERN)}catch(r){return{issue:Gn.IDENTIFY_TERMINATOR,errMsg:r.message}}return!1}else{if(di(t.PATTERN))return!1;if(Tie(t))return{issue:Gn.CUSTOM_LINE_BREAK};throw Error("non exhaustive match")}}function gIe(t,e){if(e.issue===Gn.IDENTIFY_TERMINATOR)return`Warning: unable to identify line terminator usage in pattern. + The problem is in the <${t.name}> Token Type + Root cause: ${e.errMsg}. + For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#IDENTIFY_TERMINATOR`;if(e.issue===Gn.CUSTOM_LINE_BREAK)return`Warning: A Custom Token Pattern should specify the option. + The problem is in the <${t.name}> Token Type + For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#CUSTOM_LINE_BREAK`;throw Error("non exhaustive match")}function Cie(t){return qe(t,r=>di(r)?r.charCodeAt(0):r)}function hN(t,e,r){t[e]===void 0?t[e]=[r]:t[e].push(r)}function Lc(t){return t255?255+~~(t/255):t}}var a0,Qm,GT,fN,nIe,sIe,kie,Km,$T,uN=R(()=>{"use strict";Kv();i2();Pt();qm();pie();BT();a0="PATTERN",Qm="defaultMode",GT="modes",fN=typeof new 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+`,"\r"],ensureOptimizations:!1,safeMode:!1,errorMessageProvider:pN,traceInitPerf:!1,skipValidations:!1,recoveryEnabled:!0};Object.freeze(a2);ni=class{static{o(this,"Lexer")}constructor(e,r=a2){if(this.lexerDefinition=e,this.lexerDefinitionErrors=[],this.lexerDefinitionWarning=[],this.patternIdxToConfig={},this.charCodeToPatternIdxToConfig={},this.modes=[],this.emptyGroups={},this.trackStartLines=!0,this.trackEndLines=!0,this.hasCustom=!1,this.canModeBeOptimized={},this.TRACE_INIT=(i,a)=>{if(this.traceInitPerf===!0){this.traceInitIndent++;let s=new Array(this.traceInitIndent+1).join(" ");this.traceInitIndent <${i}>`);let{time:l,value:u}=t2(a),h=l>10?console.warn:console.log;return this.traceInitIndent time: ${l}ms`),this.traceInitIndent--,u}else return a()},typeof r=="boolean")throw Error(`The second argument to the Lexer constructor is now an ILexerConfig Object. +a boolean 2nd argument is no longer supported`);this.config=pa({},a2,r);let n=this.config.traceInitPerf;n===!0?(this.traceInitMaxIdent=1/0,this.traceInitPerf=!0):typeof n=="number"&&(this.traceInitMaxIdent=n,this.traceInitPerf=!0),this.traceInitIndent=-1,this.TRACE_INIT("Lexer Constructor",()=>{let i,a=!0;this.TRACE_INIT("Lexer Config handling",()=>{if(this.config.lineTerminatorsPattern===a2.lineTerminatorsPattern)this.config.lineTerminatorsPattern=kie;else if(this.config.lineTerminatorCharacters===a2.lineTerminatorCharacters)throw Error(`Error: Missing property on the Lexer config. + For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#MISSING_LINE_TERM_CHARS`);if(r.safeMode&&r.ensureOptimizations)throw Error('"safeMode" and "ensureOptimizations" flags are mutually exclusive.');this.trackStartLines=/full|onlyStart/i.test(this.config.positionTracking),this.trackEndLines=/full/i.test(this.config.positionTracking),wt(e)?i={modes:{defaultMode:Qr(e)},defaultMode:Qm}:(a=!1,i=Qr(e))}),this.config.skipValidations===!1&&(this.TRACE_INIT("performRuntimeChecks",()=>{this.lexerDefinitionErrors=this.lexerDefinitionErrors.concat(xie(i,this.trackStartLines,this.config.lineTerminatorCharacters))}),this.TRACE_INIT("performWarningRuntimeChecks",()=>{this.lexerDefinitionWarning=this.lexerDefinitionWarning.concat(bie(i,this.trackStartLines,this.config.lineTerminatorCharacters))})),i.modes=i.modes?i.modes:{},Ee(i.modes,(l,u)=>{i.modes[u]=Kh(l,h=>er(h))});let s=Dr(i.modes);if(Ee(i.modes,(l,u)=>{this.TRACE_INIT(`Mode: <${u}> processing`,()=>{if(this.modes.push(u),this.config.skipValidations===!1&&this.TRACE_INIT("validatePatterns",()=>{this.lexerDefinitionErrors=this.lexerDefinitionErrors.concat(vie(l,s))}),Qt(this.lexerDefinitionErrors)){Fu(l);let h;this.TRACE_INIT("analyzeTokenTypes",()=>{h=yie(l,{lineTerminatorCharacters:this.config.lineTerminatorCharacters,positionTracking:r.positionTracking,ensureOptimizations:r.ensureOptimizations,safeMode:r.safeMode,tracer:this.TRACE_INIT})}),this.patternIdxToConfig[u]=h.patternIdxToConfig,this.charCodeToPatternIdxToConfig[u]=h.charCodeToPatternIdxToConfig,this.emptyGroups=pa({},this.emptyGroups,h.emptyGroups),this.hasCustom=h.hasCustom||this.hasCustom,this.canModeBeOptimized[u]=h.canBeOptimized}})}),this.defaultMode=i.defaultMode,!Qt(this.lexerDefinitionErrors)&&!this.config.deferDefinitionErrorsHandling){let u=qe(this.lexerDefinitionErrors,h=>h.message).join(`----------------------- +`);throw new Error(`Errors detected in definition of Lexer: +`+u)}Ee(this.lexerDefinitionWarning,l=>{e2(l.message)}),this.TRACE_INIT("Choosing sub-methods implementations",()=>{if(fN?(this.chopInput=ea,this.match=this.matchWithTest):(this.updateLastIndex=qn,this.match=this.matchWithExec),a&&(this.handleModes=qn),this.trackStartLines===!1&&(this.computeNewColumn=ea),this.trackEndLines===!1&&(this.updateTokenEndLineColumnLocation=qn),/full/i.test(this.config.positionTracking))this.createTokenInstance=this.createFullToken;else if(/onlyStart/i.test(this.config.positionTracking))this.createTokenInstance=this.createStartOnlyToken;else if(/onlyOffset/i.test(this.config.positionTracking))this.createTokenInstance=this.createOffsetOnlyToken;else throw Error(`Invalid config option: "${this.config.positionTracking}"`);this.hasCustom?(this.addToken=this.addTokenUsingPush,this.handlePayload=this.handlePayloadWithCustom):(this.addToken=this.addTokenUsingMemberAccess,this.handlePayload=this.handlePayloadNoCustom)}),this.TRACE_INIT("Failed Optimization Warnings",()=>{let l=Vr(this.canModeBeOptimized,(u,h,f)=>(h===!1&&u.push(f),u),[]);if(r.ensureOptimizations&&!Qt(l))throw Error(`Lexer Modes: < ${l.join(", ")} > cannot be optimized. + Disable the "ensureOptimizations" lexer config flag to silently ignore this and run the lexer in an un-optimized mode. + Or inspect the console log for details on how to resolve these issues.`)}),this.TRACE_INIT("clearRegExpParserCache",()=>{uie()}),this.TRACE_INIT("toFastProperties",()=>{r2(this)})})}tokenize(e,r=this.defaultMode){if(!Qt(this.lexerDefinitionErrors)){let i=qe(this.lexerDefinitionErrors,a=>a.message).join(`----------------------- +`);throw new Error(`Unable to Tokenize because Errors detected in definition of Lexer: +`+i)}return this.tokenizeInternal(e,r)}tokenizeInternal(e,r){let n,i,a,s,l,u,h,f,d,p,m,g,y,v,x,b,w=e,S=w.length,T=0,E=0,_=this.hasCustom?0:Math.floor(e.length/10),A=new Array(_),L=[],M=this.trackStartLines?1:void 0,N=this.trackStartLines?1:void 0,k=wie(this.emptyGroups),I=this.trackStartLines,C=this.config.lineTerminatorsPattern,O=0,D=[],P=[],F=[],B=[];Object.freeze(B);let $;function z(){return D}o(z,"getPossiblePatternsSlow");function Y(J){let Z=Lc(J),H=P[Z];return H===void 0?B:H}o(Y,"getPossiblePatternsOptimized");let Q=o(J=>{if(F.length===1&&J.tokenType.PUSH_MODE===void 0){let Z=this.config.errorMessageProvider.buildUnableToPopLexerModeMessage(J);L.push({offset:J.startOffset,line:J.startLine,column:J.startColumn,length:J.image.length,message:Z})}else{F.pop();let Z=ma(F);D=this.patternIdxToConfig[Z],P=this.charCodeToPatternIdxToConfig[Z],O=D.length;let H=this.canModeBeOptimized[Z]&&this.config.safeMode===!1;P&&H?$=Y:$=z}},"pop_mode");function X(J){F.push(J),P=this.charCodeToPatternIdxToConfig[J],D=this.patternIdxToConfig[J],O=D.length,O=D.length;let Z=this.canModeBeOptimized[J]&&this.config.safeMode===!1;P&&Z?$=Y:$=z}o(X,"push_mode"),X.call(this,r);let ie,j=this.config.recoveryEnabled;for(;Tu.length){u=s,h=f,ie=ce;break}}}break}}if(u!==null){if(d=u.length,p=ie.group,p!==void 0&&(m=ie.tokenTypeIdx,g=this.createTokenInstance(u,T,m,ie.tokenType,M,N,d),this.handlePayload(g,h),p===!1?E=this.addToken(A,E,g):k[p].push(g)),e=this.chopInput(e,d),T=T+d,N=this.computeNewColumn(N,d),I===!0&&ie.canLineTerminator===!0){let q=0,K,se;C.lastIndex=0;do K=C.test(u),K===!0&&(se=C.lastIndex-1,q++);while(K===!0);q!==0&&(M=M+q,N=d-se,this.updateTokenEndLineColumnLocation(g,p,se,q,M,N,d))}this.handleModes(ie,Q,X,g)}else{let q=T,K=M,se=N,ce=j===!1;for(;ce===!1&&T{"use strict";Pt();i2();s0();o(zu,"tokenLabel");o(gN,"hasTokenLabel");EIe="parent",Rie="categories",Nie="label",Mie="group",Iie="push_mode",Oie="pop_mode",Pie="longer_alt",Bie="line_breaks",Fie="start_chars_hint";o(VT,"createToken");o(CIe,"createTokenInternal");fo=VT({name:"EOF",pattern:ni.NA});Fu([fo]);o(o0,"createTokenInstance");o(s2,"tokenMatcher")});var Gu,zie,Ol,Jm=R(()=>{"use strict";l0();Pt();ns();Gu={buildMismatchTokenMessage({expected:t,actual:e,previous:r,ruleName:n}){return`Expecting ${gN(t)?`--> ${zu(t)} <--`:`token of type --> ${t.name} <--`} but found --> '${e.image}' <--`},buildNotAllInputParsedMessage({firstRedundant:t,ruleName:e}){return"Redundant input, expecting EOF but found: "+t.image},buildNoViableAltMessage({expectedPathsPerAlt:t,actual:e,previous:r,customUserDescription:n,ruleName:i}){let a="Expecting: ",l=` +but found: '`+na(e).image+"'";if(n)return a+n+l;{let u=Vr(t,(p,m)=>p.concat(m),[]),h=qe(u,p=>`[${qe(p,m=>zu(m)).join(", ")}]`),d=`one of these possible Token sequences: +${qe(h,(p,m)=>` ${m+1}. ${p}`).join(` +`)}`;return a+d+l}},buildEarlyExitMessage({expectedIterationPaths:t,actual:e,customUserDescription:r,ruleName:n}){let i="Expecting: ",s=` +but found: '`+na(e).image+"'";if(r)return i+r+s;{let u=`expecting at least one iteration which starts with one of these possible Token sequences:: + <${qe(t,h=>`[${qe(h,f=>zu(f)).join(",")}]`).join(" ,")}>`;return i+u+s}}};Object.freeze(Gu);zie={buildRuleNotFoundError(t,e){return"Invalid grammar, reference to a rule which is not defined: ->"+e.nonTerminalName+`<- +inside top level rule: ->`+t.name+"<-"}},Ol={buildDuplicateFoundError(t,e){function r(f){return f instanceof fr?f.terminalType.name:f instanceof Zr?f.nonTerminalName:""}o(r,"getExtraProductionArgument");let n=t.name,i=na(e),a=i.idx,s=Rs(i),l=r(i),u=a>0,h=`->${s}${u?a:""}<- ${l?`with argument: ->${l}<-`:""} + appears more than once (${e.length} times) in the top level rule: ->${n}<-. + For further details see: https://chevrotain.io/docs/FAQ.html#NUMERICAL_SUFFIXES + `;return h=h.replace(/[ \t]+/g," "),h=h.replace(/\s\s+/g,` +`),h},buildNamespaceConflictError(t){return`Namespace conflict found in grammar. +The grammar has both a Terminal(Token) and a Non-Terminal(Rule) named: <${t.name}>. +To resolve this make sure each Terminal and Non-Terminal names are unique +This is easy to accomplish by using the convention that Terminal names start with an uppercase letter +and Non-Terminal names start with a lower case letter.`},buildAlternationPrefixAmbiguityError(t){let e=qe(t.prefixPath,i=>zu(i)).join(", "),r=t.alternation.idx===0?"":t.alternation.idx;return`Ambiguous alternatives: <${t.ambiguityIndices.join(" ,")}> due to common lookahead prefix +in inside <${t.topLevelRule.name}> Rule, +<${e}> may appears as a prefix path in all these alternatives. +See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#COMMON_PREFIX +For Further details.`},buildAlternationAmbiguityError(t){let e=qe(t.prefixPath,i=>zu(i)).join(", "),r=t.alternation.idx===0?"":t.alternation.idx,n=`Ambiguous Alternatives Detected: <${t.ambiguityIndices.join(" ,")}> in inside <${t.topLevelRule.name}> Rule, +<${e}> may appears as a prefix path in all these alternatives. +`;return n=n+`See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#AMBIGUOUS_ALTERNATIVES +For Further details.`,n},buildEmptyRepetitionError(t){let e=Rs(t.repetition);return t.repetition.idx!==0&&(e+=t.repetition.idx),`The repetition <${e}> within Rule <${t.topLevelRule.name}> can never consume any tokens. +This could lead to an infinite loop.`},buildTokenNameError(t){return"deprecated"},buildEmptyAlternationError(t){return`Ambiguous empty alternative: <${t.emptyChoiceIdx+1}> in inside <${t.topLevelRule.name}> Rule. +Only the last alternative may be an empty alternative.`},buildTooManyAlternativesError(t){return`An Alternation cannot have more than 256 alternatives: + inside <${t.topLevelRule.name}> Rule. + has ${t.alternation.definition.length+1} alternatives.`},buildLeftRecursionError(t){let e=t.topLevelRule.name,r=qe(t.leftRecursionPath,a=>a.name),n=`${e} --> ${r.concat([e]).join(" --> ")}`;return`Left Recursion found in grammar. +rule: <${e}> can be invoked from itself (directly or indirectly) +without consuming any Tokens. The grammar path that causes this is: + ${n} + To fix this refactor your grammar to remove the left recursion. +see: https://en.wikipedia.org/wiki/LL_parser#Left_factoring.`},buildInvalidRuleNameError(t){return"deprecated"},buildDuplicateRuleNameError(t){let e;return t.topLevelRule instanceof ts?e=t.topLevelRule.name:e=t.topLevelRule,`Duplicate definition, rule: ->${e}<- is already defined in the grammar: ->${t.grammarName}<-`}}});function Gie(t,e){let r=new yN(t,e);return r.resolveRefs(),r.errors}var yN,$ie=R(()=>{"use strict";Ns();Pt();ns();o(Gie,"resolveGrammar");yN=class extends rs{static{o(this,"GastRefResolverVisitor")}constructor(e,r){super(),this.nameToTopRule=e,this.errMsgProvider=r,this.errors=[]}resolveRefs(){Ee(or(this.nameToTopRule),e=>{this.currTopLevel=e,e.accept(this)})}visitNonTerminal(e){let r=this.nameToTopRule[e.nonTerminalName];if(r)e.referencedRule=r;else{let n=this.errMsgProvider.buildRuleNotFoundError(this.currTopLevel,e);this.errors.push({message:n,type:Pi.UNRESOLVED_SUBRULE_REF,ruleName:this.currTopLevel.name,unresolvedRefName:e.nonTerminalName})}}}});function WT(t,e,r=[]){r=Qr(r);let n=[],i=0;function a(l){return l.concat(fi(t,i+1))}o(a,"remainingPathWith");function s(l){let u=WT(a(l),e,r);return n.concat(u)}for(o(s,"getAlternativesForProd");r.length{Qt(u.definition)===!1&&(n=s(u.definition))}),n;if(l instanceof fr)r.push(l.terminalType);else throw Error("non exhaustive match")}i++}return n.push({partialPath:r,suffixDef:fi(t,i)}),n}function qT(t,e,r,n){let i="EXIT_NONE_TERMINAL",a=[i],s="EXIT_ALTERNATIVE",l=!1,u=e.length,h=u-n-1,f=[],d=[];for(d.push({idx:-1,def:t,ruleStack:[],occurrenceStack:[]});!Qt(d);){let p=d.pop();if(p===s){l&&ma(d).idx<=h&&d.pop();continue}let m=p.def,g=p.idx,y=p.ruleStack,v=p.occurrenceStack;if(Qt(m))continue;let x=m[0];if(x===i){let b={idx:g,def:fi(m),ruleStack:Ru(y),occurrenceStack:Ru(v)};d.push(b)}else if(x instanceof fr)if(g=0;b--){let w=x.definition[b],S={idx:g,def:w.definition.concat(fi(m)),ruleStack:y,occurrenceStack:v};d.push(S),d.push(s)}else if(x instanceof Sn)d.push({idx:g,def:x.definition.concat(fi(m)),ruleStack:y,occurrenceStack:v});else if(x instanceof ts)d.push(SIe(x,g,y,v));else throw Error("non exhaustive match")}return f}function SIe(t,e,r,n){let i=Qr(r);i.push(t.name);let a=Qr(n);return a.push(1),{idx:e,def:t.definition,ruleStack:i,occurrenceStack:a}}var vN,UT,eg,HT,o2,YT,l2,c2=R(()=>{"use strict";Pt();iN();IT();ns();vN=class extends Pu{static{o(this,"AbstractNextPossibleTokensWalker")}constructor(e,r){super(),this.topProd=e,this.path=r,this.possibleTokTypes=[],this.nextProductionName="",this.nextProductionOccurrence=0,this.found=!1,this.isAtEndOfPath=!1}startWalking(){if(this.found=!1,this.path.ruleStack[0]!==this.topProd.name)throw Error("The path does not start with the walker's top Rule!");return this.ruleStack=Qr(this.path.ruleStack).reverse(),this.occurrenceStack=Qr(this.path.occurrenceStack).reverse(),this.ruleStack.pop(),this.occurrenceStack.pop(),this.updateExpectedNext(),this.walk(this.topProd),this.possibleTokTypes}walk(e,r=[]){this.found||super.walk(e,r)}walkProdRef(e,r,n){if(e.referencedRule.name===this.nextProductionName&&e.idx===this.nextProductionOccurrence){let i=r.concat(n);this.updateExpectedNext(),this.walk(e.referencedRule,i)}}updateExpectedNext(){Qt(this.ruleStack)?(this.nextProductionName="",this.nextProductionOccurrence=0,this.isAtEndOfPath=!0):(this.nextProductionName=this.ruleStack.pop(),this.nextProductionOccurrence=this.occurrenceStack.pop())}},UT=class extends vN{static{o(this,"NextAfterTokenWalker")}constructor(e,r){super(e,r),this.path=r,this.nextTerminalName="",this.nextTerminalOccurrence=0,this.nextTerminalName=this.path.lastTok.name,this.nextTerminalOccurrence=this.path.lastTokOccurrence}walkTerminal(e,r,n){if(this.isAtEndOfPath&&e.terminalType.name===this.nextTerminalName&&e.idx===this.nextTerminalOccurrence&&!this.found){let i=r.concat(n),a=new Sn({definition:i});this.possibleTokTypes=i0(a),this.found=!0}}},eg=class extends Pu{static{o(this,"AbstractNextTerminalAfterProductionWalker")}constructor(e,r){super(),this.topRule=e,this.occurrence=r,this.result={token:void 0,occurrence:void 0,isEndOfRule:void 0}}startWalking(){return this.walk(this.topRule),this.result}},HT=class extends eg{static{o(this,"NextTerminalAfterManyWalker")}walkMany(e,r,n){if(e.idx===this.occurrence){let i=na(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof fr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else 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a=i.result,l=new bN(e,t,r).startWalking(),u=new Sn({definition:a}),h=new Sn({definition:l});return qie([u,h],n)}function KT(t,e){e:for(let r=0;r{let i=e[n];return r===i||i.categoryMatchesMap[r.tokenTypeIdx]})}function jie(t){return Ia(t,e=>Ia(e,r=>Ia(r,n=>Qt(n.categoryMatches))))}var $n,bN,XT,ng=R(()=>{"use strict";Pt();c2();IT();s0();ns();(function(t){t[t.OPTION=0]="OPTION",t[t.REPETITION=1]="REPETITION",t[t.REPETITION_MANDATORY=2]="REPETITION_MANDATORY",t[t.REPETITION_MANDATORY_WITH_SEPARATOR=3]="REPETITION_MANDATORY_WITH_SEPARATOR",t[t.REPETITION_WITH_SEPARATOR=4]="REPETITION_WITH_SEPARATOR",t[t.ALTERNATION=5]="ALTERNATION"})($n||($n={}));o(u2,"getProdType");o(jT,"getLookaheadPaths");o(Uie,"buildLookaheadFuncForOr");o(Hie,"buildLookaheadFuncForOptionalProd");o(Yie,"buildAlternativesLookAheadFunc");o(Wie,"buildSingleAlternativeLookaheadFunction");bN=class extends 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i=[],a=Vr(t,(l,u,h)=>(e.definition[h].ignoreAmbiguities===!0||Ee(u,f=>{let d=[h];Ee(t,(p,m)=>{h!==m&&KT(p,f)&&e.definition[m].ignoreAmbiguities!==!0&&d.push(m)}),d.length>1&&!KT(i,f)&&(i.push(f),l.push({alts:d,path:f}))}),l),[]);return qe(a,l=>{let u=qe(l.alts,f=>f+1);return{message:n.buildAlternationAmbiguityError({topLevelRule:r,alternation:e,ambiguityIndices:u,prefixPath:l.path}),type:Pi.AMBIGUOUS_ALTS,ruleName:r.name,occurrence:e.idx,alternatives:l.alts}})}function MIe(t,e,r,n){let i=Vr(t,(s,l,u)=>{let h=qe(l,f=>({idx:u,path:f}));return s.concat(h)},[]);return wc(ga(i,s=>{if(e.definition[s.idx].ignoreAmbiguities===!0)return[];let u=s.idx,h=s.path,f=$r(i,p=>e.definition[p.idx].ignoreAmbiguities!==!0&&p.idx{let m=[p.idx+1,u+1],g=e.idx===0?"":e.idx;return{message:n.buildAlternationPrefixAmbiguityError({topLevelRule:r,alternation:e,ambiguityIndices:m,prefixPath:p.path}),type:Pi.AMBIGUOUS_PREFIX_ALTS,ruleName:r.name,occurrence:g,alternatives:m}})}))}function IIe(t,e,r){let n=[],i=qe(e,a=>a.name);return Ee(t,a=>{let s=a.name;if(Fn(i,s)){let l=r.buildNamespaceConflictError(a);n.push({message:l,type:Pi.CONFLICT_TOKENS_RULES_NAMESPACE,ruleName:s})}}),n}var wN,h2,TN,f2=R(()=>{"use strict";Pt();Ns();ns();ng();c2();s0();o(Kie,"validateLookahead");o(Qie,"validateGrammar");o(_Ie,"validateDuplicateProductions");o(LIe,"identifyProductionForDuplicates");o(Zie,"getExtraProductionArgument");wN=class extends 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l=this.getKeyForAutomaticLookahead(n,i),u=this.firstAfterRepMap[l];if(u===void 0){let p=this.getCurrRuleFullName(),m=this.getGAstProductions()[p];u=new a(m,i).startWalking(),this.firstAfterRepMap[l]=u}let h=u.token,f=u.occurrence,d=u.isEndOfRule;this.RULE_STACK.length===1&&d&&h===void 0&&(h=fo,f=1),!(h===void 0||f===void 0)&&this.shouldInRepetitionRecoveryBeTried(h,f,s)&&this.tryInRepetitionRecovery(t,e,r,h)}var EN,SN,CN,ZT,AN=R(()=>{"use strict";l0();Pt();ag();aN();Ns();EN={},SN="InRuleRecoveryException",CN=class extends Error{static{o(this,"InRuleRecoveryException")}constructor(e){super(e),this.name=SN}},ZT=class{static{o(this,"Recoverable")}initRecoverable(e){this.firstAfterRepMap={},this.resyncFollows={},this.recoveryEnabled=Xe(e,"recoveryEnabled")?e.recoveryEnabled:is.recoveryEnabled,this.recoveryEnabled&&(this.attemptInRepetitionRecovery=OIe)}getTokenToInsert(e){let r=o0(e,"",NaN,NaN,NaN,NaN,NaN,NaN);return 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strict";Pt();mae();o(FIe,"defaultVisit");o(gae,"createBaseSemanticVisitorConstructor");o(yae,"createBaseVisitorConstructorWithDefaults");(function(t){t[t.REDUNDANT_METHOD=0]="REDUNDANT_METHOD",t[t.MISSING_METHOD=1]="MISSING_METHOD"})(ON||(ON={}));o(zIe,"validateVisitor");o(GIe,"validateMissingCstMethods")});var sk,xae=R(()=>{"use strict";pae();Pt();vae();Ns();sk=class{static{o(this,"TreeBuilder")}initTreeBuilder(e){if(this.CST_STACK=[],this.outputCst=e.outputCst,this.nodeLocationTracking=Xe(e,"nodeLocationTracking")?e.nodeLocationTracking:is.nodeLocationTracking,!this.outputCst)this.cstInvocationStateUpdate=qn,this.cstFinallyStateUpdate=qn,this.cstPostTerminal=qn,this.cstPostNonTerminal=qn,this.cstPostRule=qn;else if(/full/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=MN,this.setNodeLocationFromNode=MN,this.cstPostRule=qn,this.setInitialNodeLocation=this.setInitialNodeLocationFullRecovery):(this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=this.cstPostRuleFull,this.setInitialNodeLocation=this.setInitialNodeLocationFullRegular);else if(/onlyOffset/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=NN,this.setNodeLocationFromNode=NN,this.cstPostRule=qn,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRecovery):(this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=this.cstPostRuleOnlyOffset,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRegular);else if(/none/i.test(this.nodeLocationTracking))this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=qn,this.setInitialNodeLocation=qn;else throw Error(`Invalid config option: "${e.nodeLocationTracking}"`)}setInitialNodeLocationOnlyOffsetRecovery(e){e.location={startOffset:NaN,endOffset:NaN}}setInitialNodeLocationOnlyOffsetRegular(e){e.location={startOffset:this.LA(1).startOffset,endOffset:NaN}}setInitialNodeLocationFullRecovery(e){e.location={startOffset:NaN,startLine:NaN,startColumn:NaN,endOffset:NaN,endLine:NaN,endColumn:NaN}}setInitialNodeLocationFullRegular(e){let r=this.LA(1);e.location={startOffset:r.startOffset,startLine:r.startLine,startColumn:r.startColumn,endOffset:NaN,endLine:NaN,endColumn:NaN}}cstInvocationStateUpdate(e){let r={name:e,children:Object.create(null)};this.setInitialNodeLocation(r),this.CST_STACK.push(r)}cstFinallyStateUpdate(){this.CST_STACK.pop()}cstPostRuleFull(e){let r=this.LA(0),n=e.location;n.startOffset<=r.startOffset?(n.endOffset=r.endOffset,n.endLine=r.endLine,n.endColumn=r.endColumn):(n.startOffset=NaN,n.startLine=NaN,n.startColumn=NaN)}cstPostRuleOnlyOffset(e){let r=this.LA(0),n=e.location;n.startOffset<=r.startOffset?n.endOffset=r.endOffset:n.startOffset=NaN}cstPostTerminal(e,r){let n=this.CST_STACK[this.CST_STACK.length-1];fae(n,r,e),this.setNodeLocationFromToken(n.location,r)}cstPostNonTerminal(e,r){let n=this.CST_STACK[this.CST_STACK.length-1];dae(n,r,e),this.setNodeLocationFromNode(n.location,e.location)}getBaseCstVisitorConstructor(){if(er(this.baseCstVisitorConstructor)){let e=gae(this.className,Dr(this.gastProductionsCache));return this.baseCstVisitorConstructor=e,e}return this.baseCstVisitorConstructor}getBaseCstVisitorConstructorWithDefaults(){if(er(this.baseCstVisitorWithDefaultsConstructor)){let e=yae(this.className,Dr(this.gastProductionsCache),this.getBaseCstVisitorConstructor());return this.baseCstVisitorWithDefaultsConstructor=e,e}return this.baseCstVisitorWithDefaultsConstructor}getLastExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-1]}getPreviousExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-2]}getLastExplicitRuleOccurrenceIndex(){let e=this.RULE_OCCURRENCE_STACK;return e[e.length-1]}}});var ok,bae=R(()=>{"use strict";Ns();ok=class{static{o(this,"LexerAdapter")}initLexerAdapter(){this.tokVector=[],this.tokVectorLength=0,this.currIdx=-1}set input(e){if(this.selfAnalysisDone!==!0)throw Error("Missing invocation at the end of the Parser's constructor.");this.reset(),this.tokVector=e,this.tokVectorLength=e.length}get input(){return this.tokVector}SKIP_TOKEN(){return this.currIdx<=this.tokVector.length-2?(this.consumeToken(),this.LA(1)):sg}LA(e){let r=this.currIdx+e;return r<0||this.tokVectorLength<=r?sg:this.tokVector[r]}consumeToken(){this.currIdx++}exportLexerState(){return this.currIdx}importLexerState(e){this.currIdx=e}resetLexerState(){this.currIdx=-1}moveToTerminatedState(){this.currIdx=this.tokVector.length-1}getLexerPosition(){return this.exportLexerState()}}});var lk,wae=R(()=>{"use strict";Pt();ag();Ns();Jm();f2();ns();lk=class{static{o(this,"RecognizerApi")}ACTION(e){return e.call(this)}consume(e,r,n){return this.consumeInternal(r,e,n)}subrule(e,r,n){return this.subruleInternal(r,e,n)}option(e,r){return this.optionInternal(r,e)}or(e,r){return this.orInternal(r,e)}many(e,r){return this.manyInternal(e,r)}atLeastOne(e,r){return this.atLeastOneInternal(e,r)}CONSUME(e,r){return this.consumeInternal(e,0,r)}CONSUME1(e,r){return this.consumeInternal(e,1,r)}CONSUME2(e,r){return this.consumeInternal(e,2,r)}CONSUME3(e,r){return this.consumeInternal(e,3,r)}CONSUME4(e,r){return this.consumeInternal(e,4,r)}CONSUME5(e,r){return this.consumeInternal(e,5,r)}CONSUME6(e,r){return this.consumeInternal(e,6,r)}CONSUME7(e,r){return this.consumeInternal(e,7,r)}CONSUME8(e,r){return this.consumeInternal(e,8,r)}CONSUME9(e,r){return this.consumeInternal(e,9,r)}SUBRULE(e,r){return this.subruleInternal(e,0,r)}SUBRULE1(e,r){return this.subruleInternal(e,1,r)}SUBRULE2(e,r){return this.subruleInternal(e,2,r)}SUBRULE3(e,r){return this.subruleInternal(e,3,r)}SUBRULE4(e,r){return this.subruleInternal(e,4,r)}SUBRULE5(e,r){return this.subruleInternal(e,5,r)}SUBRULE6(e,r){return this.subruleInternal(e,6,r)}SUBRULE7(e,r){return this.subruleInternal(e,7,r)}SUBRULE8(e,r){return this.subruleInternal(e,8,r)}SUBRULE9(e,r){return this.subruleInternal(e,9,r)}OPTION(e){return this.optionInternal(e,0)}OPTION1(e){return this.optionInternal(e,1)}OPTION2(e){return this.optionInternal(e,2)}OPTION3(e){return this.optionInternal(e,3)}OPTION4(e){return this.optionInternal(e,4)}OPTION5(e){return this.optionInternal(e,5)}OPTION6(e){return this.optionInternal(e,6)}OPTION7(e){return this.optionInternal(e,7)}OPTION8(e){return this.optionInternal(e,8)}OPTION9(e){return this.optionInternal(e,9)}OR(e){return this.orInternal(e,0)}OR1(e){return this.orInternal(e,1)}OR2(e){return this.orInternal(e,2)}OR3(e){return this.orInternal(e,3)}OR4(e){return this.orInternal(e,4)}OR5(e){return this.orInternal(e,5)}OR6(e){return this.orInternal(e,6)}OR7(e){return this.orInternal(e,7)}OR8(e){return this.orInternal(e,8)}OR9(e){return this.orInternal(e,9)}MANY(e){this.manyInternal(0,e)}MANY1(e){this.manyInternal(1,e)}MANY2(e){this.manyInternal(2,e)}MANY3(e){this.manyInternal(3,e)}MANY4(e){this.manyInternal(4,e)}MANY5(e){this.manyInternal(5,e)}MANY6(e){this.manyInternal(6,e)}MANY7(e){this.manyInternal(7,e)}MANY8(e){this.manyInternal(8,e)}MANY9(e){this.manyInternal(9,e)}MANY_SEP(e){this.manySepFirstInternal(0,e)}MANY_SEP1(e){this.manySepFirstInternal(1,e)}MANY_SEP2(e){this.manySepFirstInternal(2,e)}MANY_SEP3(e){this.manySepFirstInternal(3,e)}MANY_SEP4(e){this.manySepFirstInternal(4,e)}MANY_SEP5(e){this.manySepFirstInternal(5,e)}MANY_SEP6(e){this.manySepFirstInternal(6,e)}MANY_SEP7(e){this.manySepFirstInternal(7,e)}MANY_SEP8(e){this.manySepFirstInternal(8,e)}MANY_SEP9(e){this.manySepFirstInternal(9,e)}AT_LEAST_ONE(e){this.atLeastOneInternal(0,e)}AT_LEAST_ONE1(e){return this.atLeastOneInternal(1,e)}AT_LEAST_ONE2(e){this.atLeastOneInternal(2,e)}AT_LEAST_ONE3(e){this.atLeastOneInternal(3,e)}AT_LEAST_ONE4(e){this.atLeastOneInternal(4,e)}AT_LEAST_ONE5(e){this.atLeastOneInternal(5,e)}AT_LEAST_ONE6(e){this.atLeastOneInternal(6,e)}AT_LEAST_ONE7(e){this.atLeastOneInternal(7,e)}AT_LEAST_ONE8(e){this.atLeastOneInternal(8,e)}AT_LEAST_ONE9(e){this.atLeastOneInternal(9,e)}AT_LEAST_ONE_SEP(e){this.atLeastOneSepFirstInternal(0,e)}AT_LEAST_ONE_SEP1(e){this.atLeastOneSepFirstInternal(1,e)}AT_LEAST_ONE_SEP2(e){this.atLeastOneSepFirstInternal(2,e)}AT_LEAST_ONE_SEP3(e){this.atLeastOneSepFirstInternal(3,e)}AT_LEAST_ONE_SEP4(e){this.atLeastOneSepFirstInternal(4,e)}AT_LEAST_ONE_SEP5(e){this.atLeastOneSepFirstInternal(5,e)}AT_LEAST_ONE_SEP6(e){this.atLeastOneSepFirstInternal(6,e)}AT_LEAST_ONE_SEP7(e){this.atLeastOneSepFirstInternal(7,e)}AT_LEAST_ONE_SEP8(e){this.atLeastOneSepFirstInternal(8,e)}AT_LEAST_ONE_SEP9(e){this.atLeastOneSepFirstInternal(9,e)}RULE(e,r,n=og){if(Fn(this.definedRulesNames,e)){let s={message:Ol.buildDuplicateRuleNameError({topLevelRule:e,grammarName:this.className}),type:Pi.DUPLICATE_RULE_NAME,ruleName:e};this.definitionErrors.push(s)}this.definedRulesNames.push(e);let i=this.defineRule(e,r,n);return this[e]=i,i}OVERRIDE_RULE(e,r,n=og){let i=Jie(e,this.definedRulesNames,this.className);this.definitionErrors=this.definitionErrors.concat(i);let a=this.defineRule(e,r,n);return this[e]=a,a}BACKTRACK(e,r){return function(){this.isBackTrackingStack.push(1);let n=this.saveRecogState();try{return e.apply(this,r),!0}catch(i){if(nf(i))return!1;throw i}finally{this.reloadRecogState(n),this.isBackTrackingStack.pop()}}}getGAstProductions(){return this.gastProductionsCache}getSerializedGastProductions(){return NT(or(this.gastProductionsCache))}}});var ck,Tae=R(()=>{"use strict";Pt();ek();ag();ng();c2();Ns();AN();l0();s0();ck=class{static{o(this,"RecognizerEngine")}initRecognizerEngine(e,r){if(this.className=this.constructor.name,this.shortRuleNameToFull={},this.fullRuleNameToShort={},this.ruleShortNameIdx=256,this.tokenMatcher=Zm,this.subruleIdx=0,this.definedRulesNames=[],this.tokensMap={},this.isBackTrackingStack=[],this.RULE_STACK=[],this.RULE_OCCURRENCE_STACK=[],this.gastProductionsCache={},Xe(r,"serializedGrammar"))throw Error(`The Parser's configuration can no longer contain a property. + See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_6-0-0 + For Further details.`);if(wt(e)){if(Qt(e))throw Error(`A Token Vocabulary cannot be empty. + Note that the first argument for the parser constructor + is no longer a Token vector (since v4.0).`);if(typeof e[0].startOffset=="number")throw Error(`The Parser constructor no longer accepts a token vector as the first argument. + See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_4-0-0 + For Further details.`)}if(wt(e))this.tokensMap=Vr(e,(a,s)=>(a[s.name]=s,a),{});else if(Xe(e,"modes")&&Ia(Gr(or(e.modes)),Die)){let a=Gr(or(e.modes)),s=Pm(a);this.tokensMap=Vr(s,(l,u)=>(l[u.name]=u,l),{})}else if(pn(e))this.tokensMap=Qr(e);else throw new Error(" argument must be An Array of Token constructors, A dictionary of Token constructors or an IMultiModeLexerDefinition");this.tokensMap.EOF=fo;let n=Xe(e,"modes")?Gr(or(e.modes)):or(e),i=Ia(n,a=>Qt(a.categoryMatches));this.tokenMatcher=i?Zm:Bu,Fu(or(this.tokensMap))}defineRule(e,r,n){if(this.selfAnalysisDone)throw Error(`Grammar rule <${e}> may not be defined after the 'performSelfAnalysis' method has been called' +Make sure that all grammar rule definitions are done before 'performSelfAnalysis' is called.`);let i=Xe(n,"resyncEnabled")?n.resyncEnabled:og.resyncEnabled,a=Xe(n,"recoveryValueFunc")?n.recoveryValueFunc:og.recoveryValueFunc,s=this.ruleShortNameIdx<<12;this.ruleShortNameIdx++,this.shortRuleNameToFull[s]=e,this.fullRuleNameToShort[e]=s;let l;return this.outputCst===!0?l=o(function(...f){try{this.ruleInvocationStateUpdate(s,e,this.subruleIdx),r.apply(this,f);let d=this.CST_STACK[this.CST_STACK.length-1];return this.cstPostRule(d),d}catch(d){return this.invokeRuleCatch(d,i,a)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTry"):l=o(function(...f){try{return this.ruleInvocationStateUpdate(s,e,this.subruleIdx),r.apply(this,f)}catch(d){return this.invokeRuleCatch(d,i,a)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTryCst"),Object.assign(l,{ruleName:e,originalGrammarAction:r})}invokeRuleCatch(e,r,n){let i=this.RULE_STACK.length===1,a=r&&!this.isBackTracking()&&this.recoveryEnabled;if(nf(e)){let s=e;if(a){let l=this.findReSyncTokenType();if(this.isInCurrentRuleReSyncSet(l))if(s.resyncedTokens=this.reSyncTo(l),this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];return u.recoveredNode=!0,u}else return n(e);else{if(this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];u.recoveredNode=!0,s.partialCstResult=u}throw s}}else{if(i)return this.moveToTerminatedState(),n(e);throw s}}else throw e}optionInternal(e,r){let n=this.getKeyForAutomaticLookahead(512,r);return this.optionInternalLogic(e,r,n)}optionInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof e!="function"){a=e.DEF;let s=e.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=e;if(i.call(this)===!0)return a.call(this)}atLeastOneInternal(e,r){let n=this.getKeyForAutomaticLookahead(1024,e);return this.atLeastOneInternalLogic(e,r,n)}atLeastOneInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let s=r.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=r;if(i.call(this)===!0){let s=this.doSingleRepetition(a);for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a)}else throw this.raiseEarlyExitException(e,$n.REPETITION_MANDATORY,r.ERR_MSG);this.attemptInRepetitionRecovery(this.atLeastOneInternal,[e,r],i,1024,e,YT)}atLeastOneSepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1536,e);this.atLeastOneSepFirstInternalLogic(e,r,n)}atLeastOneSepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,l2],l,1536,e,l2)}else throw this.raiseEarlyExitException(e,$n.REPETITION_MANDATORY_WITH_SEPARATOR,r.ERR_MSG)}manyInternal(e,r){let n=this.getKeyForAutomaticLookahead(768,e);return this.manyInternalLogic(e,r,n)}manyInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let l=r.GATE;if(l!==void 0){let u=i;i=o(()=>l.call(this)&&u.call(this),"lookaheadFunction")}}else a=r;let s=!0;for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a);this.attemptInRepetitionRecovery(this.manyInternal,[e,r],i,768,e,HT,s)}manySepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1280,e);this.manySepFirstInternalLogic(e,r,n)}manySepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,o2],l,1280,e,o2)}}repetitionSepSecondInternal(e,r,n,i,a){for(;n();)this.CONSUME(r),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,r,n,i,a],n,1536,e,a)}doSingleRepetition(e){let r=this.getLexerPosition();return e.call(this),this.getLexerPosition()>r}orInternal(e,r){let n=this.getKeyForAutomaticLookahead(256,r),i=wt(e)?e:e.DEF,s=this.getLaFuncFromCache(n).call(this,i);if(s!==void 0)return i[s].ALT.call(this);this.raiseNoAltException(r,e.ERR_MSG)}ruleFinallyStateUpdate(){if(this.RULE_STACK.pop(),this.RULE_OCCURRENCE_STACK.pop(),this.cstFinallyStateUpdate(),this.RULE_STACK.length===0&&this.isAtEndOfInput()===!1){let e=this.LA(1),r=this.errorMessageProvider.buildNotAllInputParsedMessage({firstRedundant:e,ruleName:this.getCurrRuleFullName()});this.SAVE_ERROR(new p2(r,e))}}subruleInternal(e,r,n){let i;try{let a=n!==void 0?n.ARGS:void 0;return this.subruleIdx=r,i=e.apply(this,a),this.cstPostNonTerminal(i,n!==void 0&&n.LABEL!==void 0?n.LABEL:e.ruleName),i}catch(a){throw this.subruleInternalError(a,n,e.ruleName)}}subruleInternalError(e,r,n){throw nf(e)&&e.partialCstResult!==void 0&&(this.cstPostNonTerminal(e.partialCstResult,r!==void 0&&r.LABEL!==void 0?r.LABEL:n),delete e.partialCstResult),e}consumeInternal(e,r,n){let i;try{let a=this.LA(1);this.tokenMatcher(a,e)===!0?(this.consumeToken(),i=a):this.consumeInternalError(e,a,n)}catch(a){i=this.consumeInternalRecovery(e,r,a)}return this.cstPostTerminal(n!==void 0&&n.LABEL!==void 0?n.LABEL:e.name,i),i}consumeInternalError(e,r,n){let i,a=this.LA(0);throw n!==void 0&&n.ERR_MSG?i=n.ERR_MSG:i=this.errorMessageProvider.buildMismatchTokenMessage({expected:e,actual:r,previous:a,ruleName:this.getCurrRuleFullName()}),this.SAVE_ERROR(new c0(i,r,a))}consumeInternalRecovery(e,r,n){if(this.recoveryEnabled&&n.name==="MismatchedTokenException"&&!this.isBackTracking()){let i=this.getFollowsForInRuleRecovery(e,r);try{return this.tryInRuleRecovery(e,i)}catch(a){throw a.name===SN?n:a}}else throw n}saveRecogState(){let e=this.errors,r=Qr(this.RULE_STACK);return{errors:e,lexerState:this.exportLexerState(),RULE_STACK:r,CST_STACK:this.CST_STACK}}reloadRecogState(e){this.errors=e.errors,this.importLexerState(e.lexerState),this.RULE_STACK=e.RULE_STACK}ruleInvocationStateUpdate(e,r,n){this.RULE_OCCURRENCE_STACK.push(n),this.RULE_STACK.push(e),this.cstInvocationStateUpdate(r)}isBackTracking(){return this.isBackTrackingStack.length!==0}getCurrRuleFullName(){let e=this.getLastExplicitRuleShortName();return this.shortRuleNameToFull[e]}shortRuleNameToFullName(e){return this.shortRuleNameToFull[e]}isAtEndOfInput(){return this.tokenMatcher(this.LA(1),fo)}reset(){this.resetLexerState(),this.subruleIdx=0,this.isBackTrackingStack=[],this.errors=[],this.RULE_STACK=[],this.CST_STACK=[],this.RULE_OCCURRENCE_STACK=[]}}});var uk,kae=R(()=>{"use strict";ag();Pt();ng();Ns();uk=class{static{o(this,"ErrorHandler")}initErrorHandler(e){this._errors=[],this.errorMessageProvider=Xe(e,"errorMessageProvider")?e.errorMessageProvider:is.errorMessageProvider}SAVE_ERROR(e){if(nf(e))return e.context={ruleStack:this.getHumanReadableRuleStack(),ruleOccurrenceStack:Qr(this.RULE_OCCURRENCE_STACK)},this._errors.push(e),e;throw Error("Trying to save an Error which is not a RecognitionException")}get errors(){return Qr(this._errors)}set errors(e){this._errors=e}raiseEarlyExitException(e,r,n){let i=this.getCurrRuleFullName(),a=this.getGAstProductions()[i],l=rg(e,a,r,this.maxLookahead)[0],u=[];for(let f=1;f<=this.maxLookahead;f++)u.push(this.LA(f));let h=this.errorMessageProvider.buildEarlyExitMessage({expectedIterationPaths:l,actual:u,previous:this.LA(0),customUserDescription:n,ruleName:i});throw this.SAVE_ERROR(new m2(h,this.LA(1),this.LA(0)))}raiseNoAltException(e,r){let n=this.getCurrRuleFullName(),i=this.getGAstProductions()[n],a=tg(e,i,this.maxLookahead),s=[];for(let h=1;h<=this.maxLookahead;h++)s.push(this.LA(h));let l=this.LA(0),u=this.errorMessageProvider.buildNoViableAltMessage({expectedPathsPerAlt:a,actual:s,previous:l,customUserDescription:r,ruleName:this.getCurrRuleFullName()});throw this.SAVE_ERROR(new d2(u,this.LA(1),l))}}});var hk,Eae=R(()=>{"use strict";c2();Pt();hk=class{static{o(this,"ContentAssist")}initContentAssist(){}computeContentAssist(e,r){let n=this.gastProductionsCache[e];if(er(n))throw Error(`Rule ->${e}<- does not exist in this grammar.`);return qT([n],r,this.tokenMatcher,this.maxLookahead)}getNextPossibleTokenTypes(e){let r=na(e.ruleStack),i=this.getGAstProductions()[r];return new UT(i,e).startWalking()}}});function y2(t,e,r,n=!1){dk(r);let i=ma(this.recordingProdStack),a=wi(e)?e:e.DEF,s=new t({definition:[],idx:r});return n&&(s.separator=e.SEP),Xe(e,"MAX_LOOKAHEAD")&&(s.maxLookahead=e.MAX_LOOKAHEAD),this.recordingProdStack.push(s),a.call(this),i.definition.push(s),this.recordingProdStack.pop(),pk}function UIe(t,e){dk(e);let r=ma(this.recordingProdStack),n=wt(t)===!1,i=n===!1?t:t.DEF,a=new gn({definition:[],idx:e,ignoreAmbiguities:n&&t.IGNORE_AMBIGUITIES===!0});Xe(t,"MAX_LOOKAHEAD")&&(a.maxLookahead=t.MAX_LOOKAHEAD);let s=Nv(i,l=>wi(l.GATE));return a.hasPredicates=s,r.definition.push(a),Ee(i,l=>{let u=new Sn({definition:[]});a.definition.push(u),Xe(l,"IGNORE_AMBIGUITIES")?u.ignoreAmbiguities=l.IGNORE_AMBIGUITIES:Xe(l,"GATE")&&(u.ignoreAmbiguities=!0),this.recordingProdStack.push(u),l.ALT.call(this),this.recordingProdStack.pop()}),pk}function Aae(t){return t===0?"":`${t}`}function dk(t){if(t<0||t>Sae){let e=new Error(`Invalid DSL Method idx value: <${t}> + Idx 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https://chevrotain.io/docs/guide/resolving_grammar_errors.html#AMBIGUOUS_ALTERNATIVES +For Further details.`,n}function mOe(t){if(t instanceof Zr)return"SUBRULE";if(t instanceof Jr)return"OPTION";if(t instanceof gn)return"OR";if(t instanceof An)return"AT_LEAST_ONE";if(t instanceof _n)return"AT_LEAST_ONE_SEP";if(t instanceof mn)return"MANY_SEP";if(t instanceof br)return"MANY";if(t instanceof fr)return"CONSUME";throw Error("non exhaustive match")}function gOe(t,e,r){let n=ga(e.configs.elements,a=>a.state.transitions),i=nte(n.filter(a=>a instanceof lg).map(a=>a.tokenType),a=>a.tokenTypeIdx);return{actualToken:r,possibleTokenTypes:i,tokenPath:t}}function yOe(t,e){return t.edges[e.tokenTypeIdx]}function vOe(t,e,r){let n=new fg,i=[];for(let s of t.elements){if(r.is(s.alt)===!1)continue;if(s.state.type===ug){i.push(s);continue}let l=s.state.transitions.length;for(let u=0;u0&&!kOe(a))for(let s of i)a.add(s);return a}function xOe(t,e){if(t instanceof lg&&s2(e,t.tokenType))return 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Ge=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Ae.length-1&&(this.yylineno-=Ae.length-1);var Me=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Ae?(Ae.length===Ge.length?this.yylloc.first_column:0)+Ge[Ge.length-Ae.length].length-Ae[0].length:this.yylloc.first_column-Ce},this.options.ranges&&(this.yylloc.range=[Me[0],Me[0]+this.yyleng-Ce]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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32:this.popState(),this.begin("point_y");break;case 33:return this.popState(),46;break;case 34:return 28;case 35:return 4;case 36:return 11;case 37:return 64;case 38:return 10;case 39:return 65;case 40:return 65;case 41:return 14;case 42:return 13;case 43:return 67;case 44:return 66;case 45:return 12;case 46:return 8;case 47:return 5;case 48:return 18;case 49:return 56;case 50:return 63;case 51:return 57}},"anonymous"),rules:[/^(?:%%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n\r]+)/i,/^(?:%%[^\n]*)/i,/^(?:title\b)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?: *x-axis *)/i,/^(?: *y-axis *)/i,/^(?: *--+> *)/i,/^(?: *quadrant-1 *)/i,/^(?: *quadrant-2 *)/i,/^(?: *quadrant-3 *)/i,/^(?: *quadrant-4 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Map,V.info("clear called")}setData(e){this.data={...this.data,...e}}addPoints(e){this.data.points=[...e,...this.data.points]}addClass(e,r){this.classes.set(e,r)}setConfig(e){V.trace("setConfig called with: ",e),this.config={...this.config,...e}}setThemeConfig(e){V.trace("setThemeConfig called with: ",e),this.themeConfig={...this.themeConfig,...e}}calculateSpace(e,r,n,i){let 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this.textDimensionCalculator.getMaxDimension(this.getTickValues().map(e=>e.toString()),this.axisConfig.labelFontSize)}recalculateOuterPaddingToDrawBar(){.7*this.getTickDistance()>this.outerPadding*2&&(this.outerPadding=Math.floor(.7*this.getTickDistance()/2)),this.recalculateScale()}calculateSpaceIfDrawnHorizontally(e){let r=e.height;if(this.axisConfig.showAxisLine&&r>this.axisConfig.axisLineWidth&&(r-=this.axisConfig.axisLineWidth,this.showAxisLine=!0),this.axisConfig.showLabel){let n=this.getLabelDimension(),i=.2*e.width;this.outerPadding=Math.min(n.width/2,i);let a=n.height+this.axisConfig.labelPadding*2;this.labelTextHeight=n.height,a<=r&&(r-=a,this.showLabel=!0)}if(this.axisConfig.showTick&&r>=this.axisConfig.tickLength&&(this.showTick=!0,r-=this.axisConfig.tickLength),this.axisConfig.showTitle&&this.title){let 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49:te[oe-1].draw="participant",te[oe-1].type="addParticipant",this.$=te[oe-1];break;case 50:te[oe-3].draw="actor",te[oe-3].type="addParticipant",te[oe-3].description=ce.parseMessage(te[oe-1]),this.$=te[oe-3];break;case 51:te[oe-1].draw="actor",te[oe-1].type="addParticipant",this.$=te[oe-1];break;case 52:te[oe-1].type="destroyParticipant",this.$=te[oe-1];break;case 53:this.$=[te[oe-1],{type:"addNote",placement:te[oe-2],actor:te[oe-1].actor,text:te[oe]}];break;case 54:te[oe-2]=[].concat(te[oe-1],te[oe-1]).slice(0,2),te[oe-2][0]=te[oe-2][0].actor,te[oe-2][1]=te[oe-2][1].actor,this.$=[te[oe-1],{type:"addNote",placement:ce.PLACEMENT.OVER,actor:te[oe-2].slice(0,2),text:te[oe]}];break;case 55:this.$=[te[oe-1],{type:"addLinks",actor:te[oe-1].actor,text:te[oe]}];break;case 56:this.$=[te[oe-1],{type:"addALink",actor:te[oe-1].actor,text:te[oe]}];break;case 57:this.$=[te[oe-1],{type:"addProperties",actor:te[oe-1].actor,text:te[oe]}];break;case 58:this.$=[te[oe-1],{type:"addDetails",actor:te[oe-1].actor,text:te[oe]}];break;case 61:this.$=[te[oe-2],te[oe]];break;case 62:this.$=te[oe];break;case 63:this.$=ce.PLACEMENT.LEFTOF;break;case 64:this.$=ce.PLACEMENT.RIGHTOF;break;case 65:this.$=[te[oe-4],te[oe-1],{type:"addMessage",from:te[oe-4].actor,to:te[oe-1].actor,signalType:te[oe-3],msg:te[oe],activate:!0},{type:"activeStart",signalType:ce.LINETYPE.ACTIVE_START,actor:te[oe-1].actor}];break;case 66:this.$=[te[oe-4],te[oe-1],{type:"addMessage",from:te[oe-4].actor,to:te[oe-1].actor,signalType:te[oe-3],msg:te[oe]},{type:"activeEnd",signalType:ce.LINETYPE.ACTIVE_END,actor:te[oe-4].actor}];break;case 67:this.$=[te[oe-3],te[oe-1],{type:"addMessage",from:te[oe-3].actor,to:te[oe-1].actor,signalType:te[oe-2],msg:te[oe]}];break;case 68:this.$={type:"addParticipant",actor:te[oe]};break;case 69:this.$=ce.LINETYPE.SOLID_OPEN;break;case 70:this.$=ce.LINETYPE.DOTTED_OPEN;break;case 71:this.$=ce.LINETYPE.SOLID;break;case 72:this.$=ce.LINETYPE.BIDIRECTIONAL_SOLID;break;case 73:this.$=ce.LINETYPE.DOTTED;break;case 74:this.$=ce.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 75:this.$=ce.LINETYPE.SOLID_CROSS;break;case 76:this.$=ce.LINETYPE.DOTTED_CROSS;break;case 77:this.$=ce.LINETYPE.SOLID_POINT;break;case 78:this.$=ce.LINETYPE.DOTTED_POINT;break;case 79:this.$=ce.parseMessage(te[oe].trim().substring(1));break}},"anonymous"),table:[{3:1,4:e,5:r,6:n},{1:[3]},{3:5,4:e,5:r,6:n},{3:6,4:e,5:r,6:n},t([1,4,5,13,14,18,21,23,29,30,31,33,35,36,37,38,39,41,43,44,46,50,52,53,54,59,60,61,62,70],i,{7:7}),{1:[2,1]},{1:[2,2]},{1:[2,3],4:a,5:s,8:8,9:10,12:12,13:l,14:u,17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},t(F,[2,5]),{9:47,12:12,13:l,14:u,17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},t(F,[2,7]),t(F,[2,8]),t(F,[2,14]),{12:48,50:L,52:M,53:N},{15:[1,49]},{5:[1,50]},{5:[1,53],19:[1,51],20:[1,52]},{22:54,70:P},{22:55,70:P},{5:[1,56]},{5:[1,57]},{5:[1,58]},{5:[1,59]},{5:[1,60]},t(F,[2,29]),t(F,[2,30]),{32:[1,61]},{34:[1,62]},t(F,[2,33]),{15:[1,63]},{15:[1,64]},{15:[1,65]},{15:[1,66]},{15:[1,67]},{15:[1,68]},{15:[1,69]},{15:[1,70]},{22:71,70:P},{22:72,70:P},{22:73,70:P},{67:74,71:[1,75],72:[1,76],73:[1,77],74:[1,78],75:[1,79],76:[1,80],77:[1,81],78:[1,82],79:[1,83],80:[1,84]},{55:85,57:[1,86],65:[1,87],66:[1,88]},{22:89,70:P},{22:90,70:P},{22:91,70:P},{22:92,70:P},t([5,51,64,71,72,73,74,75,76,77,78,79,80,81],[2,68]),t(F,[2,6]),t(F,[2,15]),t(B,[2,9],{10:93}),t(F,[2,17]),{5:[1,95],19:[1,94]},{5:[1,96]},t(F,[2,21]),{5:[1,97]},{5:[1,98]},t(F,[2,24]),t(F,[2,25]),t(F,[2,26]),t(F,[2,27]),t(F,[2,28]),t(F,[2,31]),t(F,[2,32]),t($,i,{7:99}),t($,i,{7:100}),t($,i,{7:101}),t(z,i,{40:102,7:103}),t(Y,i,{42:104,7:105}),t(Y,i,{7:105,42:106}),t(Q,i,{45:107,7:108}),t($,i,{7:109}),{5:[1,111],51:[1,110]},{5:[1,113],51:[1,112]},{5:[1,114]},{22:117,68:[1,115],69:[1,116],70:P},t(X,[2,69]),t(X,[2,70]),t(X,[2,71]),t(X,[2,72]),t(X,[2,73]),t(X,[2,74]),t(X,[2,75]),t(X,[2,76]),t(X,[2,77]),t(X,[2,78]),{22:118,70:P},{22:120,58:119,70:P},{70:[2,63]},{70:[2,64]},{56:121,81:ie},{56:123,81:ie},{56:124,81:ie},{56:125,81:ie},{4:[1,128],5:[1,130],11:127,12:129,16:[1,126],50:L,52:M,53:N},{5:[1,131]},t(F,[2,19]),t(F,[2,20]),t(F,[2,22]),t(F,[2,23]),{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,132],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,133],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,134],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,135]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,46],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,49:[1,136],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,137]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,44],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,48:[1,138],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,139]},{16:[1,140]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,42],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,47:[1,141],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,142],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{15:[1,143]},t(F,[2,49]),{15:[1,144]},t(F,[2,51]),t(F,[2,52]),{22:145,70:P},{22:146,70:P},{56:147,81:ie},{56:148,81:ie},{56:149,81:ie},{64:[1,150],81:[2,62]},{5:[2,55]},{5:[2,79]},{5:[2,56]},{5:[2,57]},{5:[2,58]},t(F,[2,16]),t(B,[2,10]),{12:151,50:L,52:M,53:N},t(B,[2,12]),t(B,[2,13]),t(F,[2,18]),t(F,[2,34]),t(F,[2,35]),t(F,[2,36]),t(F,[2,37]),{15:[1,152]},t(F,[2,38]),{15:[1,153]},t(F,[2,39]),t(F,[2,40]),{15:[1,154]},t(F,[2,41]),{5:[1,155]},{5:[1,156]},{56:157,81:ie},{56:158,81:ie},{5:[2,67]},{5:[2,53]},{5:[2,54]},{22:159,70:P},t(B,[2,11]),t(z,i,{7:103,40:160}),t(Y,i,{7:105,42:161}),t(Q,i,{7:108,45:162}),t(F,[2,48]),t(F,[2,50]),{5:[2,65]},{5:[2,66]},{81:[2,61]},{16:[2,47]},{16:[2,45]},{16:[2,43]}],defaultActions:{5:[2,1],6:[2,2],87:[2,63],88:[2,64],121:[2,55],122:[2,79],123:[2,56],124:[2,57],125:[2,58],147:[2,67],148:[2,53],149:[2,54],157:[2,65],158:[2,66],159:[2,61],160:[2,47],161:[2,45],162:[2,43]},parseError:o(function(q,K){if(K.recoverable)this.trace(q);else{var se=new Error(q);throw se.hash=K,se}},"parseError"),parse:o(function(q){var K=this,se=[0],ce=[],ue=[null],te=[],De=this.table,oe="",ke=0,Ie=0,Se=0,Ue=2,Pe=1,_e=te.slice.call(arguments,1),me=Object.create(this.lexer),W={yy:{}};for(var fe in this.yy)Object.prototype.hasOwnProperty.call(this.yy,fe)&&(W.yy[fe]=this.yy[fe]);me.setInput(q,W.yy),W.yy.lexer=me,W.yy.parser=this,typeof me.yylloc>"u"&&(me.yylloc={});var ge=me.yylloc;te.push(ge);var re=me.options&&me.options.ranges;typeof W.yy.parseError=="function"?this.parseError=W.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function he(yt){se.length=se.length-2*yt,ue.length=ue.length-yt,te.length=te.length-yt}o(he,"popStack");function ne(){var yt;return yt=ce.pop()||me.lex()||Pe,typeof yt!="number"&&(yt instanceof Array&&(ce=yt,yt=ce.pop()),yt=K.symbols_[yt]||yt),yt}o(ne,"lex");for(var ae,we,Te,Ce,Ae,Ge,Me={},ye,He,ze,Ze;;){if(Te=se[se.length-1],this.defaultActions[Te]?Ce=this.defaultActions[Te]:((ae===null||typeof ae>"u")&&(ae=ne()),Ce=De[Te]&&De[Te][ae]),typeof Ce>"u"||!Ce.length||!Ce[0]){var gt="";Ze=[];for(ye in De[Te])this.terminals_[ye]&&ye>Ue&&Ze.push("'"+this.terminals_[ye]+"'");me.showPosition?gt="Parse error on line "+(ke+1)+`: +`+me.showPosition()+` +Expecting `+Ze.join(", ")+", got '"+(this.terminals_[ae]||ae)+"'":gt="Parse error on line "+(ke+1)+": Unexpected "+(ae==Pe?"end of input":"'"+(this.terminals_[ae]||ae)+"'"),this.parseError(gt,{text:me.match,token:this.terminals_[ae]||ae,line:me.yylineno,loc:ge,expected:Ze})}if(Ce[0]instanceof Array&&Ce.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Te+", token: "+ae);switch(Ce[0]){case 1:se.push(ae),ue.push(me.yytext),te.push(me.yylloc),se.push(Ce[1]),ae=null,we?(ae=we,we=null):(Ie=me.yyleng,oe=me.yytext,ke=me.yylineno,ge=me.yylloc,Se>0&&Se--);break;case 2:if(He=this.productions_[Ce[1]][1],Me.$=ue[ue.length-He],Me._$={first_line:te[te.length-(He||1)].first_line,last_line:te[te.length-1].last_line,first_column:te[te.length-(He||1)].first_column,last_column:te[te.length-1].last_column},re&&(Me._$.range=[te[te.length-(He||1)].range[0],te[te.length-1].range[1]]),Ge=this.performAction.apply(Me,[oe,Ie,ke,W.yy,Ce[1],ue,te].concat(_e)),typeof Ge<"u")return Ge;He&&(se=se.slice(0,-1*He*2),ue=ue.slice(0,-1*He),te=te.slice(0,-1*He)),se.push(this.productions_[Ce[1]][0]),ue.push(Me.$),te.push(Me._$),ze=De[se[se.length-2]][se[se.length-1]],se.push(ze);break;case 3:return!0}}return!0},"parse")},J=function(){var H={EOF:1,parseError:o(function(K,se){if(this.yy.parser)this.yy.parser.parseError(K,se);else throw new Error(K)},"parseError"),setInput:o(function(q,K){return this.yy=K||this.yy||{},this._input=q,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var q=this._input[0];this.yytext+=q,this.yyleng++,this.offset++,this.match+=q,this.matched+=q;var K=q.match(/(?:\r\n?|\n).*/g);return K?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),q},"input"),unput:o(function(q){var K=q.length,se=q.split(/(?:\r\n?|\n)/g);this._input=q+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-K),this.offset-=K;var ce=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),se.length-1&&(this.yylineno-=se.length-1);var ue=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:se?(se.length===ce.length?this.yylloc.first_column:0)+ce[ce.length-se.length].length-se[0].length:this.yylloc.first_column-K},this.options.ranges&&(this.yylloc.range=[ue[0],ue[0]+this.yyleng-K]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). +`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(q){this.unput(this.match.slice(q))},"less"),pastInput:o(function(){var q=this.matched.substr(0,this.matched.length-this.match.length);return(q.length>20?"...":"")+q.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var q=this.match;return q.length<20&&(q+=this._input.substr(0,20-q.length)),(q.substr(0,20)+(q.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var q=this.pastInput(),K=new Array(q.length+1).join("-");return q+this.upcomingInput()+` +`+K+"^"},"showPosition"),test_match:o(function(q,K){var se,ce,ue;if(this.options.backtrack_lexer&&(ue={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(ue.yylloc.range=this.yylloc.range.slice(0))),ce=q[0].match(/(?:\r\n?|\n).*/g),ce&&(this.yylineno+=ce.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:ce?ce[ce.length-1].length-ce[ce.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+q[0].length},this.yytext+=q[0],this.match+=q[0],this.matches=q,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(q[0].length),this.matched+=q[0],se=this.performAction.call(this,this.yy,this,K,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),se)return se;if(this._backtrack){for(var te in ue)this[te]=ue[te];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var q,K,se,ce;this._more||(this.yytext="",this.match="");for(var ue=this._currentRules(),te=0;teK[0].length)){if(K=se,ce=te,this.options.backtrack_lexer){if(q=this.test_match(se,ue[te]),q!==!1)return q;if(this._backtrack){K=!1;continue}else return!1}else if(!this.options.flex)break}return K?(q=this.test_match(K,ue[ce]),q!==!1?q:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text. +`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var K=this.next();return K||this.lex()},"lex"),begin:o(function(K){this.conditionStack.push(K)},"begin"),popState:o(function(){var K=this.conditionStack.length-1;return K>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(K){return K=this.conditionStack.length-1-Math.abs(K||0),K>=0?this.conditionStack[K]:"INITIAL"},"topState"),pushState:o(function(K){this.begin(K)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{"case-insensitive":!0},performAction:o(function(K,se,ce,ue){var te=ue;switch(ce){case 0:return 5;case 1:break;case 2:break;case 3:break;case 4:break;case 5:break;case 6:return 19;case 7:return this.begin("LINE"),14;break;case 8:return this.begin("ID"),50;break;case 9:return this.begin("ID"),52;break;case 10:return 13;case 11:return this.begin("ID"),53;break;case 12:return se.yytext=se.yytext.trim(),this.begin("ALIAS"),70;break;case 13:return this.popState(),this.popState(),this.begin("LINE"),51;break;case 14:return this.popState(),this.popState(),5;break;case 15:return this.begin("LINE"),36;break;case 16:return this.begin("LINE"),37;break;case 17:return this.begin("LINE"),38;break;case 18:return this.begin("LINE"),39;break;case 19:return this.begin("LINE"),49;break;case 20:return this.begin("LINE"),41;break;case 21:return this.begin("LINE"),43;break;case 22:return this.begin("LINE"),48;break;case 23:return this.begin("LINE"),44;break;case 24:return this.begin("LINE"),47;break;case 25:return this.begin("LINE"),46;break;case 26:return this.popState(),15;break;case 27:return 16;case 28:return 65;case 29:return 66;case 30:return 59;case 31:return 60;case 32:return 61;case 33:return 62;case 34:return 57;case 35:return 54;case 36:return this.begin("ID"),21;break;case 37:return this.begin("ID"),23;break;case 38:return 29;case 39:return 30;case 40:return this.begin("acc_title"),31;break;case 41:return this.popState(),"acc_title_value";break;case 42:return this.begin("acc_descr"),33;break;case 43:return this.popState(),"acc_descr_value";break;case 44:this.begin("acc_descr_multiline");break;case 45:this.popState();break;case 46:return"acc_descr_multiline_value";case 47:return 6;case 48:return 18;case 49:return 20;case 50:return 64;case 51:return 5;case 52:return se.yytext=se.yytext.trim(),70;break;case 53:return 73;case 54:return 74;case 55:return 75;case 56:return 76;case 57:return 71;case 58:return 72;case 59:return 77;case 60:return 78;case 61:return 79;case 62:return 80;case 63:return 81;case 64:return 68;case 65:return 69;case 66:return 5;case 67:return"INVALID"}},"anonymous"),rules:[/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[0-9]+(?=[ \n]+))/i,/^(?:box\b)/i,/^(?:participant\b)/i,/^(?:actor\b)/i,/^(?:create\b)/i,/^(?:destroy\b)/i,/^(?:[^\<->\->:\n,;]+?([\-]*[^\<->\->:\n,;]+?)*?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:par_over\b)/i,/^(?:and\b)/i,/^(?:critical\b)/i,/^(?:option\b)/i,/^(?:break\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:links\b)/i,/^(?:link\b)/i,/^(?:properties\b)/i,/^(?:details\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:title:\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:off\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\<->\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\<->\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:<<->>)/i,/^(?:-->>)/i,/^(?:<<-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{acc_descr_multiline:{rules:[45,46],inclusive:!1},acc_descr:{rules:[43],inclusive:!1},acc_title:{rules:[41],inclusive:!1},ID:{rules:[2,3,12],inclusive:!1},ALIAS:{rules:[2,3,13,14],inclusive:!1},LINE:{rules:[2,3,26],inclusive:!1},INITIAL:{rules:[0,1,3,4,5,6,7,8,9,10,11,15,16,17,18,19,20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36,37,38,39,40,42,44,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],inclusive:!0}}};return H}();j.lexer=J;function Z(){this.yy={}}return o(Z,"Parser"),Z.prototype=j,j.Parser=Z,new Z}();hO.parser=hO;Wue=hO});function dO(t,e){if(t.links==null)t.links=e;else for(let r in e)t.links[r]=e[r]}function Zue(t,e){if(t.properties==null)t.properties=e;else for(let r in e)t.properties[r]=e[r]}function SGe(){Mt.records.currentBox=void 0}var Mt,aGe,fO,sGe,oGe,pi,lGe,cGe,uGe,hGe,fGe,dGe,pGe,xx,mGe,gGe,yGe,vGe,xGe,Xue,A0,bGe,wGe,TGe,vx,kGe,EGe,jue,Kue,CGe,Que,Jue,AGe,ehe,pO,the=R(()=>{"use strict";_t();ut();Jk();rr();bi();Mt=new uf(()=>({prevActor:void 0,actors:new Map,createdActors:new Map,destroyedActors:new Map,boxes:[],messages:[],notes:[],sequenceNumbersEnabled:!1,wrapEnabled:void 0,currentBox:void 0,lastCreated:void 0,lastDestroyed:void 0})),aGe=o(function(t){Mt.records.boxes.push({name:t.text,wrap:t.wrap??A0(),fill:t.color,actorKeys:[]}),Mt.records.currentBox=Mt.records.boxes.slice(-1)[0]},"addBox"),fO=o(function(t,e,r,n){let i=Mt.records.currentBox,a=Mt.records.actors.get(t);if(a){if(Mt.records.currentBox&&a.box&&Mt.records.currentBox!==a.box)throw new Error(`A same participant should only be defined in one Box: ${a.name} can't be in '${a.box.name}' and in '${Mt.records.currentBox.name}' at the same time.`);if(i=a.box?a.box:Mt.records.currentBox,a.box=i,a&&e===a.name&&r==null)return}if(r?.text==null&&(r={text:e,type:n}),(n==null||r.text==null)&&(r={text:e,type:n}),Mt.records.actors.set(t,{box:i,name:e,description:r.text,wrap:r.wrap??A0(),prevActor:Mt.records.prevActor,links:{},properties:{},actorCnt:null,rectData:null,type:n??"participant"}),Mt.records.prevActor){let s=Mt.records.actors.get(Mt.records.prevActor);s&&(s.nextActor=t)}Mt.records.currentBox&&Mt.records.currentBox.actorKeys.push(t),Mt.records.prevActor=t},"addActor"),sGe=o(t=>{let e,r=0;if(!t)return 0;for(e=0;e>-",token:"->>-",line:"1",loc:{first_line:1,last_line:1,first_column:1,last_column:1},expected:["'ACTIVE_PARTICIPANT'"]},s}return Mt.records.messages.push({from:t,to:e,message:r?.text??"",wrap:r?.wrap??A0(),type:n,activate:i}),!0},"addSignal"),lGe=o(function(){return Mt.records.boxes.length>0},"hasAtLeastOneBox"),cGe=o(function(){return Mt.records.boxes.some(t=>t.name)},"hasAtLeastOneBoxWithTitle"),uGe=o(function(){return Mt.records.messages},"getMessages"),hGe=o(function(){return Mt.records.boxes},"getBoxes"),fGe=o(function(){return Mt.records.actors},"getActors"),dGe=o(function(){return Mt.records.createdActors},"getCreatedActors"),pGe=o(function(){return Mt.records.destroyedActors},"getDestroyedActors"),xx=o(function(t){return Mt.records.actors.get(t)},"getActor"),mGe=o(function(){return[...Mt.records.actors.keys()]},"getActorKeys"),gGe=o(function(){Mt.records.sequenceNumbersEnabled=!0},"enableSequenceNumbers"),yGe=o(function(){Mt.records.sequenceNumbersEnabled=!1},"disableSequenceNumbers"),vGe=o(()=>Mt.records.sequenceNumbersEnabled,"showSequenceNumbers"),xGe=o(function(t){Mt.records.wrapEnabled=t},"setWrap"),Xue=o(t=>{if(t===void 0)return{};t=t.trim();let e=/^:?wrap:/.exec(t)!==null?!0:/^:?nowrap:/.exec(t)!==null?!1:void 0;return{cleanedText:(e===void 0?t:t.replace(/^:?(?:no)?wrap:/,"")).trim(),wrap:e}},"extractWrap"),A0=o(()=>Mt.records.wrapEnabled!==void 0?Mt.records.wrapEnabled:de().sequence?.wrap??!1,"autoWrap"),bGe=o(function(){Mt.reset(),vr()},"clear"),wGe=o(function(t){let e=t.trim(),{wrap:r,cleanedText:n}=Xue(e),i={text:n,wrap:r};return V.debug(`parseMessage: ${JSON.stringify(i)}`),i},"parseMessage"),TGe=o(function(t){let e=/^((?:rgba?|hsla?)\s*\(.*\)|\w*)(.*)$/.exec(t),r=e?.[1]?e[1].trim():"transparent",n=e?.[2]?e[2].trim():void 0;if(window?.CSS)window.CSS.supports("color",r)||(r="transparent",n=t.trim());else{let s=new Option().style;s.color=r,s.color!==r&&(r="transparent",n=t.trim())}let{wrap:i,cleanedText:a}=Xue(n);return{text:a?qr(a,de()):void 0,color:r,wrap:i}},"parseBoxData"),vx={SOLID:0,DOTTED:1,NOTE:2,SOLID_CROSS:3,DOTTED_CROSS:4,SOLID_OPEN:5,DOTTED_OPEN:6,LOOP_START:10,LOOP_END:11,ALT_START:12,ALT_ELSE:13,ALT_END:14,OPT_START:15,OPT_END:16,ACTIVE_START:17,ACTIVE_END:18,PAR_START:19,PAR_AND:20,PAR_END:21,RECT_START:22,RECT_END:23,SOLID_POINT:24,DOTTED_POINT:25,AUTONUMBER:26,CRITICAL_START:27,CRITICAL_OPTION:28,CRITICAL_END:29,BREAK_START:30,BREAK_END:31,PAR_OVER_START:32,BIDIRECTIONAL_SOLID:33,BIDIRECTIONAL_DOTTED:34},kGe={FILLED:0,OPEN:1},EGe={LEFTOF:0,RIGHTOF:1,OVER:2},jue=o(function(t,e,r){let n={actor:t,placement:e,message:r.text,wrap:r.wrap??A0()},i=[].concat(t,t);Mt.records.notes.push(n),Mt.records.messages.push({from:i[0],to:i[1],message:r.text,wrap:r.wrap??A0(),type:vx.NOTE,placement:e})},"addNote"),Kue=o(function(t,e){let r=xx(t);try{let n=qr(e.text,de());n=n.replace(/&/g,"&"),n=n.replace(/=/g,"=");let i=JSON.parse(n);dO(r,i)}catch(n){V.error("error while parsing actor link text",n)}},"addLinks"),CGe=o(function(t,e){let r=xx(t);try{let n={},i=qr(e.text,de()),a=i.indexOf("@");i=i.replace(/&/g,"&"),i=i.replace(/=/g,"=");let s=i.slice(0,a-1).trim(),l=i.slice(a+1).trim();n[s]=l,dO(r,n)}catch(n){V.error("error while parsing actor link text",n)}},"addALink");o(dO,"insertLinks");Que=o(function(t,e){let r=xx(t);try{let n=qr(e.text,de()),i=JSON.parse(n);Zue(r,i)}catch(n){V.error("error while parsing actor properties text",n)}},"addProperties");o(Zue,"insertProperties");o(SGe,"boxEnd");Jue=o(function(t,e){let r=xx(t),n=document.getElementById(e.text);try{let i=n.innerHTML,a=JSON.parse(i);a.properties&&Zue(r,a.properties),a.links&&dO(r,a.links)}catch(i){V.error("error while parsing actor details text",i)}},"addDetails"),AGe=o(function(t,e){if(t?.properties!==void 0)return t.properties[e]},"getActorProperty"),ehe=o(function(t){if(Array.isArray(t))t.forEach(function(e){ehe(e)});else switch(t.type){case"sequenceIndex":Mt.records.messages.push({from:void 0,to:void 0,message:{start:t.sequenceIndex,step:t.sequenceIndexStep,visible:t.sequenceVisible},wrap:!1,type:t.signalType});break;case"addParticipant":fO(t.actor,t.actor,t.description,t.draw);break;case"createParticipant":if(Mt.records.actors.has(t.actor))throw new Error("It is not possible to have actors with the same id, even if one is destroyed before the next is created. Use 'AS' aliases to simulate the behavior");Mt.records.lastCreated=t.actor,fO(t.actor,t.actor,t.description,t.draw),Mt.records.createdActors.set(t.actor,Mt.records.messages.length);break;case"destroyParticipant":Mt.records.lastDestroyed=t.actor,Mt.records.destroyedActors.set(t.actor,Mt.records.messages.length);break;case"activeStart":pi(t.actor,void 0,void 0,t.signalType);break;case"activeEnd":pi(t.actor,void 0,void 0,t.signalType);break;case"addNote":jue(t.actor,t.placement,t.text);break;case"addLinks":Kue(t.actor,t.text);break;case"addALink":CGe(t.actor,t.text);break;case"addProperties":Que(t.actor,t.text);break;case"addDetails":Jue(t.actor,t.text);break;case"addMessage":if(Mt.records.lastCreated){if(t.to!==Mt.records.lastCreated)throw new Error("The created participant "+Mt.records.lastCreated.name+" does not have an associated creating message after its declaration. Please check the sequence diagram.");Mt.records.lastCreated=void 0}else if(Mt.records.lastDestroyed){if(t.to!==Mt.records.lastDestroyed&&t.from!==Mt.records.lastDestroyed)throw new Error("The destroyed participant "+Mt.records.lastDestroyed.name+" does not have an associated destroying message after its declaration. Please check the sequence diagram.");Mt.records.lastDestroyed=void 0}pi(t.from,t.to,t.msg,t.signalType,t.activate);break;case"boxStart":aGe(t.boxData);break;case"boxEnd":SGe();break;case"loopStart":pi(void 0,void 0,t.loopText,t.signalType);break;case"loopEnd":pi(void 0,void 0,void 0,t.signalType);break;case"rectStart":pi(void 0,void 0,t.color,t.signalType);break;case"rectEnd":pi(void 0,void 0,void 0,t.signalType);break;case"optStart":pi(void 0,void 0,t.optText,t.signalType);break;case"optEnd":pi(void 0,void 0,void 0,t.signalType);break;case"altStart":pi(void 0,void 0,t.altText,t.signalType);break;case"else":pi(void 0,void 0,t.altText,t.signalType);break;case"altEnd":pi(void 0,void 0,void 0,t.signalType);break;case"setAccTitle":kr(t.text);break;case"parStart":pi(void 0,void 0,t.parText,t.signalType);break;case"and":pi(void 0,void 0,t.parText,t.signalType);break;case"parEnd":pi(void 0,void 0,void 0,t.signalType);break;case"criticalStart":pi(void 0,void 0,t.criticalText,t.signalType);break;case"option":pi(void 0,void 0,t.optionText,t.signalType);break;case"criticalEnd":pi(void 0,void 0,void 0,t.signalType);break;case"breakStart":pi(void 0,void 0,t.breakText,t.signalType);break;case"breakEnd":pi(void 0,void 0,void 0,t.signalType);break}},"apply"),pO={addActor:fO,addMessage:oGe,addSignal:pi,addLinks:Kue,addDetails:Jue,addProperties:Que,autoWrap:A0,setWrap:xGe,enableSequenceNumbers:gGe,disableSequenceNumbers:yGe,showSequenceNumbers:vGe,getMessages:uGe,getActors:fGe,getCreatedActors:dGe,getDestroyedActors:pGe,getActor:xx,getActorKeys:mGe,getActorProperty:AGe,getAccTitle:Ar,getBoxes:hGe,getDiagramTitle:Xr,setDiagramTitle:nn,getConfig:o(()=>de().sequence,"getConfig"),clear:bGe,parseMessage:wGe,parseBoxData:TGe,LINETYPE:vx,ARROWTYPE:kGe,PLACEMENT:EGe,addNote:jue,setAccTitle:kr,apply:ehe,setAccDescription:_r,getAccDescription:Lr,hasAtLeastOneBox:lGe,hasAtLeastOneBoxWithTitle:cGe}});var _Ge,rhe,nhe=R(()=>{"use strict";_Ge=o(t=>`.actor { + stroke: ${t.actorBorder}; + fill: ${t.actorBkg}; + } + + text.actor > tspan { + fill: ${t.actorTextColor}; + stroke: none; + } + + .actor-line { + stroke: ${t.actorLineColor}; + } + + .messageLine0 { + stroke-width: 1.5; + stroke-dasharray: none; + stroke: ${t.signalColor}; + } + + .messageLine1 { + stroke-width: 1.5; + stroke-dasharray: 2, 2; + stroke: ${t.signalColor}; + } + + #arrowhead path { + fill: ${t.signalColor}; + stroke: ${t.signalColor}; + } + + .sequenceNumber { + fill: ${t.sequenceNumberColor}; + } + + #sequencenumber { + fill: ${t.signalColor}; + } + + #crosshead path { + fill: ${t.signalColor}; + stroke: ${t.signalColor}; + } + + .messageText { + fill: ${t.signalTextColor}; + stroke: none; + } + + .labelBox { + stroke: ${t.labelBoxBorderColor}; + fill: ${t.labelBoxBkgColor}; + } + + .labelText, .labelText > tspan { + fill: ${t.labelTextColor}; + stroke: none; + } + + .loopText, .loopText > tspan { + fill: ${t.loopTextColor}; + stroke: none; + } + + .loopLine { + stroke-width: 2px; + stroke-dasharray: 2, 2; + stroke: ${t.labelBoxBorderColor}; + fill: ${t.labelBoxBorderColor}; + } + + .note { + //stroke: #decc93; + stroke: ${t.noteBorderColor}; + fill: ${t.noteBkgColor}; + } + + .noteText, .noteText > tspan { + fill: ${t.noteTextColor}; + stroke: none; + } + + .activation0 { + fill: ${t.activationBkgColor}; + stroke: ${t.activationBorderColor}; + } + + .activation1 { + fill: ${t.activationBkgColor}; + stroke: ${t.activationBorderColor}; + } + + .activation2 { + fill: ${t.activationBkgColor}; + stroke: ${t.activationBorderColor}; + } + + .actorPopupMenu { + position: absolute; + } + + .actorPopupMenuPanel { + position: absolute; + fill: ${t.actorBkg}; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + filter: drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4)); +} + .actor-man line { + stroke: ${t.actorBorder}; + fill: ${t.actorBkg}; + } + .actor-man circle, line { + stroke: ${t.actorBorder}; + fill: ${t.actorBkg}; + stroke-width: 2px; + } +`,"getStyles"),rhe=_Ge});var mO,gf,ahe,she,LGe,ihe,gO,DGe,RGe,bx,_0,ohe,Bc,yO,NGe,MGe,IGe,OGe,PGe,BGe,FGe,lhe,zGe,GGe,$Ge,VGe,UGe,HGe,YGe,che,WGe,vO,qGe,si,uhe=R(()=>{"use strict";rr();Qy();xr();mO=Xi(Up(),1);qs();gf=18*2,ahe="actor-top",she="actor-bottom",LGe="actor-box",ihe="actor-man",gO=o(function(t,e){return yd(t,e)},"drawRect"),DGe=o(function(t,e,r,n,i){if(e.links===void 0||e.links===null||Object.keys(e.links).length===0)return{height:0,width:0};let a=e.links,s=e.actorCnt,l=e.rectData;var u="none";i&&(u="block !important");let h=t.append("g");h.attr("id","actor"+s+"_popup"),h.attr("class","actorPopupMenu"),h.attr("display",u);var f="";l.class!==void 0&&(f=" "+l.class);let d=l.width>r?l.width:r,p=h.append("rect");if(p.attr("class","actorPopupMenuPanel"+f),p.attr("x",l.x),p.attr("y",l.height),p.attr("fill",l.fill),p.attr("stroke",l.stroke),p.attr("width",d),p.attr("height",l.height),p.attr("rx",l.rx),p.attr("ry",l.ry),a!=null){var m=20;for(let v in a){var g=h.append("a"),y=(0,mO.sanitizeUrl)(a[v]);g.attr("xlink:href",y),g.attr("target","_blank"),qGe(n)(v,g,l.x+10,l.height+m,d,20,{class:"actor"},n),m+=30}}return p.attr("height",m),{height:l.height+m,width:d}},"drawPopup"),RGe=o(function(t){return"var pu = document.getElementById('"+t+"'); 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function(t,e,r,n){let{startx:i,stopx:a,starty:s,message:l,type:u,sequenceIndex:h,sequenceVisible:f}=e,d=Lt.calculateTextDimensions(l,L0(Ne)),p=Ky();p.x=i,p.y=s+10,p.width=a-i,p.class="messageText",p.dy="1em",p.text=l,p.fontFamily=Ne.messageFontFamily,p.fontSize=Ne.messageFontSize,p.fontWeight=Ne.messageFontWeight,p.anchor=Ne.messageAlign,p.valign="center",p.textMargin=Ne.wrapPadding,p.tspan=!1,Ni(p.text)?await bx(t,p,{startx:i,stopx:a,starty:r}):_0(t,p);let m=d.width,g;i===a?Ne.rightAngles?g=t.append("path").attr("d",`M ${i},${r} H ${i+We.getMax(Ne.width/2,m/2)} V ${r+25} H ${i}`):g=t.append("path").attr("d","M "+i+","+r+" C "+(i+60)+","+(r-10)+" "+(i+60)+","+(r+30)+" "+i+","+(r+20)):(g=t.append("line"),g.attr("x1",i),g.attr("y1",r),g.attr("x2",a),g.attr("y2",r)),u===n.db.LINETYPE.DOTTED||u===n.db.LINETYPE.DOTTED_CROSS||u===n.db.LINETYPE.DOTTED_POINT||u===n.db.LINETYPE.DOTTED_OPEN||u===n.db.LINETYPE.BIDIRECTIONAL_DOTTED?(g.style("stroke-dasharray","3, 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y="";Ne.arrowMarkerAbsolute&&(y=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,y=y.replace(/\(/g,"\\("),y=y.replace(/\)/g,"\\)")),g.attr("stroke-width",2),g.attr("stroke","none"),g.style("fill","none"),(u===n.db.LINETYPE.SOLID||u===n.db.LINETYPE.DOTTED)&&g.attr("marker-end","url("+y+"#arrowhead)"),(u===n.db.LINETYPE.BIDIRECTIONAL_SOLID||u===n.db.LINETYPE.BIDIRECTIONAL_DOTTED)&&(g.attr("marker-start","url("+y+"#arrowhead)"),g.attr("marker-end","url("+y+"#arrowhead)")),(u===n.db.LINETYPE.SOLID_POINT||u===n.db.LINETYPE.DOTTED_POINT)&&g.attr("marker-end","url("+y+"#filled-head)"),(u===n.db.LINETYPE.SOLID_CROSS||u===n.db.LINETYPE.DOTTED_CROSS)&&g.attr("marker-end","url("+y+"#crosshead)"),(f||Ne.showSequenceNumbers)&&(g.attr("marker-start","url("+y+"#sequencenumber)"),t.append("text").attr("x",i).attr("y",r+4).attr("font-family","sans-serif").attr("font-size","12px").attr("text-anchor","middle").attr("class","sequenceNumber").text(h))},"drawMessage"),QGe=o(function(t,e,r,n,i,a,s){let l=0,u=0,h,f=0;for(let d of n){let p=e.get(d),m=p.box;h&&h!=m&&(s||Ke.models.addBox(h),u+=Ne.boxMargin+h.margin),m&&m!=h&&(s||(m.x=l+u,m.y=i),u+=m.margin),p.width=p.width||Ne.width,p.height=We.getMax(p.height||Ne.height,Ne.height),p.margin=p.margin||Ne.actorMargin,f=We.getMax(f,p.height),r.get(p.name)&&(u+=p.width/2),p.x=l+u,p.starty=Ke.getVerticalPos(),Ke.insert(p.x,i,p.x+p.width,p.height),l+=p.width+u,p.box&&(p.box.width=l+m.margin-p.box.x),u=p.margin,h=p.box,Ke.models.addActor(p)}h&&!s&&Ke.models.addBox(h),Ke.bumpVerticalPos(f)},"addActorRenderingData"),bO=o(async function(t,e,r,n){if(n){let i=0;Ke.bumpVerticalPos(Ne.boxMargin*2);for(let a of r){let s=e.get(a);s.stopy||(s.stopy=Ke.getVerticalPos());let l=await si.drawActor(t,s,Ne,!0);i=We.getMax(i,l)}Ke.bumpVerticalPos(i+Ne.boxMargin)}else for(let i of r){let a=e.get(i);await si.drawActor(t,a,Ne,!1)}},"drawActors"),fhe=o(function(t,e,r,n){let i=0,a=0;for(let s of r){let l=e.get(s),u=t$e(l),h=si.drawPopup(t,l,u,Ne,Ne.forceMenus,n);h.height>i&&(i=h.height),h.width+l.x>a&&(a=h.width+l.x)}return{maxHeight:i,maxWidth:a}},"drawActorsPopup"),dhe=o(function(t){On(Ne,t),t.fontFamily&&(Ne.actorFontFamily=Ne.noteFontFamily=Ne.messageFontFamily=t.fontFamily),t.fontSize&&(Ne.actorFontSize=Ne.noteFontSize=Ne.messageFontSize=t.fontSize),t.fontWeight&&(Ne.actorFontWeight=Ne.noteFontWeight=Ne.messageFontWeight=t.fontWeight)},"setConf"),bE=o(function(t){return Ke.activations.filter(function(e){return e.actor===t})},"actorActivations"),hhe=o(function(t,e){let r=e.get(t),n=bE(t),i=n.reduce(function(s,l){return We.getMin(s,l.startx)},r.x+r.width/2-1),a=n.reduce(function(s,l){return We.getMax(s,l.stopx)},r.x+r.width/2+1);return[i,a]},"activationBounds");o(Fc,"adjustLoopHeightForWrap");o(ZGe,"adjustCreatedDestroyedData");JGe=o(async function(t,e,r,n){let{securityLevel:i,sequence:a}=de();Ne=a;let s;i==="sandbox"&&(s=$e("#i"+e));let l=i==="sandbox"?$e(s.nodes()[0].contentDocument.body):$e("body"),u=i==="sandbox"?s.nodes()[0].contentDocument:document;Ke.init(),V.debug(n.db);let h=i==="sandbox"?l.select(`[id="${e}"]`):$e(`[id="${e}"]`),f=n.db.getActors(),d=n.db.getCreatedActors(),p=n.db.getDestroyedActors(),m=n.db.getBoxes(),g=n.db.getActorKeys(),y=n.db.getMessages(),v=n.db.getDiagramTitle(),x=n.db.hasAtLeastOneBox(),b=n.db.hasAtLeastOneBoxWithTitle(),w=await e$e(f,y,n);if(Ne.height=await r$e(f,w,m),si.insertComputerIcon(h),si.insertDatabaseIcon(h),si.insertClockIcon(h),x&&(Ke.bumpVerticalPos(Ne.boxMargin),b&&Ke.bumpVerticalPos(m[0].textMaxHeight)),Ne.hideUnusedParticipants===!0){let F=new Set;y.forEach(B=>{F.add(B.from),F.add(B.to)}),g=g.filter(B=>F.has(B))}QGe(h,f,d,g,0,y,!1);let S=await a$e(y,f,w,n);si.insertArrowHead(h),si.insertArrowCrossHead(h),si.insertArrowFilledHead(h),si.insertSequenceNumber(h);function T(F,B){let $=Ke.endActivation(F);$.starty+18>B&&($.starty=B-6,B+=12),si.drawActivation(h,$,B,Ne,bE(F.from).length),Ke.insert($.startx,B-10,$.stopx,B)}o(T,"activeEnd");let E=1,_=1,A=[],L=[],M=0;for(let F of y){let B,$,z;switch(F.type){case n.db.LINETYPE.NOTE:Ke.resetVerticalPos(),$=F.noteModel,await XGe(h,$);break;case n.db.LINETYPE.ACTIVE_START:Ke.newActivation(F,h,f);break;case n.db.LINETYPE.ACTIVE_END:T(F,Ke.getVerticalPos());break;case n.db.LINETYPE.LOOP_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.LOOP_END:B=Ke.endLoop(),await si.drawLoop(h,B,"loop",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.RECT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin,Y=>Ke.newLoop(void 0,Y.message));break;case n.db.LINETYPE.RECT_END:B=Ke.endLoop(),L.push(B),Ke.models.addLoop(B),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos());break;case n.db.LINETYPE.OPT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.OPT_END:B=Ke.endLoop(),await si.drawLoop(h,B,"opt",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.ALT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.ALT_ELSE:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.ALT_END:B=Ke.endLoop(),await si.drawLoop(h,B,"alt",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.PAR_START:case n.db.LINETYPE.PAR_OVER_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y)),Ke.saveVerticalPos();break;case n.db.LINETYPE.PAR_AND:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.PAR_END:B=Ke.endLoop(),await si.drawLoop(h,B,"par",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.AUTONUMBER:E=F.message.start||E,_=F.message.step||_,F.message.visible?n.db.enableSequenceNumbers():n.db.disableSequenceNumbers();break;case n.db.LINETYPE.CRITICAL_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.CRITICAL_OPTION:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.CRITICAL_END:B=Ke.endLoop(),await si.drawLoop(h,B,"critical",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.BREAK_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.BREAK_END:B=Ke.endLoop(),await si.drawLoop(h,B,"break",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;default:try{z=F.msgModel,z.starty=Ke.getVerticalPos(),z.sequenceIndex=E,z.sequenceVisible=n.db.showSequenceNumbers();let Y=await jGe(h,z);ZGe(F,z,Y,M,f,d,p),A.push({messageModel:z,lineStartY:Y}),Ke.models.addMessage(z)}catch(Y){V.error("error while drawing message",Y)}}[n.db.LINETYPE.SOLID_OPEN,n.db.LINETYPE.DOTTED_OPEN,n.db.LINETYPE.SOLID,n.db.LINETYPE.DOTTED,n.db.LINETYPE.SOLID_CROSS,n.db.LINETYPE.DOTTED_CROSS,n.db.LINETYPE.SOLID_POINT,n.db.LINETYPE.DOTTED_POINT,n.db.LINETYPE.BIDIRECTIONAL_SOLID,n.db.LINETYPE.BIDIRECTIONAL_DOTTED].includes(F.type)&&(E=E+_),M++}V.debug("createdActors",d),V.debug("destroyedActors",p),await bO(h,f,g,!1);for(let F of A)await KGe(h,F.messageModel,F.lineStartY,n);Ne.mirrorActors&&await bO(h,f,g,!0),L.forEach(F=>si.drawBackgroundRect(h,F)),yO(h,f,g,Ne);for(let F of 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this.yy=me||this.yy||{},this._input=_e,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var _e=this._input[0];this.yytext+=_e,this.yyleng++,this.offset++,this.match+=_e,this.matched+=_e;var me=_e.match(/(?:\r\n?|\n).*/g);return me?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),_e},"input"),unput:o(function(_e){var me=_e.length,W=_e.split(/(?:\r\n?|\n)/g);this._input=_e+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-me),this.offset-=me;var 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v=g.value,x=y.value,b=v[0]===x[0]&&v[1]===x[1]&&v[2]===x[2]&&(v[3]===x[3]||(v[3]==null||v[3]===1)&&(x[3]==null||x[3]===1));if(b)return!1}return{name:t,value:d,strValue:""+e,mapped:m,field:d[1],fieldMin:parseFloat(d[2]),fieldMax:parseFloat(d[3]),valueMin:g.value,valueMax:y.value,bypass:r}}}if(h.multiple&&n!=="multiple"){var w;if(u?w=e.split(/\s+/):vn(e)?w=e:w=[e],h.evenMultiple&&w.length%2!==0)return null;for(var S=[],T=[],E=[],_="",A=!1,L=0;L0?" 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i=e.boundingBox(),a=this.width(),s=this.height();r=r===void 0?this._private.zoom:r;var l={x:(a-r*(i.x1+i.x2))/2,y:(s-r*(i.y1+i.y2))/2};return l}}},"getCenterPan"),reset:o(function(){return!this._private.panningEnabled||!this._private.zoomingEnabled?this:(this.viewport({pan:{x:0,y:0},zoom:1}),this)},"reset"),invalidateSize:o(function(){this._private.sizeCache=null},"invalidateSize"),size:o(function(){var e=this._private,r=e.container,n=this;return e.sizeCache=e.sizeCache||(r?function(){var i=n.window().getComputedStyle(r),a=o(function(l){return parseFloat(i.getPropertyValue(l))},"val");return{width:r.clientWidth-a("padding-left")-a("padding-right"),height:r.clientHeight-a("padding-top")-a("padding-bottom")}}():{width:1,height:1})},"size"),width:o(function(){return this.size().width},"width"),height:o(function(){return this.size().height},"height"),extent:o(function(){var e=this._private.pan,r=this._private.zoom,n=this.renderedExtent(),i={x1:(n.x1-e.x)/r,x2:(n.x2-e.x)/r,y1:(n.y1-e.y)/r,y2:(n.y2-e.y)/r};return i.w=i.x2-i.x1,i.h=i.y2-i.y1,i},"extent"),renderedExtent:o(function(){var e=this.width(),r=this.height();return{x1:0,y1:0,x2:e,y2:r,w:e,h:r}},"renderedExtent"),multiClickDebounceTime:o(function(e){if(e)this._private.multiClickDebounceTime=e;else return this._private.multiClickDebounceTime;return this},"multiClickDebounceTime")};q0.centre=q0.center;q0.autolockNodes=q0.autolock;q0.autoungrabifyNodes=q0.autoungrabify;jx={data:en.data({field:"data",bindingEvent:"data",allowBinding:!0,allowSetting:!0,settingEvent:"data",settingTriggersEvent:!0,triggerFnName:"trigger",allowGetting:!0,updateStyle:!0}),removeData:en.removeData({field:"data",event:"data",triggerFnName:"trigger",triggerEvent:!0,updateStyle:!0}),scratch:en.data({field:"scratch",bindingEvent:"scratch",allowBinding:!0,allowSetting:!0,settingEvent:"scratch",settingTriggersEvent:!0,triggerFnName:"trigger",allowGetting:!0,updateStyle:!0}),removeScratch:en.removeData({field:"scratch",event:"scratch",triggerFnName:"trigger",triggerEvent:!0,updateStyle:!0})};jx.attr=jx.data;jx.removeAttr=jx.removeData;Kx=o(function(e){var r=this;e=Wt({},e);var n=e.container;n&&!k6(n)&&k6(n[0])&&(n=n[0]);var i=n?n._cyreg:null;i=i||{},i&&i.cy&&(i.cy.destroy(),i={});var a=i.readies=i.readies||[];n&&(n._cyreg=i),i.cy=r;var s=Vi!==void 0&&n!==void 0&&!e.headless,l=e;l.layout=Wt({name:s?"grid":"null"},l.layout),l.renderer=Wt({name:s?"canvas":"null"},l.renderer);var u=o(function(g,y,v){return y!==void 0?y:v!==void 0?v:g},"defVal"),h=this._private={container:n,ready:!1,options:l,elements:new Ca(this),listeners:[],aniEles:new Ca(this),data:l.data||{},scratch:{},layout:null,renderer:null,destroyed:!1,notificationsEnabled:!0,minZoom:1e-50,maxZoom:1e50,zoomingEnabled:u(!0,l.zoomingEnabled),userZoomingEnabled:u(!0,l.userZoomingEnabled),panningEnabled:u(!0,l.panningEnabled),userPanningEnabled:u(!0,l.userPanningEnabled),boxSelectionEnabled:u(!0,l.boxSelectionEnabled),autolock:u(!1,l.autolock,l.autolockNodes),autoungrabify:u(!1,l.autoungrabify,l.autoungrabifyNodes),autounselectify:u(!1,l.autounselectify),styleEnabled:l.styleEnabled===void 0?s:l.styleEnabled,zoom:ft(l.zoom)?l.zoom:1,pan:{x:Mr(l.pan)&&ft(l.pan.x)?l.pan.x:0,y:Mr(l.pan)&&ft(l.pan.y)?l.pan.y:0},animation:{current:[],queue:[]},hasCompoundNodes:!1,multiClickDebounceTime:u(250,l.multiClickDebounceTime)};this.createEmitter(),this.selectionType(l.selectionType),this.zoomRange({min:l.minZoom,max:l.maxZoom});var f=o(function(g,y){var v=g.some(zHe);if(v)return u1.all(g).then(y);y(g)},"loadExtData");h.styleEnabled&&r.setStyle([]);var d=Wt({},l,l.renderer);r.initRenderer(d);var p=o(function(g,y,v){r.notifications(!1);var x=r.mutableElements();x.length>0&&x.remove(),g!=null&&(Mr(g)||vn(g))&&r.add(g),r.one("layoutready",function(w){r.notifications(!0),r.emit(w),r.one("load",y),r.emitAndNotify("load")}).one("layoutstop",function(){r.one("done",v),r.emit("done")});var b=Wt({},r._private.options.layout);b.eles=r.elements(),r.layout(b).run()},"setElesAndLayout");f([l.style,l.elements],function(m){var g=m[0],y=m[1];h.styleEnabled&&r.style().append(g),p(y,function(){r.startAnimationLoop(),h.ready=!0,jn(l.ready)&&r.on("ready",l.ready);for(var v=0;v0,u=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),h;if(xo(e.roots))h=e.roots;else if(vn(e.roots)){for(var f=[],d=0;d0;){var O=C(),D=M(O,k);if(D)O.outgoers().filter(function(ue){return ue.isNode()&&n.has(ue)}).forEach(I);else if(D===null){tn("Detected double maximal shift for node `"+O.id()+"`. 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s=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=e.sweep===void 0?2*Math.PI-2*Math.PI/a.length:e.sweep,h=u/Math.max(1,a.length-1),f,d=0,p=0;p1&&e.avoidOverlap){d*=1.75;var x=Math.cos(h)-Math.cos(0),b=Math.sin(h)-Math.sin(0),w=Math.sqrt(d*d/(x*x+b*b));f=Math.max(w,f)}var S=o(function(E,_){var A=e.startAngle+_*h*(i?1:-1),L=f*Math.cos(A),M=f*Math.sin(A),N={x:l.x+L,y:l.y+M};return N},"getPos");return n.nodes().layoutPositions(this,e,S),this};GKe={fit:!0,padding:30,startAngle:3/2*Math.PI,sweep:void 0,clockwise:!0,equidistant:!1,minNodeSpacing:10,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,height:void 0,width:void 0,spacingFactor:void 0,concentric:o(function(e){return e.degree()},"concentric"),levelWidth:o(function(e){return e.maxDegree()/4},"levelWidth"),animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(Lme,"ConcentricLayout");Lme.prototype.run=function(){for(var t=this.options,e=t,r=e.counterclockwise!==void 0?!e.counterclockwise:e.clockwise,n=t.cy,i=e.eles,a=i.nodes().not(":parent"),s=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:n.width(),h:n.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=[],h=0,f=0;f0){var T=Math.abs(b[0].value-S.value);T>=v&&(b=[],x.push(b))}b.push(S)}var E=h+e.minNodeSpacing;if(!e.avoidOverlap){var _=x.length>0&&x[0].length>1,A=Math.min(s.w,s.h)/2-E,L=A/(x.length+_?1:0);E=Math.min(E,L)}for(var M=0,N=0;N1&&e.avoidOverlap){var O=Math.cos(C)-Math.cos(0),D=Math.sin(C)-Math.sin(0),P=Math.sqrt(E*E/(O*O+D*D));M=Math.max(P,M)}k.r=M,M+=E}if(e.equidistant){for(var F=0,B=0,$=0;$=t.numIter||(XKe(n,t),n.temperature=n.temperature*t.coolingFactor,n.temperature=t.animationThreshold&&a(),E6(d)}},"frame");f()}else{for(;h;)h=s(u),u++;rpe(n,t),l()}return this};j6.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this};j6.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};VKe=o(function(e,r,n){for(var i=n.eles.edges(),a=n.eles.nodes(),s=Gs(n.boundingBox?n.boundingBox:{x1:0,y1:0,w:e.width(),h:e.height()}),l={isCompound:e.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:a.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:i.size(),temperature:n.initialTemp,clientWidth:s.w,clientHeight:s.h,boundingBox:s},u=n.eles.components(),h={},f=0;f0){l.graphSet.push(A);for(var f=0;fi.count?0:i.graph},"findLCA"),HKe=o(function t(e,r,n,i){var a=i.graphSet[n];if(-10)var d=i.nodeOverlap*f,p=Math.sqrt(l*l+u*u),m=d*l/p,g=d*u/p;else var y=R6(e,l,u),v=R6(r,-1*l,-1*u),x=v.x-y.x,b=v.y-y.y,w=x*x+b*b,p=Math.sqrt(w),d=(e.nodeRepulsion+r.nodeRepulsion)/w,m=d*x/p,g=d*b/p;e.isLocked||(e.offsetX-=m,e.offsetY-=g),r.isLocked||(r.offsetX+=m,r.offsetY+=g)}},"nodeRepulsion"),QKe=o(function(e,r,n,i){if(n>0)var a=e.maxX-r.minX;else var a=r.maxX-e.minX;if(i>0)var s=e.maxY-r.minY;else var s=r.maxY-e.minY;return a>=0&&s>=0?Math.sqrt(a*a+s*s):0},"nodesOverlap"),R6=o(function(e,r,n){var i=e.positionX,a=e.positionY,s=e.height||1,l=e.width||1,u=n/r,h=s/l,f={};return r===0&&0n?(f.x=i,f.y=a+s/2,f):0r&&-1*h<=u&&u<=h?(f.x=i-l/2,f.y=a-l*n/2/r,f):0=h)?(f.x=i+s*r/2/n,f.y=a+s/2,f):(0>n&&(u<=-1*h||u>=h)&&(f.x=i-s*r/2/n,f.y=a-s/2),f)},"findClippingPoint"),ZKe=o(function(e,r){for(var n=0;nn){var v=r.gravity*m/y,x=r.gravity*g/y;p.offsetX+=v,p.offsetY+=x}}}}},"calculateGravityForces"),eQe=o(function(e,r){var n=[],i=0,a=-1;for(n.push.apply(n,e.graphSet[0]),a+=e.graphSet[0].length;i<=a;){var s=n[i++],l=e.idToIndex[s],u=e.layoutNodes[l],h=u.children;if(0n)var a={x:n*e/i,y:n*r/i};else var a={x:e,y:r};return a},"limitForce"),nQe=o(function t(e,r){var n=e.parentId;if(n!=null){var i=r.layoutNodes[r.idToIndex[n]],a=!1;if((i.maxX==null||e.maxX+i.padRight>i.maxX)&&(i.maxX=e.maxX+i.padRight,a=!0),(i.minX==null||e.minX-i.padLefti.maxY)&&(i.maxY=e.maxY+i.padBottom,a=!0),(i.minY==null||e.minY-i.padTopx&&(g+=v+r.componentSpacing,m=0,y=0,v=0)}}},"separateComponents"),iQe={fit:!0,padding:30,boundingBox:void 0,avoidOverlap:!0,avoidOverlapPadding:10,nodeDimensionsIncludeLabels:!1,spacingFactor:void 0,condense:!1,rows:void 0,cols:void 0,position:o(function(e){},"position"),sort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(Rme,"GridLayout");Rme.prototype.run=function(){var t=this.options,e=t,r=t.cy,n=e.eles,i=n.nodes().not(":parent");e.sort&&(i=i.sort(e.sort));var a=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()});if(a.h===0||a.w===0)n.nodes().layoutPositions(this,e,function(Q){return{x:a.x1,y:a.y1}});else{var s=i.size(),l=Math.sqrt(s*a.h/a.w),u=Math.round(l),h=Math.round(a.w/a.h*l),f=o(function(X){if(X==null)return Math.min(u,h);var ie=Math.min(u,h);ie==u?u=X:h=X},"small"),d=o(function(X){if(X==null)return Math.max(u,h);var ie=Math.max(u,h);ie==u?u=X:h=X},"large"),p=e.rows,m=e.cols!=null?e.cols:e.columns;if(p!=null&&m!=null)u=p,h=m;else if(p!=null&&m==null)u=p,h=Math.ceil(s/u);else if(p==null&&m!=null)h=m,u=Math.ceil(s/h);else if(h*u>s){var g=f(),y=d();(g-1)*y>=s?f(g-1):(y-1)*g>=s&&d(y-1)}else for(;h*u=s?d(x+1):f(v+1)}var b=a.w/h,w=a.h/u;if(e.condense&&(b=0,w=0),e.avoidOverlap)for(var S=0;S=h&&(O=0,C++)},"moveToNextCell"),P={},F=0;F(O=_We(t,e,D[P],D[P+1],D[P+2],D[P+3])))return v(_,O),!0}else if(L.edgeType==="bezier"||L.edgeType==="multibezier"||L.edgeType==="self"||L.edgeType==="compound"){for(var D=L.allpts,P=0;P+5(O=AWe(t,e,D[P],D[P+1],D[P+2],D[P+3],D[P+4],D[P+5])))return v(_,O),!0}for(var F=F||A.source,B=B||A.target,$=i.getArrowWidth(M,N),z=[{name:"source",x:L.arrowStartX,y:L.arrowStartY,angle:L.srcArrowAngle},{name:"target",x:L.arrowEndX,y:L.arrowEndY,angle:L.tgtArrowAngle},{name:"mid-source",x:L.midX,y:L.midY,angle:L.midsrcArrowAngle},{name:"mid-target",x:L.midX,y:L.midY,angle:L.midtgtArrowAngle}],P=0;P0&&(x(F),x(B))}o(b,"checkEdge");function w(_,A,L){return Ul(_,A,L)}o(w,"preprop");function S(_,A){var L=_._private,M=p,N;A?N=A+"-":N="",_.boundingBox();var k=L.labelBounds[A||"main"],I=_.pstyle(N+"label").value,C=_.pstyle("text-events").strValue==="yes";if(!(!C||!I)){var O=w(L.rscratch,"labelX",A),D=w(L.rscratch,"labelY",A),P=w(L.rscratch,"labelAngle",A),F=_.pstyle(N+"text-margin-x").pfValue,B=_.pstyle(N+"text-margin-y").pfValue,$=k.x1-M-F,z=k.x2+M-F,Y=k.y1-M-B,Q=k.y2+M-B;if(P){var X=Math.cos(P),ie=Math.sin(P),j=o(function(ce,ue){return ce=ce-O,ue=ue-D,{x:ce*X-ue*ie+O,y:ce*ie+ue*X+D}},"rotate"),J=j($,Y),Z=j($,Q),H=j(z,Y),q=j(z,Q),K=[J.x+F,J.y+B,H.x+F,H.y+B,q.x+F,q.y+B,Z.x+F,Z.y+B];if(zs(t,e,K))return v(_),!0}else if(s1(k,t,e))return v(_),!0}}o(S,"checkLabel");for(var T=s.length-1;T>=0;T--){var E=s[T];E.isNode()?x(E)||S(E):b(E)||S(E)||S(E,"source")||S(E,"target")}return l};j0.getAllInBox=function(t,e,r,n){var i=this.getCachedZSortedEles().interactive,a=[],s=Math.min(t,r),l=Math.max(t,r),u=Math.min(e,n),h=Math.max(e,n);t=s,r=l,e=u,n=h;for(var f=Gs({x1:t,y1:e,x2:r,y2:n}),d=0;d0?-(Math.PI-e.ang):Math.PI+e.ang},"invertVec"),uQe=o(function(e,r,n,i,a){if(e!==ope?lpe(r,e,Gc):cQe(Jo,Gc),lpe(r,n,Jo),ape=Gc.nx*Jo.ny-Gc.ny*Jo.nx,spe=Gc.nx*Jo.nx-Gc.ny*-Jo.ny,ju=Math.asin(Math.max(-1,Math.min(1,ape))),Math.abs(ju)<1e-6){GP=r.x,$P=r.y,z0=Qg=0;return}G0=1,w6=!1,spe<0?ju<0?ju=Math.PI+ju:(ju=Math.PI-ju,G0=-1,w6=!0):ju>0&&(G0=-1,w6=!0),r.radius!==void 0?Qg=r.radius:Qg=i,O0=ju/2,f6=Math.min(Gc.len/2,Jo.len/2),a?(zc=Math.abs(Math.cos(O0)*Qg/Math.sin(O0)),zc>f6?(zc=f6,z0=Math.abs(zc*Math.sin(O0)/Math.cos(O0))):z0=Qg):(zc=Math.min(f6,Qg),z0=Math.abs(zc*Math.sin(O0)/Math.cos(O0))),VP=r.x+Jo.nx*zc,UP=r.y+Jo.ny*zc,GP=VP-Jo.ny*z0*G0,$P=UP+Jo.nx*z0*G0,Ome=r.x+Gc.nx*zc,Pme=r.y+Gc.ny*zc,ope=r},"calcCornerArc");o(Bme,"drawPreparedRoundCorner");o(mB,"getRoundCorner");$a={};$a.findMidptPtsEtc=function(t,e){var r=e.posPts,n=e.intersectionPts,i=e.vectorNormInverse,a,s=t.pstyle("source-endpoint"),l=t.pstyle("target-endpoint"),u=s.units!=null&&l.units!=null,h=o(function(T,E,_,A){var L=A-E,M=_-T,N=Math.sqrt(M*M+L*L);return{x:-L/N,y:M/N}},"recalcVectorNormInverse"),f=t.pstyle("edge-distances").value;switch(f){case"node-position":a=r;break;case"intersection":a=n;break;case"endpoints":{if(u){var d=this.manualEndptToPx(t.source()[0],s),p=$l(d,2),m=p[0],g=p[1],y=this.manualEndptToPx(t.target()[0],l),v=$l(y,2),x=v[0],b=v[1],w={x1:m,y1:g,x2:x,y2:b};i=h(m,g,x,b),a=w}else tn("Edge ".concat(t.id()," has edge-distances:endpoints specified without manual endpoints specified via source-endpoint and target-endpoint. Falling back on edge-distances:intersection (default).")),a=n;break}}return{midptPts:a,vectorNormInverse:i}};$a.findHaystackPoints=function(t){for(var e=0;e0?Math.max(Te-Ce,0):Math.min(Te+Ce,0)},"subDWH"),I=k(M,A),C=k(N,L),O=!1;b===h?x=Math.abs(I)>Math.abs(C)?i:n:b===u||b===l?(x=n,O=!0):(b===a||b===s)&&(x=i,O=!0);var D=x===n,P=D?C:I,F=D?N:M,B=$pe(F),$=!1;!(O&&(S||E))&&(b===l&&F<0||b===u&&F>0||b===a&&F>0||b===s&&F<0)&&(B*=-1,P=B*Math.abs(P),$=!0);var z;if(S){var Y=T<0?1+T:T;z=Y*P}else{var Q=T<0?P:0;z=Q+T*B}var X=o(function(Te){return Math.abs(Te)<_||Math.abs(Te)>=Math.abs(P)},"getIsTooClose"),ie=X(z),j=X(Math.abs(P)-Math.abs(z)),J=ie||j;if(J&&!$)if(D){var Z=Math.abs(F)<=p/2,H=Math.abs(M)<=m/2;if(Z){var q=(f.x1+f.x2)/2,K=f.y1,se=f.y2;r.segpts=[q,K,q,se]}else if(H){var ce=(f.y1+f.y2)/2,ue=f.x1,te=f.x2;r.segpts=[ue,ce,te,ce]}else r.segpts=[f.x1,f.y2]}else{var De=Math.abs(F)<=d/2,oe=Math.abs(N)<=g/2;if(De){var ke=(f.y1+f.y2)/2,Ie=f.x1,Se=f.x2;r.segpts=[Ie,ke,Se,ke]}else if(oe){var Ue=(f.x1+f.x2)/2,Pe=f.y1,_e=f.y2;r.segpts=[Ue,Pe,Ue,_e]}else r.segpts=[f.x2,f.y1]}else if(D){var me=f.y1+z+(v?p/2*B:0),W=f.x1,fe=f.x2;r.segpts=[W,me,fe,me]}else{var ge=f.x1+z+(v?d/2*B:0),re=f.y1,he=f.y2;r.segpts=[ge,re,ge,he]}if(r.isRound){var ne=t.pstyle("taxi-radius").value,ae=t.pstyle("radius-type").value[0]==="arc-radius";r.radii=new Array(r.segpts.length/2).fill(ne),r.isArcRadius=new Array(r.segpts.length/2).fill(ae)}};$a.tryToCorrectInvalidPoints=function(t,e){var r=t._private.rscratch;if(r.edgeType==="bezier"){var n=e.srcPos,i=e.tgtPos,a=e.srcW,s=e.srcH,l=e.tgtW,u=e.tgtH,h=e.srcShape,f=e.tgtShape,d=e.srcCornerRadius,p=e.tgtCornerRadius,m=e.srcRs,g=e.tgtRs,y=!ft(r.startX)||!ft(r.startY),v=!ft(r.arrowStartX)||!ft(r.arrowStartY),x=!ft(r.endX)||!ft(r.endY),b=!ft(r.arrowEndX)||!ft(r.arrowEndY),w=3,S=this.getArrowWidth(t.pstyle("width").pfValue,t.pstyle("arrow-scale").value)*this.arrowShapeWidth,T=w*S,E=H0({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.startX,y:r.startY}),_=EC.poolIndex()){var O=I;I=C,C=O}var D=L.srcPos=I.position(),P=L.tgtPos=C.position(),F=L.srcW=I.outerWidth(),B=L.srcH=I.outerHeight(),$=L.tgtW=C.outerWidth(),z=L.tgtH=C.outerHeight(),Y=L.srcShape=r.nodeShapes[e.getNodeShape(I)],Q=L.tgtShape=r.nodeShapes[e.getNodeShape(C)],X=L.srcCornerRadius=I.pstyle("corner-radius").value==="auto"?"auto":I.pstyle("corner-radius").pfValue,ie=L.tgtCornerRadius=C.pstyle("corner-radius").value==="auto"?"auto":C.pstyle("corner-radius").pfValue,j=L.tgtRs=C._private.rscratch,J=L.srcRs=I._private.rscratch;L.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var Z=0;Z0){var se=a,ce=B0(se,Jg(r)),ue=B0(se,Jg(K)),te=ce;if(ue2){var De=B0(se,{x:K[2],y:K[3]});De0){var he=s,ne=B0(he,Jg(r)),ae=B0(he,Jg(re)),we=ne;if(ae2){var Te=B0(he,{x:re[2],y:re[3]});Te=g||_){v={cp:S,segment:E};break}}if(v)break}var A=v.cp,L=v.segment,M=(g-x)/L.length,N=L.t1-L.t0,k=m?L.t0+N*M:L.t1-N*M;k=Hx(0,k,1),e=t1(A.p0,A.p1,A.p2,k),p=fQe(A.p0,A.p1,A.p2,k);break}case"straight":case"segments":case"haystack":{for(var I=0,C,O,D,P,F=n.allpts.length,B=0;B+3=g));B+=2);var $=g-O,z=$/C;z=Hx(0,z,1),e=yWe(D,P,z),p=Gme(D,P);break}}s("labelX",d,e.x),s("labelY",d,e.y),s("labelAutoAngle",d,p)}},"calculateEndProjection");h("source"),h("target"),this.applyLabelDimensions(t)}};Hc.applyLabelDimensions=function(t){this.applyPrefixedLabelDimensions(t),t.isEdge()&&(this.applyPrefixedLabelDimensions(t,"source"),this.applyPrefixedLabelDimensions(t,"target"))};Hc.applyPrefixedLabelDimensions=function(t,e){var r=t._private,n=this.getLabelText(t,e),i=this.calculateLabelDimensions(t,n),a=t.pstyle("line-height").pfValue,s=t.pstyle("text-wrap").strValue,l=Ul(r.rscratch,"labelWrapCachedLines",e)||[],u=s!=="wrap"?1:Math.max(l.length,1),h=i.height/u,f=h*a,d=i.width,p=i.height+(u-1)*(a-1)*h;Tf(r.rstyle,"labelWidth",e,d),Tf(r.rscratch,"labelWidth",e,d),Tf(r.rstyle,"labelHeight",e,p),Tf(r.rscratch,"labelHeight",e,p),Tf(r.rscratch,"labelLineHeight",e,f)};Hc.getLabelText=function(t,e){var r=t._private,n=e?e+"-":"",i=t.pstyle(n+"label").strValue,a=t.pstyle("text-transform").value,s=o(function(Q,X){return X?(Tf(r.rscratch,Q,e,X),X):Ul(r.rscratch,Q,e)},"rscratch");if(!i)return"";a=="none"||(a=="uppercase"?i=i.toUpperCase():a=="lowercase"&&(i=i.toLowerCase()));var l=t.pstyle("text-wrap").value;if(l==="wrap"){var u=s("labelKey");if(u!=null&&s("labelWrapKey")===u)return s("labelWrapCachedText");for(var h="\u200B",f=i.split(` +`),d=t.pstyle("text-max-width").pfValue,p=t.pstyle("text-overflow-wrap").value,m=p==="anywhere",g=[],y=/[\s\u200b]+|$/g,v=0;vd){var T=x.matchAll(y),E="",_=0,A=Tpe(T),L;try{for(A.s();!(L=A.n()).done;){var M=L.value,N=M[0],k=x.substring(_,M.index);_=M.index+N.length;var I=E.length===0?k:E+k+N,C=this.calculateLabelDimensions(t,I),O=C.width;O<=d?E+=k+N:(E&&g.push(E),E=k+N)}}catch(Y){A.e(Y)}finally{A.f()}E.match(/^[\s\u200b]+$/)||g.push(E)}else g.push(x)}s("labelWrapCachedLines",g),i=s("labelWrapCachedText",g.join(` +`)),s("labelWrapKey",u)}else if(l==="ellipsis"){var D=t.pstyle("text-max-width").pfValue,P="",F="\u2026",B=!1;if(this.calculateLabelDimensions(t,i).widthD)break;P+=i[$],$===i.length-1&&(B=!0)}return B||(P+=F),P}return i};Hc.getLabelJustification=function(t){var e=t.pstyle("text-justification").strValue,r=t.pstyle("text-halign").strValue;if(e==="auto")if(t.isNode())switch(r){case"left":return"right";case"right":return"left";default:return"center"}else return"center";else return e};Hc.calculateLabelDimensions=function(t,e){var r=this,n=r.cy.window(),i=n.document,a=U0(e,t._private.labelDimsKey),s=r.labelDimCache||(r.labelDimCache=[]),l=s[a];if(l!=null)return l;var u=0,h=t.pstyle("font-style").strValue,f=t.pstyle("font-size").pfValue,d=t.pstyle("font-family").strValue,p=t.pstyle("font-weight").strValue,m=this.labelCalcCanvas,g=this.labelCalcCanvasContext;if(!m){m=this.labelCalcCanvas=i.createElement("canvas"),g=this.labelCalcCanvasContext=m.getContext("2d");var y=m.style;y.position="absolute",y.left="-9999px",y.top="-9999px",y.zIndex="-1",y.visibility="hidden",y.pointerEvents="none"}g.font="".concat(h," ").concat(p," ").concat(f,"px ").concat(d);for(var v=0,x=0,b=e.split(` +`),w=0;w1&&arguments[1]!==void 0?arguments[1]:!0;if(e.merge(s),l)for(var u=0;u=t.desktopTapThreshold2}var Je=i(W);ze&&(t.hoverData.tapholdCancelled=!0);var Ve=o(function(){var St=t.hoverData.dragDelta=t.hoverData.dragDelta||[];St.length===0?(St.push(ye[0]),St.push(ye[1])):(St[0]+=ye[0],St[1]+=ye[1])},"updateDragDelta");ge=!0,n(Ae,["mousemove","vmousemove","tapdrag"],W,{x:ae[0],y:ae[1]});var je=o(function(){t.data.bgActivePosistion=void 0,t.hoverData.selecting||re.emit({originalEvent:W,type:"boxstart",position:{x:ae[0],y:ae[1]}}),Ce[4]=1,t.hoverData.selecting=!0,t.redrawHint("select",!0),t.redraw()},"goIntoBoxMode");if(t.hoverData.which===3){if(ze){var kt={originalEvent:W,type:"cxtdrag",position:{x:ae[0],y:ae[1]}};Me?Me.emit(kt):re.emit(kt),t.hoverData.cxtDragged=!0,(!t.hoverData.cxtOver||Ae!==t.hoverData.cxtOver)&&(t.hoverData.cxtOver&&t.hoverData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:ae[0],y:ae[1]}}),t.hoverData.cxtOver=Ae,Ae&&Ae.emit({originalEvent:W,type:"cxtdragover",position:{x:ae[0],y:ae[1]}}))}}else if(t.hoverData.dragging){if(ge=!0,re.panningEnabled()&&re.userPanningEnabled()){var at;if(t.hoverData.justStartedPan){var xt=t.hoverData.mdownPos;at={x:(ae[0]-xt[0])*he,y:(ae[1]-xt[1])*he},t.hoverData.justStartedPan=!1}else at={x:ye[0]*he,y:ye[1]*he};re.panBy(at),re.emit("dragpan"),t.hoverData.dragged=!0}ae=t.projectIntoViewport(W.clientX,W.clientY)}else if(Ce[4]==1&&(Me==null||Me.pannable())){if(ze){if(!t.hoverData.dragging&&re.boxSelectionEnabled()&&(Je||!re.panningEnabled()||!re.userPanningEnabled()))je();else if(!t.hoverData.selecting&&re.panningEnabled()&&re.userPanningEnabled()){var it=a(Me,t.hoverData.downs);it&&(t.hoverData.dragging=!0,t.hoverData.justStartedPan=!0,Ce[4]=0,t.data.bgActivePosistion=Jg(we),t.redrawHint("select",!0),t.redraw())}Me&&Me.pannable()&&Me.active()&&Me.unactivate()}}else{if(Me&&Me.pannable()&&Me.active()&&Me.unactivate(),(!Me||!Me.grabbed())&&Ae!=Ge&&(Ge&&n(Ge,["mouseout","tapdragout"],W,{x:ae[0],y:ae[1]}),Ae&&n(Ae,["mouseover","tapdragover"],W,{x:ae[0],y:ae[1]}),t.hoverData.last=Ae),Me)if(ze){if(re.boxSelectionEnabled()&&Je)Me&&Me.grabbed()&&(v(He),Me.emit("freeon"),He.emit("free"),t.dragData.didDrag&&(Me.emit("dragfreeon"),He.emit("dragfree"))),je();else if(Me&&Me.grabbed()&&t.nodeIsDraggable(Me)){var dt=!t.dragData.didDrag;dt&&t.redrawHint("eles",!0),t.dragData.didDrag=!0,t.hoverData.draggingEles||g(He,{inDragLayer:!0});var lt={x:0,y:0};if(ft(ye[0])&&ft(ye[1])&&(lt.x+=ye[0],lt.y+=ye[1],dt)){var It=t.hoverData.dragDelta;It&&ft(It[0])&&ft(It[1])&&(lt.x+=It[0],lt.y+=It[1])}t.hoverData.draggingEles=!0,He.silentShift(lt).emit("position drag"),t.redrawHint("drag",!0),t.redraw()}}else Ve();ge=!0}if(Ce[2]=ae[0],Ce[3]=ae[1],ge)return W.stopPropagation&&W.stopPropagation(),W.preventDefault&&W.preventDefault(),!1}},"mousemoveHandler"),!1);var M,N,k;t.registerBinding(e,"mouseup",o(function(W){if(!(t.hoverData.which===1&&W.which!==1&&t.hoverData.capture)){var fe=t.hoverData.capture;if(fe){t.hoverData.capture=!1;var ge=t.cy,re=t.projectIntoViewport(W.clientX,W.clientY),he=t.selection,ne=t.findNearestElement(re[0],re[1],!0,!1),ae=t.dragData.possibleDragElements,we=t.hoverData.down,Te=i(W);if(t.data.bgActivePosistion&&(t.redrawHint("select",!0),t.redraw()),t.hoverData.tapholdCancelled=!0,t.data.bgActivePosistion=void 0,we&&we.unactivate(),t.hoverData.which===3){var Ce={originalEvent:W,type:"cxttapend",position:{x:re[0],y:re[1]}};if(we?we.emit(Ce):ge.emit(Ce),!t.hoverData.cxtDragged){var Ae={originalEvent:W,type:"cxttap",position:{x:re[0],y:re[1]}};we?we.emit(Ae):ge.emit(Ae)}t.hoverData.cxtDragged=!1,t.hoverData.which=null}else if(t.hoverData.which===1){if(n(ne,["mouseup","tapend","vmouseup"],W,{x:re[0],y:re[1]}),!t.dragData.didDrag&&!t.hoverData.dragged&&!t.hoverData.selecting&&!t.hoverData.isOverThresholdDrag&&(n(we,["click","tap","vclick"],W,{x:re[0],y:re[1]}),N=!1,W.timeStamp-k<=ge.multiClickDebounceTime()?(M&&clearTimeout(M),N=!0,k=null,n(we,["dblclick","dbltap","vdblclick"],W,{x:re[0],y:re[1]})):(M=setTimeout(function(){N||n(we,["oneclick","onetap","voneclick"],W,{x:re[0],y:re[1]})},ge.multiClickDebounceTime()),k=W.timeStamp)),we==null&&!t.dragData.didDrag&&!t.hoverData.selecting&&!t.hoverData.dragged&&!i(W)&&(ge.$(r).unselect(["tapunselect"]),ae.length>0&&t.redrawHint("eles",!0),t.dragData.possibleDragElements=ae=ge.collection()),ne==we&&!t.dragData.didDrag&&!t.hoverData.selecting&&ne!=null&&ne._private.selectable&&(t.hoverData.dragging||(ge.selectionType()==="additive"||Te?ne.selected()?ne.unselect(["tapunselect"]):ne.select(["tapselect"]):Te||(ge.$(r).unmerge(ne).unselect(["tapunselect"]),ne.select(["tapselect"]))),t.redrawHint("eles",!0)),t.hoverData.selecting){var Ge=ge.collection(t.getAllInBox(he[0],he[1],he[2],he[3]));t.redrawHint("select",!0),Ge.length>0&&t.redrawHint("eles",!0),ge.emit({type:"boxend",originalEvent:W,position:{x:re[0],y:re[1]}});var Me=o(function(ze){return ze.selectable()&&!ze.selected()},"eleWouldBeSelected");ge.selectionType()==="additive"||Te||ge.$(r).unmerge(Ge).unselect(),Ge.emit("box").stdFilter(Me).select().emit("boxselect"),t.redraw()}if(t.hoverData.dragging&&(t.hoverData.dragging=!1,t.redrawHint("select",!0),t.redrawHint("eles",!0),t.redraw()),!he[4]){t.redrawHint("drag",!0),t.redrawHint("eles",!0);var ye=we&&we.grabbed();v(ae),ye&&(we.emit("freeon"),ae.emit("free"),t.dragData.didDrag&&(we.emit("dragfreeon"),ae.emit("dragfree")))}}he[4]=0,t.hoverData.down=null,t.hoverData.cxtStarted=!1,t.hoverData.draggingEles=!1,t.hoverData.selecting=!1,t.hoverData.isOverThresholdDrag=!1,t.dragData.didDrag=!1,t.hoverData.dragged=!1,t.hoverData.dragDelta=[],t.hoverData.mdownPos=null,t.hoverData.mdownGPos=null}}},"mouseupHandler"),!1);var I=o(function(W){if(!t.scrollingPage){var fe=t.cy,ge=fe.zoom(),re=fe.pan(),he=t.projectIntoViewport(W.clientX,W.clientY),ne=[he[0]*ge+re.x,he[1]*ge+re.y];if(t.hoverData.draggingEles||t.hoverData.dragging||t.hoverData.cxtStarted||A()){W.preventDefault();return}if(fe.panningEnabled()&&fe.userPanningEnabled()&&fe.zoomingEnabled()&&fe.userZoomingEnabled()){W.preventDefault(),t.data.wheelZooming=!0,clearTimeout(t.data.wheelTimeout),t.data.wheelTimeout=setTimeout(function(){t.data.wheelZooming=!1,t.redrawHint("eles",!0),t.redraw()},150);var ae;W.deltaY!=null?ae=W.deltaY/-250:W.wheelDeltaY!=null?ae=W.wheelDeltaY/1e3:ae=W.wheelDelta/1e3,ae=ae*t.wheelSensitivity;var we=W.deltaMode===1;we&&(ae*=33);var Te=fe.zoom()*Math.pow(10,ae);W.type==="gesturechange"&&(Te=t.gestureStartZoom*W.scale),fe.zoom({level:Te,renderedPosition:{x:ne[0],y:ne[1]}}),fe.emit(W.type==="gesturechange"?"pinchzoom":"scrollzoom")}}},"wheelHandler");t.registerBinding(t.container,"wheel",I,!0),t.registerBinding(e,"scroll",o(function(W){t.scrollingPage=!0,clearTimeout(t.scrollingPageTimeout),t.scrollingPageTimeout=setTimeout(function(){t.scrollingPage=!1},250)},"scrollHandler"),!0),t.registerBinding(t.container,"gesturestart",o(function(W){t.gestureStartZoom=t.cy.zoom(),t.hasTouchStarted||W.preventDefault()},"gestureStartHandler"),!0),t.registerBinding(t.container,"gesturechange",function(me){t.hasTouchStarted||I(me)},!0),t.registerBinding(t.container,"mouseout",o(function(W){var fe=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseout",position:{x:fe[0],y:fe[1]}})},"mouseOutHandler"),!1),t.registerBinding(t.container,"mouseover",o(function(W){var fe=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseover",position:{x:fe[0],y:fe[1]}})},"mouseOverHandler"),!1);var C,O,D,P,F,B,$,z,Y,Q,X,ie,j,J=o(function(W,fe,ge,re){return Math.sqrt((ge-W)*(ge-W)+(re-fe)*(re-fe))},"distance"),Z=o(function(W,fe,ge,re){return(ge-W)*(ge-W)+(re-fe)*(re-fe)},"distanceSq"),H;t.registerBinding(t.container,"touchstart",H=o(function(W){if(t.hasTouchStarted=!0,!!L(W)){b(),t.touchData.capture=!0,t.data.bgActivePosistion=void 0;var fe=t.cy,ge=t.touchData.now,re=t.touchData.earlier;if(W.touches[0]){var he=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);ge[0]=he[0],ge[1]=he[1]}if(W.touches[1]){var he=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);ge[2]=he[0],ge[3]=he[1]}if(W.touches[2]){var he=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);ge[4]=he[0],ge[5]=he[1]}if(W.touches[1]){t.touchData.singleTouchMoved=!0,v(t.dragData.touchDragEles);var ne=t.findContainerClientCoords();Y=ne[0],Q=ne[1],X=ne[2],ie=ne[3],C=W.touches[0].clientX-Y,O=W.touches[0].clientY-Q,D=W.touches[1].clientX-Y,P=W.touches[1].clientY-Q,j=0<=C&&C<=X&&0<=D&&D<=X&&0<=O&&O<=ie&&0<=P&&P<=ie;var ae=fe.pan(),we=fe.zoom();F=J(C,O,D,P),B=Z(C,O,D,P),$=[(C+D)/2,(O+P)/2],z=[($[0]-ae.x)/we,($[1]-ae.y)/we];var Te=200,Ce=Te*Te;if(B=1){for(var gt=t.touchData.startPosition=[null,null,null,null,null,null],yt=0;yt=t.touchTapThreshold2}if(fe&&t.touchData.cxt){W.preventDefault();var gt=W.touches[0].clientX-Y,yt=W.touches[0].clientY-Q,tt=W.touches[1].clientX-Y,Ye=W.touches[1].clientY-Q,Je=Z(gt,yt,tt,Ye),Ve=Je/B,je=150,kt=je*je,at=1.5,xt=at*at;if(Ve>=xt||Je>=kt){t.touchData.cxt=!1,t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var it={originalEvent:W,type:"cxttapend",position:{x:he[0],y:he[1]}};t.touchData.start?(t.touchData.start.unactivate().emit(it),t.touchData.start=null):re.emit(it)}}if(fe&&t.touchData.cxt){var it={originalEvent:W,type:"cxtdrag",position:{x:he[0],y:he[1]}};t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.touchData.start?t.touchData.start.emit(it):re.emit(it),t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxtDragged=!0;var dt=t.findNearestElement(he[0],he[1],!0,!0);(!t.touchData.cxtOver||dt!==t.touchData.cxtOver)&&(t.touchData.cxtOver&&t.touchData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:he[0],y:he[1]}}),t.touchData.cxtOver=dt,dt&&dt.emit({originalEvent:W,type:"cxtdragover",position:{x:he[0],y:he[1]}}))}else if(fe&&W.touches[2]&&re.boxSelectionEnabled())W.preventDefault(),t.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,t.touchData.selecting||re.emit({originalEvent:W,type:"boxstart",position:{x:he[0],y:he[1]}}),t.touchData.selecting=!0,t.touchData.didSelect=!0,ge[4]=1,!ge||ge.length===0||ge[0]===void 0?(ge[0]=(he[0]+he[2]+he[4])/3,ge[1]=(he[1]+he[3]+he[5])/3,ge[2]=(he[0]+he[2]+he[4])/3+1,ge[3]=(he[1]+he[3]+he[5])/3+1):(ge[2]=(he[0]+he[2]+he[4])/3,ge[3]=(he[1]+he[3]+he[5])/3),t.redrawHint("select",!0),t.redraw();else if(fe&&W.touches[1]&&!t.touchData.didSelect&&re.zoomingEnabled()&&re.panningEnabled()&&re.userZoomingEnabled()&&re.userPanningEnabled()){W.preventDefault(),t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var lt=t.dragData.touchDragEles;if(lt){t.redrawHint("drag",!0);for(var It=0;It0&&!t.hoverData.draggingEles&&!t.swipePanning&&t.data.bgActivePosistion!=null&&(t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.redraw())}},"touchmoveHandler"),!1);var K;t.registerBinding(e,"touchcancel",K=o(function(W){var fe=t.touchData.start;t.touchData.capture=!1,fe&&fe.unactivate()},"touchcancelHandler"));var se,ce,ue,te;if(t.registerBinding(e,"touchend",se=o(function(W){var fe=t.touchData.start,ge=t.touchData.capture;if(ge)W.touches.length===0&&(t.touchData.capture=!1),W.preventDefault();else return;var re=t.selection;t.swipePanning=!1,t.hoverData.draggingEles=!1;var he=t.cy,ne=he.zoom(),ae=t.touchData.now,we=t.touchData.earlier;if(W.touches[0]){var Te=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);ae[0]=Te[0],ae[1]=Te[1]}if(W.touches[1]){var Te=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);ae[2]=Te[0],ae[3]=Te[1]}if(W.touches[2]){var Te=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);ae[4]=Te[0],ae[5]=Te[1]}fe&&fe.unactivate();var Ce;if(t.touchData.cxt){if(Ce={originalEvent:W,type:"cxttapend",position:{x:ae[0],y:ae[1]}},fe?fe.emit(Ce):he.emit(Ce),!t.touchData.cxtDragged){var Ae={originalEvent:W,type:"cxttap",position:{x:ae[0],y:ae[1]}};fe?fe.emit(Ae):he.emit(Ae)}t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxt=!1,t.touchData.start=null,t.redraw();return}if(!W.touches[2]&&he.boxSelectionEnabled()&&t.touchData.selecting){t.touchData.selecting=!1;var Ge=he.collection(t.getAllInBox(re[0],re[1],re[2],re[3]));re[0]=void 0,re[1]=void 0,re[2]=void 0,re[3]=void 0,re[4]=0,t.redrawHint("select",!0),he.emit({type:"boxend",originalEvent:W,position:{x:ae[0],y:ae[1]}});var Me=o(function(kt){return kt.selectable()&&!kt.selected()},"eleWouldBeSelected");Ge.emit("box").stdFilter(Me).select().emit("boxselect"),Ge.nonempty()&&t.redrawHint("eles",!0),t.redraw()}if(fe?.unactivate(),W.touches[2])t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);else if(!W.touches[1]){if(!W.touches[0]){if(!W.touches[0]){t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var ye=t.dragData.touchDragEles;if(fe!=null){var He=fe._private.grabbed;v(ye),t.redrawHint("drag",!0),t.redrawHint("eles",!0),He&&(fe.emit("freeon"),ye.emit("free"),t.dragData.didDrag&&(fe.emit("dragfreeon"),ye.emit("dragfree"))),n(fe,["touchend","tapend","vmouseup","tapdragout"],W,{x:ae[0],y:ae[1]}),fe.unactivate(),t.touchData.start=null}else{var ze=t.findNearestElement(ae[0],ae[1],!0,!0);n(ze,["touchend","tapend","vmouseup","tapdragout"],W,{x:ae[0],y:ae[1]})}var Ze=t.touchData.startPosition[0]-ae[0],gt=Ze*Ze,yt=t.touchData.startPosition[1]-ae[1],tt=yt*yt,Ye=gt+tt,Je=Ye*ne*ne;t.touchData.singleTouchMoved||(fe||he.$(":selected").unselect(["tapunselect"]),n(fe,["tap","vclick"],W,{x:ae[0],y:ae[1]}),ce=!1,W.timeStamp-te<=he.multiClickDebounceTime()?(ue&&clearTimeout(ue),ce=!0,te=null,n(fe,["dbltap","vdblclick"],W,{x:ae[0],y:ae[1]})):(ue=setTimeout(function(){ce||n(fe,["onetap","voneclick"],W,{x:ae[0],y:ae[1]})},he.multiClickDebounceTime()),te=W.timeStamp)),fe!=null&&!t.dragData.didDrag&&fe._private.selectable&&Je"u"){var De=[],oe=o(function(W){return{clientX:W.clientX,clientY:W.clientY,force:1,identifier:W.pointerId,pageX:W.pageX,pageY:W.pageY,radiusX:W.width/2,radiusY:W.height/2,screenX:W.screenX,screenY:W.screenY,target:W.target}},"makeTouch"),ke=o(function(W){return{event:W,touch:oe(W)}},"makePointer"),Ie=o(function(W){De.push(ke(W))},"addPointer"),Se=o(function(W){for(var fe=0;fe0)return Y[0]}return null},"getCurveT"),g=Object.keys(p),y=0;y0?m:Hpe(a,s,e,r,n,i,l,u)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){u=u==="auto"?Y0(i,a):u;var h=2*u;if(Qu(e,r,this.points,s,l,i,a-h,[0,-1],n)||Qu(e,r,this.points,s,l,i-h,a,[0,-1],n))return!0;var f=i/2+2*n,d=a/2+2*n,p=[s-f,l-d,s-f,l,s+f,l,s+f,l-d];return!!(zs(e,r,p)||$0(e,r,h,h,s+i/2-u,l+a/2-u,n)||$0(e,r,h,h,s-i/2+u,l+a/2-u,n))},"checkPoint")}};Ju.registerNodeShapes=function(){var t=this.nodeShapes={},e=this;this.generateEllipse(),this.generatePolygon("triangle",ls(3,0)),this.generateRoundPolygon("round-triangle",ls(3,0)),this.generatePolygon("rectangle",ls(4,0)),t.square=t.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();{var r=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",r),this.generateRoundPolygon("round-diamond",r)}this.generatePolygon("pentagon",ls(5,0)),this.generateRoundPolygon("round-pentagon",ls(5,0)),this.generatePolygon("hexagon",ls(6,0)),this.generateRoundPolygon("round-hexagon",ls(6,0)),this.generatePolygon("heptagon",ls(7,0)),this.generateRoundPolygon("round-heptagon",ls(7,0)),this.generatePolygon("octagon",ls(8,0)),this.generateRoundPolygon("round-octagon",ls(8,0));var n=new Array(20);{var i=NP(5,0),a=NP(5,Math.PI/5),s=.5*(3-Math.sqrt(5));s*=1.57;for(var l=0;l=e.deqFastCost*S)break}else if(h){if(b>=e.deqCost*m||b>=e.deqAvgCost*p)break}else if(w>=e.deqNoDrawCost*LP)break;var T=e.deq(n,v,y);if(T.length>0)for(var E=0;E0&&(e.onDeqd(n,g),!h&&e.shouldRedraw(n,g,v,y)&&a())},"dequeue"),l=e.priority||JP;i.beforeRender(s,l(n))}},"setupDequeueingImpl")},"setupDequeueing")},pQe=function(){function t(e){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:C6;XP(this,t),this.idsByKey=new Vc,this.keyForId=new Vc,this.cachesByLvl=new Vc,this.lvls=[],this.getKey=e,this.doesEleInvalidateKey=r}return o(t,"ElementTextureCacheLookup"),jP(t,[{key:"getIdsFor",value:o(function(r){r==null&&oi("Can not get id list for null key");var n=this.idsByKey,i=this.idsByKey.get(r);return i||(i=new c1,n.set(r,i)),i},"getIdsFor")},{key:"addIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).add(n)},"addIdForKey")},{key:"deleteIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).delete(n)},"deleteIdForKey")},{key:"getNumberOfIdsForKey",value:o(function(r){return r==null?0:this.getIdsFor(r).size},"getNumberOfIdsForKey")},{key:"updateKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);this.deleteIdForKey(i,n),this.addIdForKey(a,n),this.keyForId.set(n,a)},"updateKeyMappingFor")},{key:"deleteKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n);this.deleteIdForKey(i,n),this.keyForId.delete(n)},"deleteKeyMappingFor")},{key:"keyHasChangedFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);return i!==a},"keyHasChangedFor")},{key:"isInvalid",value:o(function(r){return this.keyHasChangedFor(r)||this.doesEleInvalidateKey(r)},"isInvalid")},{key:"getCachesAt",value:o(function(r){var n=this.cachesByLvl,i=this.lvls,a=n.get(r);return a||(a=new Vc,n.set(r,a),i.push(r)),a},"getCachesAt")},{key:"getCache",value:o(function(r,n){return this.getCachesAt(n).get(r)},"getCache")},{key:"get",value:o(function(r,n){var i=this.getKey(r),a=this.getCache(i,n);return 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this.rect.x+this.rect.width/2},h.prototype.getCenterY=function(){return this.rect.y+this.rect.height/2},h.prototype.getCenter=function(){return new u(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},h.prototype.getLocation=function(){return new u(this.rect.x,this.rect.y)},h.prototype.getRect=function(){return this.rect},h.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},h.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},h.prototype.setRect=function(d,p){this.rect.x=d.x,this.rect.y=d.y,this.rect.width=p.width,this.rect.height=p.height},h.prototype.setCenter=function(d,p){this.rect.x=d-this.rect.width/2,this.rect.y=p-this.rect.height/2},h.prototype.setLocation=function(d,p){this.rect.x=d,this.rect.y=p},h.prototype.moveBy=function(d,p){this.rect.x+=d,this.rect.y+=p},h.prototype.getEdgeListToNode=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(y.target==d){if(y.source!=g)throw"Incorrect edge source!";p.push(y)}}),p},h.prototype.getEdgesBetween=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(!(y.source==g||y.target==g))throw"Incorrect edge source and/or target";(y.target==d||y.source==d)&&p.push(y)}),p},h.prototype.getNeighborsList=function(){var d=new Set,p=this;return p.edges.forEach(function(m){if(m.source==p)d.add(m.target);else{if(m.target!=p)throw"Incorrect incidency!";d.add(m.source)}}),d},h.prototype.withChildren=function(){var d=new Set,p,m;if(d.add(this),this.child!=null)for(var g=this.child.getNodes(),y=0;yp&&(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)),this.labelHeight>m&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-m)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-m),this.setHeight(this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(6),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var S=x.source.owner.getEdges().indexOf(x);if(S==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(S,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),S=w.length,T=0;Tv&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,S,T,E,_,A=this.nodes,L=A.length,M=0;Mw&&(y=w),vT&&(x=T),bw&&(y=w),vT&&(x=T),b=this.nodes.length){var L=0;v.forEach(function(M){M.owner==g&&L++}),L==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(5),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d=s.getRight()?l[0]+=Math.min(s.getX()-a.getX(),a.getRight()-s.getRight()):s.getX()<=a.getX()&&s.getRight()>=a.getRight()&&(l[0]+=Math.min(a.getX()-s.getX(),s.getRight()-a.getRight())),a.getY()<=s.getY()&&a.getBottom()>=s.getBottom()?l[1]+=Math.min(s.getY()-a.getY(),a.getBottom()-s.getBottom()):s.getY()<=a.getY()&&s.getBottom()>=a.getBottom()&&(l[1]+=Math.min(a.getY()-s.getY(),s.getBottom()-a.getBottom()));var f=Math.abs((s.getCenterY()-a.getCenterY())/(s.getCenterX()-a.getCenterX()));s.getCenterY()===a.getCenterY()&&s.getCenterX()===a.getCenterX()&&(f=1);var d=f*l[0],p=l[1]/f;l[0]d)return l[0]=u,l[1]=m,l[2]=f,l[3]=A,!1;if(hf)return l[0]=p,l[1]=h,l[2]=E,l[3]=d,!1;if(uf?(l[0]=y,l[1]=v,k=!0):(l[0]=g,l[1]=m,k=!0):C===D&&(u>f?(l[0]=p,l[1]=m,k=!0):(l[0]=x,l[1]=v,k=!0)),-O===D?f>u?(l[2]=_,l[3]=A,I=!0):(l[2]=E,l[3]=T,I=!0):O===D&&(f>u?(l[2]=S,l[3]=T,I=!0):(l[2]=L,l[3]=A,I=!0)),k&&I)return!1;if(u>f?h>d?(P=this.getCardinalDirection(C,D,4),F=this.getCardinalDirection(O,D,2)):(P=this.getCardinalDirection(-C,D,3),F=this.getCardinalDirection(-O,D,1)):h>d?(P=this.getCardinalDirection(-C,D,1),F=this.getCardinalDirection(-O,D,3)):(P=this.getCardinalDirection(C,D,2),F=this.getCardinalDirection(O,D,4)),!k)switch(P){case 1:$=m,B=u+-w/D,l[0]=B,l[1]=$;break;case 2:B=x,$=h+b*D,l[0]=B,l[1]=$;break;case 3:$=v,B=u+w/D,l[0]=B,l[1]=$;break;case 4:B=y,$=h+-b*D,l[0]=B,l[1]=$;break}if(!I)switch(F){case 1:Y=T,z=f+-N/D,l[2]=z,l[3]=Y;break;case 2:z=L,Y=d+M*D,l[2]=z,l[3]=Y;break;case 3:Y=A,z=f+N/D,l[2]=z,l[3]=Y;break;case 4:z=_,Y=d+-M*D,l[2]=z,l[3]=Y;break}}return!1},i.getCardinalDirection=function(a,s,l){return a>s?l:1+l%4},i.getIntersection=function(a,s,l,u){if(u==null)return this.getIntersection2(a,s,l);var h=a.x,f=a.y,d=s.x,p=s.y,m=l.x,g=l.y,y=u.x,v=u.y,x=void 0,b=void 0,w=void 0,S=void 0,T=void 0,E=void 0,_=void 0,A=void 0,L=void 0;return w=p-f,T=h-d,_=d*f-h*p,S=v-g,E=m-y,A=y*g-m*v,L=w*E-S*T,L===0?null:(x=(T*A-E*_)/L,b=(S*_-w*A)/L,new n(x,b))},i.angleOfVector=function(a,s,l,u){var h=void 0;return a!==l?(h=Math.atan((u-s)/(l-a)),l0?1:i<0?-1:0},n.floor=function(i){return i<0?Math.ceil(i):Math.floor(i)},n.ceil=function(i){return i<0?Math.floor(i):Math.ceil(i)},t.exports=n},function(t,e,r){"use strict";function n(){}o(n,"Integer"),n.MAX_VALUE=2147483647,n.MIN_VALUE=-2147483648,t.exports=n},function(t,e,r){"use strict";var n=function(){function h(f,d){for(var p=0;p"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var _=E.getEdges(),x=0;x<_.length;x++){var A=_[x].getOtherEnd(E);if(S.get(E)!=A)if(!b.has(A))w.push(A),S.set(A,E);else{g=!1;break}}}if(!g)m=[];else{var L=[].concat(n(b));m.push(L);for(var x=0;x-1&&T.splice(N,1)}b=new Set,S=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x0){for(var v=this.edgeToDummyNodes.get(y),x=0;x=0&&g.splice(A,1);var L=S.getNeighborsList();L.forEach(function(k){if(y.indexOf(k)<0){var I=v.get(k),C=I-1;C==1&&E.push(k),v.set(k,C)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(4);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);pa.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var 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T.getLogger().debug("Lexa: (["),this.pushState("NODE"),36;break;case 65:return T.getLogger().debug("Lexa: )"),this.pushState("NODE"),36;break;case 66:return this.pushState("NODE"),36;break;case 67:return this.pushState("NODE"),36;break;case 68:return this.pushState("NODE"),36;break;case 69:return this.pushState("NODE"),36;break;case 70:return this.pushState("NODE"),36;break;case 71:return this.pushState("NODE"),36;break;case 72:return this.pushState("NODE"),36;break;case 73:return T.getLogger().debug("Lexa: ["),this.pushState("NODE"),36;break;case 74:return this.pushState("BLOCK_ARROW"),T.getLogger().debug("LEX ARR START"),38;break;case 75:return T.getLogger().debug("Lex: NODE_ID",E.yytext),32;break;case 76:return T.getLogger().debug("Lex: EOF",E.yytext),8;break;case 77:this.pushState("md_string");break;case 78:this.pushState("md_string");break;case 79:return"NODE_DESCR";case 80:this.popState();break;case 81:T.getLogger().debug("Lex: Starting 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g=this.child.getNodes(),y=0;yp?(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)):this.labelPosHorizontal=="right"&&this.setWidth(p+this.labelWidth)),this.labelHeight&&(this.labelPosVertical=="top"?(this.rect.y-=this.labelHeight,this.setHeight(m+this.labelHeight)):this.labelPosVertical=="center"&&this.labelHeight>m?(this.rect.y-=(this.labelHeight-m)/2,this.setHeight(this.labelHeight)):this.labelPosVertical=="bottom"&&this.setHeight(m+this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";var n=r(0);function i(){}o(i,"FDLayoutConstants");for(var a in n)i[a]=n[a];i.MAX_ITERATIONS=2500,i.DEFAULT_EDGE_LENGTH=50,i.DEFAULT_SPRING_STRENGTH=.45,i.DEFAULT_REPULSION_STRENGTH=4500,i.DEFAULT_GRAVITY_STRENGTH=.4,i.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,i.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,i.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,i.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,i.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,i.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,i.COOLING_ADAPTATION_FACTOR=.33,i.ADAPTATION_LOWER_NODE_LIMIT=1e3,i.ADAPTATION_UPPER_NODE_LIMIT=5e3,i.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,i.MAX_NODE_DISPLACEMENT=i.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,i.MIN_REPULSION_DIST=i.DEFAULT_EDGE_LENGTH/10,i.CONVERGENCE_CHECK_PERIOD=100,i.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,i.MIN_EDGE_LENGTH=1,i.GRID_CALCULATION_CHECK_PERIOD=10,t.exports=i},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(7),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var S=x.source.owner.getEdges().indexOf(x);if(S==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(S,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),S=w.length,T=0;Tv&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,S,T,E,_,A=this.nodes,L=A.length,M=0;Mw&&(y=w),vT&&(x=T),bw&&(y=w),vT&&(x=T),b=this.nodes.length){var L=0;v.forEach(function(M){M.owner==g&&L++}),L==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(6),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d=s.getRight()?l[0]+=Math.min(s.getX()-a.getX(),a.getRight()-s.getRight()):s.getX()<=a.getX()&&s.getRight()>=a.getRight()&&(l[0]+=Math.min(a.getX()-s.getX(),s.getRight()-a.getRight())),a.getY()<=s.getY()&&a.getBottom()>=s.getBottom()?l[1]+=Math.min(s.getY()-a.getY(),a.getBottom()-s.getBottom()):s.getY()<=a.getY()&&s.getBottom()>=a.getBottom()&&(l[1]+=Math.min(a.getY()-s.getY(),s.getBottom()-a.getBottom()));var f=Math.abs((s.getCenterY()-a.getCenterY())/(s.getCenterX()-a.getCenterX()));s.getCenterY()===a.getCenterY()&&s.getCenterX()===a.getCenterX()&&(f=1);var d=f*l[0],p=l[1]/f;l[0]d)return l[0]=u,l[1]=m,l[2]=f,l[3]=A,!1;if(hf)return l[0]=p,l[1]=h,l[2]=E,l[3]=d,!1;if(uf?(l[0]=y,l[1]=v,k=!0):(l[0]=g,l[1]=m,k=!0):C===D&&(u>f?(l[0]=p,l[1]=m,k=!0):(l[0]=x,l[1]=v,k=!0)),-O===D?f>u?(l[2]=_,l[3]=A,I=!0):(l[2]=E,l[3]=T,I=!0):O===D&&(f>u?(l[2]=S,l[3]=T,I=!0):(l[2]=L,l[3]=A,I=!0)),k&&I)return!1;if(u>f?h>d?(P=this.getCardinalDirection(C,D,4),F=this.getCardinalDirection(O,D,2)):(P=this.getCardinalDirection(-C,D,3),F=this.getCardinalDirection(-O,D,1)):h>d?(P=this.getCardinalDirection(-C,D,1),F=this.getCardinalDirection(-O,D,3)):(P=this.getCardinalDirection(C,D,2),F=this.getCardinalDirection(O,D,4)),!k)switch(P){case 1:$=m,B=u+-w/D,l[0]=B,l[1]=$;break;case 2:B=x,$=h+b*D,l[0]=B,l[1]=$;break;case 3:$=v,B=u+w/D,l[0]=B,l[1]=$;break;case 4:B=y,$=h+-b*D,l[0]=B,l[1]=$;break}if(!I)switch(F){case 1:Y=T,z=f+-N/D,l[2]=z,l[3]=Y;break;case 2:z=L,Y=d+M*D,l[2]=z,l[3]=Y;break;case 3:Y=A,z=f+N/D,l[2]=z,l[3]=Y;break;case 4:z=_,Y=d+-M*D,l[2]=z,l[3]=Y;break}}return!1},i.getCardinalDirection=function(a,s,l){return a>s?l:1+l%4},i.getIntersection=function(a,s,l,u){if(u==null)return this.getIntersection2(a,s,l);var h=a.x,f=a.y,d=s.x,p=s.y,m=l.x,g=l.y,y=u.x,v=u.y,x=void 0,b=void 0,w=void 0,S=void 0,T=void 0,E=void 0,_=void 0,A=void 0,L=void 0;return w=p-f,T=h-d,_=d*f-h*p,S=v-g,E=m-y,A=y*g-m*v,L=w*E-S*T,L===0?null:(x=(T*A-E*_)/L,b=(S*_-w*A)/L,new n(x,b))},i.angleOfVector=function(a,s,l,u){var h=void 0;return a!==l?(h=Math.atan((u-s)/(l-a)),l=0){var v=(-m+Math.sqrt(m*m-4*p*g))/(2*p),x=(-m-Math.sqrt(m*m-4*p*g))/(2*p),b=null;return v>=0&&v<=1?[v]:x>=0&&x<=1?[x]:b}else return null},i.HALF_PI=.5*Math.PI,i.ONE_AND_HALF_PI=1.5*Math.PI,i.TWO_PI=2*Math.PI,i.THREE_PI=3*Math.PI,t.exports=i},function(t,e,r){"use strict";function n(){}o(n,"IMath"),n.sign=function(i){return i>0?1:i<0?-1:0},n.floor=function(i){return i<0?Math.ceil(i):Math.floor(i)},n.ceil=function(i){return i<0?Math.floor(i):Math.ceil(i)},t.exports=n},function(t,e,r){"use strict";function n(){}o(n,"Integer"),n.MAX_VALUE=2147483647,n.MIN_VALUE=-2147483648,t.exports=n},function(t,e,r){"use strict";var n=function(){function h(f,d){for(var p=0;p"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var _=E.getEdges(),x=0;x<_.length;x++){var A=_[x].getOtherEnd(E);if(S.get(E)!=A)if(!b.has(A))w.push(A),S.set(A,E);else{g=!1;break}}}if(!g)m=[];else{var L=[].concat(n(b));m.push(L);for(var x=0;x-1&&T.splice(N,1)}b=new Set,S=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x0){for(var v=this.edgeToDummyNodes.get(y),x=0;x=0&&g.splice(A,1);var L=S.getNeighborsList();L.forEach(function(k){if(y.indexOf(k)<0){var I=v.get(k),C=I-1;C==1&&E.push(k),v.set(k,C)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(5);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);pa.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var d=this.getAllEdges(),p,m=0;m0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&d&&this.updateGrid(),b=new Set,m=0;mw||b>w)&&(d.gravitationForceX=-this.gravityConstant*y,d.gravitationForceY=-this.gravityConstant*v)):(w=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,d.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var d,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),d=this.totalDisplacement=x.length||w>=x[0].length)){for(var S=0;Sh},"_defaultCompareFunction")}]),l}();t.exports=s},function(t,e,r){"use strict";function n(){}o(n,"SVD"),n.svd=function(i){this.U=null,this.V=null,this.s=null,this.m=0,this.n=0,this.m=i.length,this.n=i[0].length;var a=Math.min(this.m,this.n);this.s=function(it){for(var dt=[];it-- >0;)dt.push(0);return dt}(Math.min(this.m+1,this.n)),this.U=function(it){var dt=o(function lt(It){if(It.length==0)return 0;for(var mt=[],St=0;St0;)dt.push(0);return dt}(this.n),l=function(it){for(var dt=[];it-- >0;)dt.push(0);return dt}(this.m),u=!0,h=!0,f=Math.min(this.m-1,this.n),d=Math.max(0,Math.min(this.n-2,this.m)),p=0;p=0;D--)if(this.s[D]!==0){for(var P=D+1;P=0;X--){if(function(it,dt){return it&&dt}(X0;){var ue=void 0,te=void 0;for(ue=I-2;ue>=-1&&ue!==-1;ue--)if(Math.abs(s[ue])<=ce+se*(Math.abs(this.s[ue])+Math.abs(this.s[ue+1]))){s[ue]=0;break}if(ue===I-2)te=4;else{var De=void 0;for(De=I-1;De>=ue&&De!==ue;De--){var oe=(De!==I?Math.abs(s[De]):0)+(De!==ue+1?Math.abs(s[De-1]):0);if(Math.abs(this.s[De])<=ce+se*oe){this.s[De]=0;break}}De===ue?te=3:De===I-1?te=1:(te=2,ue=De)}switch(ue++,te){case 1:{var ke=s[I-2];s[I-2]=0;for(var Ie=I-2;Ie>=ue;Ie--){var Se=n.hypot(this.s[Ie],ke),Ue=this.s[Ie]/Se,Pe=ke/Se;if(this.s[Ie]=Se,Ie!==ue&&(ke=-Pe*s[Ie-1],s[Ie-1]=Ue*s[Ie-1]),h)for(var _e=0;_e=this.s[ue+1]);){var je=this.s[ue];if(this.s[ue]=this.s[ue+1],this.s[ue+1]=je,h&&ueMath.abs(a)?(s=a/i,s=Math.abs(i)*Math.sqrt(1+s*s)):a!=0?(s=i/a,s=Math.abs(a)*Math.sqrt(1+s*s)):s=0,s},t.exports=n},function(t,e,r){"use strict";var n=function(){function s(l,u){for(var h=0;h2&&arguments[2]!==void 0?arguments[2]:1,f=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,d=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;i(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=f,this.gap_penalty=d,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},i.emit=function(a,s){for(var l=0;l{"use strict";o(function(e,r){typeof vb=="object"&&typeof QB=="object"?QB.exports=r(KB()):typeof define=="function"&&define.amd?define(["layout-base"],r):typeof vb=="object"?vb.coseBase=r(KB()):e.coseBase=r(e.layoutBase)},"webpackUniversalModuleDefinition")(vb,function(t){return(()=>{"use strict";var e={45:(a,s,l)=>{var u={};u.layoutBase=l(551),u.CoSEConstants=l(806),u.CoSEEdge=l(767),u.CoSEGraph=l(880),u.CoSEGraphManager=l(578),u.CoSELayout=l(765),u.CoSENode=l(991),u.ConstraintHandler=l(902),a.exports=u},806:(a,s,l)=>{var u=l(551).FDLayoutConstants;function h(){}o(h,"CoSEConstants");for(var f in u)h[f]=u[f];h.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,h.DEFAULT_RADIAL_SEPARATION=u.DEFAULT_EDGE_LENGTH,h.DEFAULT_COMPONENT_SEPERATION=60,h.TILE=!0,h.TILING_PADDING_VERTICAL=10,h.TILING_PADDING_HORIZONTAL=10,h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!0,h.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,h.TREE_REDUCTION_ON_INCREMENTAL=!0,h.PURE_INCREMENTAL=h.DEFAULT_INCREMENTAL,a.exports=h},767:(a,s,l)=>{var u=l(551).FDLayoutEdge;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEEdge"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},880:(a,s,l)=>{var u=l(551).LGraph;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEGraph"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},578:(a,s,l)=>{var u=l(551).LGraphManager;function h(d){u.call(this,d)}o(h,"CoSEGraphManager"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},765:(a,s,l)=>{var u=l(551).FDLayout,h=l(578),f=l(880),d=l(991),p=l(767),m=l(806),g=l(902),y=l(551).FDLayoutConstants,v=l(551).LayoutConstants,x=l(551).Point,b=l(551).PointD,w=l(551).DimensionD,S=l(551).Layout,T=l(551).Integer,E=l(551).IGeometry,_=l(551).LGraph,A=l(551).Transform,L=l(551).LinkedList;function M(){u.call(this),this.toBeTiled={},this.constraints={}}o(M,"CoSELayout"),M.prototype=Object.create(u.prototype);for(var N in u)M[N]=u[N];M.prototype.newGraphManager=function(){var k=new h(this);return this.graphManager=k,k},M.prototype.newGraph=function(k){return new f(null,this.graphManager,k)},M.prototype.newNode=function(k){return new d(this.graphManager,k)},M.prototype.newEdge=function(k){return new p(null,null,k)},M.prototype.initParameters=function(){u.prototype.initParameters.call(this,arguments),this.isSubLayout||(m.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=m.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=m.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=y.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=y.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=y.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},M.prototype.initSpringEmbedder=function(){u.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/y.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},M.prototype.layout=function(){var k=v.DEFAULT_CREATE_BENDS_AS_NEEDED;return k&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},M.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(m.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var I=new Set(this.getAllNodes()),C=this.nodesWithGravity.filter(function(P){return I.has(P)});this.graphManager.setAllNodesToApplyGravitation(C)}}else{var k=this.getFlatForest();if(k.length>0)this.positionNodesRadially(k);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var I=new Set(this.getAllNodes()),C=this.nodesWithGravity.filter(function(O){return I.has(O)});this.graphManager.setAllNodesToApplyGravitation(C),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(g.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),m.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},M.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%y.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var k=new Set(this.getAllNodes()),I=this.nodesWithGravity.filter(function(D){return k.has(D)});this.graphManager.setAllNodesToApplyGravitation(I),this.graphManager.updateBounds(),this.updateGrid(),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var C=!this.isTreeGrowing&&!this.isGrowthFinished,O=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(C,O),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},M.prototype.getPositionsData=function(){for(var k=this.graphManager.getAllNodes(),I={},C=0;C0&&this.updateDisplacements();for(var C=0;C0&&(O.fixedNodeWeight=P)}}if(this.constraints.relativePlacementConstraint){var F=new Map,B=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(J){k.fixedNodesOnHorizontal.add(J),k.fixedNodesOnVertical.add(J)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var $=this.constraints.alignmentConstraint.vertical,C=0;C<$.length;C++)this.dummyToNodeForVerticalAlignment.set("dummy"+C,[]),$[C].forEach(function(Z){F.set(Z,"dummy"+C),k.dummyToNodeForVerticalAlignment.get("dummy"+C).push(Z),k.fixedNodeSet.has(Z)&&k.fixedNodesOnHorizontal.add("dummy"+C)});if(this.constraints.alignmentConstraint.horizontal)for(var z=this.constraints.alignmentConstraint.horizontal,C=0;C=2*J.length/3;q--)Z=Math.floor(Math.random()*(q+1)),H=J[q],J[q]=J[Z],J[Z]=H;return J},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(J){if(J.left){var Z=F.has(J.left)?F.get(J.left):J.left,H=F.has(J.right)?F.get(J.right):J.right;k.nodesInRelativeHorizontal.includes(Z)||(k.nodesInRelativeHorizontal.push(Z),k.nodeToRelativeConstraintMapHorizontal.set(Z,[]),k.dummyToNodeForVerticalAlignment.has(Z)?k.nodeToTempPositionMapHorizontal.set(Z,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(Z)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(Z,k.idToNodeMap.get(Z).getCenterX())),k.nodesInRelativeHorizontal.includes(H)||(k.nodesInRelativeHorizontal.push(H),k.nodeToRelativeConstraintMapHorizontal.set(H,[]),k.dummyToNodeForVerticalAlignment.has(H)?k.nodeToTempPositionMapHorizontal.set(H,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(H)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(H,k.idToNodeMap.get(H).getCenterX())),k.nodeToRelativeConstraintMapHorizontal.get(Z).push({right:H,gap:J.gap}),k.nodeToRelativeConstraintMapHorizontal.get(H).push({left:Z,gap:J.gap})}else{var q=B.has(J.top)?B.get(J.top):J.top,K=B.has(J.bottom)?B.get(J.bottom):J.bottom;k.nodesInRelativeVertical.includes(q)||(k.nodesInRelativeVertical.push(q),k.nodeToRelativeConstraintMapVertical.set(q,[]),k.dummyToNodeForHorizontalAlignment.has(q)?k.nodeToTempPositionMapVertical.set(q,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(q)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(q,k.idToNodeMap.get(q).getCenterY())),k.nodesInRelativeVertical.includes(K)||(k.nodesInRelativeVertical.push(K),k.nodeToRelativeConstraintMapVertical.set(K,[]),k.dummyToNodeForHorizontalAlignment.has(K)?k.nodeToTempPositionMapVertical.set(K,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(K)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(K,k.idToNodeMap.get(K).getCenterY())),k.nodeToRelativeConstraintMapVertical.get(q).push({bottom:K,gap:J.gap}),k.nodeToRelativeConstraintMapVertical.get(K).push({top:q,gap:J.gap})}});else{var Y=new Map,Q=new Map;this.constraints.relativePlacementConstraint.forEach(function(J){if(J.left){var Z=F.has(J.left)?F.get(J.left):J.left,H=F.has(J.right)?F.get(J.right):J.right;Y.has(Z)?Y.get(Z).push(H):Y.set(Z,[H]),Y.has(H)?Y.get(H).push(Z):Y.set(H,[Z])}else{var q=B.has(J.top)?B.get(J.top):J.top,K=B.has(J.bottom)?B.get(J.bottom):J.bottom;Q.has(q)?Q.get(q).push(K):Q.set(q,[K]),Q.has(K)?Q.get(K).push(q):Q.set(K,[q])}});var X=o(function(Z,H){var q=[],K=[],se=new L,ce=new Set,ue=0;return Z.forEach(function(te,De){if(!ce.has(De)){q[ue]=[],K[ue]=!1;var oe=De;for(se.push(oe),ce.add(oe),q[ue].push(oe);se.length!=0;){oe=se.shift(),H.has(oe)&&(K[ue]=!0);var ke=Z.get(oe);ke.forEach(function(Ie){ce.has(Ie)||(se.push(Ie),ce.add(Ie),q[ue].push(Ie))})}ue++}}),{components:q,isFixed:K}},"constructComponents"),ie=X(Y,k.fixedNodesOnHorizontal);this.componentsOnHorizontal=ie.components,this.fixedComponentsOnHorizontal=ie.isFixed;var j=X(Q,k.fixedNodesOnVertical);this.componentsOnVertical=j.components,this.fixedComponentsOnVertical=j.isFixed}}},M.prototype.updateDisplacements=function(){var k=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(j){var J=k.idToNodeMap.get(j.nodeId);J.displacementX=0,J.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var I=this.constraints.alignmentConstraint.vertical,C=0;C1){var B;for(B=0;BO&&(O=Math.floor(F.y)),P=Math.floor(F.x+m.DEFAULT_COMPONENT_SEPERATION)}this.transform(new b(v.WORLD_CENTER_X-F.x/2,v.WORLD_CENTER_Y-F.y/2))},M.radialLayout=function(k,I,C){var O=Math.max(this.maxDiagonalInTree(k),m.DEFAULT_RADIAL_SEPARATION);M.branchRadialLayout(I,null,0,359,0,O);var D=_.calculateBounds(k),P=new A;P.setDeviceOrgX(D.getMinX()),P.setDeviceOrgY(D.getMinY()),P.setWorldOrgX(C.x),P.setWorldOrgY(C.y);for(var F=0;F1;){var q=H[0];H.splice(0,1);var K=X.indexOf(q);K>=0&&X.splice(K,1),J--,ie--}I!=null?Z=(X.indexOf(H[0])+1)%J:Z=0;for(var se=Math.abs(O-C)/ie,ce=Z;j!=ie;ce=++ce%J){var ue=X[ce].getOtherEnd(k);if(ue!=I){var te=(C+j*se)%360,De=(te+se)%360;M.branchRadialLayout(ue,k,te,De,D+P,P),j++}}},M.maxDiagonalInTree=function(k){for(var I=T.MIN_VALUE,C=0;CI&&(I=D)}return I},M.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},M.prototype.groupZeroDegreeMembers=function(){var k=this,I={};this.memberGroups={},this.idToDummyNode={};for(var C=[],O=this.graphManager.getAllNodes(),D=0;D"u"&&(I[B]=[]),I[B]=I[B].concat(P)}Object.keys(I).forEach(function($){if(I[$].length>1){var z="DummyCompound_"+$;k.memberGroups[z]=I[$];var Y=I[$][0].getParent(),Q=new d(k.graphManager);Q.id=z,Q.paddingLeft=Y.paddingLeft||0,Q.paddingRight=Y.paddingRight||0,Q.paddingBottom=Y.paddingBottom||0,Q.paddingTop=Y.paddingTop||0,k.idToDummyNode[z]=Q;var X=k.getGraphManager().add(k.newGraph(),Q),ie=Y.getChild();ie.add(Q);for(var j=0;jD?(O.rect.x-=(O.labelWidth-D)/2,O.setWidth(O.labelWidth),O.labelMarginLeft=(O.labelWidth-D)/2):O.labelPosHorizontal=="right"&&O.setWidth(D+O.labelWidth)),O.labelHeight&&(O.labelPosVertical=="top"?(O.rect.y-=O.labelHeight,O.setHeight(P+O.labelHeight),O.labelMarginTop=O.labelHeight):O.labelPosVertical=="center"&&O.labelHeight>P?(O.rect.y-=(O.labelHeight-P)/2,O.setHeight(O.labelHeight),O.labelMarginTop=(O.labelHeight-P)/2):O.labelPosVertical=="bottom"&&O.setHeight(P+O.labelHeight))}})},M.prototype.repopulateCompounds=function(){for(var k=this.compoundOrder.length-1;k>=0;k--){var I=this.compoundOrder[k],C=I.id,O=I.paddingLeft,D=I.paddingTop,P=I.labelMarginLeft,F=I.labelMarginTop;this.adjustLocations(this.tiledMemberPack[C],I.rect.x,I.rect.y,O,D,P,F)}},M.prototype.repopulateZeroDegreeMembers=function(){var k=this,I=this.tiledZeroDegreePack;Object.keys(I).forEach(function(C){var O=k.idToDummyNode[C],D=O.paddingLeft,P=O.paddingTop,F=O.labelMarginLeft,B=O.labelMarginTop;k.adjustLocations(I[C],O.rect.x,O.rect.y,D,P,F,B)})},M.prototype.getToBeTiled=function(k){var I=k.id;if(this.toBeTiled[I]!=null)return this.toBeTiled[I];var C=k.getChild();if(C==null)return this.toBeTiled[I]=!1,!1;for(var O=C.getNodes(),D=0;D0)return this.toBeTiled[I]=!1,!1;if(P.getChild()==null){this.toBeTiled[P.id]=!1;continue}if(!this.getToBeTiled(P))return this.toBeTiled[I]=!1,!1}return this.toBeTiled[I]=!0,!0},M.prototype.getNodeDegree=function(k){for(var I=k.id,C=k.getEdges(),O=0,D=0;DY&&(Y=X.rect.height)}C+=Y+k.verticalPadding}},M.prototype.tileCompoundMembers=function(k,I){var C=this;this.tiledMemberPack=[],Object.keys(k).forEach(function(O){var D=I[O];if(C.tiledMemberPack[O]=C.tileNodes(k[O],D.paddingLeft+D.paddingRight),D.rect.width=C.tiledMemberPack[O].width,D.rect.height=C.tiledMemberPack[O].height,D.setCenter(C.tiledMemberPack[O].centerX,C.tiledMemberPack[O].centerY),D.labelMarginLeft=0,D.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var P=D.rect.width,F=D.rect.height;D.labelWidth&&(D.labelPosHorizontal=="left"?(D.rect.x-=D.labelWidth,D.setWidth(P+D.labelWidth),D.labelMarginLeft=D.labelWidth):D.labelPosHorizontal=="center"&&D.labelWidth>P?(D.rect.x-=(D.labelWidth-P)/2,D.setWidth(D.labelWidth),D.labelMarginLeft=(D.labelWidth-P)/2):D.labelPosHorizontal=="right"&&D.setWidth(P+D.labelWidth)),D.labelHeight&&(D.labelPosVertical=="top"?(D.rect.y-=D.labelHeight,D.setHeight(F+D.labelHeight),D.labelMarginTop=D.labelHeight):D.labelPosVertical=="center"&&D.labelHeight>F?(D.rect.y-=(D.labelHeight-F)/2,D.setHeight(D.labelHeight),D.labelMarginTop=(D.labelHeight-F)/2):D.labelPosVertical=="bottom"&&D.setHeight(F+D.labelHeight))}})},M.prototype.tileNodes=function(k,I){var C=this.tileNodesByFavoringDim(k,I,!0),O=this.tileNodesByFavoringDim(k,I,!1),D=this.getOrgRatio(C),P=this.getOrgRatio(O),F;return PB&&(B=j.getWidth())});var $=P/D,z=F/D,Y=Math.pow(C-O,2)+4*($+O)*(z+C)*D,Q=(O-C+Math.sqrt(Y))/(2*($+O)),X;I?(X=Math.ceil(Q),X==Q&&X++):X=Math.floor(Q);var ie=X*($+O)-O;return B>ie&&(ie=B),ie+=O*2,ie},M.prototype.tileNodesByFavoringDim=function(k,I,C){var O=m.TILING_PADDING_VERTICAL,D=m.TILING_PADDING_HORIZONTAL,P=m.TILING_COMPARE_BY,F={rows:[],rowWidth:[],rowHeight:[],width:0,height:I,verticalPadding:O,horizontalPadding:D,centerX:0,centerY:0};P&&(F.idealRowWidth=this.calcIdealRowWidth(k,C));var B=o(function(J){return J.rect.width*J.rect.height},"getNodeArea"),$=o(function(J,Z){return B(Z)-B(J)},"areaCompareFcn");k.sort(function(j,J){var Z=$;return F.idealRowWidth?(Z=P,Z(j.id,J.id)):Z(j,J)});for(var z=0,Y=0,Q=0;Q0&&(F+=k.horizontalPadding),k.rowWidth[C]=F,k.width0&&(B+=k.verticalPadding);var $=0;B>k.rowHeight[C]&&($=k.rowHeight[C],k.rowHeight[C]=B,$=k.rowHeight[C]-$),k.height+=$,k.rows[C].push(I)},M.prototype.getShortestRowIndex=function(k){for(var I=-1,C=Number.MAX_VALUE,O=0;OC&&(I=O,C=k.rowWidth[O]);return 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u=l(551).FDLayoutNode,h=l(551).IMath;function f(p,m,g,y){u.call(this,p,m,g,y)}o(f,"CoSENode"),f.prototype=Object.create(u.prototype);for(var d in u)f[d]=u[d];f.prototype.calculateDisplacement=function(){var p=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementX=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementX)),Math.abs(this.displacementY)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementY=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},f.prototype.propogateDisplacementToChildren=function(p,m){for(var g=this.getChild().getNodes(),y,v=0;v{function u(g){if(Array.isArray(g)){for(var y=0,v=Array(g.length);y0){var Je=0;Ye.forEach(function(je){we=="horizontal"?(ye.set(je,x.has(je)?b[x.get(je)]:Ce.get(je)),Je+=ye.get(je)):(ye.set(je,x.has(je)?w[x.get(je)]:Ce.get(je)),Je+=ye.get(je))}),Je=Je/Ye.length,tt.forEach(function(je){Te.has(je)||ye.set(je,Je)})}else{var Ve=0;tt.forEach(function(je){we=="horizontal"?Ve+=x.has(je)?b[x.get(je)]:Ce.get(je):Ve+=x.has(je)?w[x.get(je)]:Ce.get(je)}),Ve=Ve/tt.length,tt.forEach(function(je){ye.set(je,Ve)})}});for(var Ze=o(function(){var Ye=ze.shift(),Je=ae.get(Ye);Je.forEach(function(Ve){if(ye.get(Ve.id)je&&(je=mt),Stkt&&(kt=St)}}catch(Qn){xt=!0,it=Qn}finally{try{!at&&dt.return&&dt.return()}finally{if(xt)throw it}}var gr=(Je+je)/2-(Ve+kt)/2,xn=!0,jt=!1,rn=void 0;try{for(var Er=tt[Symbol.iterator](),Kn;!(xn=(Kn=Er.next()).done);xn=!0){var hn=Kn.value;ye.set(hn,ye.get(hn)+gr)}}catch(Qn){jt=!0,rn=Qn}finally{try{!xn&&Er.return&&Er.return()}finally{if(jt)throw rn}}})}return ye},"findAppropriatePositionForRelativePlacement"),N=o(function(ae){var we=0,Te=0,Ce=0,Ae=0;if(ae.forEach(function(He){He.left?b[x.get(He.left)]-b[x.get(He.right)]>=0?we++:Te++:w[x.get(He.top)]-w[x.get(He.bottom)]>=0?Ce++:Ae++}),we>Te&&Ce>Ae)for(var Ge=0;GeTe)for(var Me=0;MeAe)for(var ye=0;ye1)y.fixedNodeConstraint.forEach(function(ne,ae){O[ae]=[ne.position.x,ne.position.y],D[ae]=[b[x.get(ne.nodeId)],w[x.get(ne.nodeId)]]}),P=!0;else if(y.alignmentConstraint)(function(){var ne=0;if(y.alignmentConstraint.vertical){for(var ae=y.alignmentConstraint.vertical,we=o(function(ye){var He=new Set;ae[ye].forEach(function(gt){He.add(gt)});var ze=new Set([].concat(u(He)).filter(function(gt){return B.has(gt)})),Ze=void 0;ze.size>0?Ze=b[x.get(ze.values().next().value)]:Ze=L(He).x,ae[ye].forEach(function(gt){O[ne]=[Ze,w[x.get(gt)]],D[ne]=[b[x.get(gt)],w[x.get(gt)]],ne++})},"_loop2"),Te=0;Te0?Ze=b[x.get(ze.values().next().value)]:Ze=L(He).y,Ce[ye].forEach(function(gt){O[ne]=[b[x.get(gt)],Ze],D[ne]=[b[x.get(gt)],w[x.get(gt)]],ne++})},"_loop3"),Ge=0;GeQ&&(Q=Y[ie].length,X=ie);if(Q0){var Ue={x:0,y:0};y.fixedNodeConstraint.forEach(function(ne,ae){var we={x:b[x.get(ne.nodeId)],y:w[x.get(ne.nodeId)]},Te=ne.position,Ce=A(Te,we);Ue.x+=Ce.x,Ue.y+=Ce.y}),Ue.x/=y.fixedNodeConstraint.length,Ue.y/=y.fixedNodeConstraint.length,b.forEach(function(ne,ae){b[ae]+=Ue.x}),w.forEach(function(ne,ae){w[ae]+=Ue.y}),y.fixedNodeConstraint.forEach(function(ne){b[x.get(ne.nodeId)]=ne.position.x,w[x.get(ne.nodeId)]=ne.position.y})}if(y.alignmentConstraint){if(y.alignmentConstraint.vertical)for(var Pe=y.alignmentConstraint.vertical,_e=o(function(ae){var we=new Set;Pe[ae].forEach(function(Ae){we.add(Ae)});var Te=new Set([].concat(u(we)).filter(function(Ae){return B.has(Ae)})),Ce=void 0;Te.size>0?Ce=b[x.get(Te.values().next().value)]:Ce=L(we).x,we.forEach(function(Ae){B.has(Ae)||(b[x.get(Ae)]=Ce)})},"_loop4"),me=0;me0?Ce=w[x.get(Te.values().next().value)]:Ce=L(we).y,we.forEach(function(Ae){B.has(Ae)||(w[x.get(Ae)]=Ce)})},"_loop5"),ge=0;ge{a.exports=t}},r={};function n(a){var s=r[a];if(s!==void 0)return s.exports;var l=r[a]={exports:{}};return e[a](l,l.exports,n),l.exports}o(n,"__webpack_require__");var i=n(45);return i})()})});var X1e=gi((xb,JB)=>{"use strict";o(function(e,r){typeof xb=="object"&&typeof JB=="object"?JB.exports=r(ZB()):typeof define=="function"&&define.amd?define(["cose-base"],r):typeof xb=="object"?xb.cytoscapeFcose=r(ZB()):e.cytoscapeFcose=r(e.coseBase)},"webpackUniversalModuleDefinition")(xb,function(t){return(()=>{"use strict";var e={658:a=>{a.exports=Object.assign!=null?Object.assign.bind(Object):function(s){for(var l=arguments.length,u=Array(l>1?l-1:0),h=1;h{var u=function(){function d(p,m){var g=[],y=!0,v=!1,x=void 0;try{for(var b=p[Symbol.iterator](),w;!(y=(w=b.next()).done)&&(g.push(w.value),!(m&&g.length===m));y=!0);}catch(S){v=!0,x=S}finally{try{!y&&b.return&&b.return()}finally{if(v)throw x}}return g}return o(d,"sliceIterator"),function(p,m){if(Array.isArray(p))return p;if(Symbol.iterator in Object(p))return d(p,m);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),h=l(140).layoutBase.LinkedList,f={};f.getTopMostNodes=function(d){for(var p={},m=0;m0&&P.merge(z)});for(var F=0;F1){w=x[0],S=w.connectedEdges().length,x.forEach(function(D){D.connectedEdges().length0&&g.set("dummy"+(g.size+1),_),A},f.relocateComponent=function(d,p,m){if(!m.fixedNodeConstraint){var g=Number.POSITIVE_INFINITY,y=Number.NEGATIVE_INFINITY,v=Number.POSITIVE_INFINITY,x=Number.NEGATIVE_INFINITY;if(m.quality=="draft"){var b=!0,w=!1,S=void 0;try{for(var T=p.nodeIndexes[Symbol.iterator](),E;!(b=(E=T.next()).done);b=!0){var _=E.value,A=u(_,2),L=A[0],M=A[1],N=m.cy.getElementById(L);if(N){var k=N.boundingBox(),I=p.xCoords[M]-k.w/2,C=p.xCoords[M]+k.w/2,O=p.yCoords[M]-k.h/2,D=p.yCoords[M]+k.h/2;Iy&&(y=C),Ox&&(x=D)}}}catch(z){w=!0,S=z}finally{try{!b&&T.return&&T.return()}finally{if(w)throw S}}var P=d.x-(y+g)/2,F=d.y-(x+v)/2;p.xCoords=p.xCoords.map(function(z){return z+P}),p.yCoords=p.yCoords.map(function(z){return z+F})}else{Object.keys(p).forEach(function(z){var Y=p[z],Q=Y.getRect().x,X=Y.getRect().x+Y.getRect().width,ie=Y.getRect().y,j=Y.getRect().y+Y.getRect().height;Qy&&(y=X),iex&&(x=j)});var B=d.x-(y+g)/2,$=d.y-(x+v)/2;Object.keys(p).forEach(function(z){var Y=p[z];Y.setCenter(Y.getCenterX()+B,Y.getCenterY()+$)})}}},f.calcBoundingBox=function(d,p,m,g){for(var y=Number.MAX_SAFE_INTEGER,v=Number.MIN_SAFE_INTEGER,x=Number.MAX_SAFE_INTEGER,b=Number.MIN_SAFE_INTEGER,w=void 0,S=void 0,T=void 0,E=void 0,_=d.descendants().not(":parent"),A=_.length,L=0;Lw&&(y=w),vT&&(x=T),b{var u=l(548),h=l(140).CoSELayout,f=l(140).CoSENode,d=l(140).layoutBase.PointD,p=l(140).layoutBase.DimensionD,m=l(140).layoutBase.LayoutConstants,g=l(140).layoutBase.FDLayoutConstants,y=l(140).CoSEConstants,v=o(function(b,w){var S=b.cy,T=b.eles,E=T.nodes(),_=T.edges(),A=void 0,L=void 0,M=void 0,N={};b.randomize&&(A=w.nodeIndexes,L=w.xCoords,M=w.yCoords);var k=o(function(z){return typeof z=="function"},"isFn"),I=o(function(z,Y){return k(z)?z(Y):z},"optFn"),C=u.calcParentsWithoutChildren(S,T),O=o(function $(z,Y,Q,X){for(var ie=Y.length,j=0;j0){var se=void 0;se=Q.getGraphManager().add(Q.newGraph(),H),$(se,Z,Q,X)}}},"processChildrenList"),D=o(function(z,Y,Q){for(var X=0,ie=0,j=0;j0?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=X/ie:k(b.idealEdgeLength)?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=50:y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=b.idealEdgeLength,y.MIN_REPULSION_DIST=g.MIN_REPULSION_DIST=g.DEFAULT_EDGE_LENGTH/10,y.DEFAULT_RADIAL_SEPARATION=g.DEFAULT_EDGE_LENGTH)},"processEdges"),P=o(function(z,Y){Y.fixedNodeConstraint&&(z.constraints.fixedNodeConstraint=Y.fixedNodeConstraint),Y.alignmentConstraint&&(z.constraints.alignmentConstraint=Y.alignmentConstraint),Y.relativePlacementConstraint&&(z.constraints.relativePlacementConstraint=Y.relativePlacementConstraint)},"processConstraints");b.nestingFactor!=null&&(y.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=g.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=b.nestingFactor),b.gravity!=null&&(y.DEFAULT_GRAVITY_STRENGTH=g.DEFAULT_GRAVITY_STRENGTH=b.gravity),b.numIter!=null&&(y.MAX_ITERATIONS=g.MAX_ITERATIONS=b.numIter),b.gravityRange!=null&&(y.DEFAULT_GRAVITY_RANGE_FACTOR=g.DEFAULT_GRAVITY_RANGE_FACTOR=b.gravityRange),b.gravityCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_STRENGTH=g.DEFAULT_COMPOUND_GRAVITY_STRENGTH=b.gravityCompound),b.gravityRangeCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=g.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=b.gravityRangeCompound),b.initialEnergyOnIncremental!=null&&(y.DEFAULT_COOLING_FACTOR_INCREMENTAL=g.DEFAULT_COOLING_FACTOR_INCREMENTAL=b.initialEnergyOnIncremental),b.tilingCompareBy!=null&&(y.TILING_COMPARE_BY=b.tilingCompareBy),b.quality=="proof"?m.QUALITY=2:m.QUALITY=0,y.NODE_DIMENSIONS_INCLUDE_LABELS=g.NODE_DIMENSIONS_INCLUDE_LABELS=m.NODE_DIMENSIONS_INCLUDE_LABELS=b.nodeDimensionsIncludeLabels,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!b.randomize,y.ANIMATE=g.ANIMATE=m.ANIMATE=b.animate,y.TILE=b.tile,y.TILING_PADDING_VERTICAL=typeof b.tilingPaddingVertical=="function"?b.tilingPaddingVertical.call():b.tilingPaddingVertical,y.TILING_PADDING_HORIZONTAL=typeof b.tilingPaddingHorizontal=="function"?b.tilingPaddingHorizontal.call():b.tilingPaddingHorizontal,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!0,y.PURE_INCREMENTAL=!b.randomize,m.DEFAULT_UNIFORM_LEAF_NODE_SIZES=b.uniformNodeDimensions,b.step=="transformed"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!1),b.step=="enforced"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!1),b.step=="cose"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!0),b.step=="all"&&(b.randomize?y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0:y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!0),b.fixedNodeConstraint||b.alignmentConstraint||b.relativePlacementConstraint?y.TREE_REDUCTION_ON_INCREMENTAL=!1:y.TREE_REDUCTION_ON_INCREMENTAL=!0;var F=new h,B=F.newGraphManager();return O(B.addRoot(),u.getTopMostNodes(E),F,b),D(F,B,_),P(F,b),F.runLayout(),N},"coseLayout");a.exports={coseLayout:v}},212:(a,s,l)=>{var u=function(){function b(w,S){for(var T=0;T0)if(D){var B=d.getTopMostNodes(T.eles.nodes());if(k=d.connectComponents(E,T.eles,B),k.forEach(function(oe){var ke=oe.boundingBox();I.push({x:ke.x1+ke.w/2,y:ke.y1+ke.h/2})}),T.randomize&&k.forEach(function(oe){T.eles=oe,A.push(m(T))}),T.quality=="default"||T.quality=="proof"){var $=E.collection();if(T.tile){var z=new Map,Y=[],Q=[],X=0,ie={nodeIndexes:z,xCoords:Y,yCoords:Q},j=[];if(k.forEach(function(oe,ke){oe.edges().length==0&&(oe.nodes().forEach(function(Ie,Se){$.merge(oe.nodes()[Se]),Ie.isParent()||(ie.nodeIndexes.set(oe.nodes()[Se].id(),X++),ie.xCoords.push(oe.nodes()[0].position().x),ie.yCoords.push(oe.nodes()[0].position().y))}),j.push(ke))}),$.length>1){var J=$.boundingBox();I.push({x:J.x1+J.w/2,y:J.y1+J.h/2}),k.push($),A.push(ie);for(var Z=j.length-1;Z>=0;Z--)k.splice(j[Z],1),A.splice(j[Z],1),I.splice(j[Z],1)}}k.forEach(function(oe,ke){T.eles=oe,N.push(y(T,A[ke])),d.relocateComponent(I[ke],N[ke],T)})}else k.forEach(function(oe,ke){d.relocateComponent(I[ke],A[ke],T)});var H=new Set;if(k.length>1){var q=[],K=_.filter(function(oe){return oe.css("display")=="none"});k.forEach(function(oe,ke){var Ie=void 0;if(T.quality=="draft"&&(Ie=A[ke].nodeIndexes),oe.nodes().not(K).length>0){var Se={};Se.edges=[],Se.nodes=[];var Ue=void 0;oe.nodes().not(K).forEach(function(Pe){if(T.quality=="draft")if(!Pe.isParent())Ue=Ie.get(Pe.id()),Se.nodes.push({x:A[ke].xCoords[Ue]-Pe.boundingbox().w/2,y:A[ke].yCoords[Ue]-Pe.boundingbox().h/2,width:Pe.boundingbox().w,height:Pe.boundingbox().h});else{var _e=d.calcBoundingBox(Pe,A[ke].xCoords,A[ke].yCoords,Ie);Se.nodes.push({x:_e.topLeftX,y:_e.topLeftY,width:_e.width,height:_e.height})}else N[ke][Pe.id()]&&Se.nodes.push({x:N[ke][Pe.id()].getLeft(),y:N[ke][Pe.id()].getTop(),width:N[ke][Pe.id()].getWidth(),height:N[ke][Pe.id()].getHeight()})}),oe.edges().forEach(function(Pe){var _e=Pe.source(),me=Pe.target();if(_e.css("display")!="none"&&me.css("display")!="none")if(T.quality=="draft"){var W=Ie.get(_e.id()),fe=Ie.get(me.id()),ge=[],re=[];if(_e.isParent()){var he=d.calcBoundingBox(_e,A[ke].xCoords,A[ke].yCoords,Ie);ge.push(he.topLeftX+he.width/2),ge.push(he.topLeftY+he.height/2)}else ge.push(A[ke].xCoords[W]),ge.push(A[ke].yCoords[W]);if(me.isParent()){var ne=d.calcBoundingBox(me,A[ke].xCoords,A[ke].yCoords,Ie);re.push(ne.topLeftX+ne.width/2),re.push(ne.topLeftY+ne.height/2)}else re.push(A[ke].xCoords[fe]),re.push(A[ke].yCoords[fe]);Se.edges.push({startX:ge[0],startY:ge[1],endX:re[0],endY:re[1]})}else 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r={name:t.filename,buffer:t.input.slice(0,-1),position:t.position,line:t.line,column:t.position-t.lineStart};return r.snippet=Met(r),new $s(e,r)}o(ave,"generateError");function Gt(t,e){throw ave(t,e)}o(Gt,"throwError");function TC(t,e){t.onWarning&&t.onWarning.call(null,ave(t,e))}o(TC,"throwWarning");var Pye={YAML:o(function(e,r,n){var i,a,s;e.version!==null&&Gt(e,"duplication of %YAML directive"),n.length!==1&&Gt(e,"YAML directive accepts exactly one argument"),i=/^([0-9]+)\.([0-9]+)$/.exec(n[0]),i===null&&Gt(e,"ill-formed argument of the YAML directive"),a=parseInt(i[1],10),s=parseInt(i[2],10),a!==1&&Gt(e,"unacceptable YAML version of the document"),e.version=n[0],e.checkLineBreaks=s<2,s!==1&&s!==2&&TC(e,"unsupported YAML version of the document")},"handleYamlDirective"),TAG:o(function(e,r,n){var i,a;n.length!==2&&Gt(e,"TAG directive accepts exactly two arguments"),i=n[0],a=n[1],tve.test(i)||Gt(e,"ill-formed tag handle (first argument) of the TAG 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s=t.input.charCodeAt(++t.position);while(s!==0&&!qc(s));break}if(qc(s))break;for(r=t.position;s!==0&&!Vs(s);)s=t.input.charCodeAt(++t.position);i.push(t.input.slice(r,t.position))}s!==0&&fF(t),Bf.call(Pye,n)?Pye[n](t,n,i):TC(t,'unknown document directive "'+n+'"')}if(Ai(t,!0,-1),t.lineIndent===0&&t.input.charCodeAt(t.position)===45&&t.input.charCodeAt(t.position+1)===45&&t.input.charCodeAt(t.position+2)===45?(t.position+=3,Ai(t,!0,-1)):a&&Gt(t,"directives end mark is expected"),N1(t,t.lineIndent-1,wC,!1,!0),Ai(t,!0,-1),t.checkLineBreaks&&ztt.test(t.input.slice(e,t.position))&&TC(t,"non-ASCII line breaks are interpreted as content"),t.documents.push(t.result),t.position===t.lineStart&&CC(t)){t.input.charCodeAt(t.position)===46&&(t.position+=3,Ai(t,!0,-1));return}if(t.position"u"&&(r=e,e=null);var n=sve(t,r);if(typeof e!="function")return n;for(var i=0,a=n.length;i=55296&&r<=56319&&e+1=56320&&n<=57343)?(r-55296)*1024+n-56320+65536:r}o(kb,"codePointAt");function mve(t){var e=/^\n* /;return e.test(t)}o(mve,"needIndentIndicator");var gve=1,cF=2,yve=3,vve=4,L1=5;function Rrt(t,e,r,n,i,a,s,l){var u,h=0,f=null,d=!1,p=!1,m=n!==-1,g=-1,y=Lrt(kb(t,0))&&Drt(kb(t,t.length-1));if(e||s)for(u=0;u=65536?u+=2:u++){if(h=kb(t,u),!Ab(h))return L1;y=y&&$ye(h,f,l),f=h}else{for(u=0;u=65536?u+=2:u++){if(h=kb(t,u),h===Cb)d=!0,m&&(p=p||u-g-1>n&&t[g+1]!==" ",g=u);else if(!Ab(h))return L1;y=y&&$ye(h,f,l),f=h}p=p||m&&u-g-1>n&&t[g+1]!==" "}return!d&&!p?y&&!s&&!i(t)?gve:a===Sb?L1:cF:r>9&&mve(t)?L1:s?a===Sb?L1:cF:p?vve:yve}o(Rrt,"chooseScalarStyle");function Nrt(t,e,r,n,i){t.dump=function(){if(e.length===0)return t.quotingType===Sb?'""':"''";if(!t.noCompatMode&&(Trt.indexOf(e)!==-1||krt.test(e)))return t.quotingType===Sb?'"'+e+'"':"'"+e+"'";var a=t.indent*Math.max(1,r),s=t.lineWidth===-1?-1:Math.max(Math.min(t.lineWidth,40),t.lineWidth-a),l=n||t.flowLevel>-1&&r>=t.flowLevel;function u(h){return _rt(t,h)}switch(o(u,"testAmbiguity"),Rrt(e,l,t.indent,s,u,t.quotingType,t.forceQuotes&&!n,i)){case gve:return e;case cF:return"'"+e.replace(/'/g,"''")+"'";case yve:return"|"+Vye(e,t.indent)+Uye(zye(e,a));case vve:return">"+Vye(e,t.indent)+Uye(zye(Mrt(e,s),a));case L1:return'"'+Irt(e)+'"';default:throw new $s("impossible error: invalid scalar style")}}()}o(Nrt,"writeScalar");function Vye(t,e){var r=mve(t)?String(e):"",n=t[t.length-1]===` +`,i=n&&(t[t.length-2]===` +`||t===` +`),a=i?"+":n?"":"-";return r+a+` +`}o(Vye,"blockHeader");function Uye(t){return t[t.length-1]===` +`?t.slice(0,-1):t}o(Uye,"dropEndingNewline");function Mrt(t,e){for(var r=/(\n+)([^\n]*)/g,n=function(){var h=t.indexOf(` +`);return h=h!==-1?h:t.length,r.lastIndex=h,Hye(t.slice(0,h),e)}(),i=t[0]===` +`||t[0]===" ",a,s;s=r.exec(t);){var l=s[1],u=s[2];a=u[0]===" ",n+=l+(!i&&!a&&u!==""?` +`:"")+Hye(u,e),i=a}return n}o(Mrt,"foldString");function Hye(t,e){if(t===""||t[0]===" ")return t;for(var r=/ [^ ]/g,n,i=0,a,s=0,l=0,u="";n=r.exec(t);)l=n.index,l-i>e&&(a=s>i?s:l,u+=` +`+t.slice(i,a),i=a+1),s=l;return u+=` 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Engelschall (http://engelschall.com) + Licensed under The MIT License (http://opensource.org/licenses/MIT) + *) + (*! + Event object based on jQuery events, MIT license + + https://jquery.org/license/ + https://tldrlegal.com/license/mit-license + https://github.com/jquery/jquery/blob/master/src/event.js + *) + (*! Bezier curve function generator. Copyright Gaetan Renaudeau. MIT License: http://en.wikipedia.org/wiki/MIT_License *) + (*! Runge-Kutta spring physics function generator. Adapted from Framer.js, copyright Koen Bok. MIT License: http://en.wikipedia.org/wiki/MIT_License *) + +js-yaml/dist/js-yaml.mjs: + (*! js-yaml 4.1.0 https://github.com/nodeca/js-yaml @license MIT *) +*/ +globalThis.mermaid = globalThis.__esbuild_esm_mermaid.default; diff --git a/vignettes/references.bib b/vignettes/references.bib new file mode 100644 index 000000000..5460f49a8 --- /dev/null +++ b/vignettes/references.bib @@ -0,0 +1,109 @@ +@inproceedings{dempster1983, + Address = {New York}, + Author = {Dempster, A. P. and Rubin, D. B.}, + Booktitle = {Incomplete Data in Sample Surveys}, + Date-Added = {2011-01-25 08:31:11 +0100}, + Date-Modified = {2011-10-02 10:56:44 +0000}, + Pages = {3-10}, + Publisher = {Academic Press}, + Title = {Introduction}, + Volume = {2}, + Year = {1983}} + +@Article{kavelaars2022, +author = {Kavelaars, X. M. and {van Ginkel}, J. R. and {van Buuren}, S.}, +title = {Multiple imputation in data that grow over time: A comparison of three strategies}, +journal = {Multivariate Behavioral Research}, +volume = {57}, +number = {2-3}, +pages = {513--523}, +year = {2022}, +location = {}, +keywords = {}} + +@article{little1988, + Author = {Little, R. J. A.}, + Date-Modified = {2011-10-02 13:23:01 +0000}, + Journal = {Journal of Business Economics and Statistics}, + Number = {3}, + Pages = {287-301}, + Title = {Missing-data adjustments in large surveys (with discussion)}, + Volume = {6}, + Year = {1988}} + +@article{morris2014, +author = {Morris, T. P. and White, I. R. and Royston, P.}, +title = {Tuning multiple imputation by predictive mean matching and local residual draws}, +journal = {BMC Medical Reseach Methods}, +volume = {14}, +number = {}, +pages = {75}, +year = {2014}, +location = {}, +keywords = {}} + +@book{rubin1987, + Address = {New York}, + Author = {Rubin, D. B.}, + Date-Modified = {2011-10-02 14:41:28 +0000}, + Keywords = {Nonresponse}, + Publisher = {John Wiley \& Sons}, + Title = {Multiple Imputation for Nonresponse in Surveys}, + Year = {1987}} + +@article{schenker1996, + Author = {Schenker, N. and Taylor, J. M. G.}, + Journal = {Computational Statistics \& Data Analysis}, + Keywords = {Multiple imputation}, + Number = {4}, + Pages = {425-446}, + Title = {Partially parametric techniques for multiple imputation}, + Volume = {22}, + Year = {1996}} + +@Article{vanbuuren2007, +author = {{van Buuren}, S.}, +title = {Multiple imputation of discrete and continuous data by fully conditional specification}, +journal = {Statistical Methods in Medical Research}, +volume = {16}, +number = {3}, +pages = {219-242}, +year = {2007}, +abstract = {}, +location = {}, +keywords = {Multiple imputation; ERC}} + +@Article{vanbuuren2011, +author = {{van Buuren}, S. and Groothuis-Oudshoorn, K.}, +title = {{MICE}: Multivariate Imputation by Chained Equations in {R}}, +journal = {Journal of Statistical Software}, +volume = {45}, +number = {3}, +pages = {1–67}, +year = {2011}, +location = {}, +keywords = {ERC}} + +@Book{vanbuuren2018, +author = {{van Buuren}, S.}, +title = {Flexible Imputation of Missing Data. Second Edition}, +volume = {}, +pages = {}, +editor = {}, +publisher = {Chapman & Hall/CRC Press}, +address = {Boca Raton, FL}, +year = {2018}, +url = {https://stefvanbuuren.name/fimd/}, +abstract = {}} + +@Article{vink2015, +author = {Vink, G. and Lazendic, G. and {van Buuren}, S.}, +title = {Partioned predictive mean matching as a large data multilevel imputation technique}, +journal = {Psychological Test and Assessment Modeling}, +volume = {57}, +number = {4}, +pages = {577--594}, +year = {2015}, +abstract = {}, +location = {}, +keywords = {}} From 9a53aa363975f6867413f50e8ca228ce7f3892ff Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sat, 22 Mar 2025 11:22:35 +0100 Subject: [PATCH 078/147] Clean up; Store .qmd vignette with underscore to prevent pkgdown for using it --- .Rbuildignore | 2 +- _pkgdown.yml | 12 +- ...ation_model.qmd => _imputation_models.qmd} | 0 vignettes/articles/.gitignore | 2 - .../libs/quarto-diagram/mermaid-init.js | 275 --- .../libs/quarto-diagram/mermaid.css | 13 - .../libs/quarto-diagram/mermaid.min.js | 2186 ----------------- 7 files changed, 6 insertions(+), 2484 deletions(-) rename vignettes/{imputation_model.qmd => _imputation_models.qmd} (100%) delete mode 100644 vignettes/articles/.gitignore delete mode 100644 vignettes/imputation_model_files/libs/quarto-diagram/mermaid-init.js delete mode 100644 vignettes/imputation_model_files/libs/quarto-diagram/mermaid.css delete mode 100644 vignettes/imputation_model_files/libs/quarto-diagram/mermaid.min.js diff --git a/.Rbuildignore b/.Rbuildignore index 486e80d98..0421be473 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -20,4 +20,4 @@ vignettes/ ^pkgdown$ ^LICENSE\.md$ ^\.github$ -^CRAN-SUBMISSION$ \ No newline at end of file +^CRAN-SUBMISSION$ diff --git a/_pkgdown.yml b/_pkgdown.yml index 588ebbe44..386755431 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -126,7 +126,6 @@ reference: - estimice - norm.draw - .norm.draw - - .pmm.match - title: Multivariate amputation desc: | Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies. @@ -174,9 +173,8 @@ reference: - supports.transparent - version articles: -- title: General - navbar: ~ - contents: - - overview - - oldfriends - + - title: General + navbar: ~ + contents: + - overview + - oldfriends diff --git a/vignettes/imputation_model.qmd b/vignettes/_imputation_models.qmd similarity index 100% rename from vignettes/imputation_model.qmd rename to vignettes/_imputation_models.qmd diff --git a/vignettes/articles/.gitignore b/vignettes/articles/.gitignore deleted file mode 100644 index 097b24163..000000000 --- a/vignettes/articles/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -*.html -*.R diff --git a/vignettes/imputation_model_files/libs/quarto-diagram/mermaid-init.js b/vignettes/imputation_model_files/libs/quarto-diagram/mermaid-init.js deleted file mode 100644 index 48b7f5ce0..000000000 --- a/vignettes/imputation_model_files/libs/quarto-diagram/mermaid-init.js +++ /dev/null @@ -1,275 +0,0 @@ -// mermaid-init.js -// 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kFigHeight = "figHeight"; - const options = this.resolveOptions(svgEl); - let width = svgEl.getAttribute("width"); - let height = svgEl.getAttribute("height"); - const getViewBox = () => { - const vb = svgEl.attributes.getNamedItem("viewBox").value; // do it the roundabout way so that viewBox isn't dropped by deno_dom and text/html - if (!vb) return undefined; - const lst = vb.trim().split(" ").map(Number); - if (lst.length !== 4) return undefined; - if (lst.some(isNaN)) return undefined; - return lst; - }; - if (!width || !height) { - // attempt to resolve figure dimensions via viewBox - const viewBox = getViewBox(); - if (viewBox !== undefined) { - const [_mx, _my, vbWidth, vbHeight] = viewBox; - width = `${vbWidth}px`; - height = `${vbHeight}px`; - } else { - throw new Error( - "Mermaid generated an SVG without a viewbox attribute. Without knowing the diagram dimensions, quarto cannot convert it to a PNG" - ); - } - } - - let svgWidthInInches, svgHeightInInches; - - if ( - (width.slice(0, -2) === "pt" && height.slice(0, -2) === "pt") || - (width.slice(0, -2) === "px" && height.slice(0, -2) === "px") || - (!isNaN(Number(width)) && !isNaN(Number(height))) - ) { - // we assume 96 dpi which is generally what seems to be used. - svgWidthInInches = Number(width.slice(0, -2)) / 96; - svgHeightInInches = Number(height.slice(0, -2)) / 96; - } - const viewBox = getViewBox(); - if (viewBox !== undefined) { - // assume width and height come from viewbox. - const [_mx, _my, vbWidth, vbHeight] = viewBox; - svgWidthInInches = vbWidth / 96; - svgHeightInInches = vbHeight / 96; - } else { - throw new Error( - "Internal Error: Couldn't resolve width and height of SVG" - ); - } - const svgWidthOverHeight = svgWidthInInches / svgHeightInInches; - let widthInInches, heightInInches; - - if (options[kFigWidth] && options[kFigHeight]) { 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cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.darkMode)for(let r=0;r{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Nz=o(t=>{let e=new QC;return e.calculate(t),e},"getThemeVariables")});var ZC,Iz,Oz=R(()=>{"use strict";al();up();ZC=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=Et(this.primaryColor,16),this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=ot(this.background),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=Et(ot("#323D47"),10),this.lineColor="calculated",this.border1="#ccc",this.border2=Ws(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#F9FFFE",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="calculated",this.activationBkgColor="calculated",this.sequenceNumberColor="black",this.sectionBkgColor=Dt("#EAE8D9",30),this.altSectionBkgColor="calculated",this.sectionBkgColor2="#EAE8D9",this.excludeBkgColor=Dt(this.sectionBkgColor,10),this.taskBorderColor=Ws(255,255,255,70),this.taskBkgColor="calculated",this.taskTextColor="calculated",this.taskTextLightColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor=Ws(255,255,255,50),this.activeTaskBkgColor="#81B1DB",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="grey",this.critBorderColor="#E83737",this.critBkgColor="#E83737",this.taskTextDarkColor="calculated",this.todayLineColor="#DB5757",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="calculated",this.errorBkgColor="#a44141",this.errorTextColor="#ddd"}updateColors(){this.secondBkg=Et(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Et(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.actorBorder,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Et(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Oe(this.primaryColor,{h:64}),this.fillType3=Oe(this.secondaryColor,{h:64}),this.fillType4=Oe(this.primaryColor,{h:-64}),this.fillType5=Oe(this.secondaryColor,{h:-64}),this.fillType6=Oe(this.primaryColor,{h:128}),this.fillType7=Oe(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330});for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Iz=o(t=>{let e=new ZC;return e.calculate(t),e},"getThemeVariables")});var JC,hp,jb=R(()=>{"use strict";al();up();j1();JC=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Oe(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 0.8)",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.sectionBkgColor=Ws(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.updateColors()}updateColors(){this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Dt(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Dt(this.tertiaryColor,40);for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},hp=o(t=>{let e=new JC;return e.calculate(t),e},"getThemeVariables")});var e7,Pz,Bz=R(()=>{"use strict";al();j1();up();e7=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Et("#cde498",10),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.primaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.actorBorder=Dt(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Dt(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Dt(this.tertiaryColor,40);for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Pz=o(t=>{let e=new e7;return e.calculate(t),e},"getThemeVariables")});var t7,Fz,zz=R(()=>{"use strict";al();up();j1();t7=class{static{o(this,"Theme")}constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=Et(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor=this.actorBorder,this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.secondBkg=Et(this.contrast,55),this.border2=this.contrast,this.actorBorder=Et(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.actorBorder,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor="#999",this.noteBkgColor="#666",this.noteTextColor="#fff",this.cScale0=this.cScale0||"#555",this.cScale1=this.cScale1||"#F4F4F4",this.cScale2=this.cScale2||"#555",this.cScale3=this.cScale3||"#BBB",this.cScale4=this.cScale4||"#777",this.cScale5=this.cScale5||"#999",this.cScale6=this.cScale6||"#DDD",this.cScale7=this.cScale7||"#FFF",this.cScale8=this.cScale8||"#DDD",this.cScale9=this.cScale9||"#BBB",this.cScale10=this.cScale10||"#999",this.cScale11=this.cScale11||"#777";for(let 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r=="function"||!r.unexpandable:fh.hasOwnProperty(e)&&!fh[e].primitive}},SG=/^[₊₋₌₍₎₀₁₂₃₄₅₆₇₈₉ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓᵦᵧᵨᵩᵪ]/,c4=Object.freeze({"\u208A":"+","\u208B":"-","\u208C":"=","\u208D":"(","\u208E":")","\u2080":"0","\u2081":"1","\u2082":"2","\u2083":"3","\u2084":"4","\u2085":"5","\u2086":"6","\u2087":"7","\u2088":"8","\u2089":"9","\u2090":"a","\u2091":"e","\u2095":"h","\u1D62":"i","\u2C7C":"j","\u2096":"k","\u2097":"l","\u2098":"m","\u2099":"n","\u2092":"o","\u209A":"p","\u1D63":"r","\u209B":"s","\u209C":"t","\u1D64":"u","\u1D65":"v","\u2093":"x","\u1D66":"\u03B2","\u1D67":"\u03B3","\u1D68":"\u03C1","\u1D69":"\u03D5","\u1D6A":"\u03C7","\u207A":"+","\u207B":"-","\u207C":"=","\u207D":"(","\u207E":")","\u2070":"0","\xB9":"1","\xB2":"2","\xB3":"3","\u2074":"4","\u2075":"5","\u2076":"6","\u2077":"7","\u2078":"8","\u2079":"9","\u1D2C":"A","\u1D2E":"B","\u1D30":"D","\u1D31":"E","\u1D33":"G","\u1D34":"H","\u1D35":"I","\u1D36":"J","\u1D37":"K","\u1D38":"L","\u1D39":"M","\u1D3A":"N","\u1D3C":"O","\u1D3E":"P","\u1D3F":"R","\u1D40":"T","\u1D41":"U","\u2C7D":"V","\u1D42":"W","\u1D43":"a","\u1D47":"b","\u1D9C":"c","\u1D48":"d","\u1D49":"e","\u1DA0":"f","\u1D4D":"g",\u02B0:"h","\u2071":"i",\u02B2:"j","\u1D4F":"k",\u02E1:"l","\u1D50":"m",\u207F:"n","\u1D52":"o","\u1D56":"p",\u02B3:"r",\u02E2:"s","\u1D57":"t","\u1D58":"u","\u1D5B":"v",\u02B7:"w",\u02E3:"x",\u02B8:"y","\u1DBB":"z","\u1D5D":"\u03B2","\u1D5E":"\u03B3","\u1D5F":"\u03B4","\u1D60":"\u03D5","\u1D61":"\u03C7","\u1DBF":"\u03B8"}),x7={"\u0301":{text:"\\'",math:"\\acute"},"\u0300":{text:"\\`",math:"\\grave"},"\u0308":{text:'\\"',math:"\\ddot"},"\u0303":{text:"\\~",math:"\\tilde"},"\u0304":{text:"\\=",math:"\\bar"},"\u0306":{text:"\\u",math:"\\breve"},"\u030C":{text:"\\v",math:"\\check"},"\u0302":{text:"\\^",math:"\\hat"},"\u0307":{text:"\\.",math:"\\dot"},"\u030A":{text:"\\r",math:"\\mathring"},"\u030B":{text:"\\H"},"\u0327":{text:"\\c"}},AG={\u00E1:"a\u0301",\u00E0:"a\u0300",\u00E4:"a\u0308",\u01DF:"a\u0308\u0304",\u00E3:"a\u0303",\u0101:"a\u0304",\u0103:"a\u0306",\u1EAF:"a\u0306\u0301",\u1EB1:"a\u0306\u0300",\u1EB5:"a\u0306\u0303",\u01CE:"a\u030C",\u00E2:"a\u0302",\u1EA5:"a\u0302\u0301",\u1EA7:"a\u0302\u0300",\u1EAB:"a\u0302\u0303",\u0227:"a\u0307",\u01E1:"a\u0307\u0304",\u00E5:"a\u030A",\u01FB:"a\u030A\u0301",\u1E03:"b\u0307",\u0107:"c\u0301",\u1E09:"c\u0327\u0301",\u010D:"c\u030C",\u0109:"c\u0302",\u010B:"c\u0307",\u00E7:"c\u0327",\u010F:"d\u030C",\u1E0B:"d\u0307",\u1E11:"d\u0327",\u00E9:"e\u0301",\u00E8:"e\u0300",\u00EB:"e\u0308",\u1EBD:"e\u0303",\u0113:"e\u0304",\u1E17:"e\u0304\u0301",\u1E15:"e\u0304\u0300",\u0115:"e\u0306",\u1E1D:"e\u0327\u0306",\u011B:"e\u030C",\u00EA:"e\u0302",\u1EBF:"e\u0302\u0301",\u1EC1:"e\u0302\u0300",\u1EC5:"e\u0302\u0303",\u0117:"e\u0307",\u0229:"e\u0327",\u1E1F:"f\u0307",\u01F5:"g\u0301",\u1E21:"g\u0304",\u011F:"g\u0306",\u01E7:"g\u030C",\u011D:"g\u0302",\u0121:"g\u0307",\u0123:"g\u0327",\u1E27:"h\u0308",\u021F:"h\u030C",\u0125:"h\u0302",\u1E23:"h\u0307",\u1E29:"h\u0327",\u00ED:"i\u0301",\u00EC:"i\u0300",\u00EF:"i\u0308",\u1E2F:"i\u0308\u0301",\u0129:"i\u0303",\u012B:"i\u0304",\u012D:"i\u0306",\u01D0:"i\u030C",\u00EE:"i\u0302",\u01F0:"j\u030C",\u0135:"j\u0302",\u1E31:"k\u0301",\u01E9:"k\u030C",\u0137:"k\u0327",\u013A:"l\u0301",\u013E:"l\u030C",\u013C:"l\u0327",\u1E3F:"m\u0301",\u1E41:"m\u0307",\u0144:"n\u0301",\u01F9:"n\u0300",\u00F1:"n\u0303",\u0148:"n\u030C",\u1E45:"n\u0307",\u0146:"n\u0327",\u00F3:"o\u0301",\u00F2:"o\u0300",\u00F6:"o\u0308",\u022B:"o\u0308\u0304",\u00F5:"o\u0303",\u1E4D:"o\u0303\u0301",\u1E4F:"o\u0303\u0308",\u022D:"o\u0303\u0304",\u014D:"o\u0304",\u1E53:"o\u0304\u0301",\u1E51:"o\u0304\u0300",\u014F:"o\u0306",\u01D2:"o\u030C",\u00F4:"o\u0302",\u1ED1:"o\u0302\u0301",\u1ED3:"o\u0302\u0300",\u1ED7:"o\u0302\u0303",\u022F:"o\u0307",\u0231:"o\u0307\u0304",\u0151:"o\u030B",\u1E55:"p\u0301",\u1E57:"p\u0307",\u0155:"r\u0301",\u0159:"r\u030C",\u1E59:"r\u0307",\u0157:"r\u0327",\u015B:"s\u0301",\u1E65:"s\u0301\u0307",\u0161:"s\u030C",\u1E67:"s\u030C\u0307",\u015D:"s\u0302",\u1E61:"s\u0307",\u015F:"s\u0327",\u1E97:"t\u0308",\u0165:"t\u030C",\u1E6B:"t\u0307",\u0163:"t\u0327",\u00FA:"u\u0301",\u00F9:"u\u0300",\u00FC:"u\u0308",\u01D8:"u\u0308\u0301",\u01DC:"u\u0308\u0300",\u01D6:"u\u0308\u0304",\u01DA:"u\u0308\u030C",\u0169:"u\u0303",\u1E79:"u\u0303\u0301",\u016B:"u\u0304",\u1E7B:"u\u0304\u0308",\u016D:"u\u0306",\u01D4:"u\u030C",\u00FB:"u\u0302",\u016F:"u\u030A",\u0171:"u\u030B",\u1E7D:"v\u0303",\u1E83:"w\u0301",\u1E81:"w\u0300",\u1E85:"w\u0308",\u0175:"w\u0302",\u1E87:"w\u0307",\u1E98:"w\u030A",\u1E8D:"x\u0308",\u1E8B:"x\u0307",\u00FD:"y\u0301",\u1EF3:"y\u0300",\u00FF:"y\u0308",\u1EF9:"y\u0303",\u0233:"y\u0304",\u0177:"y\u0302",\u1E8F:"y\u0307",\u1E99:"y\u030A",\u017A:"z\u0301",\u017E:"z\u030C",\u1E91:"z\u0302",\u017C:"z\u0307",\u00C1:"A\u0301",\u00C0:"A\u0300",\u00C4:"A\u0308",\u01DE:"A\u0308\u0304",\u00C3:"A\u0303",\u0100:"A\u0304",\u0102:"A\u0306",\u1EAE:"A\u0306\u0301",\u1EB0:"A\u0306\u0300",\u1EB4:"A\u0306\u0303",\u01CD:"A\u03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this.expect("}"),this.nextToken=r,n}parseExpression(e,r){for(var n=[];;){this.mode==="math"&&this.consumeSpaces();var i=this.fetch();if(t.endOfExpression.indexOf(i.text)!==-1||r&&i.text===r||e&&fh[i.text]&&fh[i.text].infix)break;var a=this.parseAtom(r);if(a){if(a.type==="internal")continue}else break;n.push(a)}return this.mode==="text"&&this.formLigatures(n),this.handleInfixNodes(n)}handleInfixNodes(e){for(var r=-1,n,i=0;i=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+r[0]+'" used in math mode',e);var l=wn[this.mode][r].group,u=Xs.range(e),h;if(hxe.hasOwnProperty(l)){var f=l;h={type:"atom",mode:this.mode,family:f,loc:u,text:r}}else h={type:l,mode:this.mode,loc:u,text:r};s=h}else if(r.charCodeAt(0)>=128)this.settings.strict&&(LG(r.charCodeAt(0))?this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+r[0]+'" used in math mode',e):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode 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2:if(Hs=this.productions_[us[1]][1],sh.$=Ot[Ot.length-Hs],sh._$={first_line:pe[pe.length-(Hs||1)].first_line,last_line:pe[pe.length-1].last_line,first_column:pe[pe.length-(Hs||1)].first_column,last_column:pe[pe.length-1].last_column},ah&&(sh._$.range=[pe[pe.length-(Hs||1)].range[0],pe[pe.length-1].range[1]]),Wl=this.performAction.apply(sh,[be,Xc,Ir,ko.yy,us[1],Ot,pe].concat(O1)),typeof Wl<"u")return Wl;Hs&&($t=$t.slice(0,-1*Hs*2),Ot=Ot.slice(0,-1*Hs),pe=pe.slice(0,-1*Hs)),$t.push(this.productions_[us[1]][0]),Ot.push(sh.$),pe.push(sh._$),B1=ur[$t[$t.length-2]][$t[$t.length-1]],$t.push(B1);break;case 3:return!0}}return!0},"parse")},_a=function(){var qi={EOF:1,parseError:o(function(At,$t){if(this.yy.parser)this.yy.parser.parseError(At,$t);else throw new Error(At)},"parseError"),setInput:o(function(ht,At){return this.yy=At||this.yy||{},this._input=ht,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var ht=this._input[0];this.yytext+=ht,this.yyleng++,this.offset++,this.match+=ht,this.matched+=ht;var At=ht.match(/(?:\r\n?|\n).*/g);return At?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),ht},"input"),unput:o(function(ht){var At=ht.length,$t=ht.split(/(?:\r\n?|\n)/g);this._input=ht+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-At),this.offset-=At;var rt=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),$t.length-1&&(this.yylineno-=$t.length-1);var Ot=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:$t?($t.length===rt.length?this.yylloc.first_column:0)+rt[rt.length-$t.length].length-$t[0].length:this.yylloc.first_column-At},this.options.ranges&&(this.yylloc.range=[Ot[0],Ot[0]+this.yyleng-At]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). -`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(ht){this.unput(this.match.slice(ht))},"less"),pastInput:o(function(){var ht=this.matched.substr(0,this.matched.length-this.match.length);return(ht.length>20?"...":"")+ht.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var ht=this.match;return ht.length<20&&(ht+=this._input.substr(0,20-ht.length)),(ht.substr(0,20)+(ht.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var ht=this.pastInput(),At=new Array(ht.length+1).join("-");return ht+this.upcomingInput()+` -`+At+"^"},"showPosition"),test_match:o(function(ht,At){var $t,rt,Ot;if(this.options.backtrack_lexer&&(Ot={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Ot.yylloc.range=this.yylloc.range.slice(0))),rt=ht[0].match(/(?:\r\n?|\n).*/g),rt&&(this.yylineno+=rt.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:rt?rt[rt.length-1].length-rt[rt.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+ht[0].length},this.yytext+=ht[0],this.match+=ht[0],this.matches=ht,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(ht[0].length),this.matched+=ht[0],$t=this.performAction.call(this,this.yy,this,At,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),$t)return $t;if(this._backtrack){for(var pe in Ot)this[pe]=Ot[pe];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var ht,At,$t,rt;this._more||(this.yytext="",this.match="");for(var Ot=this._currentRules(),pe=0;peAt[0].length)){if(At=$t,rt=pe,this.options.backtrack_lexer){if(ht=this.test_match($t,Ot[pe]),ht!==!1)return ht;if(this._backtrack){At=!1;continue}else return!1}else if(!this.options.flex)break}return At?(ht=this.test_match(At,Ot[rt]),ht!==!1?ht:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text. -`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var At=this.next();return At||this.lex()},"lex"),begin:o(function(At){this.conditionStack.push(At)},"begin"),popState:o(function(){var At=this.conditionStack.length-1;return At>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(At){return At=this.conditionStack.length-1-Math.abs(At||0),At>=0?this.conditionStack[At]:"INITIAL"},"topState"),pushState:o(function(At){this.begin(At)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(At,$t,rt,Ot){var pe=Ot;switch(rt){case 0:return this.begin("acc_title"),34;break;case 1:return this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),36;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:this.begin("callbackname");break;case 8:this.popState();break;case 9:this.popState(),this.begin("callbackargs");break;case 10:return 92;case 11:this.popState();break;case 12:return 93;case 13:return"MD_STR";case 14:this.popState();break;case 15:this.begin("md_string");break;case 16:return"STR";case 17:this.popState();break;case 18:this.pushState("string");break;case 19:return 81;case 20:return 99;case 21:return 82;case 22:return 101;case 23:return 83;case 24:return 84;case 25:return 94;case 26:this.begin("click");break;case 27:this.popState();break;case 28:return 85;case 29:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 30:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 31:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 32:return 27;case 33:return 32;case 34:return 95;case 35:return 95;case 36:return 95;case 37:return 95;case 38:return this.popState(),13;break;case 39:return this.popState(),14;break;case 40:return this.popState(),14;break;case 41:return this.popState(),14;break;case 42:return this.popState(),14;break;case 43:return this.popState(),14;break;case 44:return this.popState(),14;break;case 45:return this.popState(),14;break;case 46:return this.popState(),14;break;case 47:return this.popState(),14;break;case 48:return this.popState(),14;break;case 49:return 118;case 50:return 119;case 51:return 120;case 52:return 121;case 53:return 102;case 54:return 108;case 55:return 44;case 56:return 58;case 57:return 42;case 58:return 8;case 59:return 103;case 60:return 112;case 61:return this.popState(),75;break;case 62:return this.pushState("edgeText"),73;break;case 63:return 116;case 64:return this.popState(),75;break;case 65:return this.pushState("thickEdgeText"),73;break;case 66:return 116;case 67:return this.popState(),75;break;case 68:return this.pushState("dottedEdgeText"),73;break;case 69:return 116;case 70:return 75;case 71:return this.popState(),51;break;case 72:return"TEXT";case 73:return this.pushState("ellipseText"),50;break;case 74:return this.popState(),53;break;case 75:return this.pushState("text"),52;break;case 76:return this.popState(),55;break;case 77:return this.pushState("text"),54;break;case 78:return 56;case 79:return this.pushState("text"),65;break;case 80:return this.popState(),62;break;case 81:return this.pushState("text"),61;break;case 82:return this.popState(),47;break;case 83:return this.pushState("text"),46;break;case 84:return this.popState(),67;break;case 85:return this.popState(),69;break;case 86:return 114;case 87:return this.pushState("trapText"),66;break;case 88:return this.pushState("trapText"),68;break;case 89:return 115;case 90:return 65;case 91:return 87;case 92:return"SEP";case 93:return 86;case 94:return 112;case 95:return 108;case 96:return 42;case 97:return 106;case 98:return 111;case 99:return 113;case 100:return this.popState(),60;break;case 101:return this.pushState("text"),60;break;case 102:return this.popState(),49;break;case 103:return this.pushState("text"),48;break;case 104:return this.popState(),31;break;case 105:return this.pushState("text"),29;break;case 106:return this.popState(),64;break;case 107:return this.pushState("text"),63;break;case 108:return"TEXT";case 109:return"QUOTE";case 110:return 9;case 111:return 10;case 112:return 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24:this.$={attributeType:C[D-2],attributeName:C[D-1],attributeKeyTypeList:C[D]};break;case 25:this.$={attributeType:C[D-2],attributeName:C[D-1],attributeComment:C[D]};break;case 26:this.$={attributeType:C[D-3],attributeName:C[D-2],attributeKeyTypeList:C[D-1],attributeComment:C[D]};break;case 27:case 28:case 31:this.$=C[D];break;case 30:C[D-2].push(C[D]),this.$=C[D-2];break;case 32:this.$=C[D].replace(/"/g,"");break;case 33:this.$={cardA:C[D],relType:C[D-1],cardB:C[D-2]};break;case 34:this.$=k.Cardinality.ZERO_OR_ONE;break;case 35:this.$=k.Cardinality.ZERO_OR_MORE;break;case 36:this.$=k.Cardinality.ONE_OR_MORE;break;case 37:this.$=k.Cardinality.ONLY_ONE;break;case 38:this.$=k.Cardinality.MD_PARENT;break;case 39:this.$=k.Identification.NON_IDENTIFYING;break;case 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N=new Error(L);throw N.hash=M,N}},"parseError"),parse:o(function(L){var M=this,N=[0],k=[],I=[null],C=[],O=this.table,D="",P=0,F=0,B=0,$=2,z=1,Y=C.slice.call(arguments,1),Q=Object.create(this.lexer),X={yy:{}};for(var ie in this.yy)Object.prototype.hasOwnProperty.call(this.yy,ie)&&(X.yy[ie]=this.yy[ie]);Q.setInput(L,X.yy),X.yy.lexer=Q,X.yy.parser=this,typeof Q.yylloc>"u"&&(Q.yylloc={});var j=Q.yylloc;C.push(j);var J=Q.options&&Q.options.ranges;typeof X.yy.parseError=="function"?this.parseError=X.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Z(Pe){N.length=N.length-2*Pe,I.length=I.length-Pe,C.length=C.length-Pe}o(Z,"popStack");function H(){var Pe;return Pe=k.pop()||Q.lex()||z,typeof Pe!="number"&&(Pe instanceof Array&&(k=Pe,Pe=k.pop()),Pe=M.symbols_[Pe]||Pe),Pe}o(H,"lex");for(var q,K,se,ce,ue,te,De={},oe,ke,Ie,Se;;){if(se=N[N.length-1],this.defaultActions[se]?ce=this.defaultActions[se]:((q===null||typeof q>"u")&&(q=H()),ce=O[se]&&O[se][q]),typeof 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I=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:N?(N.length===k.length?this.yylloc.first_column:0)+k[k.length-N.length].length-N[0].length:this.yylloc.first_column-M},this.options.ranges&&(this.yylloc.range=[I[0],I[0]+this.yyleng-M]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),24;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:return 10;case 8:break;case 9:return 8;case 10:return 28;case 11:return 48;case 12:return 4;case 13:return this.begin("block"),15;break;case 14:return 36;case 15:break;case 16:return 37;case 17:return 34;case 18:return 34;case 19:return 38;case 20:break;case 21:return this.popState(),17;break;case 22:return N.yytext[0];case 23:return 18;case 24:return 19;case 25:return 41;case 26:return 43;case 27:return 43;case 28:return 43;case 29:return 41;case 30:return 41;case 31:return 42;case 32:return 42;case 33:return 42;case 34:return 42;case 35:return 42;case 36:return 43;case 37:return 42;case 38:return 43;case 39:return 44;case 40:return 44;case 41:return 44;case 42:return 44;case 43:return 41;case 44:return 42;case 45:return 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n=this.config.traceInitPerf;n===!0?(this.traceInitMaxIdent=1/0,this.traceInitPerf=!0):typeof n=="number"&&(this.traceInitMaxIdent=n,this.traceInitPerf=!0),this.traceInitIndent=-1,this.TRACE_INIT("Lexer Constructor",()=>{let i,a=!0;this.TRACE_INIT("Lexer Config handling",()=>{if(this.config.lineTerminatorsPattern===a2.lineTerminatorsPattern)this.config.lineTerminatorsPattern=kie;else if(this.config.lineTerminatorCharacters===a2.lineTerminatorCharacters)throw Error(`Error: Missing property on the Lexer config. - For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#MISSING_LINE_TERM_CHARS`);if(r.safeMode&&r.ensureOptimizations)throw Error('"safeMode" and "ensureOptimizations" flags are mutually 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h;this.TRACE_INIT("analyzeTokenTypes",()=>{h=yie(l,{lineTerminatorCharacters:this.config.lineTerminatorCharacters,positionTracking:r.positionTracking,ensureOptimizations:r.ensureOptimizations,safeMode:r.safeMode,tracer:this.TRACE_INIT})}),this.patternIdxToConfig[u]=h.patternIdxToConfig,this.charCodeToPatternIdxToConfig[u]=h.charCodeToPatternIdxToConfig,this.emptyGroups=pa({},this.emptyGroups,h.emptyGroups),this.hasCustom=h.hasCustom||this.hasCustom,this.canModeBeOptimized[u]=h.canBeOptimized}})}),this.defaultMode=i.defaultMode,!Qt(this.lexerDefinitionErrors)&&!this.config.deferDefinitionErrorsHandling){let u=qe(this.lexerDefinitionErrors,h=>h.message).join(`----------------------- -`);throw new Error(`Errors detected in definition of Lexer: -`+u)}Ee(this.lexerDefinitionWarning,l=>{e2(l.message)}),this.TRACE_INIT("Choosing sub-methods implementations",()=>{if(fN?(this.chopInput=ea,this.match=this.matchWithTest):(this.updateLastIndex=qn,this.match=this.matchWithExec),a&&(this.handleModes=qn),this.trackStartLines===!1&&(this.computeNewColumn=ea),this.trackEndLines===!1&&(this.updateTokenEndLineColumnLocation=qn),/full/i.test(this.config.positionTracking))this.createTokenInstance=this.createFullToken;else if(/onlyStart/i.test(this.config.positionTracking))this.createTokenInstance=this.createStartOnlyToken;else if(/onlyOffset/i.test(this.config.positionTracking))this.createTokenInstance=this.createOffsetOnlyToken;else throw Error(`Invalid config option: "${this.config.positionTracking}"`);this.hasCustom?(this.addToken=this.addTokenUsingPush,this.handlePayload=this.handlePayloadWithCustom):(this.addToken=this.addTokenUsingMemberAccess,this.handlePayload=this.handlePayloadNoCustom)}),this.TRACE_INIT("Failed Optimization Warnings",()=>{let l=Vr(this.canModeBeOptimized,(u,h,f)=>(h===!1&&u.push(f),u),[]);if(r.ensureOptimizations&&!Qt(l))throw Error(`Lexer Modes: < ${l.join(", ")} > cannot be optimized. - Disable the "ensureOptimizations" lexer config flag to silently ignore this and run the lexer in an un-optimized mode. - Or inspect the console log for details on how to resolve these issues.`)}),this.TRACE_INIT("clearRegExpParserCache",()=>{uie()}),this.TRACE_INIT("toFastProperties",()=>{r2(this)})})}tokenize(e,r=this.defaultMode){if(!Qt(this.lexerDefinitionErrors)){let i=qe(this.lexerDefinitionErrors,a=>a.message).join(`----------------------- -`);throw new Error(`Unable to Tokenize because Errors detected in definition of Lexer: -`+i)}return this.tokenizeInternal(e,r)}tokenizeInternal(e,r){let n,i,a,s,l,u,h,f,d,p,m,g,y,v,x,b,w=e,S=w.length,T=0,E=0,_=this.hasCustom?0:Math.floor(e.length/10),A=new Array(_),L=[],M=this.trackStartLines?1:void 0,N=this.trackStartLines?1:void 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ie,j=this.config.recoveryEnabled;for(;Tu.length){u=s,h=f,ie=ce;break}}}break}}if(u!==null){if(d=u.length,p=ie.group,p!==void 0&&(m=ie.tokenTypeIdx,g=this.createTokenInstance(u,T,m,ie.tokenType,M,N,d),this.handlePayload(g,h),p===!1?E=this.addToken(A,E,g):k[p].push(g)),e=this.chopInput(e,d),T=T+d,N=this.computeNewColumn(N,d),I===!0&&ie.canLineTerminator===!0){let q=0,K,se;C.lastIndex=0;do K=C.test(u),K===!0&&(se=C.lastIndex-1,q++);while(K===!0);q!==0&&(M=M+q,N=d-se,this.updateTokenEndLineColumnLocation(g,p,se,q,M,N,d))}this.handleModes(ie,Q,X,g)}else{let q=T,K=M,se=N,ce=j===!1;for(;ce===!1&&T{"use strict";Pt();i2();s0();o(zu,"tokenLabel");o(gN,"hasTokenLabel");EIe="parent",Rie="categories",Nie="label",Mie="group",Iie="push_mode",Oie="pop_mode",Pie="longer_alt",Bie="line_breaks",Fie="start_chars_hint";o(VT,"createToken");o(CIe,"createTokenInternal");fo=VT({name:"EOF",pattern:ni.NA});Fu([fo]);o(o0,"createTokenInstance");o(s2,"tokenMatcher")});var Gu,zie,Ol,Jm=R(()=>{"use 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easy to accomplish by using the convention that Terminal names start with an uppercase letter -and Non-Terminal names start with a lower case letter.`},buildAlternationPrefixAmbiguityError(t){let e=qe(t.prefixPath,i=>zu(i)).join(", "),r=t.alternation.idx===0?"":t.alternation.idx;return`Ambiguous alternatives: <${t.ambiguityIndices.join(" ,")}> due to common lookahead prefix -in inside <${t.topLevelRule.name}> Rule, -<${e}> may appears as a prefix path in all these alternatives. -See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#COMMON_PREFIX -For Further details.`},buildAlternationAmbiguityError(t){let e=qe(t.prefixPath,i=>zu(i)).join(", "),r=t.alternation.idx===0?"":t.alternation.idx,n=`Ambiguous Alternatives Detected: <${t.ambiguityIndices.join(" ,")}> in inside <${t.topLevelRule.name}> Rule, -<${e}> may appears as a prefix path in all these alternatives. -`;return n=n+`See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#AMBIGUOUS_ALTERNATIVES -For Further details.`,n},buildEmptyRepetitionError(t){let e=Rs(t.repetition);return t.repetition.idx!==0&&(e+=t.repetition.idx),`The repetition <${e}> within Rule <${t.topLevelRule.name}> can never consume any tokens. -This could lead to an infinite loop.`},buildTokenNameError(t){return"deprecated"},buildEmptyAlternationError(t){return`Ambiguous empty alternative: <${t.emptyChoiceIdx+1}> in inside <${t.topLevelRule.name}> Rule. -Only the last alternative may be an empty alternative.`},buildTooManyAlternativesError(t){return`An Alternation cannot have more than 256 alternatives: - inside <${t.topLevelRule.name}> Rule. - has ${t.alternation.definition.length+1} alternatives.`},buildLeftRecursionError(t){let e=t.topLevelRule.name,r=qe(t.leftRecursionPath,a=>a.name),n=`${e} --> ${r.concat([e]).join(" --> ")}`;return`Left Recursion found in grammar. -rule: <${e}> can be invoked from itself (directly or indirectly) -without consuming any Tokens. The grammar path that causes this is: - ${n} - To fix this refactor your grammar to remove the left recursion. -see: https://en.wikipedia.org/wiki/LL_parser#Left_factoring.`},buildInvalidRuleNameError(t){return"deprecated"},buildDuplicateRuleNameError(t){let e;return t.topLevelRule instanceof ts?e=t.topLevelRule.name:e=t.topLevelRule,`Duplicate definition, rule: ->${e}<- is already defined in the grammar: ->${t.grammarName}<-`}}});function Gie(t,e){let r=new yN(t,e);return r.resolveRefs(),r.errors}var yN,$ie=R(()=>{"use strict";Ns();Pt();ns();o(Gie,"resolveGrammar");yN=class extends rs{static{o(this,"GastRefResolverVisitor")}constructor(e,r){super(),this.nameToTopRule=e,this.errMsgProvider=r,this.errors=[]}resolveRefs(){Ee(or(this.nameToTopRule),e=>{this.currTopLevel=e,e.accept(this)})}visitNonTerminal(e){let r=this.nameToTopRule[e.nonTerminalName];if(r)e.referencedRule=r;else{let n=this.errMsgProvider.buildRuleNotFoundError(this.currTopLevel,e);this.errors.push({message:n,type:Pi.UNRESOLVED_SUBRULE_REF,ruleName:this.currTopLevel.name,unresolvedRefName:e.nonTerminalName})}}}});function WT(t,e,r=[]){r=Qr(r);let n=[],i=0;function a(l){return l.concat(fi(t,i+1))}o(a,"remainingPathWith");function s(l){let u=WT(a(l),e,r);return n.concat(u)}for(o(s,"getAlternativesForProd");r.length{Qt(u.definition)===!1&&(n=s(u.definition))}),n;if(l instanceof fr)r.push(l.terminalType);else throw Error("non exhaustive match")}i++}return n.push({partialPath:r,suffixDef:fi(t,i)}),n}function qT(t,e,r,n){let i="EXIT_NONE_TERMINAL",a=[i],s="EXIT_ALTERNATIVE",l=!1,u=e.length,h=u-n-1,f=[],d=[];for(d.push({idx:-1,def:t,ruleStack:[],occurrenceStack:[]});!Qt(d);){let p=d.pop();if(p===s){l&&ma(d).idx<=h&&d.pop();continue}let m=p.def,g=p.idx,y=p.ruleStack,v=p.occurrenceStack;if(Qt(m))continue;let x=m[0];if(x===i){let b={idx:g,def:fi(m),ruleStack:Ru(y),occurrenceStack:Ru(v)};d.push(b)}else if(x 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this.ruleStack=Qr(this.path.ruleStack).reverse(),this.occurrenceStack=Qr(this.path.occurrenceStack).reverse(),this.ruleStack.pop(),this.occurrenceStack.pop(),this.updateExpectedNext(),this.walk(this.topProd),this.possibleTokTypes}walk(e,r=[]){this.found||super.walk(e,r)}walkProdRef(e,r,n){if(e.referencedRule.name===this.nextProductionName&&e.idx===this.nextProductionOccurrence){let i=r.concat(n);this.updateExpectedNext(),this.walk(e.referencedRule,i)}}updateExpectedNext(){Qt(this.ruleStack)?(this.nextProductionName="",this.nextProductionOccurrence=0,this.isAtEndOfPath=!0):(this.nextProductionName=this.ruleStack.pop(),this.nextProductionOccurrence=this.occurrenceStack.pop())}},UT=class extends vN{static{o(this,"NextAfterTokenWalker")}constructor(e,r){super(e,r),this.path=r,this.nextTerminalName="",this.nextTerminalOccurrence=0,this.nextTerminalName=this.path.lastTok.name,this.nextTerminalOccurrence=this.path.lastTokOccurrence}walkTerminal(e,r,n){if(this.isAtEndOfPath&&e.terminalType.name===this.nextTerminalName&&e.idx===this.nextTerminalOccurrence&&!this.found){let i=r.concat(n),a=new Sn({definition:i});this.possibleTokTypes=i0(a),this.found=!0}}},eg=class extends Pu{static{o(this,"AbstractNextTerminalAfterProductionWalker")}constructor(e,r){super(),this.topRule=e,this.occurrence=r,this.result={token:void 0,occurrence:void 0,isEndOfRule:void 0}}startWalking(){return this.walk(this.topRule),this.result}},HT=class extends eg{static{o(this,"NextTerminalAfterManyWalker")}walkMany(e,r,n){if(e.idx===this.occurrence){let i=na(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof fr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else 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i=[],a=Vr(t,(l,u,h)=>(e.definition[h].ignoreAmbiguities===!0||Ee(u,f=>{let d=[h];Ee(t,(p,m)=>{h!==m&&KT(p,f)&&e.definition[m].ignoreAmbiguities!==!0&&d.push(m)}),d.length>1&&!KT(i,f)&&(i.push(f),l.push({alts:d,path:f}))}),l),[]);return qe(a,l=>{let u=qe(l.alts,f=>f+1);return{message:n.buildAlternationAmbiguityError({topLevelRule:r,alternation:e,ambiguityIndices:u,prefixPath:l.path}),type:Pi.AMBIGUOUS_ALTS,ruleName:r.name,occurrence:e.idx,alternatives:l.alts}})}function MIe(t,e,r,n){let i=Vr(t,(s,l,u)=>{let h=qe(l,f=>({idx:u,path:f}));return s.concat(h)},[]);return wc(ga(i,s=>{if(e.definition[s.idx].ignoreAmbiguities===!0)return[];let u=s.idx,h=s.path,f=$r(i,p=>e.definition[p.idx].ignoreAmbiguities!==!0&&p.idx{let m=[p.idx+1,u+1],g=e.idx===0?"":e.idx;return{message:n.buildAlternationPrefixAmbiguityError({topLevelRule:r,alternation:e,ambiguityIndices:m,prefixPath:p.path}),type:Pi.AMBIGUOUS_PREFIX_ALTS,ruleName:r.name,occurrence:g,alternatives:m}})}))}function IIe(t,e,r){let n=[],i=qe(e,a=>a.name);return Ee(t,a=>{let s=a.name;if(Fn(i,s)){let l=r.buildNamespaceConflictError(a);n.push({message:l,type:Pi.CONFLICT_TOKENS_RULES_NAMESPACE,ruleName:s})}}),n}var wN,h2,TN,f2=R(()=>{"use strict";Pt();Ns();ns();ng();c2();s0();o(Kie,"validateLookahead");o(Qie,"validateGrammar");o(_Ie,"validateDuplicateProductions");o(LIe,"identifyProductionForDuplicates");o(Zie,"getExtraProductionArgument");wN=class extends 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l=this.getKeyForAutomaticLookahead(n,i),u=this.firstAfterRepMap[l];if(u===void 0){let p=this.getCurrRuleFullName(),m=this.getGAstProductions()[p];u=new a(m,i).startWalking(),this.firstAfterRepMap[l]=u}let h=u.token,f=u.occurrence,d=u.isEndOfRule;this.RULE_STACK.length===1&&d&&h===void 0&&(h=fo,f=1),!(h===void 0||f===void 0)&&this.shouldInRepetitionRecoveryBeTried(h,f,s)&&this.tryInRepetitionRecovery(t,e,r,h)}var EN,SN,CN,ZT,AN=R(()=>{"use strict";l0();Pt();ag();aN();Ns();EN={},SN="InRuleRecoveryException",CN=class extends Error{static{o(this,"InRuleRecoveryException")}constructor(e){super(e),this.name=SN}},ZT=class{static{o(this,"Recoverable")}initRecoverable(e){this.firstAfterRepMap={},this.resyncFollows={},this.recoveryEnabled=Xe(e,"recoveryEnabled")?e.recoveryEnabled:is.recoveryEnabled,this.recoveryEnabled&&(this.attemptInRepetitionRecovery=OIe)}getTokenToInsert(e){let r=o0(e,"",NaN,NaN,NaN,NaN,NaN,NaN);return 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strict";Pt();mae();o(FIe,"defaultVisit");o(gae,"createBaseSemanticVisitorConstructor");o(yae,"createBaseVisitorConstructorWithDefaults");(function(t){t[t.REDUNDANT_METHOD=0]="REDUNDANT_METHOD",t[t.MISSING_METHOD=1]="MISSING_METHOD"})(ON||(ON={}));o(zIe,"validateVisitor");o(GIe,"validateMissingCstMethods")});var sk,xae=R(()=>{"use strict";pae();Pt();vae();Ns();sk=class{static{o(this,"TreeBuilder")}initTreeBuilder(e){if(this.CST_STACK=[],this.outputCst=e.outputCst,this.nodeLocationTracking=Xe(e,"nodeLocationTracking")?e.nodeLocationTracking:is.nodeLocationTracking,!this.outputCst)this.cstInvocationStateUpdate=qn,this.cstFinallyStateUpdate=qn,this.cstPostTerminal=qn,this.cstPostNonTerminal=qn,this.cstPostRule=qn;else if(/full/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=MN,this.setNodeLocationFromNode=MN,this.cstPostRule=qn,this.setInitialNodeLocation=this.setInitialNodeLocationFullRecovery):(this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=this.cstPostRuleFull,this.setInitialNodeLocation=this.setInitialNodeLocationFullRegular);else if(/onlyOffset/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=NN,this.setNodeLocationFromNode=NN,this.cstPostRule=qn,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRecovery):(this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=this.cstPostRuleOnlyOffset,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRegular);else if(/none/i.test(this.nodeLocationTracking))this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=qn,this.setInitialNodeLocation=qn;else throw Error(`Invalid config option: "${e.nodeLocationTracking}"`)}setInitialNodeLocationOnlyOffsetRecovery(e){e.location={startOffset:NaN,endOffset:NaN}}setInitialNodeLocationOnlyOffsetRegular(e){e.location={startOffset:this.LA(1).startOffset,endOffset:NaN}}setInitialNodeLocationFullRecovery(e){e.location={startOffset:NaN,startLine:NaN,startColumn:NaN,endOffset:NaN,endLine:NaN,endColumn:NaN}}setInitialNodeLocationFullRegular(e){let r=this.LA(1);e.location={startOffset:r.startOffset,startLine:r.startLine,startColumn:r.startColumn,endOffset:NaN,endLine:NaN,endColumn:NaN}}cstInvocationStateUpdate(e){let r={name:e,children:Object.create(null)};this.setInitialNodeLocation(r),this.CST_STACK.push(r)}cstFinallyStateUpdate(){this.CST_STACK.pop()}cstPostRuleFull(e){let r=this.LA(0),n=e.location;n.startOffset<=r.startOffset?(n.endOffset=r.endOffset,n.endLine=r.endLine,n.endColumn=r.endColumn):(n.startOffset=NaN,n.startLine=NaN,n.startColumn=NaN)}cstPostRuleOnlyOffset(e){let r=this.LA(0),n=e.location;n.startOffset<=r.startOffset?n.endOffset=r.endOffset:n.startOffset=NaN}cstPostTerminal(e,r){let n=this.CST_STACK[this.CST_STACK.length-1];fae(n,r,e),this.setNodeLocationFromToken(n.location,r)}cstPostNonTerminal(e,r){let n=this.CST_STACK[this.CST_STACK.length-1];dae(n,r,e),this.setNodeLocationFromNode(n.location,e.location)}getBaseCstVisitorConstructor(){if(er(this.baseCstVisitorConstructor)){let e=gae(this.className,Dr(this.gastProductionsCache));return this.baseCstVisitorConstructor=e,e}return this.baseCstVisitorConstructor}getBaseCstVisitorConstructorWithDefaults(){if(er(this.baseCstVisitorWithDefaultsConstructor)){let e=yae(this.className,Dr(this.gastProductionsCache),this.getBaseCstVisitorConstructor());return this.baseCstVisitorWithDefaultsConstructor=e,e}return this.baseCstVisitorWithDefaultsConstructor}getLastExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-1]}getPreviousExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-2]}getLastExplicitRuleOccurrenceIndex(){let e=this.RULE_OCCURRENCE_STACK;return e[e.length-1]}}});var ok,bae=R(()=>{"use strict";Ns();ok=class{static{o(this,"LexerAdapter")}initLexerAdapter(){this.tokVector=[],this.tokVectorLength=0,this.currIdx=-1}set input(e){if(this.selfAnalysisDone!==!0)throw Error("Missing invocation at the end of the Parser's constructor.");this.reset(),this.tokVector=e,this.tokVectorLength=e.length}get input(){return this.tokVector}SKIP_TOKEN(){return this.currIdx<=this.tokVector.length-2?(this.consumeToken(),this.LA(1)):sg}LA(e){let r=this.currIdx+e;return r<0||this.tokVectorLength<=r?sg:this.tokVector[r]}consumeToken(){this.currIdx++}exportLexerState(){return this.currIdx}importLexerState(e){this.currIdx=e}resetLexerState(){this.currIdx=-1}moveToTerminatedState(){this.currIdx=this.tokVector.length-1}getLexerPosition(){return this.exportLexerState()}}});var lk,wae=R(()=>{"use strict";Pt();ag();Ns();Jm();f2();ns();lk=class{static{o(this,"RecognizerApi")}ACTION(e){return e.call(this)}consume(e,r,n){return this.consumeInternal(r,e,n)}subrule(e,r,n){return this.subruleInternal(r,e,n)}option(e,r){return this.optionInternal(r,e)}or(e,r){return this.orInternal(r,e)}many(e,r){return this.manyInternal(e,r)}atLeastOne(e,r){return this.atLeastOneInternal(e,r)}CONSUME(e,r){return this.consumeInternal(e,0,r)}CONSUME1(e,r){return this.consumeInternal(e,1,r)}CONSUME2(e,r){return this.consumeInternal(e,2,r)}CONSUME3(e,r){return this.consumeInternal(e,3,r)}CONSUME4(e,r){return this.consumeInternal(e,4,r)}CONSUME5(e,r){return this.consumeInternal(e,5,r)}CONSUME6(e,r){return this.consumeInternal(e,6,r)}CONSUME7(e,r){return this.consumeInternal(e,7,r)}CONSUME8(e,r){return this.consumeInternal(e,8,r)}CONSUME9(e,r){return this.consumeInternal(e,9,r)}SUBRULE(e,r){return this.subruleInternal(e,0,r)}SUBRULE1(e,r){return this.subruleInternal(e,1,r)}SUBRULE2(e,r){return this.subruleInternal(e,2,r)}SUBRULE3(e,r){return this.subruleInternal(e,3,r)}SUBRULE4(e,r){return this.subruleInternal(e,4,r)}SUBRULE5(e,r){return this.subruleInternal(e,5,r)}SUBRULE6(e,r){return this.subruleInternal(e,6,r)}SUBRULE7(e,r){return this.subruleInternal(e,7,r)}SUBRULE8(e,r){return this.subruleInternal(e,8,r)}SUBRULE9(e,r){return this.subruleInternal(e,9,r)}OPTION(e){return this.optionInternal(e,0)}OPTION1(e){return this.optionInternal(e,1)}OPTION2(e){return this.optionInternal(e,2)}OPTION3(e){return this.optionInternal(e,3)}OPTION4(e){return this.optionInternal(e,4)}OPTION5(e){return this.optionInternal(e,5)}OPTION6(e){return this.optionInternal(e,6)}OPTION7(e){return this.optionInternal(e,7)}OPTION8(e){return this.optionInternal(e,8)}OPTION9(e){return this.optionInternal(e,9)}OR(e){return this.orInternal(e,0)}OR1(e){return this.orInternal(e,1)}OR2(e){return this.orInternal(e,2)}OR3(e){return this.orInternal(e,3)}OR4(e){return this.orInternal(e,4)}OR5(e){return this.orInternal(e,5)}OR6(e){return this.orInternal(e,6)}OR7(e){return this.orInternal(e,7)}OR8(e){return this.orInternal(e,8)}OR9(e){return this.orInternal(e,9)}MANY(e){this.manyInternal(0,e)}MANY1(e){this.manyInternal(1,e)}MANY2(e){this.manyInternal(2,e)}MANY3(e){this.manyInternal(3,e)}MANY4(e){this.manyInternal(4,e)}MANY5(e){this.manyInternal(5,e)}MANY6(e){this.manyInternal(6,e)}MANY7(e){this.manyInternal(7,e)}MANY8(e){this.manyInternal(8,e)}MANY9(e){this.manyInternal(9,e)}MANY_SEP(e){this.manySepFirstInternal(0,e)}MANY_SEP1(e){this.manySepFirstInternal(1,e)}MANY_SEP2(e){this.manySepFirstInternal(2,e)}MANY_SEP3(e){this.manySepFirstInternal(3,e)}MANY_SEP4(e){this.manySepFirstInternal(4,e)}MANY_SEP5(e){this.manySepFirstInternal(5,e)}MANY_SEP6(e){this.manySepFirstInternal(6,e)}MANY_SEP7(e){this.manySepFirstInternal(7,e)}MANY_SEP8(e){this.manySepFirstInternal(8,e)}MANY_SEP9(e){this.manySepFirstInternal(9,e)}AT_LEAST_ONE(e){this.atLeastOneInternal(0,e)}AT_LEAST_ONE1(e){return this.atLeastOneInternal(1,e)}AT_LEAST_ONE2(e){this.atLeastOneInternal(2,e)}AT_LEAST_ONE3(e){this.atLeastOneInternal(3,e)}AT_LEAST_ONE4(e){this.atLeastOneInternal(4,e)}AT_LEAST_ONE5(e){this.atLeastOneInternal(5,e)}AT_LEAST_ONE6(e){this.atLeastOneInternal(6,e)}AT_LEAST_ONE7(e){this.atLeastOneInternal(7,e)}AT_LEAST_ONE8(e){this.atLeastOneInternal(8,e)}AT_LEAST_ONE9(e){this.atLeastOneInternal(9,e)}AT_LEAST_ONE_SEP(e){this.atLeastOneSepFirstInternal(0,e)}AT_LEAST_ONE_SEP1(e){this.atLeastOneSepFirstInternal(1,e)}AT_LEAST_ONE_SEP2(e){this.atLeastOneSepFirstInternal(2,e)}AT_LEAST_ONE_SEP3(e){this.atLeastOneSepFirstInternal(3,e)}AT_LEAST_ONE_SEP4(e){this.atLeastOneSepFirstInternal(4,e)}AT_LEAST_ONE_SEP5(e){this.atLeastOneSepFirstInternal(5,e)}AT_LEAST_ONE_SEP6(e){this.atLeastOneSepFirstInternal(6,e)}AT_LEAST_ONE_SEP7(e){this.atLeastOneSepFirstInternal(7,e)}AT_LEAST_ONE_SEP8(e){this.atLeastOneSepFirstInternal(8,e)}AT_LEAST_ONE_SEP9(e){this.atLeastOneSepFirstInternal(9,e)}RULE(e,r,n=og){if(Fn(this.definedRulesNames,e)){let s={message:Ol.buildDuplicateRuleNameError({topLevelRule:e,grammarName:this.className}),type:Pi.DUPLICATE_RULE_NAME,ruleName:e};this.definitionErrors.push(s)}this.definedRulesNames.push(e);let i=this.defineRule(e,r,n);return this[e]=i,i}OVERRIDE_RULE(e,r,n=og){let i=Jie(e,this.definedRulesNames,this.className);this.definitionErrors=this.definitionErrors.concat(i);let a=this.defineRule(e,r,n);return this[e]=a,a}BACKTRACK(e,r){return function(){this.isBackTrackingStack.push(1);let n=this.saveRecogState();try{return e.apply(this,r),!0}catch(i){if(nf(i))return!1;throw i}finally{this.reloadRecogState(n),this.isBackTrackingStack.pop()}}}getGAstProductions(){return this.gastProductionsCache}getSerializedGastProductions(){return NT(or(this.gastProductionsCache))}}});var ck,Tae=R(()=>{"use strict";Pt();ek();ag();ng();c2();Ns();AN();l0();s0();ck=class{static{o(this,"RecognizerEngine")}initRecognizerEngine(e,r){if(this.className=this.constructor.name,this.shortRuleNameToFull={},this.fullRuleNameToShort={},this.ruleShortNameIdx=256,this.tokenMatcher=Zm,this.subruleIdx=0,this.definedRulesNames=[],this.tokensMap={},this.isBackTrackingStack=[],this.RULE_STACK=[],this.RULE_OCCURRENCE_STACK=[],this.gastProductionsCache={},Xe(r,"serializedGrammar"))throw Error(`The Parser's configuration can no longer contain a property. - See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_6-0-0 - For Further details.`);if(wt(e)){if(Qt(e))throw Error(`A Token Vocabulary cannot be empty. - Note that the first argument for the parser constructor - is no longer a Token vector (since v4.0).`);if(typeof e[0].startOffset=="number")throw Error(`The Parser constructor no longer accepts a token vector as the first argument. - See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_4-0-0 - For Further details.`)}if(wt(e))this.tokensMap=Vr(e,(a,s)=>(a[s.name]=s,a),{});else if(Xe(e,"modes")&&Ia(Gr(or(e.modes)),Die)){let a=Gr(or(e.modes)),s=Pm(a);this.tokensMap=Vr(s,(l,u)=>(l[u.name]=u,l),{})}else if(pn(e))this.tokensMap=Qr(e);else throw new Error(" argument must be An Array of Token constructors, A dictionary of Token constructors or an IMultiModeLexerDefinition");this.tokensMap.EOF=fo;let n=Xe(e,"modes")?Gr(or(e.modes)):or(e),i=Ia(n,a=>Qt(a.categoryMatches));this.tokenMatcher=i?Zm:Bu,Fu(or(this.tokensMap))}defineRule(e,r,n){if(this.selfAnalysisDone)throw Error(`Grammar rule <${e}> may not be defined after the 'performSelfAnalysis' method has been called' -Make sure that all grammar rule definitions are done before 'performSelfAnalysis' is called.`);let i=Xe(n,"resyncEnabled")?n.resyncEnabled:og.resyncEnabled,a=Xe(n,"recoveryValueFunc")?n.recoveryValueFunc:og.recoveryValueFunc,s=this.ruleShortNameIdx<<12;this.ruleShortNameIdx++,this.shortRuleNameToFull[s]=e,this.fullRuleNameToShort[e]=s;let l;return this.outputCst===!0?l=o(function(...f){try{this.ruleInvocationStateUpdate(s,e,this.subruleIdx),r.apply(this,f);let d=this.CST_STACK[this.CST_STACK.length-1];return this.cstPostRule(d),d}catch(d){return this.invokeRuleCatch(d,i,a)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTry"):l=o(function(...f){try{return this.ruleInvocationStateUpdate(s,e,this.subruleIdx),r.apply(this,f)}catch(d){return this.invokeRuleCatch(d,i,a)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTryCst"),Object.assign(l,{ruleName:e,originalGrammarAction:r})}invokeRuleCatch(e,r,n){let i=this.RULE_STACK.length===1,a=r&&!this.isBackTracking()&&this.recoveryEnabled;if(nf(e)){let s=e;if(a){let l=this.findReSyncTokenType();if(this.isInCurrentRuleReSyncSet(l))if(s.resyncedTokens=this.reSyncTo(l),this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];return u.recoveredNode=!0,u}else return n(e);else{if(this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];u.recoveredNode=!0,s.partialCstResult=u}throw s}}else{if(i)return this.moveToTerminatedState(),n(e);throw s}}else throw e}optionInternal(e,r){let n=this.getKeyForAutomaticLookahead(512,r);return this.optionInternalLogic(e,r,n)}optionInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof e!="function"){a=e.DEF;let s=e.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=e;if(i.call(this)===!0)return a.call(this)}atLeastOneInternal(e,r){let n=this.getKeyForAutomaticLookahead(1024,e);return this.atLeastOneInternalLogic(e,r,n)}atLeastOneInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let s=r.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=r;if(i.call(this)===!0){let s=this.doSingleRepetition(a);for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a)}else throw this.raiseEarlyExitException(e,$n.REPETITION_MANDATORY,r.ERR_MSG);this.attemptInRepetitionRecovery(this.atLeastOneInternal,[e,r],i,1024,e,YT)}atLeastOneSepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1536,e);this.atLeastOneSepFirstInternalLogic(e,r,n)}atLeastOneSepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,l2],l,1536,e,l2)}else throw this.raiseEarlyExitException(e,$n.REPETITION_MANDATORY_WITH_SEPARATOR,r.ERR_MSG)}manyInternal(e,r){let n=this.getKeyForAutomaticLookahead(768,e);return this.manyInternalLogic(e,r,n)}manyInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let l=r.GATE;if(l!==void 0){let u=i;i=o(()=>l.call(this)&&u.call(this),"lookaheadFunction")}}else a=r;let s=!0;for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a);this.attemptInRepetitionRecovery(this.manyInternal,[e,r],i,768,e,HT,s)}manySepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1280,e);this.manySepFirstInternalLogic(e,r,n)}manySepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,o2],l,1280,e,o2)}}repetitionSepSecondInternal(e,r,n,i,a){for(;n();)this.CONSUME(r),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,r,n,i,a],n,1536,e,a)}doSingleRepetition(e){let r=this.getLexerPosition();return e.call(this),this.getLexerPosition()>r}orInternal(e,r){let n=this.getKeyForAutomaticLookahead(256,r),i=wt(e)?e:e.DEF,s=this.getLaFuncFromCache(n).call(this,i);if(s!==void 0)return i[s].ALT.call(this);this.raiseNoAltException(r,e.ERR_MSG)}ruleFinallyStateUpdate(){if(this.RULE_STACK.pop(),this.RULE_OCCURRENCE_STACK.pop(),this.cstFinallyStateUpdate(),this.RULE_STACK.length===0&&this.isAtEndOfInput()===!1){let e=this.LA(1),r=this.errorMessageProvider.buildNotAllInputParsedMessage({firstRedundant:e,ruleName:this.getCurrRuleFullName()});this.SAVE_ERROR(new p2(r,e))}}subruleInternal(e,r,n){let i;try{let a=n!==void 0?n.ARGS:void 0;return this.subruleIdx=r,i=e.apply(this,a),this.cstPostNonTerminal(i,n!==void 0&&n.LABEL!==void 0?n.LABEL:e.ruleName),i}catch(a){throw this.subruleInternalError(a,n,e.ruleName)}}subruleInternalError(e,r,n){throw nf(e)&&e.partialCstResult!==void 0&&(this.cstPostNonTerminal(e.partialCstResult,r!==void 0&&r.LABEL!==void 0?r.LABEL:n),delete e.partialCstResult),e}consumeInternal(e,r,n){let i;try{let a=this.LA(1);this.tokenMatcher(a,e)===!0?(this.consumeToken(),i=a):this.consumeInternalError(e,a,n)}catch(a){i=this.consumeInternalRecovery(e,r,a)}return this.cstPostTerminal(n!==void 0&&n.LABEL!==void 0?n.LABEL:e.name,i),i}consumeInternalError(e,r,n){let i,a=this.LA(0);throw n!==void 0&&n.ERR_MSG?i=n.ERR_MSG:i=this.errorMessageProvider.buildMismatchTokenMessage({expected:e,actual:r,previous:a,ruleName:this.getCurrRuleFullName()}),this.SAVE_ERROR(new c0(i,r,a))}consumeInternalRecovery(e,r,n){if(this.recoveryEnabled&&n.name==="MismatchedTokenException"&&!this.isBackTracking()){let i=this.getFollowsForInRuleRecovery(e,r);try{return this.tryInRuleRecovery(e,i)}catch(a){throw a.name===SN?n:a}}else throw n}saveRecogState(){let e=this.errors,r=Qr(this.RULE_STACK);return{errors:e,lexerState:this.exportLexerState(),RULE_STACK:r,CST_STACK:this.CST_STACK}}reloadRecogState(e){this.errors=e.errors,this.importLexerState(e.lexerState),this.RULE_STACK=e.RULE_STACK}ruleInvocationStateUpdate(e,r,n){this.RULE_OCCURRENCE_STACK.push(n),this.RULE_STACK.push(e),this.cstInvocationStateUpdate(r)}isBackTracking(){return this.isBackTrackingStack.length!==0}getCurrRuleFullName(){let e=this.getLastExplicitRuleShortName();return this.shortRuleNameToFull[e]}shortRuleNameToFullName(e){return this.shortRuleNameToFull[e]}isAtEndOfInput(){return this.tokenMatcher(this.LA(1),fo)}reset(){this.resetLexerState(),this.subruleIdx=0,this.isBackTrackingStack=[],this.errors=[],this.RULE_STACK=[],this.CST_STACK=[],this.RULE_OCCURRENCE_STACK=[]}}});var uk,kae=R(()=>{"use strict";ag();Pt();ng();Ns();uk=class{static{o(this,"ErrorHandler")}initErrorHandler(e){this._errors=[],this.errorMessageProvider=Xe(e,"errorMessageProvider")?e.errorMessageProvider:is.errorMessageProvider}SAVE_ERROR(e){if(nf(e))return e.context={ruleStack:this.getHumanReadableRuleStack(),ruleOccurrenceStack:Qr(this.RULE_OCCURRENCE_STACK)},this._errors.push(e),e;throw Error("Trying to save an Error which is not a RecognitionException")}get errors(){return Qr(this._errors)}set errors(e){this._errors=e}raiseEarlyExitException(e,r,n){let i=this.getCurrRuleFullName(),a=this.getGAstProductions()[i],l=rg(e,a,r,this.maxLookahead)[0],u=[];for(let f=1;f<=this.maxLookahead;f++)u.push(this.LA(f));let h=this.errorMessageProvider.buildEarlyExitMessage({expectedIterationPaths:l,actual:u,previous:this.LA(0),customUserDescription:n,ruleName:i});throw this.SAVE_ERROR(new m2(h,this.LA(1),this.LA(0)))}raiseNoAltException(e,r){let n=this.getCurrRuleFullName(),i=this.getGAstProductions()[n],a=tg(e,i,this.maxLookahead),s=[];for(let h=1;h<=this.maxLookahead;h++)s.push(this.LA(h));let l=this.LA(0),u=this.errorMessageProvider.buildNoViableAltMessage({expectedPathsPerAlt:a,actual:s,previous:l,customUserDescription:r,ruleName:this.getCurrRuleFullName()});throw this.SAVE_ERROR(new d2(u,this.LA(1),l))}}});var hk,Eae=R(()=>{"use strict";c2();Pt();hk=class{static{o(this,"ContentAssist")}initContentAssist(){}computeContentAssist(e,r){let n=this.gastProductionsCache[e];if(er(n))throw Error(`Rule ->${e}<- does not exist in this grammar.`);return qT([n],r,this.tokenMatcher,this.maxLookahead)}getNextPossibleTokenTypes(e){let r=na(e.ruleStack),i=this.getGAstProductions()[r];return new UT(i,e).startWalking()}}});function y2(t,e,r,n=!1){dk(r);let i=ma(this.recordingProdStack),a=wi(e)?e:e.DEF,s=new t({definition:[],idx:r});return n&&(s.separator=e.SEP),Xe(e,"MAX_LOOKAHEAD")&&(s.maxLookahead=e.MAX_LOOKAHEAD),this.recordingProdStack.push(s),a.call(this),i.definition.push(s),this.recordingProdStack.pop(),pk}function UIe(t,e){dk(e);let r=ma(this.recordingProdStack),n=wt(t)===!1,i=n===!1?t:t.DEF,a=new gn({definition:[],idx:e,ignoreAmbiguities:n&&t.IGNORE_AMBIGUITIES===!0});Xe(t,"MAX_LOOKAHEAD")&&(a.maxLookahead=t.MAX_LOOKAHEAD);let s=Nv(i,l=>wi(l.GATE));return a.hasPredicates=s,r.definition.push(a),Ee(i,l=>{let u=new Sn({definition:[]});a.definition.push(u),Xe(l,"IGNORE_AMBIGUITIES")?u.ignoreAmbiguities=l.IGNORE_AMBIGUITIES:Xe(l,"GATE")&&(u.ignoreAmbiguities=!0),this.recordingProdStack.push(u),l.ALT.call(this),this.recordingProdStack.pop()}),pk}function Aae(t){return t===0?"":`${t}`}function dk(t){if(t<0||t>Sae){let e=new Error(`Invalid DSL Method idx value: <${t}> - Idx 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Ge=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Ae.length-1&&(this.yylineno-=Ae.length-1);var Me=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Ae?(Ae.length===Ge.length?this.yylloc.first_column:0)+Ge[Ge.length-Ae.length].length-Ae[0].length:this.yylloc.first_column-Ce},this.options.ranges&&(this.yylloc.range=[Me[0],Me[0]+this.yyleng-Ce]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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32:this.popState(),this.begin("point_y");break;case 33:return this.popState(),46;break;case 34:return 28;case 35:return 4;case 36:return 11;case 37:return 64;case 38:return 10;case 39:return 65;case 40:return 65;case 41:return 14;case 42:return 13;case 43:return 67;case 44:return 66;case 45:return 12;case 46:return 8;case 47:return 5;case 48:return 18;case 49:return 56;case 50:return 63;case 51:return 57}},"anonymous"),rules:[/^(?:%%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n\r]+)/i,/^(?:%%[^\n]*)/i,/^(?:title\b)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?: *x-axis *)/i,/^(?: *y-axis *)/i,/^(?: *--+> *)/i,/^(?: *quadrant-1 *)/i,/^(?: *quadrant-2 *)/i,/^(?: *quadrant-3 *)/i,/^(?: *quadrant-4 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Map,V.info("clear called")}setData(e){this.data={...this.data,...e}}addPoints(e){this.data.points=[...e,...this.data.points]}addClass(e,r){this.classes.set(e,r)}setConfig(e){V.trace("setConfig called with: ",e),this.config={...this.config,...e}}setThemeConfig(e){V.trace("setThemeConfig called with: ",e),this.themeConfig={...this.themeConfig,...e}}calculateSpace(e,r,n,i){let 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this.textDimensionCalculator.getMaxDimension(this.getTickValues().map(e=>e.toString()),this.axisConfig.labelFontSize)}recalculateOuterPaddingToDrawBar(){.7*this.getTickDistance()>this.outerPadding*2&&(this.outerPadding=Math.floor(.7*this.getTickDistance()/2)),this.recalculateScale()}calculateSpaceIfDrawnHorizontally(e){let r=e.height;if(this.axisConfig.showAxisLine&&r>this.axisConfig.axisLineWidth&&(r-=this.axisConfig.axisLineWidth,this.showAxisLine=!0),this.axisConfig.showLabel){let n=this.getLabelDimension(),i=.2*e.width;this.outerPadding=Math.min(n.width/2,i);let a=n.height+this.axisConfig.labelPadding*2;this.labelTextHeight=n.height,a<=r&&(r-=a,this.showLabel=!0)}if(this.axisConfig.showTick&&r>=this.axisConfig.tickLength&&(this.showTick=!0,r-=this.axisConfig.tickLength),this.axisConfig.showTitle&&this.title){let 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49:te[oe-1].draw="participant",te[oe-1].type="addParticipant",this.$=te[oe-1];break;case 50:te[oe-3].draw="actor",te[oe-3].type="addParticipant",te[oe-3].description=ce.parseMessage(te[oe-1]),this.$=te[oe-3];break;case 51:te[oe-1].draw="actor",te[oe-1].type="addParticipant",this.$=te[oe-1];break;case 52:te[oe-1].type="destroyParticipant",this.$=te[oe-1];break;case 53:this.$=[te[oe-1],{type:"addNote",placement:te[oe-2],actor:te[oe-1].actor,text:te[oe]}];break;case 54:te[oe-2]=[].concat(te[oe-1],te[oe-1]).slice(0,2),te[oe-2][0]=te[oe-2][0].actor,te[oe-2][1]=te[oe-2][1].actor,this.$=[te[oe-1],{type:"addNote",placement:ce.PLACEMENT.OVER,actor:te[oe-2].slice(0,2),text:te[oe]}];break;case 55:this.$=[te[oe-1],{type:"addLinks",actor:te[oe-1].actor,text:te[oe]}];break;case 56:this.$=[te[oe-1],{type:"addALink",actor:te[oe-1].actor,text:te[oe]}];break;case 57:this.$=[te[oe-1],{type:"addProperties",actor:te[oe-1].actor,text:te[oe]}];break;case 58:this.$=[te[oe-1],{type:"addDetails",actor:te[oe-1].actor,text:te[oe]}];break;case 61:this.$=[te[oe-2],te[oe]];break;case 62:this.$=te[oe];break;case 63:this.$=ce.PLACEMENT.LEFTOF;break;case 64:this.$=ce.PLACEMENT.RIGHTOF;break;case 65:this.$=[te[oe-4],te[oe-1],{type:"addMessage",from:te[oe-4].actor,to:te[oe-1].actor,signalType:te[oe-3],msg:te[oe],activate:!0},{type:"activeStart",signalType:ce.LINETYPE.ACTIVE_START,actor:te[oe-1].actor}];break;case 66:this.$=[te[oe-4],te[oe-1],{type:"addMessage",from:te[oe-4].actor,to:te[oe-1].actor,signalType:te[oe-3],msg:te[oe]},{type:"activeEnd",signalType:ce.LINETYPE.ACTIVE_END,actor:te[oe-4].actor}];break;case 67:this.$=[te[oe-3],te[oe-1],{type:"addMessage",from:te[oe-3].actor,to:te[oe-1].actor,signalType:te[oe-2],msg:te[oe]}];break;case 68:this.$={type:"addParticipant",actor:te[oe]};break;case 69:this.$=ce.LINETYPE.SOLID_OPEN;break;case 70:this.$=ce.LINETYPE.DOTTED_OPEN;break;case 71:this.$=ce.LINETYPE.SOLID;break;case 72:this.$=ce.LINETYPE.BIDIRECTIONAL_SOLID;break;case 73:this.$=ce.LINETYPE.DOTTED;break;case 74:this.$=ce.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 75:this.$=ce.LINETYPE.SOLID_CROSS;break;case 76:this.$=ce.LINETYPE.DOTTED_CROSS;break;case 77:this.$=ce.LINETYPE.SOLID_POINT;break;case 78:this.$=ce.LINETYPE.DOTTED_POINT;break;case 79:this.$=ce.parseMessage(te[oe].trim().substring(1));break}},"anonymous"),table:[{3:1,4:e,5:r,6:n},{1:[3]},{3:5,4:e,5:r,6:n},{3:6,4:e,5:r,6:n},t([1,4,5,13,14,18,21,23,29,30,31,33,35,36,37,38,39,41,43,44,46,50,52,53,54,59,60,61,62,70],i,{7:7}),{1:[2,1]},{1:[2,2]},{1:[2,3],4:a,5:s,8:8,9:10,12:12,13:l,14:u,17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},t(F,[2,5]),{9:47,12:12,13:l,14:u,17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},t(F,[2,7]),t(F,[2,8]),t(F,[2,14]),{12:48,50:L,52:M,53:N},{15:[1,49]},{5:[1,50]},{5:[1,53],19:[1,51],20:[1,52]},{22:54,70:P},{22:55,70:P},{5:[1,56]},{5:[1,57]},{5:[1,58]},{5:[1,59]},{5:[1,60]},t(F,[2,29]),t(F,[2,30]),{32:[1,61]},{34:[1,62]},t(F,[2,33]),{15:[1,63]},{15:[1,64]},{15:[1,65]},{15:[1,66]},{15:[1,67]},{15:[1,68]},{15:[1,69]},{15:[1,70]},{22:71,70:P},{22:72,70:P},{22:73,70:P},{67:74,71:[1,75],72:[1,76],73:[1,77],74:[1,78],75:[1,79],76:[1,80],77:[1,81],78:[1,82],79:[1,83],80:[1,84]},{55:85,57:[1,86],65:[1,87],66:[1,88]},{22:89,70:P},{22:90,70:P},{22:91,70:P},{22:92,70:P},t([5,51,64,71,72,73,74,75,76,77,78,79,80,81],[2,68]),t(F,[2,6]),t(F,[2,15]),t(B,[2,9],{10:93}),t(F,[2,17]),{5:[1,95],19:[1,94]},{5:[1,96]},t(F,[2,21]),{5:[1,97]},{5:[1,98]},t(F,[2,24]),t(F,[2,25]),t(F,[2,26]),t(F,[2,27]),t(F,[2,28]),t(F,[2,31]),t(F,[2,32]),t($,i,{7:99}),t($,i,{7:100}),t($,i,{7:101}),t(z,i,{40:102,7:103}),t(Y,i,{42:104,7:105}),t(Y,i,{7:105,42:106}),t(Q,i,{45:107,7:108}),t($,i,{7:109}),{5:[1,111],51:[1,110]},{5:[1,113],51:[1,112]},{5:[1,114]},{22:117,68:[1,115],69:[1,116],70:P},t(X,[2,69]),t(X,[2,70]),t(X,[2,71]),t(X,[2,72]),t(X,[2,73]),t(X,[2,74]),t(X,[2,75]),t(X,[2,76]),t(X,[2,77]),t(X,[2,78]),{22:118,70:P},{22:120,58:119,70:P},{70:[2,63]},{70:[2,64]},{56:121,81:ie},{56:123,81:ie},{56:124,81:ie},{56:125,81:ie},{4:[1,128],5:[1,130],11:127,12:129,16:[1,126],50:L,52:M,53:N},{5:[1,131]},t(F,[2,19]),t(F,[2,20]),t(F,[2,22]),t(F,[2,23]),{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,132],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,133],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,134],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,135]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,46],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,49:[1,136],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,137]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,44],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,48:[1,138],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,139]},{16:[1,140]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,42],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,47:[1,141],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,142],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{15:[1,143]},t(F,[2,49]),{15:[1,144]},t(F,[2,51]),t(F,[2,52]),{22:145,70:P},{22:146,70:P},{56:147,81:ie},{56:148,81:ie},{56:149,81:ie},{64:[1,150],81:[2,62]},{5:[2,55]},{5:[2,79]},{5:[2,56]},{5:[2,57]},{5:[2,58]},t(F,[2,16]),t(B,[2,10]),{12:151,50:L,52:M,53:N},t(B,[2,12]),t(B,[2,13]),t(F,[2,18]),t(F,[2,34]),t(F,[2,35]),t(F,[2,36]),t(F,[2,37]),{15:[1,152]},t(F,[2,38]),{15:[1,153]},t(F,[2,39]),t(F,[2,40]),{15:[1,154]},t(F,[2,41]),{5:[1,155]},{5:[1,156]},{56:157,81:ie},{56:158,81:ie},{5:[2,67]},{5:[2,53]},{5:[2,54]},{22:159,70:P},t(B,[2,11]),t(z,i,{7:103,40:160}),t(Y,i,{7:105,42:161}),t(Q,i,{7:108,45:162}),t(F,[2,48]),t(F,[2,50]),{5:[2,65]},{5:[2,66]},{81:[2,61]},{16:[2,47]},{16:[2,45]},{16:[2,43]}],defaultActions:{5:[2,1],6:[2,2],87:[2,63],88:[2,64],121:[2,55],122:[2,79],123:[2,56],124:[2,57],125:[2,58],147:[2,67],148:[2,53],149:[2,54],157:[2,65],158:[2,66],159:[2,61],160:[2,47],161:[2,45],162:[2,43]},parseError:o(function(q,K){if(K.recoverable)this.trace(q);else{var se=new Error(q);throw se.hash=K,se}},"parseError"),parse:o(function(q){var K=this,se=[0],ce=[],ue=[null],te=[],De=this.table,oe="",ke=0,Ie=0,Se=0,Ue=2,Pe=1,_e=te.slice.call(arguments,1),me=Object.create(this.lexer),W={yy:{}};for(var fe in this.yy)Object.prototype.hasOwnProperty.call(this.yy,fe)&&(W.yy[fe]=this.yy[fe]);me.setInput(q,W.yy),W.yy.lexer=me,W.yy.parser=this,typeof me.yylloc>"u"&&(me.yylloc={});var ge=me.yylloc;te.push(ge);var re=me.options&&me.options.ranges;typeof W.yy.parseError=="function"?this.parseError=W.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function he(yt){se.length=se.length-2*yt,ue.length=ue.length-yt,te.length=te.length-yt}o(he,"popStack");function ne(){var yt;return yt=ce.pop()||me.lex()||Pe,typeof yt!="number"&&(yt instanceof Array&&(ce=yt,yt=ce.pop()),yt=K.symbols_[yt]||yt),yt}o(ne,"lex");for(var ae,we,Te,Ce,Ae,Ge,Me={},ye,He,ze,Ze;;){if(Te=se[se.length-1],this.defaultActions[Te]?Ce=this.defaultActions[Te]:((ae===null||typeof ae>"u")&&(ae=ne()),Ce=De[Te]&&De[Te][ae]),typeof Ce>"u"||!Ce.length||!Ce[0]){var gt="";Ze=[];for(ye in De[Te])this.terminals_[ye]&&ye>Ue&&Ze.push("'"+this.terminals_[ye]+"'");me.showPosition?gt="Parse error on line "+(ke+1)+`: -`+me.showPosition()+` -Expecting `+Ze.join(", ")+", got '"+(this.terminals_[ae]||ae)+"'":gt="Parse error on line "+(ke+1)+": Unexpected "+(ae==Pe?"end of input":"'"+(this.terminals_[ae]||ae)+"'"),this.parseError(gt,{text:me.match,token:this.terminals_[ae]||ae,line:me.yylineno,loc:ge,expected:Ze})}if(Ce[0]instanceof Array&&Ce.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Te+", token: "+ae);switch(Ce[0]){case 1:se.push(ae),ue.push(me.yytext),te.push(me.yylloc),se.push(Ce[1]),ae=null,we?(ae=we,we=null):(Ie=me.yyleng,oe=me.yytext,ke=me.yylineno,ge=me.yylloc,Se>0&&Se--);break;case 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this.yy=K||this.yy||{},this._input=q,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var q=this._input[0];this.yytext+=q,this.yyleng++,this.offset++,this.match+=q,this.matched+=q;var K=q.match(/(?:\r\n?|\n).*/g);return K?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),q},"input"),unput:o(function(q){var K=q.length,se=q.split(/(?:\r\n?|\n)/g);this._input=q+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-K),this.offset-=K;var ce=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),se.length-1&&(this.yylineno-=se.length-1);var ue=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:se?(se.length===ce.length?this.yylloc.first_column:0)+ce[ce.length-se.length].length-se[0].length:this.yylloc.first_column-K},this.options.ranges&&(this.yylloc.range=[ue[0],ue[0]+this.yyleng-K]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). -`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(q){this.unput(this.match.slice(q))},"less"),pastInput:o(function(){var q=this.matched.substr(0,this.matched.length-this.match.length);return(q.length>20?"...":"")+q.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var q=this.match;return q.length<20&&(q+=this._input.substr(0,20-q.length)),(q.substr(0,20)+(q.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var q=this.pastInput(),K=new Array(q.length+1).join("-");return q+this.upcomingInput()+` -`+K+"^"},"showPosition"),test_match:o(function(q,K){var se,ce,ue;if(this.options.backtrack_lexer&&(ue={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(ue.yylloc.range=this.yylloc.range.slice(0))),ce=q[0].match(/(?:\r\n?|\n).*/g),ce&&(this.yylineno+=ce.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:ce?ce[ce.length-1].length-ce[ce.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+q[0].length},this.yytext+=q[0],this.match+=q[0],this.matches=q,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(q[0].length),this.matched+=q[0],se=this.performAction.call(this,this.yy,this,K,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),se)return se;if(this._backtrack){for(var te in ue)this[te]=ue[te];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var q,K,se,ce;this._more||(this.yytext="",this.match="");for(var ue=this._currentRules(),te=0;teK[0].length)){if(K=se,ce=te,this.options.backtrack_lexer){if(q=this.test_match(se,ue[te]),q!==!1)return q;if(this._backtrack){K=!1;continue}else return!1}else if(!this.options.flex)break}return K?(q=this.test_match(K,ue[ce]),q!==!1?q:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text. -`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var K=this.next();return K||this.lex()},"lex"),begin:o(function(K){this.conditionStack.push(K)},"begin"),popState:o(function(){var K=this.conditionStack.length-1;return K>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(K){return K=this.conditionStack.length-1-Math.abs(K||0),K>=0?this.conditionStack[K]:"INITIAL"},"topState"),pushState:o(function(K){this.begin(K)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{"case-insensitive":!0},performAction:o(function(K,se,ce,ue){var te=ue;switch(ce){case 0:return 5;case 1:break;case 2:break;case 3:break;case 4:break;case 5:break;case 6:return 19;case 7:return this.begin("LINE"),14;break;case 8:return this.begin("ID"),50;break;case 9:return this.begin("ID"),52;break;case 10:return 13;case 11:return this.begin("ID"),53;break;case 12:return se.yytext=se.yytext.trim(),this.begin("ALIAS"),70;break;case 13:return this.popState(),this.popState(),this.begin("LINE"),51;break;case 14:return this.popState(),this.popState(),5;break;case 15:return this.begin("LINE"),36;break;case 16:return this.begin("LINE"),37;break;case 17:return this.begin("LINE"),38;break;case 18:return this.begin("LINE"),39;break;case 19:return this.begin("LINE"),49;break;case 20:return this.begin("LINE"),41;break;case 21:return this.begin("LINE"),43;break;case 22:return this.begin("LINE"),48;break;case 23:return this.begin("LINE"),44;break;case 24:return this.begin("LINE"),47;break;case 25:return this.begin("LINE"),46;break;case 26:return this.popState(),15;break;case 27:return 16;case 28:return 65;case 29:return 66;case 30:return 59;case 31:return 60;case 32:return 61;case 33:return 62;case 34:return 57;case 35:return 54;case 36:return this.begin("ID"),21;break;case 37:return this.begin("ID"),23;break;case 38:return 29;case 39:return 30;case 40:return this.begin("acc_title"),31;break;case 41:return this.popState(),"acc_title_value";break;case 42:return this.begin("acc_descr"),33;break;case 43:return this.popState(),"acc_descr_value";break;case 44:this.begin("acc_descr_multiline");break;case 45:this.popState();break;case 46:return"acc_descr_multiline_value";case 47:return 6;case 48:return 18;case 49:return 20;case 50:return 64;case 51:return 5;case 52:return se.yytext=se.yytext.trim(),70;break;case 53:return 73;case 54:return 74;case 55:return 75;case 56:return 76;case 57:return 71;case 58:return 72;case 59:return 77;case 60:return 78;case 61:return 79;case 62:return 80;case 63:return 81;case 64:return 68;case 65:return 69;case 66:return 5;case 67:return"INVALID"}},"anonymous"),rules:[/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[0-9]+(?=[ \n]+))/i,/^(?:box\b)/i,/^(?:participant\b)/i,/^(?:actor\b)/i,/^(?:create\b)/i,/^(?:destroy\b)/i,/^(?:[^\<->\->:\n,;]+?([\-]*[^\<->\->:\n,;]+?)*?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:par_over\b)/i,/^(?:and\b)/i,/^(?:critical\b)/i,/^(?:option\b)/i,/^(?:break\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:links\b)/i,/^(?:link\b)/i,/^(?:properties\b)/i,/^(?:details\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:title:\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:off\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\<->\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\<->\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:<<->>)/i,/^(?:-->>)/i,/^(?:<<-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{acc_descr_multiline:{rules:[45,46],inclusive:!1},acc_descr:{rules:[43],inclusive:!1},acc_title:{rules:[41],inclusive:!1},ID:{rules:[2,3,12],inclusive:!1},ALIAS:{rules:[2,3,13,14],inclusive:!1},LINE:{rules:[2,3,26],inclusive:!1},INITIAL:{rules:[0,1,3,4,5,6,7,8,9,10,11,15,16,17,18,19,20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36,37,38,39,40,42,44,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],inclusive:!0}}};return H}();j.lexer=J;function Z(){this.yy={}}return o(Z,"Parser"),Z.prototype=j,j.Parser=Z,new Z}();hO.parser=hO;Wue=hO});function dO(t,e){if(t.links==null)t.links=e;else for(let r in e)t.links[r]=e[r]}function Zue(t,e){if(t.properties==null)t.properties=e;else for(let r in e)t.properties[r]=e[r]}function SGe(){Mt.records.currentBox=void 0}var Mt,aGe,fO,sGe,oGe,pi,lGe,cGe,uGe,hGe,fGe,dGe,pGe,xx,mGe,gGe,yGe,vGe,xGe,Xue,A0,bGe,wGe,TGe,vx,kGe,EGe,jue,Kue,CGe,Que,Jue,AGe,ehe,pO,the=R(()=>{"use strict";_t();ut();Jk();rr();bi();Mt=new uf(()=>({prevActor:void 0,actors:new Map,createdActors:new Map,destroyedActors:new Map,boxes:[],messages:[],notes:[],sequenceNumbersEnabled:!1,wrapEnabled:void 0,currentBox:void 0,lastCreated:void 0,lastDestroyed:void 0})),aGe=o(function(t){Mt.records.boxes.push({name:t.text,wrap:t.wrap??A0(),fill:t.color,actorKeys:[]}),Mt.records.currentBox=Mt.records.boxes.slice(-1)[0]},"addBox"),fO=o(function(t,e,r,n){let i=Mt.records.currentBox,a=Mt.records.actors.get(t);if(a){if(Mt.records.currentBox&&a.box&&Mt.records.currentBox!==a.box)throw new Error(`A same participant should only be defined in one Box: ${a.name} can't be in '${a.box.name}' and in '${Mt.records.currentBox.name}' at the same time.`);if(i=a.box?a.box:Mt.records.currentBox,a.box=i,a&&e===a.name&&r==null)return}if(r?.text==null&&(r={text:e,type:n}),(n==null||r.text==null)&&(r={text:e,type:n}),Mt.records.actors.set(t,{box:i,name:e,description:r.text,wrap:r.wrap??A0(),prevActor:Mt.records.prevActor,links:{},properties:{},actorCnt:null,rectData:null,type:n??"participant"}),Mt.records.prevActor){let s=Mt.records.actors.get(Mt.records.prevActor);s&&(s.nextActor=t)}Mt.records.currentBox&&Mt.records.currentBox.actorKeys.push(t),Mt.records.prevActor=t},"addActor"),sGe=o(t=>{let e,r=0;if(!t)return 0;for(e=0;e>-",token:"->>-",line:"1",loc:{first_line:1,last_line:1,first_column:1,last_column:1},expected:["'ACTIVE_PARTICIPANT'"]},s}return Mt.records.messages.push({from:t,to:e,message:r?.text??"",wrap:r?.wrap??A0(),type:n,activate:i}),!0},"addSignal"),lGe=o(function(){return Mt.records.boxes.length>0},"hasAtLeastOneBox"),cGe=o(function(){return Mt.records.boxes.some(t=>t.name)},"hasAtLeastOneBoxWithTitle"),uGe=o(function(){return Mt.records.messages},"getMessages"),hGe=o(function(){return Mt.records.boxes},"getBoxes"),fGe=o(function(){return Mt.records.actors},"getActors"),dGe=o(function(){return Mt.records.createdActors},"getCreatedActors"),pGe=o(function(){return Mt.records.destroyedActors},"getDestroyedActors"),xx=o(function(t){return Mt.records.actors.get(t)},"getActor"),mGe=o(function(){return[...Mt.records.actors.keys()]},"getActorKeys"),gGe=o(function(){Mt.records.sequenceNumbersEnabled=!0},"enableSequenceNumbers"),yGe=o(function(){Mt.records.sequenceNumbersEnabled=!1},"disableSequenceNumbers"),vGe=o(()=>Mt.records.sequenceNumbersEnabled,"showSequenceNumbers"),xGe=o(function(t){Mt.records.wrapEnabled=t},"setWrap"),Xue=o(t=>{if(t===void 0)return{};t=t.trim();let e=/^:?wrap:/.exec(t)!==null?!0:/^:?nowrap:/.exec(t)!==null?!1:void 0;return{cleanedText:(e===void 0?t:t.replace(/^:?(?:no)?wrap:/,"")).trim(),wrap:e}},"extractWrap"),A0=o(()=>Mt.records.wrapEnabled!==void 0?Mt.records.wrapEnabled:de().sequence?.wrap??!1,"autoWrap"),bGe=o(function(){Mt.reset(),vr()},"clear"),wGe=o(function(t){let e=t.trim(),{wrap:r,cleanedText:n}=Xue(e),i={text:n,wrap:r};return V.debug(`parseMessage: ${JSON.stringify(i)}`),i},"parseMessage"),TGe=o(function(t){let e=/^((?:rgba?|hsla?)\s*\(.*\)|\w*)(.*)$/.exec(t),r=e?.[1]?e[1].trim():"transparent",n=e?.[2]?e[2].trim():void 0;if(window?.CSS)window.CSS.supports("color",r)||(r="transparent",n=t.trim());else{let s=new Option().style;s.color=r,s.color!==r&&(r="transparent",n=t.trim())}let{wrap:i,cleanedText:a}=Xue(n);return{text:a?qr(a,de()):void 0,color:r,wrap:i}},"parseBoxData"),vx={SOLID:0,DOTTED:1,NOTE:2,SOLID_CROSS:3,DOTTED_CROSS:4,SOLID_OPEN:5,DOTTED_OPEN:6,LOOP_START:10,LOOP_END:11,ALT_START:12,ALT_ELSE:13,ALT_END:14,OPT_START:15,OPT_END:16,ACTIVE_START:17,ACTIVE_END:18,PAR_START:19,PAR_AND:20,PAR_END:21,RECT_START:22,RECT_END:23,SOLID_POINT:24,DOTTED_POINT:25,AUTONUMBER:26,CRITICAL_START:27,CRITICAL_OPTION:28,CRITICAL_END:29,BREAK_START:30,BREAK_END:31,PAR_OVER_START:32,BIDIRECTIONAL_SOLID:33,BIDIRECTIONAL_DOTTED:34},kGe={FILLED:0,OPEN:1},EGe={LEFTOF:0,RIGHTOF:1,OVER:2},jue=o(function(t,e,r){let n={actor:t,placement:e,message:r.text,wrap:r.wrap??A0()},i=[].concat(t,t);Mt.records.notes.push(n),Mt.records.messages.push({from:i[0],to:i[1],message:r.text,wrap:r.wrap??A0(),type:vx.NOTE,placement:e})},"addNote"),Kue=o(function(t,e){let r=xx(t);try{let n=qr(e.text,de());n=n.replace(/&/g,"&"),n=n.replace(/=/g,"=");let i=JSON.parse(n);dO(r,i)}catch(n){V.error("error while parsing actor link text",n)}},"addLinks"),CGe=o(function(t,e){let r=xx(t);try{let n={},i=qr(e.text,de()),a=i.indexOf("@");i=i.replace(/&/g,"&"),i=i.replace(/=/g,"=");let s=i.slice(0,a-1).trim(),l=i.slice(a+1).trim();n[s]=l,dO(r,n)}catch(n){V.error("error while parsing actor link text",n)}},"addALink");o(dO,"insertLinks");Que=o(function(t,e){let r=xx(t);try{let n=qr(e.text,de()),i=JSON.parse(n);Zue(r,i)}catch(n){V.error("error while parsing actor properties text",n)}},"addProperties");o(Zue,"insertProperties");o(SGe,"boxEnd");Jue=o(function(t,e){let r=xx(t),n=document.getElementById(e.text);try{let i=n.innerHTML,a=JSON.parse(i);a.properties&&Zue(r,a.properties),a.links&&dO(r,a.links)}catch(i){V.error("error while parsing actor details text",i)}},"addDetails"),AGe=o(function(t,e){if(t?.properties!==void 0)return t.properties[e]},"getActorProperty"),ehe=o(function(t){if(Array.isArray(t))t.forEach(function(e){ehe(e)});else switch(t.type){case"sequenceIndex":Mt.records.messages.push({from:void 0,to:void 0,message:{start:t.sequenceIndex,step:t.sequenceIndexStep,visible:t.sequenceVisible},wrap:!1,type:t.signalType});break;case"addParticipant":fO(t.actor,t.actor,t.description,t.draw);break;case"createParticipant":if(Mt.records.actors.has(t.actor))throw new Error("It is not possible to have actors with the same id, even if one is destroyed before the next is created. Use 'AS' aliases to simulate the behavior");Mt.records.lastCreated=t.actor,fO(t.actor,t.actor,t.description,t.draw),Mt.records.createdActors.set(t.actor,Mt.records.messages.length);break;case"destroyParticipant":Mt.records.lastDestroyed=t.actor,Mt.records.destroyedActors.set(t.actor,Mt.records.messages.length);break;case"activeStart":pi(t.actor,void 0,void 0,t.signalType);break;case"activeEnd":pi(t.actor,void 0,void 0,t.signalType);break;case"addNote":jue(t.actor,t.placement,t.text);break;case"addLinks":Kue(t.actor,t.text);break;case"addALink":CGe(t.actor,t.text);break;case"addProperties":Que(t.actor,t.text);break;case"addDetails":Jue(t.actor,t.text);break;case"addMessage":if(Mt.records.lastCreated){if(t.to!==Mt.records.lastCreated)throw new Error("The created participant "+Mt.records.lastCreated.name+" does not have an associated creating message after its declaration. Please check the sequence diagram.");Mt.records.lastCreated=void 0}else if(Mt.records.lastDestroyed){if(t.to!==Mt.records.lastDestroyed&&t.from!==Mt.records.lastDestroyed)throw new Error("The destroyed participant "+Mt.records.lastDestroyed.name+" does not have an associated destroying message after its declaration. Please check the sequence diagram.");Mt.records.lastDestroyed=void 0}pi(t.from,t.to,t.msg,t.signalType,t.activate);break;case"boxStart":aGe(t.boxData);break;case"boxEnd":SGe();break;case"loopStart":pi(void 0,void 0,t.loopText,t.signalType);break;case"loopEnd":pi(void 0,void 0,void 0,t.signalType);break;case"rectStart":pi(void 0,void 0,t.color,t.signalType);break;case"rectEnd":pi(void 0,void 0,void 0,t.signalType);break;case"optStart":pi(void 0,void 0,t.optText,t.signalType);break;case"optEnd":pi(void 0,void 0,void 0,t.signalType);break;case"altStart":pi(void 0,void 0,t.altText,t.signalType);break;case"else":pi(void 0,void 0,t.altText,t.signalType);break;case"altEnd":pi(void 0,void 0,void 0,t.signalType);break;case"setAccTitle":kr(t.text);break;case"parStart":pi(void 0,void 0,t.parText,t.signalType);break;case"and":pi(void 0,void 0,t.parText,t.signalType);break;case"parEnd":pi(void 0,void 0,void 0,t.signalType);break;case"criticalStart":pi(void 0,void 0,t.criticalText,t.signalType);break;case"option":pi(void 0,void 0,t.optionText,t.signalType);break;case"criticalEnd":pi(void 0,void 0,void 0,t.signalType);break;case"breakStart":pi(void 0,void 0,t.breakText,t.signalType);break;case"breakEnd":pi(void 0,void 0,void 0,t.signalType);break}},"apply"),pO={addActor:fO,addMessage:oGe,addSignal:pi,addLinks:Kue,addDetails:Jue,addProperties:Que,autoWrap:A0,setWrap:xGe,enableSequenceNumbers:gGe,disableSequenceNumbers:yGe,showSequenceNumbers:vGe,getMessages:uGe,getActors:fGe,getCreatedActors:dGe,getDestroyedActors:pGe,getActor:xx,getActorKeys:mGe,getActorProperty:AGe,getAccTitle:Ar,getBoxes:hGe,getDiagramTitle:Xr,setDiagramTitle:nn,getConfig:o(()=>de().sequence,"getConfig"),clear:bGe,parseMessage:wGe,parseBoxData:TGe,LINETYPE:vx,ARROWTYPE:kGe,PLACEMENT:EGe,addNote:jue,setAccTitle:kr,apply:ehe,setAccDescription:_r,getAccDescription:Lr,hasAtLeastOneBox:lGe,hasAtLeastOneBoxWithTitle:cGe}});var _Ge,rhe,nhe=R(()=>{"use strict";_Ge=o(t=>`.actor { - stroke: ${t.actorBorder}; - fill: ${t.actorBkg}; - } - - text.actor > tspan { - fill: ${t.actorTextColor}; - stroke: none; - } - - .actor-line { - stroke: ${t.actorLineColor}; - } - - .messageLine0 { - stroke-width: 1.5; - stroke-dasharray: none; - stroke: ${t.signalColor}; - } - - .messageLine1 { - stroke-width: 1.5; - stroke-dasharray: 2, 2; - stroke: ${t.signalColor}; - } - - #arrowhead path { - fill: ${t.signalColor}; - stroke: ${t.signalColor}; - } - - .sequenceNumber { - fill: ${t.sequenceNumberColor}; - } - - #sequencenumber { - fill: ${t.signalColor}; - } - - #crosshead path { - fill: ${t.signalColor}; - stroke: ${t.signalColor}; - } - - .messageText { - fill: ${t.signalTextColor}; - stroke: none; - } - - .labelBox { - stroke: ${t.labelBoxBorderColor}; - fill: ${t.labelBoxBkgColor}; - } - - .labelText, .labelText > tspan { - fill: ${t.labelTextColor}; - stroke: none; - } - - .loopText, .loopText > tspan { - fill: ${t.loopTextColor}; - stroke: none; - } - - .loopLine { - stroke-width: 2px; - stroke-dasharray: 2, 2; 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function(t,e,r,n){let{startx:i,stopx:a,starty:s,message:l,type:u,sequenceIndex:h,sequenceVisible:f}=e,d=Lt.calculateTextDimensions(l,L0(Ne)),p=Ky();p.x=i,p.y=s+10,p.width=a-i,p.class="messageText",p.dy="1em",p.text=l,p.fontFamily=Ne.messageFontFamily,p.fontSize=Ne.messageFontSize,p.fontWeight=Ne.messageFontWeight,p.anchor=Ne.messageAlign,p.valign="center",p.textMargin=Ne.wrapPadding,p.tspan=!1,Ni(p.text)?await bx(t,p,{startx:i,stopx:a,starty:r}):_0(t,p);let m=d.width,g;i===a?Ne.rightAngles?g=t.append("path").attr("d",`M ${i},${r} H ${i+We.getMax(Ne.width/2,m/2)} V ${r+25} H ${i}`):g=t.append("path").attr("d","M "+i+","+r+" C "+(i+60)+","+(r-10)+" "+(i+60)+","+(r+30)+" "+i+","+(r+20)):(g=t.append("line"),g.attr("x1",i),g.attr("y1",r),g.attr("x2",a),g.attr("y2",r)),u===n.db.LINETYPE.DOTTED||u===n.db.LINETYPE.DOTTED_CROSS||u===n.db.LINETYPE.DOTTED_POINT||u===n.db.LINETYPE.DOTTED_OPEN||u===n.db.LINETYPE.BIDIRECTIONAL_DOTTED?(g.style("stroke-dasharray","3, 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y="";Ne.arrowMarkerAbsolute&&(y=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,y=y.replace(/\(/g,"\\("),y=y.replace(/\)/g,"\\)")),g.attr("stroke-width",2),g.attr("stroke","none"),g.style("fill","none"),(u===n.db.LINETYPE.SOLID||u===n.db.LINETYPE.DOTTED)&&g.attr("marker-end","url("+y+"#arrowhead)"),(u===n.db.LINETYPE.BIDIRECTIONAL_SOLID||u===n.db.LINETYPE.BIDIRECTIONAL_DOTTED)&&(g.attr("marker-start","url("+y+"#arrowhead)"),g.attr("marker-end","url("+y+"#arrowhead)")),(u===n.db.LINETYPE.SOLID_POINT||u===n.db.LINETYPE.DOTTED_POINT)&&g.attr("marker-end","url("+y+"#filled-head)"),(u===n.db.LINETYPE.SOLID_CROSS||u===n.db.LINETYPE.DOTTED_CROSS)&&g.attr("marker-end","url("+y+"#crosshead)"),(f||Ne.showSequenceNumbers)&&(g.attr("marker-start","url("+y+"#sequencenumber)"),t.append("text").attr("x",i).attr("y",r+4).attr("font-family","sans-serif").attr("font-size","12px").attr("text-anchor","middle").attr("class","sequenceNumber").text(h))},"drawMessage"),QGe=o(function(t,e,r,n,i,a,s){let l=0,u=0,h,f=0;for(let d of n){let p=e.get(d),m=p.box;h&&h!=m&&(s||Ke.models.addBox(h),u+=Ne.boxMargin+h.margin),m&&m!=h&&(s||(m.x=l+u,m.y=i),u+=m.margin),p.width=p.width||Ne.width,p.height=We.getMax(p.height||Ne.height,Ne.height),p.margin=p.margin||Ne.actorMargin,f=We.getMax(f,p.height),r.get(p.name)&&(u+=p.width/2),p.x=l+u,p.starty=Ke.getVerticalPos(),Ke.insert(p.x,i,p.x+p.width,p.height),l+=p.width+u,p.box&&(p.box.width=l+m.margin-p.box.x),u=p.margin,h=p.box,Ke.models.addActor(p)}h&&!s&&Ke.models.addBox(h),Ke.bumpVerticalPos(f)},"addActorRenderingData"),bO=o(async function(t,e,r,n){if(n){let i=0;Ke.bumpVerticalPos(Ne.boxMargin*2);for(let a of r){let s=e.get(a);s.stopy||(s.stopy=Ke.getVerticalPos());let l=await si.drawActor(t,s,Ne,!0);i=We.getMax(i,l)}Ke.bumpVerticalPos(i+Ne.boxMargin)}else for(let i of r){let a=e.get(i);await si.drawActor(t,a,Ne,!1)}},"drawActors"),fhe=o(function(t,e,r,n){let i=0,a=0;for(let s of r){let l=e.get(s),u=t$e(l),h=si.drawPopup(t,l,u,Ne,Ne.forceMenus,n);h.height>i&&(i=h.height),h.width+l.x>a&&(a=h.width+l.x)}return{maxHeight:i,maxWidth:a}},"drawActorsPopup"),dhe=o(function(t){On(Ne,t),t.fontFamily&&(Ne.actorFontFamily=Ne.noteFontFamily=Ne.messageFontFamily=t.fontFamily),t.fontSize&&(Ne.actorFontSize=Ne.noteFontSize=Ne.messageFontSize=t.fontSize),t.fontWeight&&(Ne.actorFontWeight=Ne.noteFontWeight=Ne.messageFontWeight=t.fontWeight)},"setConf"),bE=o(function(t){return Ke.activations.filter(function(e){return e.actor===t})},"actorActivations"),hhe=o(function(t,e){let r=e.get(t),n=bE(t),i=n.reduce(function(s,l){return We.getMin(s,l.startx)},r.x+r.width/2-1),a=n.reduce(function(s,l){return We.getMax(s,l.stopx)},r.x+r.width/2+1);return[i,a]},"activationBounds");o(Fc,"adjustLoopHeightForWrap");o(ZGe,"adjustCreatedDestroyedData");JGe=o(async function(t,e,r,n){let{securityLevel:i,sequence:a}=de();Ne=a;let s;i==="sandbox"&&(s=$e("#i"+e));let l=i==="sandbox"?$e(s.nodes()[0].contentDocument.body):$e("body"),u=i==="sandbox"?s.nodes()[0].contentDocument:document;Ke.init(),V.debug(n.db);let h=i==="sandbox"?l.select(`[id="${e}"]`):$e(`[id="${e}"]`),f=n.db.getActors(),d=n.db.getCreatedActors(),p=n.db.getDestroyedActors(),m=n.db.getBoxes(),g=n.db.getActorKeys(),y=n.db.getMessages(),v=n.db.getDiagramTitle(),x=n.db.hasAtLeastOneBox(),b=n.db.hasAtLeastOneBoxWithTitle(),w=await e$e(f,y,n);if(Ne.height=await r$e(f,w,m),si.insertComputerIcon(h),si.insertDatabaseIcon(h),si.insertClockIcon(h),x&&(Ke.bumpVerticalPos(Ne.boxMargin),b&&Ke.bumpVerticalPos(m[0].textMaxHeight)),Ne.hideUnusedParticipants===!0){let F=new Set;y.forEach(B=>{F.add(B.from),F.add(B.to)}),g=g.filter(B=>F.has(B))}QGe(h,f,d,g,0,y,!1);let S=await a$e(y,f,w,n);si.insertArrowHead(h),si.insertArrowCrossHead(h),si.insertArrowFilledHead(h),si.insertSequenceNumber(h);function T(F,B){let $=Ke.endActivation(F);$.starty+18>B&&($.starty=B-6,B+=12),si.drawActivation(h,$,B,Ne,bE(F.from).length),Ke.insert($.startx,B-10,$.stopx,B)}o(T,"activeEnd");let E=1,_=1,A=[],L=[],M=0;for(let F of y){let B,$,z;switch(F.type){case n.db.LINETYPE.NOTE:Ke.resetVerticalPos(),$=F.noteModel,await XGe(h,$);break;case n.db.LINETYPE.ACTIVE_START:Ke.newActivation(F,h,f);break;case n.db.LINETYPE.ACTIVE_END:T(F,Ke.getVerticalPos());break;case n.db.LINETYPE.LOOP_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.LOOP_END:B=Ke.endLoop(),await si.drawLoop(h,B,"loop",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.RECT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin,Y=>Ke.newLoop(void 0,Y.message));break;case n.db.LINETYPE.RECT_END:B=Ke.endLoop(),L.push(B),Ke.models.addLoop(B),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos());break;case n.db.LINETYPE.OPT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.OPT_END:B=Ke.endLoop(),await si.drawLoop(h,B,"opt",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.ALT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.ALT_ELSE:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.ALT_END:B=Ke.endLoop(),await si.drawLoop(h,B,"alt",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.PAR_START:case n.db.LINETYPE.PAR_OVER_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y)),Ke.saveVerticalPos();break;case n.db.LINETYPE.PAR_AND:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.PAR_END:B=Ke.endLoop(),await si.drawLoop(h,B,"par",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.AUTONUMBER:E=F.message.start||E,_=F.message.step||_,F.message.visible?n.db.enableSequenceNumbers():n.db.disableSequenceNumbers();break;case n.db.LINETYPE.CRITICAL_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.CRITICAL_OPTION:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.CRITICAL_END:B=Ke.endLoop(),await si.drawLoop(h,B,"critical",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.BREAK_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.BREAK_END:B=Ke.endLoop(),await si.drawLoop(h,B,"break",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;default:try{z=F.msgModel,z.starty=Ke.getVerticalPos(),z.sequenceIndex=E,z.sequenceVisible=n.db.showSequenceNumbers();let Y=await jGe(h,z);ZGe(F,z,Y,M,f,d,p),A.push({messageModel:z,lineStartY:Y}),Ke.models.addMessage(z)}catch(Y){V.error("error while drawing message",Y)}}[n.db.LINETYPE.SOLID_OPEN,n.db.LINETYPE.DOTTED_OPEN,n.db.LINETYPE.SOLID,n.db.LINETYPE.DOTTED,n.db.LINETYPE.SOLID_CROSS,n.db.LINETYPE.DOTTED_CROSS,n.db.LINETYPE.SOLID_POINT,n.db.LINETYPE.DOTTED_POINT,n.db.LINETYPE.BIDIRECTIONAL_SOLID,n.db.LINETYPE.BIDIRECTIONAL_DOTTED].includes(F.type)&&(E=E+_),M++}V.debug("createdActors",d),V.debug("destroyedActors",p),await bO(h,f,g,!1);for(let F of A)await KGe(h,F.messageModel,F.lineStartY,n);Ne.mirrorActors&&await bO(h,f,g,!0),L.forEach(F=>si.drawBackgroundRect(h,F)),yO(h,f,g,Ne);for(let F of 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this.yy=me||this.yy||{},this._input=_e,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var _e=this._input[0];this.yytext+=_e,this.yyleng++,this.offset++,this.match+=_e,this.matched+=_e;var me=_e.match(/(?:\r\n?|\n).*/g);return me?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),_e},"input"),unput:o(function(_e){var me=_e.length,W=_e.split(/(?:\r\n?|\n)/g);this._input=_e+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-me),this.offset-=me;var 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v=g.value,x=y.value,b=v[0]===x[0]&&v[1]===x[1]&&v[2]===x[2]&&(v[3]===x[3]||(v[3]==null||v[3]===1)&&(x[3]==null||x[3]===1));if(b)return!1}return{name:t,value:d,strValue:""+e,mapped:m,field:d[1],fieldMin:parseFloat(d[2]),fieldMax:parseFloat(d[3]),valueMin:g.value,valueMax:y.value,bypass:r}}}if(h.multiple&&n!=="multiple"){var w;if(u?w=e.split(/\s+/):vn(e)?w=e:w=[e],h.evenMultiple&&w.length%2!==0)return null;for(var S=[],T=[],E=[],_="",A=!1,L=0;L0?" 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i=e.boundingBox(),a=this.width(),s=this.height();r=r===void 0?this._private.zoom:r;var l={x:(a-r*(i.x1+i.x2))/2,y:(s-r*(i.y1+i.y2))/2};return l}}},"getCenterPan"),reset:o(function(){return!this._private.panningEnabled||!this._private.zoomingEnabled?this:(this.viewport({pan:{x:0,y:0},zoom:1}),this)},"reset"),invalidateSize:o(function(){this._private.sizeCache=null},"invalidateSize"),size:o(function(){var e=this._private,r=e.container,n=this;return e.sizeCache=e.sizeCache||(r?function(){var i=n.window().getComputedStyle(r),a=o(function(l){return parseFloat(i.getPropertyValue(l))},"val");return{width:r.clientWidth-a("padding-left")-a("padding-right"),height:r.clientHeight-a("padding-top")-a("padding-bottom")}}():{width:1,height:1})},"size"),width:o(function(){return this.size().width},"width"),height:o(function(){return this.size().height},"height"),extent:o(function(){var e=this._private.pan,r=this._private.zoom,n=this.renderedExtent(),i={x1:(n.x1-e.x)/r,x2:(n.x2-e.x)/r,y1:(n.y1-e.y)/r,y2:(n.y2-e.y)/r};return i.w=i.x2-i.x1,i.h=i.y2-i.y1,i},"extent"),renderedExtent:o(function(){var e=this.width(),r=this.height();return{x1:0,y1:0,x2:e,y2:r,w:e,h:r}},"renderedExtent"),multiClickDebounceTime:o(function(e){if(e)this._private.multiClickDebounceTime=e;else return this._private.multiClickDebounceTime;return this},"multiClickDebounceTime")};q0.centre=q0.center;q0.autolockNodes=q0.autolock;q0.autoungrabifyNodes=q0.autoungrabify;jx={data:en.data({field:"data",bindingEvent:"data",allowBinding:!0,allowSetting:!0,settingEvent:"data",settingTriggersEvent:!0,triggerFnName:"trigger",allowGetting:!0,updateStyle:!0}),removeData:en.removeData({field:"data",event:"data",triggerFnName:"trigger",triggerEvent:!0,updateStyle:!0}),scratch:en.data({field:"scratch",bindingEvent:"scratch",allowBinding:!0,allowSetting:!0,settingEvent:"scratch",settingTriggersEvent:!0,triggerFnName:"trigger",allowGetting:!0,updateStyle:!0}),removeScratch:en.removeData({field:"scratch",event:"scratch",triggerFnName:"trigger",triggerEvent:!0,updateStyle:!0})};jx.attr=jx.data;jx.removeAttr=jx.removeData;Kx=o(function(e){var r=this;e=Wt({},e);var n=e.container;n&&!k6(n)&&k6(n[0])&&(n=n[0]);var i=n?n._cyreg:null;i=i||{},i&&i.cy&&(i.cy.destroy(),i={});var a=i.readies=i.readies||[];n&&(n._cyreg=i),i.cy=r;var s=Vi!==void 0&&n!==void 0&&!e.headless,l=e;l.layout=Wt({name:s?"grid":"null"},l.layout),l.renderer=Wt({name:s?"canvas":"null"},l.renderer);var u=o(function(g,y,v){return y!==void 0?y:v!==void 0?v:g},"defVal"),h=this._private={container:n,ready:!1,options:l,elements:new Ca(this),listeners:[],aniEles:new Ca(this),data:l.data||{},scratch:{},layout:null,renderer:null,destroyed:!1,notificationsEnabled:!0,minZoom:1e-50,maxZoom:1e50,zoomingEnabled:u(!0,l.zoomingEnabled),userZoomingEnabled:u(!0,l.userZoomingEnabled),panningEnabled:u(!0,l.panningEnabled),userPanningEnabled:u(!0,l.userPanningEnabled),boxSelectionEnabled:u(!0,l.boxSelectionEnabled),autolock:u(!1,l.autolock,l.autolockNodes),autoungrabify:u(!1,l.autoungrabify,l.autoungrabifyNodes),autounselectify:u(!1,l.autounselectify),styleEnabled:l.styleEnabled===void 0?s:l.styleEnabled,zoom:ft(l.zoom)?l.zoom:1,pan:{x:Mr(l.pan)&&ft(l.pan.x)?l.pan.x:0,y:Mr(l.pan)&&ft(l.pan.y)?l.pan.y:0},animation:{current:[],queue:[]},hasCompoundNodes:!1,multiClickDebounceTime:u(250,l.multiClickDebounceTime)};this.createEmitter(),this.selectionType(l.selectionType),this.zoomRange({min:l.minZoom,max:l.maxZoom});var f=o(function(g,y){var v=g.some(zHe);if(v)return u1.all(g).then(y);y(g)},"loadExtData");h.styleEnabled&&r.setStyle([]);var d=Wt({},l,l.renderer);r.initRenderer(d);var p=o(function(g,y,v){r.notifications(!1);var x=r.mutableElements();x.length>0&&x.remove(),g!=null&&(Mr(g)||vn(g))&&r.add(g),r.one("layoutready",function(w){r.notifications(!0),r.emit(w),r.one("load",y),r.emitAndNotify("load")}).one("layoutstop",function(){r.one("done",v),r.emit("done")});var b=Wt({},r._private.options.layout);b.eles=r.elements(),r.layout(b).run()},"setElesAndLayout");f([l.style,l.elements],function(m){var g=m[0],y=m[1];h.styleEnabled&&r.style().append(g),p(y,function(){r.startAnimationLoop(),h.ready=!0,jn(l.ready)&&r.on("ready",l.ready);for(var v=0;v0,u=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),h;if(xo(e.roots))h=e.roots;else if(vn(e.roots)){for(var f=[],d=0;d0;){var O=C(),D=M(O,k);if(D)O.outgoers().filter(function(ue){return ue.isNode()&&n.has(ue)}).forEach(I);else if(D===null){tn("Detected double maximal shift for node `"+O.id()+"`. 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s=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=e.sweep===void 0?2*Math.PI-2*Math.PI/a.length:e.sweep,h=u/Math.max(1,a.length-1),f,d=0,p=0;p1&&e.avoidOverlap){d*=1.75;var x=Math.cos(h)-Math.cos(0),b=Math.sin(h)-Math.sin(0),w=Math.sqrt(d*d/(x*x+b*b));f=Math.max(w,f)}var S=o(function(E,_){var A=e.startAngle+_*h*(i?1:-1),L=f*Math.cos(A),M=f*Math.sin(A),N={x:l.x+L,y:l.y+M};return N},"getPos");return n.nodes().layoutPositions(this,e,S),this};GKe={fit:!0,padding:30,startAngle:3/2*Math.PI,sweep:void 0,clockwise:!0,equidistant:!1,minNodeSpacing:10,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,height:void 0,width:void 0,spacingFactor:void 0,concentric:o(function(e){return e.degree()},"concentric"),levelWidth:o(function(e){return e.maxDegree()/4},"levelWidth"),animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(Lme,"ConcentricLayout");Lme.prototype.run=function(){for(var t=this.options,e=t,r=e.counterclockwise!==void 0?!e.counterclockwise:e.clockwise,n=t.cy,i=e.eles,a=i.nodes().not(":parent"),s=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:n.width(),h:n.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=[],h=0,f=0;f0){var T=Math.abs(b[0].value-S.value);T>=v&&(b=[],x.push(b))}b.push(S)}var E=h+e.minNodeSpacing;if(!e.avoidOverlap){var _=x.length>0&&x[0].length>1,A=Math.min(s.w,s.h)/2-E,L=A/(x.length+_?1:0);E=Math.min(E,L)}for(var M=0,N=0;N1&&e.avoidOverlap){var O=Math.cos(C)-Math.cos(0),D=Math.sin(C)-Math.sin(0),P=Math.sqrt(E*E/(O*O+D*D));M=Math.max(P,M)}k.r=M,M+=E}if(e.equidistant){for(var F=0,B=0,$=0;$=t.numIter||(XKe(n,t),n.temperature=n.temperature*t.coolingFactor,n.temperature=t.animationThreshold&&a(),E6(d)}},"frame");f()}else{for(;h;)h=s(u),u++;rpe(n,t),l()}return this};j6.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this};j6.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};VKe=o(function(e,r,n){for(var i=n.eles.edges(),a=n.eles.nodes(),s=Gs(n.boundingBox?n.boundingBox:{x1:0,y1:0,w:e.width(),h:e.height()}),l={isCompound:e.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:a.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:i.size(),temperature:n.initialTemp,clientWidth:s.w,clientHeight:s.h,boundingBox:s},u=n.eles.components(),h={},f=0;f0){l.graphSet.push(A);for(var f=0;fi.count?0:i.graph},"findLCA"),HKe=o(function t(e,r,n,i){var a=i.graphSet[n];if(-10)var d=i.nodeOverlap*f,p=Math.sqrt(l*l+u*u),m=d*l/p,g=d*u/p;else var y=R6(e,l,u),v=R6(r,-1*l,-1*u),x=v.x-y.x,b=v.y-y.y,w=x*x+b*b,p=Math.sqrt(w),d=(e.nodeRepulsion+r.nodeRepulsion)/w,m=d*x/p,g=d*b/p;e.isLocked||(e.offsetX-=m,e.offsetY-=g),r.isLocked||(r.offsetX+=m,r.offsetY+=g)}},"nodeRepulsion"),QKe=o(function(e,r,n,i){if(n>0)var a=e.maxX-r.minX;else var a=r.maxX-e.minX;if(i>0)var s=e.maxY-r.minY;else var s=r.maxY-e.minY;return a>=0&&s>=0?Math.sqrt(a*a+s*s):0},"nodesOverlap"),R6=o(function(e,r,n){var i=e.positionX,a=e.positionY,s=e.height||1,l=e.width||1,u=n/r,h=s/l,f={};return r===0&&0n?(f.x=i,f.y=a+s/2,f):0r&&-1*h<=u&&u<=h?(f.x=i-l/2,f.y=a-l*n/2/r,f):0=h)?(f.x=i+s*r/2/n,f.y=a+s/2,f):(0>n&&(u<=-1*h||u>=h)&&(f.x=i-s*r/2/n,f.y=a-s/2),f)},"findClippingPoint"),ZKe=o(function(e,r){for(var n=0;nn){var v=r.gravity*m/y,x=r.gravity*g/y;p.offsetX+=v,p.offsetY+=x}}}}},"calculateGravityForces"),eQe=o(function(e,r){var n=[],i=0,a=-1;for(n.push.apply(n,e.graphSet[0]),a+=e.graphSet[0].length;i<=a;){var s=n[i++],l=e.idToIndex[s],u=e.layoutNodes[l],h=u.children;if(0n)var a={x:n*e/i,y:n*r/i};else var a={x:e,y:r};return a},"limitForce"),nQe=o(function t(e,r){var n=e.parentId;if(n!=null){var i=r.layoutNodes[r.idToIndex[n]],a=!1;if((i.maxX==null||e.maxX+i.padRight>i.maxX)&&(i.maxX=e.maxX+i.padRight,a=!0),(i.minX==null||e.minX-i.padLefti.maxY)&&(i.maxY=e.maxY+i.padBottom,a=!0),(i.minY==null||e.minY-i.padTopx&&(g+=v+r.componentSpacing,m=0,y=0,v=0)}}},"separateComponents"),iQe={fit:!0,padding:30,boundingBox:void 0,avoidOverlap:!0,avoidOverlapPadding:10,nodeDimensionsIncludeLabels:!1,spacingFactor:void 0,condense:!1,rows:void 0,cols:void 0,position:o(function(e){},"position"),sort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(Rme,"GridLayout");Rme.prototype.run=function(){var t=this.options,e=t,r=t.cy,n=e.eles,i=n.nodes().not(":parent");e.sort&&(i=i.sort(e.sort));var a=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()});if(a.h===0||a.w===0)n.nodes().layoutPositions(this,e,function(Q){return{x:a.x1,y:a.y1}});else{var s=i.size(),l=Math.sqrt(s*a.h/a.w),u=Math.round(l),h=Math.round(a.w/a.h*l),f=o(function(X){if(X==null)return Math.min(u,h);var ie=Math.min(u,h);ie==u?u=X:h=X},"small"),d=o(function(X){if(X==null)return Math.max(u,h);var ie=Math.max(u,h);ie==u?u=X:h=X},"large"),p=e.rows,m=e.cols!=null?e.cols:e.columns;if(p!=null&&m!=null)u=p,h=m;else if(p!=null&&m==null)u=p,h=Math.ceil(s/u);else if(p==null&&m!=null)h=m,u=Math.ceil(s/h);else if(h*u>s){var g=f(),y=d();(g-1)*y>=s?f(g-1):(y-1)*g>=s&&d(y-1)}else for(;h*u=s?d(x+1):f(v+1)}var b=a.w/h,w=a.h/u;if(e.condense&&(b=0,w=0),e.avoidOverlap)for(var S=0;S=h&&(O=0,C++)},"moveToNextCell"),P={},F=0;F(O=_We(t,e,D[P],D[P+1],D[P+2],D[P+3])))return v(_,O),!0}else if(L.edgeType==="bezier"||L.edgeType==="multibezier"||L.edgeType==="self"||L.edgeType==="compound"){for(var D=L.allpts,P=0;P+5(O=AWe(t,e,D[P],D[P+1],D[P+2],D[P+3],D[P+4],D[P+5])))return v(_,O),!0}for(var F=F||A.source,B=B||A.target,$=i.getArrowWidth(M,N),z=[{name:"source",x:L.arrowStartX,y:L.arrowStartY,angle:L.srcArrowAngle},{name:"target",x:L.arrowEndX,y:L.arrowEndY,angle:L.tgtArrowAngle},{name:"mid-source",x:L.midX,y:L.midY,angle:L.midsrcArrowAngle},{name:"mid-target",x:L.midX,y:L.midY,angle:L.midtgtArrowAngle}],P=0;P0&&(x(F),x(B))}o(b,"checkEdge");function w(_,A,L){return Ul(_,A,L)}o(w,"preprop");function S(_,A){var L=_._private,M=p,N;A?N=A+"-":N="",_.boundingBox();var k=L.labelBounds[A||"main"],I=_.pstyle(N+"label").value,C=_.pstyle("text-events").strValue==="yes";if(!(!C||!I)){var O=w(L.rscratch,"labelX",A),D=w(L.rscratch,"labelY",A),P=w(L.rscratch,"labelAngle",A),F=_.pstyle(N+"text-margin-x").pfValue,B=_.pstyle(N+"text-margin-y").pfValue,$=k.x1-M-F,z=k.x2+M-F,Y=k.y1-M-B,Q=k.y2+M-B;if(P){var X=Math.cos(P),ie=Math.sin(P),j=o(function(ce,ue){return ce=ce-O,ue=ue-D,{x:ce*X-ue*ie+O,y:ce*ie+ue*X+D}},"rotate"),J=j($,Y),Z=j($,Q),H=j(z,Y),q=j(z,Q),K=[J.x+F,J.y+B,H.x+F,H.y+B,q.x+F,q.y+B,Z.x+F,Z.y+B];if(zs(t,e,K))return v(_),!0}else if(s1(k,t,e))return v(_),!0}}o(S,"checkLabel");for(var T=s.length-1;T>=0;T--){var E=s[T];E.isNode()?x(E)||S(E):b(E)||S(E)||S(E,"source")||S(E,"target")}return l};j0.getAllInBox=function(t,e,r,n){var i=this.getCachedZSortedEles().interactive,a=[],s=Math.min(t,r),l=Math.max(t,r),u=Math.min(e,n),h=Math.max(e,n);t=s,r=l,e=u,n=h;for(var f=Gs({x1:t,y1:e,x2:r,y2:n}),d=0;d0?-(Math.PI-e.ang):Math.PI+e.ang},"invertVec"),uQe=o(function(e,r,n,i,a){if(e!==ope?lpe(r,e,Gc):cQe(Jo,Gc),lpe(r,n,Jo),ape=Gc.nx*Jo.ny-Gc.ny*Jo.nx,spe=Gc.nx*Jo.nx-Gc.ny*-Jo.ny,ju=Math.asin(Math.max(-1,Math.min(1,ape))),Math.abs(ju)<1e-6){GP=r.x,$P=r.y,z0=Qg=0;return}G0=1,w6=!1,spe<0?ju<0?ju=Math.PI+ju:(ju=Math.PI-ju,G0=-1,w6=!0):ju>0&&(G0=-1,w6=!0),r.radius!==void 0?Qg=r.radius:Qg=i,O0=ju/2,f6=Math.min(Gc.len/2,Jo.len/2),a?(zc=Math.abs(Math.cos(O0)*Qg/Math.sin(O0)),zc>f6?(zc=f6,z0=Math.abs(zc*Math.sin(O0)/Math.cos(O0))):z0=Qg):(zc=Math.min(f6,Qg),z0=Math.abs(zc*Math.sin(O0)/Math.cos(O0))),VP=r.x+Jo.nx*zc,UP=r.y+Jo.ny*zc,GP=VP-Jo.ny*z0*G0,$P=UP+Jo.nx*z0*G0,Ome=r.x+Gc.nx*zc,Pme=r.y+Gc.ny*zc,ope=r},"calcCornerArc");o(Bme,"drawPreparedRoundCorner");o(mB,"getRoundCorner");$a={};$a.findMidptPtsEtc=function(t,e){var r=e.posPts,n=e.intersectionPts,i=e.vectorNormInverse,a,s=t.pstyle("source-endpoint"),l=t.pstyle("target-endpoint"),u=s.units!=null&&l.units!=null,h=o(function(T,E,_,A){var L=A-E,M=_-T,N=Math.sqrt(M*M+L*L);return{x:-L/N,y:M/N}},"recalcVectorNormInverse"),f=t.pstyle("edge-distances").value;switch(f){case"node-position":a=r;break;case"intersection":a=n;break;case"endpoints":{if(u){var d=this.manualEndptToPx(t.source()[0],s),p=$l(d,2),m=p[0],g=p[1],y=this.manualEndptToPx(t.target()[0],l),v=$l(y,2),x=v[0],b=v[1],w={x1:m,y1:g,x2:x,y2:b};i=h(m,g,x,b),a=w}else tn("Edge ".concat(t.id()," has edge-distances:endpoints specified without manual endpoints specified via source-endpoint and target-endpoint. Falling back on edge-distances:intersection (default).")),a=n;break}}return{midptPts:a,vectorNormInverse:i}};$a.findHaystackPoints=function(t){for(var e=0;e0?Math.max(Te-Ce,0):Math.min(Te+Ce,0)},"subDWH"),I=k(M,A),C=k(N,L),O=!1;b===h?x=Math.abs(I)>Math.abs(C)?i:n:b===u||b===l?(x=n,O=!0):(b===a||b===s)&&(x=i,O=!0);var D=x===n,P=D?C:I,F=D?N:M,B=$pe(F),$=!1;!(O&&(S||E))&&(b===l&&F<0||b===u&&F>0||b===a&&F>0||b===s&&F<0)&&(B*=-1,P=B*Math.abs(P),$=!0);var z;if(S){var Y=T<0?1+T:T;z=Y*P}else{var Q=T<0?P:0;z=Q+T*B}var X=o(function(Te){return Math.abs(Te)<_||Math.abs(Te)>=Math.abs(P)},"getIsTooClose"),ie=X(z),j=X(Math.abs(P)-Math.abs(z)),J=ie||j;if(J&&!$)if(D){var Z=Math.abs(F)<=p/2,H=Math.abs(M)<=m/2;if(Z){var q=(f.x1+f.x2)/2,K=f.y1,se=f.y2;r.segpts=[q,K,q,se]}else if(H){var ce=(f.y1+f.y2)/2,ue=f.x1,te=f.x2;r.segpts=[ue,ce,te,ce]}else r.segpts=[f.x1,f.y2]}else{var De=Math.abs(F)<=d/2,oe=Math.abs(N)<=g/2;if(De){var ke=(f.y1+f.y2)/2,Ie=f.x1,Se=f.x2;r.segpts=[Ie,ke,Se,ke]}else if(oe){var Ue=(f.x1+f.x2)/2,Pe=f.y1,_e=f.y2;r.segpts=[Ue,Pe,Ue,_e]}else r.segpts=[f.x2,f.y1]}else if(D){var me=f.y1+z+(v?p/2*B:0),W=f.x1,fe=f.x2;r.segpts=[W,me,fe,me]}else{var ge=f.x1+z+(v?d/2*B:0),re=f.y1,he=f.y2;r.segpts=[ge,re,ge,he]}if(r.isRound){var ne=t.pstyle("taxi-radius").value,ae=t.pstyle("radius-type").value[0]==="arc-radius";r.radii=new Array(r.segpts.length/2).fill(ne),r.isArcRadius=new Array(r.segpts.length/2).fill(ae)}};$a.tryToCorrectInvalidPoints=function(t,e){var r=t._private.rscratch;if(r.edgeType==="bezier"){var n=e.srcPos,i=e.tgtPos,a=e.srcW,s=e.srcH,l=e.tgtW,u=e.tgtH,h=e.srcShape,f=e.tgtShape,d=e.srcCornerRadius,p=e.tgtCornerRadius,m=e.srcRs,g=e.tgtRs,y=!ft(r.startX)||!ft(r.startY),v=!ft(r.arrowStartX)||!ft(r.arrowStartY),x=!ft(r.endX)||!ft(r.endY),b=!ft(r.arrowEndX)||!ft(r.arrowEndY),w=3,S=this.getArrowWidth(t.pstyle("width").pfValue,t.pstyle("arrow-scale").value)*this.arrowShapeWidth,T=w*S,E=H0({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.startX,y:r.startY}),_=EC.poolIndex()){var O=I;I=C,C=O}var D=L.srcPos=I.position(),P=L.tgtPos=C.position(),F=L.srcW=I.outerWidth(),B=L.srcH=I.outerHeight(),$=L.tgtW=C.outerWidth(),z=L.tgtH=C.outerHeight(),Y=L.srcShape=r.nodeShapes[e.getNodeShape(I)],Q=L.tgtShape=r.nodeShapes[e.getNodeShape(C)],X=L.srcCornerRadius=I.pstyle("corner-radius").value==="auto"?"auto":I.pstyle("corner-radius").pfValue,ie=L.tgtCornerRadius=C.pstyle("corner-radius").value==="auto"?"auto":C.pstyle("corner-radius").pfValue,j=L.tgtRs=C._private.rscratch,J=L.srcRs=I._private.rscratch;L.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var Z=0;Z0){var se=a,ce=B0(se,Jg(r)),ue=B0(se,Jg(K)),te=ce;if(ue2){var De=B0(se,{x:K[2],y:K[3]});De0){var he=s,ne=B0(he,Jg(r)),ae=B0(he,Jg(re)),we=ne;if(ae2){var Te=B0(he,{x:re[2],y:re[3]});Te=g||_){v={cp:S,segment:E};break}}if(v)break}var A=v.cp,L=v.segment,M=(g-x)/L.length,N=L.t1-L.t0,k=m?L.t0+N*M:L.t1-N*M;k=Hx(0,k,1),e=t1(A.p0,A.p1,A.p2,k),p=fQe(A.p0,A.p1,A.p2,k);break}case"straight":case"segments":case"haystack":{for(var I=0,C,O,D,P,F=n.allpts.length,B=0;B+3=g));B+=2);var $=g-O,z=$/C;z=Hx(0,z,1),e=yWe(D,P,z),p=Gme(D,P);break}}s("labelX",d,e.x),s("labelY",d,e.y),s("labelAutoAngle",d,p)}},"calculateEndProjection");h("source"),h("target"),this.applyLabelDimensions(t)}};Hc.applyLabelDimensions=function(t){this.applyPrefixedLabelDimensions(t),t.isEdge()&&(this.applyPrefixedLabelDimensions(t,"source"),this.applyPrefixedLabelDimensions(t,"target"))};Hc.applyPrefixedLabelDimensions=function(t,e){var r=t._private,n=this.getLabelText(t,e),i=this.calculateLabelDimensions(t,n),a=t.pstyle("line-height").pfValue,s=t.pstyle("text-wrap").strValue,l=Ul(r.rscratch,"labelWrapCachedLines",e)||[],u=s!=="wrap"?1:Math.max(l.length,1),h=i.height/u,f=h*a,d=i.width,p=i.height+(u-1)*(a-1)*h;Tf(r.rstyle,"labelWidth",e,d),Tf(r.rscratch,"labelWidth",e,d),Tf(r.rstyle,"labelHeight",e,p),Tf(r.rscratch,"labelHeight",e,p),Tf(r.rscratch,"labelLineHeight",e,f)};Hc.getLabelText=function(t,e){var r=t._private,n=e?e+"-":"",i=t.pstyle(n+"label").strValue,a=t.pstyle("text-transform").value,s=o(function(Q,X){return X?(Tf(r.rscratch,Q,e,X),X):Ul(r.rscratch,Q,e)},"rscratch");if(!i)return"";a=="none"||(a=="uppercase"?i=i.toUpperCase():a=="lowercase"&&(i=i.toLowerCase()));var l=t.pstyle("text-wrap").value;if(l==="wrap"){var u=s("labelKey");if(u!=null&&s("labelWrapKey")===u)return s("labelWrapCachedText");for(var h="\u200B",f=i.split(` -`),d=t.pstyle("text-max-width").pfValue,p=t.pstyle("text-overflow-wrap").value,m=p==="anywhere",g=[],y=/[\s\u200b]+|$/g,v=0;vd){var T=x.matchAll(y),E="",_=0,A=Tpe(T),L;try{for(A.s();!(L=A.n()).done;){var M=L.value,N=M[0],k=x.substring(_,M.index);_=M.index+N.length;var I=E.length===0?k:E+k+N,C=this.calculateLabelDimensions(t,I),O=C.width;O<=d?E+=k+N:(E&&g.push(E),E=k+N)}}catch(Y){A.e(Y)}finally{A.f()}E.match(/^[\s\u200b]+$/)||g.push(E)}else g.push(x)}s("labelWrapCachedLines",g),i=s("labelWrapCachedText",g.join(` -`)),s("labelWrapKey",u)}else if(l==="ellipsis"){var D=t.pstyle("text-max-width").pfValue,P="",F="\u2026",B=!1;if(this.calculateLabelDimensions(t,i).widthD)break;P+=i[$],$===i.length-1&&(B=!0)}return B||(P+=F),P}return i};Hc.getLabelJustification=function(t){var e=t.pstyle("text-justification").strValue,r=t.pstyle("text-halign").strValue;if(e==="auto")if(t.isNode())switch(r){case"left":return"right";case"right":return"left";default:return"center"}else return"center";else return e};Hc.calculateLabelDimensions=function(t,e){var r=this,n=r.cy.window(),i=n.document,a=U0(e,t._private.labelDimsKey),s=r.labelDimCache||(r.labelDimCache=[]),l=s[a];if(l!=null)return l;var u=0,h=t.pstyle("font-style").strValue,f=t.pstyle("font-size").pfValue,d=t.pstyle("font-family").strValue,p=t.pstyle("font-weight").strValue,m=this.labelCalcCanvas,g=this.labelCalcCanvasContext;if(!m){m=this.labelCalcCanvas=i.createElement("canvas"),g=this.labelCalcCanvasContext=m.getContext("2d");var y=m.style;y.position="absolute",y.left="-9999px",y.top="-9999px",y.zIndex="-1",y.visibility="hidden",y.pointerEvents="none"}g.font="".concat(h," ").concat(p," ").concat(f,"px ").concat(d);for(var v=0,x=0,b=e.split(` -`),w=0;w1&&arguments[1]!==void 0?arguments[1]:!0;if(e.merge(s),l)for(var u=0;u=t.desktopTapThreshold2}var Je=i(W);ze&&(t.hoverData.tapholdCancelled=!0);var Ve=o(function(){var St=t.hoverData.dragDelta=t.hoverData.dragDelta||[];St.length===0?(St.push(ye[0]),St.push(ye[1])):(St[0]+=ye[0],St[1]+=ye[1])},"updateDragDelta");ge=!0,n(Ae,["mousemove","vmousemove","tapdrag"],W,{x:ae[0],y:ae[1]});var je=o(function(){t.data.bgActivePosistion=void 0,t.hoverData.selecting||re.emit({originalEvent:W,type:"boxstart",position:{x:ae[0],y:ae[1]}}),Ce[4]=1,t.hoverData.selecting=!0,t.redrawHint("select",!0),t.redraw()},"goIntoBoxMode");if(t.hoverData.which===3){if(ze){var kt={originalEvent:W,type:"cxtdrag",position:{x:ae[0],y:ae[1]}};Me?Me.emit(kt):re.emit(kt),t.hoverData.cxtDragged=!0,(!t.hoverData.cxtOver||Ae!==t.hoverData.cxtOver)&&(t.hoverData.cxtOver&&t.hoverData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:ae[0],y:ae[1]}}),t.hoverData.cxtOver=Ae,Ae&&Ae.emit({originalEvent:W,type:"cxtdragover",position:{x:ae[0],y:ae[1]}}))}}else if(t.hoverData.dragging){if(ge=!0,re.panningEnabled()&&re.userPanningEnabled()){var at;if(t.hoverData.justStartedPan){var xt=t.hoverData.mdownPos;at={x:(ae[0]-xt[0])*he,y:(ae[1]-xt[1])*he},t.hoverData.justStartedPan=!1}else at={x:ye[0]*he,y:ye[1]*he};re.panBy(at),re.emit("dragpan"),t.hoverData.dragged=!0}ae=t.projectIntoViewport(W.clientX,W.clientY)}else if(Ce[4]==1&&(Me==null||Me.pannable())){if(ze){if(!t.hoverData.dragging&&re.boxSelectionEnabled()&&(Je||!re.panningEnabled()||!re.userPanningEnabled()))je();else if(!t.hoverData.selecting&&re.panningEnabled()&&re.userPanningEnabled()){var it=a(Me,t.hoverData.downs);it&&(t.hoverData.dragging=!0,t.hoverData.justStartedPan=!0,Ce[4]=0,t.data.bgActivePosistion=Jg(we),t.redrawHint("select",!0),t.redraw())}Me&&Me.pannable()&&Me.active()&&Me.unactivate()}}else{if(Me&&Me.pannable()&&Me.active()&&Me.unactivate(),(!Me||!Me.grabbed())&&Ae!=Ge&&(Ge&&n(Ge,["mouseout","tapdragout"],W,{x:ae[0],y:ae[1]}),Ae&&n(Ae,["mouseover","tapdragover"],W,{x:ae[0],y:ae[1]}),t.hoverData.last=Ae),Me)if(ze){if(re.boxSelectionEnabled()&&Je)Me&&Me.grabbed()&&(v(He),Me.emit("freeon"),He.emit("free"),t.dragData.didDrag&&(Me.emit("dragfreeon"),He.emit("dragfree"))),je();else if(Me&&Me.grabbed()&&t.nodeIsDraggable(Me)){var dt=!t.dragData.didDrag;dt&&t.redrawHint("eles",!0),t.dragData.didDrag=!0,t.hoverData.draggingEles||g(He,{inDragLayer:!0});var lt={x:0,y:0};if(ft(ye[0])&&ft(ye[1])&&(lt.x+=ye[0],lt.y+=ye[1],dt)){var It=t.hoverData.dragDelta;It&&ft(It[0])&&ft(It[1])&&(lt.x+=It[0],lt.y+=It[1])}t.hoverData.draggingEles=!0,He.silentShift(lt).emit("position drag"),t.redrawHint("drag",!0),t.redraw()}}else Ve();ge=!0}if(Ce[2]=ae[0],Ce[3]=ae[1],ge)return W.stopPropagation&&W.stopPropagation(),W.preventDefault&&W.preventDefault(),!1}},"mousemoveHandler"),!1);var M,N,k;t.registerBinding(e,"mouseup",o(function(W){if(!(t.hoverData.which===1&&W.which!==1&&t.hoverData.capture)){var fe=t.hoverData.capture;if(fe){t.hoverData.capture=!1;var ge=t.cy,re=t.projectIntoViewport(W.clientX,W.clientY),he=t.selection,ne=t.findNearestElement(re[0],re[1],!0,!1),ae=t.dragData.possibleDragElements,we=t.hoverData.down,Te=i(W);if(t.data.bgActivePosistion&&(t.redrawHint("select",!0),t.redraw()),t.hoverData.tapholdCancelled=!0,t.data.bgActivePosistion=void 0,we&&we.unactivate(),t.hoverData.which===3){var Ce={originalEvent:W,type:"cxttapend",position:{x:re[0],y:re[1]}};if(we?we.emit(Ce):ge.emit(Ce),!t.hoverData.cxtDragged){var Ae={originalEvent:W,type:"cxttap",position:{x:re[0],y:re[1]}};we?we.emit(Ae):ge.emit(Ae)}t.hoverData.cxtDragged=!1,t.hoverData.which=null}else if(t.hoverData.which===1){if(n(ne,["mouseup","tapend","vmouseup"],W,{x:re[0],y:re[1]}),!t.dragData.didDrag&&!t.hoverData.dragged&&!t.hoverData.selecting&&!t.hoverData.isOverThresholdDrag&&(n(we,["click","tap","vclick"],W,{x:re[0],y:re[1]}),N=!1,W.timeStamp-k<=ge.multiClickDebounceTime()?(M&&clearTimeout(M),N=!0,k=null,n(we,["dblclick","dbltap","vdblclick"],W,{x:re[0],y:re[1]})):(M=setTimeout(function(){N||n(we,["oneclick","onetap","voneclick"],W,{x:re[0],y:re[1]})},ge.multiClickDebounceTime()),k=W.timeStamp)),we==null&&!t.dragData.didDrag&&!t.hoverData.selecting&&!t.hoverData.dragged&&!i(W)&&(ge.$(r).unselect(["tapunselect"]),ae.length>0&&t.redrawHint("eles",!0),t.dragData.possibleDragElements=ae=ge.collection()),ne==we&&!t.dragData.didDrag&&!t.hoverData.selecting&&ne!=null&&ne._private.selectable&&(t.hoverData.dragging||(ge.selectionType()==="additive"||Te?ne.selected()?ne.unselect(["tapunselect"]):ne.select(["tapselect"]):Te||(ge.$(r).unmerge(ne).unselect(["tapunselect"]),ne.select(["tapselect"]))),t.redrawHint("eles",!0)),t.hoverData.selecting){var Ge=ge.collection(t.getAllInBox(he[0],he[1],he[2],he[3]));t.redrawHint("select",!0),Ge.length>0&&t.redrawHint("eles",!0),ge.emit({type:"boxend",originalEvent:W,position:{x:re[0],y:re[1]}});var Me=o(function(ze){return ze.selectable()&&!ze.selected()},"eleWouldBeSelected");ge.selectionType()==="additive"||Te||ge.$(r).unmerge(Ge).unselect(),Ge.emit("box").stdFilter(Me).select().emit("boxselect"),t.redraw()}if(t.hoverData.dragging&&(t.hoverData.dragging=!1,t.redrawHint("select",!0),t.redrawHint("eles",!0),t.redraw()),!he[4]){t.redrawHint("drag",!0),t.redrawHint("eles",!0);var ye=we&&we.grabbed();v(ae),ye&&(we.emit("freeon"),ae.emit("free"),t.dragData.didDrag&&(we.emit("dragfreeon"),ae.emit("dragfree")))}}he[4]=0,t.hoverData.down=null,t.hoverData.cxtStarted=!1,t.hoverData.draggingEles=!1,t.hoverData.selecting=!1,t.hoverData.isOverThresholdDrag=!1,t.dragData.didDrag=!1,t.hoverData.dragged=!1,t.hoverData.dragDelta=[],t.hoverData.mdownPos=null,t.hoverData.mdownGPos=null}}},"mouseupHandler"),!1);var I=o(function(W){if(!t.scrollingPage){var fe=t.cy,ge=fe.zoom(),re=fe.pan(),he=t.projectIntoViewport(W.clientX,W.clientY),ne=[he[0]*ge+re.x,he[1]*ge+re.y];if(t.hoverData.draggingEles||t.hoverData.dragging||t.hoverData.cxtStarted||A()){W.preventDefault();return}if(fe.panningEnabled()&&fe.userPanningEnabled()&&fe.zoomingEnabled()&&fe.userZoomingEnabled()){W.preventDefault(),t.data.wheelZooming=!0,clearTimeout(t.data.wheelTimeout),t.data.wheelTimeout=setTimeout(function(){t.data.wheelZooming=!1,t.redrawHint("eles",!0),t.redraw()},150);var ae;W.deltaY!=null?ae=W.deltaY/-250:W.wheelDeltaY!=null?ae=W.wheelDeltaY/1e3:ae=W.wheelDelta/1e3,ae=ae*t.wheelSensitivity;var we=W.deltaMode===1;we&&(ae*=33);var Te=fe.zoom()*Math.pow(10,ae);W.type==="gesturechange"&&(Te=t.gestureStartZoom*W.scale),fe.zoom({level:Te,renderedPosition:{x:ne[0],y:ne[1]}}),fe.emit(W.type==="gesturechange"?"pinchzoom":"scrollzoom")}}},"wheelHandler");t.registerBinding(t.container,"wheel",I,!0),t.registerBinding(e,"scroll",o(function(W){t.scrollingPage=!0,clearTimeout(t.scrollingPageTimeout),t.scrollingPageTimeout=setTimeout(function(){t.scrollingPage=!1},250)},"scrollHandler"),!0),t.registerBinding(t.container,"gesturestart",o(function(W){t.gestureStartZoom=t.cy.zoom(),t.hasTouchStarted||W.preventDefault()},"gestureStartHandler"),!0),t.registerBinding(t.container,"gesturechange",function(me){t.hasTouchStarted||I(me)},!0),t.registerBinding(t.container,"mouseout",o(function(W){var fe=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseout",position:{x:fe[0],y:fe[1]}})},"mouseOutHandler"),!1),t.registerBinding(t.container,"mouseover",o(function(W){var fe=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseover",position:{x:fe[0],y:fe[1]}})},"mouseOverHandler"),!1);var C,O,D,P,F,B,$,z,Y,Q,X,ie,j,J=o(function(W,fe,ge,re){return Math.sqrt((ge-W)*(ge-W)+(re-fe)*(re-fe))},"distance"),Z=o(function(W,fe,ge,re){return(ge-W)*(ge-W)+(re-fe)*(re-fe)},"distanceSq"),H;t.registerBinding(t.container,"touchstart",H=o(function(W){if(t.hasTouchStarted=!0,!!L(W)){b(),t.touchData.capture=!0,t.data.bgActivePosistion=void 0;var fe=t.cy,ge=t.touchData.now,re=t.touchData.earlier;if(W.touches[0]){var he=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);ge[0]=he[0],ge[1]=he[1]}if(W.touches[1]){var he=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);ge[2]=he[0],ge[3]=he[1]}if(W.touches[2]){var he=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);ge[4]=he[0],ge[5]=he[1]}if(W.touches[1]){t.touchData.singleTouchMoved=!0,v(t.dragData.touchDragEles);var ne=t.findContainerClientCoords();Y=ne[0],Q=ne[1],X=ne[2],ie=ne[3],C=W.touches[0].clientX-Y,O=W.touches[0].clientY-Q,D=W.touches[1].clientX-Y,P=W.touches[1].clientY-Q,j=0<=C&&C<=X&&0<=D&&D<=X&&0<=O&&O<=ie&&0<=P&&P<=ie;var ae=fe.pan(),we=fe.zoom();F=J(C,O,D,P),B=Z(C,O,D,P),$=[(C+D)/2,(O+P)/2],z=[($[0]-ae.x)/we,($[1]-ae.y)/we];var Te=200,Ce=Te*Te;if(B=1){for(var gt=t.touchData.startPosition=[null,null,null,null,null,null],yt=0;yt=t.touchTapThreshold2}if(fe&&t.touchData.cxt){W.preventDefault();var gt=W.touches[0].clientX-Y,yt=W.touches[0].clientY-Q,tt=W.touches[1].clientX-Y,Ye=W.touches[1].clientY-Q,Je=Z(gt,yt,tt,Ye),Ve=Je/B,je=150,kt=je*je,at=1.5,xt=at*at;if(Ve>=xt||Je>=kt){t.touchData.cxt=!1,t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var it={originalEvent:W,type:"cxttapend",position:{x:he[0],y:he[1]}};t.touchData.start?(t.touchData.start.unactivate().emit(it),t.touchData.start=null):re.emit(it)}}if(fe&&t.touchData.cxt){var it={originalEvent:W,type:"cxtdrag",position:{x:he[0],y:he[1]}};t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.touchData.start?t.touchData.start.emit(it):re.emit(it),t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxtDragged=!0;var dt=t.findNearestElement(he[0],he[1],!0,!0);(!t.touchData.cxtOver||dt!==t.touchData.cxtOver)&&(t.touchData.cxtOver&&t.touchData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:he[0],y:he[1]}}),t.touchData.cxtOver=dt,dt&&dt.emit({originalEvent:W,type:"cxtdragover",position:{x:he[0],y:he[1]}}))}else if(fe&&W.touches[2]&&re.boxSelectionEnabled())W.preventDefault(),t.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,t.touchData.selecting||re.emit({originalEvent:W,type:"boxstart",position:{x:he[0],y:he[1]}}),t.touchData.selecting=!0,t.touchData.didSelect=!0,ge[4]=1,!ge||ge.length===0||ge[0]===void 0?(ge[0]=(he[0]+he[2]+he[4])/3,ge[1]=(he[1]+he[3]+he[5])/3,ge[2]=(he[0]+he[2]+he[4])/3+1,ge[3]=(he[1]+he[3]+he[5])/3+1):(ge[2]=(he[0]+he[2]+he[4])/3,ge[3]=(he[1]+he[3]+he[5])/3),t.redrawHint("select",!0),t.redraw();else if(fe&&W.touches[1]&&!t.touchData.didSelect&&re.zoomingEnabled()&&re.panningEnabled()&&re.userZoomingEnabled()&&re.userPanningEnabled()){W.preventDefault(),t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var lt=t.dragData.touchDragEles;if(lt){t.redrawHint("drag",!0);for(var It=0;It0&&!t.hoverData.draggingEles&&!t.swipePanning&&t.data.bgActivePosistion!=null&&(t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.redraw())}},"touchmoveHandler"),!1);var K;t.registerBinding(e,"touchcancel",K=o(function(W){var fe=t.touchData.start;t.touchData.capture=!1,fe&&fe.unactivate()},"touchcancelHandler"));var se,ce,ue,te;if(t.registerBinding(e,"touchend",se=o(function(W){var fe=t.touchData.start,ge=t.touchData.capture;if(ge)W.touches.length===0&&(t.touchData.capture=!1),W.preventDefault();else return;var re=t.selection;t.swipePanning=!1,t.hoverData.draggingEles=!1;var he=t.cy,ne=he.zoom(),ae=t.touchData.now,we=t.touchData.earlier;if(W.touches[0]){var Te=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);ae[0]=Te[0],ae[1]=Te[1]}if(W.touches[1]){var Te=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);ae[2]=Te[0],ae[3]=Te[1]}if(W.touches[2]){var Te=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);ae[4]=Te[0],ae[5]=Te[1]}fe&&fe.unactivate();var Ce;if(t.touchData.cxt){if(Ce={originalEvent:W,type:"cxttapend",position:{x:ae[0],y:ae[1]}},fe?fe.emit(Ce):he.emit(Ce),!t.touchData.cxtDragged){var Ae={originalEvent:W,type:"cxttap",position:{x:ae[0],y:ae[1]}};fe?fe.emit(Ae):he.emit(Ae)}t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxt=!1,t.touchData.start=null,t.redraw();return}if(!W.touches[2]&&he.boxSelectionEnabled()&&t.touchData.selecting){t.touchData.selecting=!1;var Ge=he.collection(t.getAllInBox(re[0],re[1],re[2],re[3]));re[0]=void 0,re[1]=void 0,re[2]=void 0,re[3]=void 0,re[4]=0,t.redrawHint("select",!0),he.emit({type:"boxend",originalEvent:W,position:{x:ae[0],y:ae[1]}});var Me=o(function(kt){return kt.selectable()&&!kt.selected()},"eleWouldBeSelected");Ge.emit("box").stdFilter(Me).select().emit("boxselect"),Ge.nonempty()&&t.redrawHint("eles",!0),t.redraw()}if(fe?.unactivate(),W.touches[2])t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);else if(!W.touches[1]){if(!W.touches[0]){if(!W.touches[0]){t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var ye=t.dragData.touchDragEles;if(fe!=null){var He=fe._private.grabbed;v(ye),t.redrawHint("drag",!0),t.redrawHint("eles",!0),He&&(fe.emit("freeon"),ye.emit("free"),t.dragData.didDrag&&(fe.emit("dragfreeon"),ye.emit("dragfree"))),n(fe,["touchend","tapend","vmouseup","tapdragout"],W,{x:ae[0],y:ae[1]}),fe.unactivate(),t.touchData.start=null}else{var ze=t.findNearestElement(ae[0],ae[1],!0,!0);n(ze,["touchend","tapend","vmouseup","tapdragout"],W,{x:ae[0],y:ae[1]})}var Ze=t.touchData.startPosition[0]-ae[0],gt=Ze*Ze,yt=t.touchData.startPosition[1]-ae[1],tt=yt*yt,Ye=gt+tt,Je=Ye*ne*ne;t.touchData.singleTouchMoved||(fe||he.$(":selected").unselect(["tapunselect"]),n(fe,["tap","vclick"],W,{x:ae[0],y:ae[1]}),ce=!1,W.timeStamp-te<=he.multiClickDebounceTime()?(ue&&clearTimeout(ue),ce=!0,te=null,n(fe,["dbltap","vdblclick"],W,{x:ae[0],y:ae[1]})):(ue=setTimeout(function(){ce||n(fe,["onetap","voneclick"],W,{x:ae[0],y:ae[1]})},he.multiClickDebounceTime()),te=W.timeStamp)),fe!=null&&!t.dragData.didDrag&&fe._private.selectable&&Je"u"){var De=[],oe=o(function(W){return{clientX:W.clientX,clientY:W.clientY,force:1,identifier:W.pointerId,pageX:W.pageX,pageY:W.pageY,radiusX:W.width/2,radiusY:W.height/2,screenX:W.screenX,screenY:W.screenY,target:W.target}},"makeTouch"),ke=o(function(W){return{event:W,touch:oe(W)}},"makePointer"),Ie=o(function(W){De.push(ke(W))},"addPointer"),Se=o(function(W){for(var fe=0;fe0)return Y[0]}return null},"getCurveT"),g=Object.keys(p),y=0;y0?m:Hpe(a,s,e,r,n,i,l,u)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){u=u==="auto"?Y0(i,a):u;var h=2*u;if(Qu(e,r,this.points,s,l,i,a-h,[0,-1],n)||Qu(e,r,this.points,s,l,i-h,a,[0,-1],n))return!0;var f=i/2+2*n,d=a/2+2*n,p=[s-f,l-d,s-f,l,s+f,l,s+f,l-d];return!!(zs(e,r,p)||$0(e,r,h,h,s+i/2-u,l+a/2-u,n)||$0(e,r,h,h,s-i/2+u,l+a/2-u,n))},"checkPoint")}};Ju.registerNodeShapes=function(){var t=this.nodeShapes={},e=this;this.generateEllipse(),this.generatePolygon("triangle",ls(3,0)),this.generateRoundPolygon("round-triangle",ls(3,0)),this.generatePolygon("rectangle",ls(4,0)),t.square=t.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();{var r=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",r),this.generateRoundPolygon("round-diamond",r)}this.generatePolygon("pentagon",ls(5,0)),this.generateRoundPolygon("round-pentagon",ls(5,0)),this.generatePolygon("hexagon",ls(6,0)),this.generateRoundPolygon("round-hexagon",ls(6,0)),this.generatePolygon("heptagon",ls(7,0)),this.generateRoundPolygon("round-heptagon",ls(7,0)),this.generatePolygon("octagon",ls(8,0)),this.generateRoundPolygon("round-octagon",ls(8,0));var n=new Array(20);{var i=NP(5,0),a=NP(5,Math.PI/5),s=.5*(3-Math.sqrt(5));s*=1.57;for(var l=0;l=e.deqFastCost*S)break}else if(h){if(b>=e.deqCost*m||b>=e.deqAvgCost*p)break}else if(w>=e.deqNoDrawCost*LP)break;var T=e.deq(n,v,y);if(T.length>0)for(var E=0;E0&&(e.onDeqd(n,g),!h&&e.shouldRedraw(n,g,v,y)&&a())},"dequeue"),l=e.priority||JP;i.beforeRender(s,l(n))}},"setupDequeueingImpl")},"setupDequeueing")},pQe=function(){function t(e){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:C6;XP(this,t),this.idsByKey=new Vc,this.keyForId=new Vc,this.cachesByLvl=new Vc,this.lvls=[],this.getKey=e,this.doesEleInvalidateKey=r}return o(t,"ElementTextureCacheLookup"),jP(t,[{key:"getIdsFor",value:o(function(r){r==null&&oi("Can not get id list for null key");var n=this.idsByKey,i=this.idsByKey.get(r);return i||(i=new c1,n.set(r,i)),i},"getIdsFor")},{key:"addIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).add(n)},"addIdForKey")},{key:"deleteIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).delete(n)},"deleteIdForKey")},{key:"getNumberOfIdsForKey",value:o(function(r){return r==null?0:this.getIdsFor(r).size},"getNumberOfIdsForKey")},{key:"updateKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);this.deleteIdForKey(i,n),this.addIdForKey(a,n),this.keyForId.set(n,a)},"updateKeyMappingFor")},{key:"deleteKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n);this.deleteIdForKey(i,n),this.keyForId.delete(n)},"deleteKeyMappingFor")},{key:"keyHasChangedFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);return i!==a},"keyHasChangedFor")},{key:"isInvalid",value:o(function(r){return this.keyHasChangedFor(r)||this.doesEleInvalidateKey(r)},"isInvalid")},{key:"getCachesAt",value:o(function(r){var n=this.cachesByLvl,i=this.lvls,a=n.get(r);return a||(a=new Vc,n.set(r,a),i.push(r)),a},"getCachesAt")},{key:"getCache",value:o(function(r,n){return this.getCachesAt(n).get(r)},"getCache")},{key:"get",value:o(function(r,n){var i=this.getKey(r),a=this.getCache(i,n);return 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this.rect.x+this.rect.width/2},h.prototype.getCenterY=function(){return this.rect.y+this.rect.height/2},h.prototype.getCenter=function(){return new u(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},h.prototype.getLocation=function(){return new u(this.rect.x,this.rect.y)},h.prototype.getRect=function(){return this.rect},h.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},h.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},h.prototype.setRect=function(d,p){this.rect.x=d.x,this.rect.y=d.y,this.rect.width=p.width,this.rect.height=p.height},h.prototype.setCenter=function(d,p){this.rect.x=d-this.rect.width/2,this.rect.y=p-this.rect.height/2},h.prototype.setLocation=function(d,p){this.rect.x=d,this.rect.y=p},h.prototype.moveBy=function(d,p){this.rect.x+=d,this.rect.y+=p},h.prototype.getEdgeListToNode=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(y.target==d){if(y.source!=g)throw"Incorrect edge source!";p.push(y)}}),p},h.prototype.getEdgesBetween=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(!(y.source==g||y.target==g))throw"Incorrect edge source and/or target";(y.target==d||y.source==d)&&p.push(y)}),p},h.prototype.getNeighborsList=function(){var d=new Set,p=this;return p.edges.forEach(function(m){if(m.source==p)d.add(m.target);else{if(m.target!=p)throw"Incorrect incidency!";d.add(m.source)}}),d},h.prototype.withChildren=function(){var d=new Set,p,m;if(d.add(this),this.child!=null)for(var g=this.child.getNodes(),y=0;yp&&(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)),this.labelHeight>m&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-m)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-m),this.setHeight(this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(6),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var S=x.source.owner.getEdges().indexOf(x);if(S==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(S,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),S=w.length,T=0;Tv&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,S,T,E,_,A=this.nodes,L=A.length,M=0;Mw&&(y=w),vT&&(x=T),bw&&(y=w),vT&&(x=T),b=this.nodes.length){var L=0;v.forEach(function(M){M.owner==g&&L++}),L==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(5),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d=s.getRight()?l[0]+=Math.min(s.getX()-a.getX(),a.getRight()-s.getRight()):s.getX()<=a.getX()&&s.getRight()>=a.getRight()&&(l[0]+=Math.min(a.getX()-s.getX(),s.getRight()-a.getRight())),a.getY()<=s.getY()&&a.getBottom()>=s.getBottom()?l[1]+=Math.min(s.getY()-a.getY(),a.getBottom()-s.getBottom()):s.getY()<=a.getY()&&s.getBottom()>=a.getBottom()&&(l[1]+=Math.min(a.getY()-s.getY(),s.getBottom()-a.getBottom()));var f=Math.abs((s.getCenterY()-a.getCenterY())/(s.getCenterX()-a.getCenterX()));s.getCenterY()===a.getCenterY()&&s.getCenterX()===a.getCenterX()&&(f=1);var d=f*l[0],p=l[1]/f;l[0]d)return l[0]=u,l[1]=m,l[2]=f,l[3]=A,!1;if(hf)return l[0]=p,l[1]=h,l[2]=E,l[3]=d,!1;if(uf?(l[0]=y,l[1]=v,k=!0):(l[0]=g,l[1]=m,k=!0):C===D&&(u>f?(l[0]=p,l[1]=m,k=!0):(l[0]=x,l[1]=v,k=!0)),-O===D?f>u?(l[2]=_,l[3]=A,I=!0):(l[2]=E,l[3]=T,I=!0):O===D&&(f>u?(l[2]=S,l[3]=T,I=!0):(l[2]=L,l[3]=A,I=!0)),k&&I)return!1;if(u>f?h>d?(P=this.getCardinalDirection(C,D,4),F=this.getCardinalDirection(O,D,2)):(P=this.getCardinalDirection(-C,D,3),F=this.getCardinalDirection(-O,D,1)):h>d?(P=this.getCardinalDirection(-C,D,1),F=this.getCardinalDirection(-O,D,3)):(P=this.getCardinalDirection(C,D,2),F=this.getCardinalDirection(O,D,4)),!k)switch(P){case 1:$=m,B=u+-w/D,l[0]=B,l[1]=$;break;case 2:B=x,$=h+b*D,l[0]=B,l[1]=$;break;case 3:$=v,B=u+w/D,l[0]=B,l[1]=$;break;case 4:B=y,$=h+-b*D,l[0]=B,l[1]=$;break}if(!I)switch(F){case 1:Y=T,z=f+-N/D,l[2]=z,l[3]=Y;break;case 2:z=L,Y=d+M*D,l[2]=z,l[3]=Y;break;case 3:Y=A,z=f+N/D,l[2]=z,l[3]=Y;break;case 4:z=_,Y=d+-M*D,l[2]=z,l[3]=Y;break}}return!1},i.getCardinalDirection=function(a,s,l){return a>s?l:1+l%4},i.getIntersection=function(a,s,l,u){if(u==null)return this.getIntersection2(a,s,l);var h=a.x,f=a.y,d=s.x,p=s.y,m=l.x,g=l.y,y=u.x,v=u.y,x=void 0,b=void 0,w=void 0,S=void 0,T=void 0,E=void 0,_=void 0,A=void 0,L=void 0;return w=p-f,T=h-d,_=d*f-h*p,S=v-g,E=m-y,A=y*g-m*v,L=w*E-S*T,L===0?null:(x=(T*A-E*_)/L,b=(S*_-w*A)/L,new n(x,b))},i.angleOfVector=function(a,s,l,u){var h=void 0;return a!==l?(h=Math.atan((u-s)/(l-a)),l0?1:i<0?-1:0},n.floor=function(i){return i<0?Math.ceil(i):Math.floor(i)},n.ceil=function(i){return i<0?Math.floor(i):Math.ceil(i)},t.exports=n},function(t,e,r){"use strict";function n(){}o(n,"Integer"),n.MAX_VALUE=2147483647,n.MIN_VALUE=-2147483648,t.exports=n},function(t,e,r){"use strict";var n=function(){function h(f,d){for(var p=0;p"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var _=E.getEdges(),x=0;x<_.length;x++){var A=_[x].getOtherEnd(E);if(S.get(E)!=A)if(!b.has(A))w.push(A),S.set(A,E);else{g=!1;break}}}if(!g)m=[];else{var L=[].concat(n(b));m.push(L);for(var x=0;x-1&&T.splice(N,1)}b=new Set,S=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x0){for(var v=this.edgeToDummyNodes.get(y),x=0;x=0&&g.splice(A,1);var L=S.getNeighborsList();L.forEach(function(k){if(y.indexOf(k)<0){var I=v.get(k),C=I-1;C==1&&E.push(k),v.set(k,C)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(4);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);pa.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var 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g=this.child.getNodes(),y=0;yp?(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)):this.labelPosHorizontal=="right"&&this.setWidth(p+this.labelWidth)),this.labelHeight&&(this.labelPosVertical=="top"?(this.rect.y-=this.labelHeight,this.setHeight(m+this.labelHeight)):this.labelPosVertical=="center"&&this.labelHeight>m?(this.rect.y-=(this.labelHeight-m)/2,this.setHeight(this.labelHeight)):this.labelPosVertical=="bottom"&&this.setHeight(m+this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";var n=r(0);function i(){}o(i,"FDLayoutConstants");for(var a in n)i[a]=n[a];i.MAX_ITERATIONS=2500,i.DEFAULT_EDGE_LENGTH=50,i.DEFAULT_SPRING_STRENGTH=.45,i.DEFAULT_REPULSION_STRENGTH=4500,i.DEFAULT_GRAVITY_STRENGTH=.4,i.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,i.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,i.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,i.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,i.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,i.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,i.COOLING_ADAPTATION_FACTOR=.33,i.ADAPTATION_LOWER_NODE_LIMIT=1e3,i.ADAPTATION_UPPER_NODE_LIMIT=5e3,i.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,i.MAX_NODE_DISPLACEMENT=i.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,i.MIN_REPULSION_DIST=i.DEFAULT_EDGE_LENGTH/10,i.CONVERGENCE_CHECK_PERIOD=100,i.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,i.MIN_EDGE_LENGTH=1,i.GRID_CALCULATION_CHECK_PERIOD=10,t.exports=i},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(7),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var S=x.source.owner.getEdges().indexOf(x);if(S==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(S,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),S=w.length,T=0;Tv&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,S,T,E,_,A=this.nodes,L=A.length,M=0;Mw&&(y=w),vT&&(x=T),bw&&(y=w),vT&&(x=T),b=this.nodes.length){var L=0;v.forEach(function(M){M.owner==g&&L++}),L==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(6),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d=s.getRight()?l[0]+=Math.min(s.getX()-a.getX(),a.getRight()-s.getRight()):s.getX()<=a.getX()&&s.getRight()>=a.getRight()&&(l[0]+=Math.min(a.getX()-s.getX(),s.getRight()-a.getRight())),a.getY()<=s.getY()&&a.getBottom()>=s.getBottom()?l[1]+=Math.min(s.getY()-a.getY(),a.getBottom()-s.getBottom()):s.getY()<=a.getY()&&s.getBottom()>=a.getBottom()&&(l[1]+=Math.min(a.getY()-s.getY(),s.getBottom()-a.getBottom()));var f=Math.abs((s.getCenterY()-a.getCenterY())/(s.getCenterX()-a.getCenterX()));s.getCenterY()===a.getCenterY()&&s.getCenterX()===a.getCenterX()&&(f=1);var d=f*l[0],p=l[1]/f;l[0]d)return l[0]=u,l[1]=m,l[2]=f,l[3]=A,!1;if(hf)return l[0]=p,l[1]=h,l[2]=E,l[3]=d,!1;if(uf?(l[0]=y,l[1]=v,k=!0):(l[0]=g,l[1]=m,k=!0):C===D&&(u>f?(l[0]=p,l[1]=m,k=!0):(l[0]=x,l[1]=v,k=!0)),-O===D?f>u?(l[2]=_,l[3]=A,I=!0):(l[2]=E,l[3]=T,I=!0):O===D&&(f>u?(l[2]=S,l[3]=T,I=!0):(l[2]=L,l[3]=A,I=!0)),k&&I)return!1;if(u>f?h>d?(P=this.getCardinalDirection(C,D,4),F=this.getCardinalDirection(O,D,2)):(P=this.getCardinalDirection(-C,D,3),F=this.getCardinalDirection(-O,D,1)):h>d?(P=this.getCardinalDirection(-C,D,1),F=this.getCardinalDirection(-O,D,3)):(P=this.getCardinalDirection(C,D,2),F=this.getCardinalDirection(O,D,4)),!k)switch(P){case 1:$=m,B=u+-w/D,l[0]=B,l[1]=$;break;case 2:B=x,$=h+b*D,l[0]=B,l[1]=$;break;case 3:$=v,B=u+w/D,l[0]=B,l[1]=$;break;case 4:B=y,$=h+-b*D,l[0]=B,l[1]=$;break}if(!I)switch(F){case 1:Y=T,z=f+-N/D,l[2]=z,l[3]=Y;break;case 2:z=L,Y=d+M*D,l[2]=z,l[3]=Y;break;case 3:Y=A,z=f+N/D,l[2]=z,l[3]=Y;break;case 4:z=_,Y=d+-M*D,l[2]=z,l[3]=Y;break}}return!1},i.getCardinalDirection=function(a,s,l){return a>s?l:1+l%4},i.getIntersection=function(a,s,l,u){if(u==null)return this.getIntersection2(a,s,l);var h=a.x,f=a.y,d=s.x,p=s.y,m=l.x,g=l.y,y=u.x,v=u.y,x=void 0,b=void 0,w=void 0,S=void 0,T=void 0,E=void 0,_=void 0,A=void 0,L=void 0;return w=p-f,T=h-d,_=d*f-h*p,S=v-g,E=m-y,A=y*g-m*v,L=w*E-S*T,L===0?null:(x=(T*A-E*_)/L,b=(S*_-w*A)/L,new n(x,b))},i.angleOfVector=function(a,s,l,u){var h=void 0;return a!==l?(h=Math.atan((u-s)/(l-a)),l=0){var v=(-m+Math.sqrt(m*m-4*p*g))/(2*p),x=(-m-Math.sqrt(m*m-4*p*g))/(2*p),b=null;return v>=0&&v<=1?[v]:x>=0&&x<=1?[x]:b}else return null},i.HALF_PI=.5*Math.PI,i.ONE_AND_HALF_PI=1.5*Math.PI,i.TWO_PI=2*Math.PI,i.THREE_PI=3*Math.PI,t.exports=i},function(t,e,r){"use strict";function n(){}o(n,"IMath"),n.sign=function(i){return i>0?1:i<0?-1:0},n.floor=function(i){return i<0?Math.ceil(i):Math.floor(i)},n.ceil=function(i){return i<0?Math.floor(i):Math.ceil(i)},t.exports=n},function(t,e,r){"use strict";function n(){}o(n,"Integer"),n.MAX_VALUE=2147483647,n.MIN_VALUE=-2147483648,t.exports=n},function(t,e,r){"use strict";var n=function(){function h(f,d){for(var p=0;p"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var _=E.getEdges(),x=0;x<_.length;x++){var A=_[x].getOtherEnd(E);if(S.get(E)!=A)if(!b.has(A))w.push(A),S.set(A,E);else{g=!1;break}}}if(!g)m=[];else{var L=[].concat(n(b));m.push(L);for(var x=0;x-1&&T.splice(N,1)}b=new Set,S=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x0){for(var v=this.edgeToDummyNodes.get(y),x=0;x=0&&g.splice(A,1);var L=S.getNeighborsList();L.forEach(function(k){if(y.indexOf(k)<0){var I=v.get(k),C=I-1;C==1&&E.push(k),v.set(k,C)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(5);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);pa.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var d=this.getAllEdges(),p,m=0;m0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&d&&this.updateGrid(),b=new Set,m=0;mw||b>w)&&(d.gravitationForceX=-this.gravityConstant*y,d.gravitationForceY=-this.gravityConstant*v)):(w=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,d.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var d,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),d=this.totalDisplacement=x.length||w>=x[0].length)){for(var S=0;Sh},"_defaultCompareFunction")}]),l}();t.exports=s},function(t,e,r){"use strict";function n(){}o(n,"SVD"),n.svd=function(i){this.U=null,this.V=null,this.s=null,this.m=0,this.n=0,this.m=i.length,this.n=i[0].length;var a=Math.min(this.m,this.n);this.s=function(it){for(var dt=[];it-- >0;)dt.push(0);return dt}(Math.min(this.m+1,this.n)),this.U=function(it){var dt=o(function lt(It){if(It.length==0)return 0;for(var mt=[],St=0;St0;)dt.push(0);return dt}(this.n),l=function(it){for(var dt=[];it-- >0;)dt.push(0);return dt}(this.m),u=!0,h=!0,f=Math.min(this.m-1,this.n),d=Math.max(0,Math.min(this.n-2,this.m)),p=0;p=0;D--)if(this.s[D]!==0){for(var P=D+1;P=0;X--){if(function(it,dt){return it&&dt}(X0;){var ue=void 0,te=void 0;for(ue=I-2;ue>=-1&&ue!==-1;ue--)if(Math.abs(s[ue])<=ce+se*(Math.abs(this.s[ue])+Math.abs(this.s[ue+1]))){s[ue]=0;break}if(ue===I-2)te=4;else{var De=void 0;for(De=I-1;De>=ue&&De!==ue;De--){var oe=(De!==I?Math.abs(s[De]):0)+(De!==ue+1?Math.abs(s[De-1]):0);if(Math.abs(this.s[De])<=ce+se*oe){this.s[De]=0;break}}De===ue?te=3:De===I-1?te=1:(te=2,ue=De)}switch(ue++,te){case 1:{var ke=s[I-2];s[I-2]=0;for(var Ie=I-2;Ie>=ue;Ie--){var Se=n.hypot(this.s[Ie],ke),Ue=this.s[Ie]/Se,Pe=ke/Se;if(this.s[Ie]=Se,Ie!==ue&&(ke=-Pe*s[Ie-1],s[Ie-1]=Ue*s[Ie-1]),h)for(var _e=0;_e=this.s[ue+1]);){var je=this.s[ue];if(this.s[ue]=this.s[ue+1],this.s[ue+1]=je,h&&ueMath.abs(a)?(s=a/i,s=Math.abs(i)*Math.sqrt(1+s*s)):a!=0?(s=i/a,s=Math.abs(a)*Math.sqrt(1+s*s)):s=0,s},t.exports=n},function(t,e,r){"use strict";var n=function(){function s(l,u){for(var h=0;h2&&arguments[2]!==void 0?arguments[2]:1,f=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,d=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;i(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=f,this.gap_penalty=d,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},i.emit=function(a,s){for(var l=0;l{"use strict";o(function(e,r){typeof vb=="object"&&typeof QB=="object"?QB.exports=r(KB()):typeof define=="function"&&define.amd?define(["layout-base"],r):typeof vb=="object"?vb.coseBase=r(KB()):e.coseBase=r(e.layoutBase)},"webpackUniversalModuleDefinition")(vb,function(t){return(()=>{"use strict";var e={45:(a,s,l)=>{var u={};u.layoutBase=l(551),u.CoSEConstants=l(806),u.CoSEEdge=l(767),u.CoSEGraph=l(880),u.CoSEGraphManager=l(578),u.CoSELayout=l(765),u.CoSENode=l(991),u.ConstraintHandler=l(902),a.exports=u},806:(a,s,l)=>{var u=l(551).FDLayoutConstants;function h(){}o(h,"CoSEConstants");for(var f in u)h[f]=u[f];h.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,h.DEFAULT_RADIAL_SEPARATION=u.DEFAULT_EDGE_LENGTH,h.DEFAULT_COMPONENT_SEPERATION=60,h.TILE=!0,h.TILING_PADDING_VERTICAL=10,h.TILING_PADDING_HORIZONTAL=10,h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!0,h.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,h.TREE_REDUCTION_ON_INCREMENTAL=!0,h.PURE_INCREMENTAL=h.DEFAULT_INCREMENTAL,a.exports=h},767:(a,s,l)=>{var u=l(551).FDLayoutEdge;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEEdge"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},880:(a,s,l)=>{var u=l(551).LGraph;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEGraph"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},578:(a,s,l)=>{var u=l(551).LGraphManager;function h(d){u.call(this,d)}o(h,"CoSEGraphManager"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},765:(a,s,l)=>{var u=l(551).FDLayout,h=l(578),f=l(880),d=l(991),p=l(767),m=l(806),g=l(902),y=l(551).FDLayoutConstants,v=l(551).LayoutConstants,x=l(551).Point,b=l(551).PointD,w=l(551).DimensionD,S=l(551).Layout,T=l(551).Integer,E=l(551).IGeometry,_=l(551).LGraph,A=l(551).Transform,L=l(551).LinkedList;function M(){u.call(this),this.toBeTiled={},this.constraints={}}o(M,"CoSELayout"),M.prototype=Object.create(u.prototype);for(var N in u)M[N]=u[N];M.prototype.newGraphManager=function(){var k=new h(this);return this.graphManager=k,k},M.prototype.newGraph=function(k){return new f(null,this.graphManager,k)},M.prototype.newNode=function(k){return new d(this.graphManager,k)},M.prototype.newEdge=function(k){return new p(null,null,k)},M.prototype.initParameters=function(){u.prototype.initParameters.call(this,arguments),this.isSubLayout||(m.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=m.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=m.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=y.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=y.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=y.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},M.prototype.initSpringEmbedder=function(){u.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/y.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},M.prototype.layout=function(){var k=v.DEFAULT_CREATE_BENDS_AS_NEEDED;return k&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},M.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(m.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var I=new Set(this.getAllNodes()),C=this.nodesWithGravity.filter(function(P){return I.has(P)});this.graphManager.setAllNodesToApplyGravitation(C)}}else{var k=this.getFlatForest();if(k.length>0)this.positionNodesRadially(k);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var I=new Set(this.getAllNodes()),C=this.nodesWithGravity.filter(function(O){return I.has(O)});this.graphManager.setAllNodesToApplyGravitation(C),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(g.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),m.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},M.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%y.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var k=new Set(this.getAllNodes()),I=this.nodesWithGravity.filter(function(D){return k.has(D)});this.graphManager.setAllNodesToApplyGravitation(I),this.graphManager.updateBounds(),this.updateGrid(),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var C=!this.isTreeGrowing&&!this.isGrowthFinished,O=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(C,O),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},M.prototype.getPositionsData=function(){for(var k=this.graphManager.getAllNodes(),I={},C=0;C0&&this.updateDisplacements();for(var C=0;C0&&(O.fixedNodeWeight=P)}}if(this.constraints.relativePlacementConstraint){var F=new Map,B=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(J){k.fixedNodesOnHorizontal.add(J),k.fixedNodesOnVertical.add(J)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var $=this.constraints.alignmentConstraint.vertical,C=0;C<$.length;C++)this.dummyToNodeForVerticalAlignment.set("dummy"+C,[]),$[C].forEach(function(Z){F.set(Z,"dummy"+C),k.dummyToNodeForVerticalAlignment.get("dummy"+C).push(Z),k.fixedNodeSet.has(Z)&&k.fixedNodesOnHorizontal.add("dummy"+C)});if(this.constraints.alignmentConstraint.horizontal)for(var z=this.constraints.alignmentConstraint.horizontal,C=0;C=2*J.length/3;q--)Z=Math.floor(Math.random()*(q+1)),H=J[q],J[q]=J[Z],J[Z]=H;return J},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(J){if(J.left){var Z=F.has(J.left)?F.get(J.left):J.left,H=F.has(J.right)?F.get(J.right):J.right;k.nodesInRelativeHorizontal.includes(Z)||(k.nodesInRelativeHorizontal.push(Z),k.nodeToRelativeConstraintMapHorizontal.set(Z,[]),k.dummyToNodeForVerticalAlignment.has(Z)?k.nodeToTempPositionMapHorizontal.set(Z,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(Z)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(Z,k.idToNodeMap.get(Z).getCenterX())),k.nodesInRelativeHorizontal.includes(H)||(k.nodesInRelativeHorizontal.push(H),k.nodeToRelativeConstraintMapHorizontal.set(H,[]),k.dummyToNodeForVerticalAlignment.has(H)?k.nodeToTempPositionMapHorizontal.set(H,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(H)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(H,k.idToNodeMap.get(H).getCenterX())),k.nodeToRelativeConstraintMapHorizontal.get(Z).push({right:H,gap:J.gap}),k.nodeToRelativeConstraintMapHorizontal.get(H).push({left:Z,gap:J.gap})}else{var q=B.has(J.top)?B.get(J.top):J.top,K=B.has(J.bottom)?B.get(J.bottom):J.bottom;k.nodesInRelativeVertical.includes(q)||(k.nodesInRelativeVertical.push(q),k.nodeToRelativeConstraintMapVertical.set(q,[]),k.dummyToNodeForHorizontalAlignment.has(q)?k.nodeToTempPositionMapVertical.set(q,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(q)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(q,k.idToNodeMap.get(q).getCenterY())),k.nodesInRelativeVertical.includes(K)||(k.nodesInRelativeVertical.push(K),k.nodeToRelativeConstraintMapVertical.set(K,[]),k.dummyToNodeForHorizontalAlignment.has(K)?k.nodeToTempPositionMapVertical.set(K,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(K)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(K,k.idToNodeMap.get(K).getCenterY())),k.nodeToRelativeConstraintMapVertical.get(q).push({bottom:K,gap:J.gap}),k.nodeToRelativeConstraintMapVertical.get(K).push({top:q,gap:J.gap})}});else{var Y=new Map,Q=new Map;this.constraints.relativePlacementConstraint.forEach(function(J){if(J.left){var Z=F.has(J.left)?F.get(J.left):J.left,H=F.has(J.right)?F.get(J.right):J.right;Y.has(Z)?Y.get(Z).push(H):Y.set(Z,[H]),Y.has(H)?Y.get(H).push(Z):Y.set(H,[Z])}else{var q=B.has(J.top)?B.get(J.top):J.top,K=B.has(J.bottom)?B.get(J.bottom):J.bottom;Q.has(q)?Q.get(q).push(K):Q.set(q,[K]),Q.has(K)?Q.get(K).push(q):Q.set(K,[q])}});var X=o(function(Z,H){var q=[],K=[],se=new L,ce=new Set,ue=0;return Z.forEach(function(te,De){if(!ce.has(De)){q[ue]=[],K[ue]=!1;var oe=De;for(se.push(oe),ce.add(oe),q[ue].push(oe);se.length!=0;){oe=se.shift(),H.has(oe)&&(K[ue]=!0);var ke=Z.get(oe);ke.forEach(function(Ie){ce.has(Ie)||(se.push(Ie),ce.add(Ie),q[ue].push(Ie))})}ue++}}),{components:q,isFixed:K}},"constructComponents"),ie=X(Y,k.fixedNodesOnHorizontal);this.componentsOnHorizontal=ie.components,this.fixedComponentsOnHorizontal=ie.isFixed;var j=X(Q,k.fixedNodesOnVertical);this.componentsOnVertical=j.components,this.fixedComponentsOnVertical=j.isFixed}}},M.prototype.updateDisplacements=function(){var k=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(j){var J=k.idToNodeMap.get(j.nodeId);J.displacementX=0,J.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var I=this.constraints.alignmentConstraint.vertical,C=0;C1){var B;for(B=0;BO&&(O=Math.floor(F.y)),P=Math.floor(F.x+m.DEFAULT_COMPONENT_SEPERATION)}this.transform(new b(v.WORLD_CENTER_X-F.x/2,v.WORLD_CENTER_Y-F.y/2))},M.radialLayout=function(k,I,C){var O=Math.max(this.maxDiagonalInTree(k),m.DEFAULT_RADIAL_SEPARATION);M.branchRadialLayout(I,null,0,359,0,O);var D=_.calculateBounds(k),P=new A;P.setDeviceOrgX(D.getMinX()),P.setDeviceOrgY(D.getMinY()),P.setWorldOrgX(C.x),P.setWorldOrgY(C.y);for(var F=0;F1;){var q=H[0];H.splice(0,1);var K=X.indexOf(q);K>=0&&X.splice(K,1),J--,ie--}I!=null?Z=(X.indexOf(H[0])+1)%J:Z=0;for(var se=Math.abs(O-C)/ie,ce=Z;j!=ie;ce=++ce%J){var ue=X[ce].getOtherEnd(k);if(ue!=I){var te=(C+j*se)%360,De=(te+se)%360;M.branchRadialLayout(ue,k,te,De,D+P,P),j++}}},M.maxDiagonalInTree=function(k){for(var I=T.MIN_VALUE,C=0;CI&&(I=D)}return I},M.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},M.prototype.groupZeroDegreeMembers=function(){var k=this,I={};this.memberGroups={},this.idToDummyNode={};for(var C=[],O=this.graphManager.getAllNodes(),D=0;D"u"&&(I[B]=[]),I[B]=I[B].concat(P)}Object.keys(I).forEach(function($){if(I[$].length>1){var z="DummyCompound_"+$;k.memberGroups[z]=I[$];var Y=I[$][0].getParent(),Q=new d(k.graphManager);Q.id=z,Q.paddingLeft=Y.paddingLeft||0,Q.paddingRight=Y.paddingRight||0,Q.paddingBottom=Y.paddingBottom||0,Q.paddingTop=Y.paddingTop||0,k.idToDummyNode[z]=Q;var X=k.getGraphManager().add(k.newGraph(),Q),ie=Y.getChild();ie.add(Q);for(var j=0;jD?(O.rect.x-=(O.labelWidth-D)/2,O.setWidth(O.labelWidth),O.labelMarginLeft=(O.labelWidth-D)/2):O.labelPosHorizontal=="right"&&O.setWidth(D+O.labelWidth)),O.labelHeight&&(O.labelPosVertical=="top"?(O.rect.y-=O.labelHeight,O.setHeight(P+O.labelHeight),O.labelMarginTop=O.labelHeight):O.labelPosVertical=="center"&&O.labelHeight>P?(O.rect.y-=(O.labelHeight-P)/2,O.setHeight(O.labelHeight),O.labelMarginTop=(O.labelHeight-P)/2):O.labelPosVertical=="bottom"&&O.setHeight(P+O.labelHeight))}})},M.prototype.repopulateCompounds=function(){for(var k=this.compoundOrder.length-1;k>=0;k--){var I=this.compoundOrder[k],C=I.id,O=I.paddingLeft,D=I.paddingTop,P=I.labelMarginLeft,F=I.labelMarginTop;this.adjustLocations(this.tiledMemberPack[C],I.rect.x,I.rect.y,O,D,P,F)}},M.prototype.repopulateZeroDegreeMembers=function(){var k=this,I=this.tiledZeroDegreePack;Object.keys(I).forEach(function(C){var O=k.idToDummyNode[C],D=O.paddingLeft,P=O.paddingTop,F=O.labelMarginLeft,B=O.labelMarginTop;k.adjustLocations(I[C],O.rect.x,O.rect.y,D,P,F,B)})},M.prototype.getToBeTiled=function(k){var I=k.id;if(this.toBeTiled[I]!=null)return this.toBeTiled[I];var C=k.getChild();if(C==null)return this.toBeTiled[I]=!1,!1;for(var O=C.getNodes(),D=0;D0)return this.toBeTiled[I]=!1,!1;if(P.getChild()==null){this.toBeTiled[P.id]=!1;continue}if(!this.getToBeTiled(P))return this.toBeTiled[I]=!1,!1}return this.toBeTiled[I]=!0,!0},M.prototype.getNodeDegree=function(k){for(var I=k.id,C=k.getEdges(),O=0,D=0;DY&&(Y=X.rect.height)}C+=Y+k.verticalPadding}},M.prototype.tileCompoundMembers=function(k,I){var C=this;this.tiledMemberPack=[],Object.keys(k).forEach(function(O){var D=I[O];if(C.tiledMemberPack[O]=C.tileNodes(k[O],D.paddingLeft+D.paddingRight),D.rect.width=C.tiledMemberPack[O].width,D.rect.height=C.tiledMemberPack[O].height,D.setCenter(C.tiledMemberPack[O].centerX,C.tiledMemberPack[O].centerY),D.labelMarginLeft=0,D.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var P=D.rect.width,F=D.rect.height;D.labelWidth&&(D.labelPosHorizontal=="left"?(D.rect.x-=D.labelWidth,D.setWidth(P+D.labelWidth),D.labelMarginLeft=D.labelWidth):D.labelPosHorizontal=="center"&&D.labelWidth>P?(D.rect.x-=(D.labelWidth-P)/2,D.setWidth(D.labelWidth),D.labelMarginLeft=(D.labelWidth-P)/2):D.labelPosHorizontal=="right"&&D.setWidth(P+D.labelWidth)),D.labelHeight&&(D.labelPosVertical=="top"?(D.rect.y-=D.labelHeight,D.setHeight(F+D.labelHeight),D.labelMarginTop=D.labelHeight):D.labelPosVertical=="center"&&D.labelHeight>F?(D.rect.y-=(D.labelHeight-F)/2,D.setHeight(D.labelHeight),D.labelMarginTop=(D.labelHeight-F)/2):D.labelPosVertical=="bottom"&&D.setHeight(F+D.labelHeight))}})},M.prototype.tileNodes=function(k,I){var C=this.tileNodesByFavoringDim(k,I,!0),O=this.tileNodesByFavoringDim(k,I,!1),D=this.getOrgRatio(C),P=this.getOrgRatio(O),F;return PB&&(B=j.getWidth())});var $=P/D,z=F/D,Y=Math.pow(C-O,2)+4*($+O)*(z+C)*D,Q=(O-C+Math.sqrt(Y))/(2*($+O)),X;I?(X=Math.ceil(Q),X==Q&&X++):X=Math.floor(Q);var ie=X*($+O)-O;return B>ie&&(ie=B),ie+=O*2,ie},M.prototype.tileNodesByFavoringDim=function(k,I,C){var O=m.TILING_PADDING_VERTICAL,D=m.TILING_PADDING_HORIZONTAL,P=m.TILING_COMPARE_BY,F={rows:[],rowWidth:[],rowHeight:[],width:0,height:I,verticalPadding:O,horizontalPadding:D,centerX:0,centerY:0};P&&(F.idealRowWidth=this.calcIdealRowWidth(k,C));var B=o(function(J){return J.rect.width*J.rect.height},"getNodeArea"),$=o(function(J,Z){return B(Z)-B(J)},"areaCompareFcn");k.sort(function(j,J){var Z=$;return F.idealRowWidth?(Z=P,Z(j.id,J.id)):Z(j,J)});for(var z=0,Y=0,Q=0;Q0&&(F+=k.horizontalPadding),k.rowWidth[C]=F,k.width0&&(B+=k.verticalPadding);var $=0;B>k.rowHeight[C]&&($=k.rowHeight[C],k.rowHeight[C]=B,$=k.rowHeight[C]-$),k.height+=$,k.rows[C].push(I)},M.prototype.getShortestRowIndex=function(k){for(var I=-1,C=Number.MAX_VALUE,O=0;OC&&(I=O,C=k.rowWidth[O]);return 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u=l(551).FDLayoutNode,h=l(551).IMath;function f(p,m,g,y){u.call(this,p,m,g,y)}o(f,"CoSENode"),f.prototype=Object.create(u.prototype);for(var d in u)f[d]=u[d];f.prototype.calculateDisplacement=function(){var p=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementX=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementX)),Math.abs(this.displacementY)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementY=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},f.prototype.propogateDisplacementToChildren=function(p,m){for(var g=this.getChild().getNodes(),y,v=0;v{function u(g){if(Array.isArray(g)){for(var y=0,v=Array(g.length);y0){var Je=0;Ye.forEach(function(je){we=="horizontal"?(ye.set(je,x.has(je)?b[x.get(je)]:Ce.get(je)),Je+=ye.get(je)):(ye.set(je,x.has(je)?w[x.get(je)]:Ce.get(je)),Je+=ye.get(je))}),Je=Je/Ye.length,tt.forEach(function(je){Te.has(je)||ye.set(je,Je)})}else{var Ve=0;tt.forEach(function(je){we=="horizontal"?Ve+=x.has(je)?b[x.get(je)]:Ce.get(je):Ve+=x.has(je)?w[x.get(je)]:Ce.get(je)}),Ve=Ve/tt.length,tt.forEach(function(je){ye.set(je,Ve)})}});for(var Ze=o(function(){var Ye=ze.shift(),Je=ae.get(Ye);Je.forEach(function(Ve){if(ye.get(Ve.id)je&&(je=mt),Stkt&&(kt=St)}}catch(Qn){xt=!0,it=Qn}finally{try{!at&&dt.return&&dt.return()}finally{if(xt)throw it}}var gr=(Je+je)/2-(Ve+kt)/2,xn=!0,jt=!1,rn=void 0;try{for(var Er=tt[Symbol.iterator](),Kn;!(xn=(Kn=Er.next()).done);xn=!0){var hn=Kn.value;ye.set(hn,ye.get(hn)+gr)}}catch(Qn){jt=!0,rn=Qn}finally{try{!xn&&Er.return&&Er.return()}finally{if(jt)throw rn}}})}return ye},"findAppropriatePositionForRelativePlacement"),N=o(function(ae){var we=0,Te=0,Ce=0,Ae=0;if(ae.forEach(function(He){He.left?b[x.get(He.left)]-b[x.get(He.right)]>=0?we++:Te++:w[x.get(He.top)]-w[x.get(He.bottom)]>=0?Ce++:Ae++}),we>Te&&Ce>Ae)for(var Ge=0;GeTe)for(var Me=0;MeAe)for(var ye=0;ye1)y.fixedNodeConstraint.forEach(function(ne,ae){O[ae]=[ne.position.x,ne.position.y],D[ae]=[b[x.get(ne.nodeId)],w[x.get(ne.nodeId)]]}),P=!0;else if(y.alignmentConstraint)(function(){var ne=0;if(y.alignmentConstraint.vertical){for(var ae=y.alignmentConstraint.vertical,we=o(function(ye){var He=new Set;ae[ye].forEach(function(gt){He.add(gt)});var ze=new Set([].concat(u(He)).filter(function(gt){return B.has(gt)})),Ze=void 0;ze.size>0?Ze=b[x.get(ze.values().next().value)]:Ze=L(He).x,ae[ye].forEach(function(gt){O[ne]=[Ze,w[x.get(gt)]],D[ne]=[b[x.get(gt)],w[x.get(gt)]],ne++})},"_loop2"),Te=0;Te0?Ze=b[x.get(ze.values().next().value)]:Ze=L(He).y,Ce[ye].forEach(function(gt){O[ne]=[b[x.get(gt)],Ze],D[ne]=[b[x.get(gt)],w[x.get(gt)]],ne++})},"_loop3"),Ge=0;GeQ&&(Q=Y[ie].length,X=ie);if(Q0){var Ue={x:0,y:0};y.fixedNodeConstraint.forEach(function(ne,ae){var we={x:b[x.get(ne.nodeId)],y:w[x.get(ne.nodeId)]},Te=ne.position,Ce=A(Te,we);Ue.x+=Ce.x,Ue.y+=Ce.y}),Ue.x/=y.fixedNodeConstraint.length,Ue.y/=y.fixedNodeConstraint.length,b.forEach(function(ne,ae){b[ae]+=Ue.x}),w.forEach(function(ne,ae){w[ae]+=Ue.y}),y.fixedNodeConstraint.forEach(function(ne){b[x.get(ne.nodeId)]=ne.position.x,w[x.get(ne.nodeId)]=ne.position.y})}if(y.alignmentConstraint){if(y.alignmentConstraint.vertical)for(var Pe=y.alignmentConstraint.vertical,_e=o(function(ae){var we=new Set;Pe[ae].forEach(function(Ae){we.add(Ae)});var Te=new Set([].concat(u(we)).filter(function(Ae){return B.has(Ae)})),Ce=void 0;Te.size>0?Ce=b[x.get(Te.values().next().value)]:Ce=L(we).x,we.forEach(function(Ae){B.has(Ae)||(b[x.get(Ae)]=Ce)})},"_loop4"),me=0;me0?Ce=w[x.get(Te.values().next().value)]:Ce=L(we).y,we.forEach(function(Ae){B.has(Ae)||(w[x.get(Ae)]=Ce)})},"_loop5"),ge=0;ge{a.exports=t}},r={};function n(a){var s=r[a];if(s!==void 0)return s.exports;var l=r[a]={exports:{}};return e[a](l,l.exports,n),l.exports}o(n,"__webpack_require__");var i=n(45);return i})()})});var X1e=gi((xb,JB)=>{"use strict";o(function(e,r){typeof xb=="object"&&typeof JB=="object"?JB.exports=r(ZB()):typeof define=="function"&&define.amd?define(["cose-base"],r):typeof xb=="object"?xb.cytoscapeFcose=r(ZB()):e.cytoscapeFcose=r(e.coseBase)},"webpackUniversalModuleDefinition")(xb,function(t){return(()=>{"use strict";var e={658:a=>{a.exports=Object.assign!=null?Object.assign.bind(Object):function(s){for(var l=arguments.length,u=Array(l>1?l-1:0),h=1;h{var u=function(){function d(p,m){var g=[],y=!0,v=!1,x=void 0;try{for(var b=p[Symbol.iterator](),w;!(y=(w=b.next()).done)&&(g.push(w.value),!(m&&g.length===m));y=!0);}catch(S){v=!0,x=S}finally{try{!y&&b.return&&b.return()}finally{if(v)throw x}}return g}return o(d,"sliceIterator"),function(p,m){if(Array.isArray(p))return p;if(Symbol.iterator in Object(p))return d(p,m);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),h=l(140).layoutBase.LinkedList,f={};f.getTopMostNodes=function(d){for(var p={},m=0;m0&&P.merge(z)});for(var F=0;F1){w=x[0],S=w.connectedEdges().length,x.forEach(function(D){D.connectedEdges().length0&&g.set("dummy"+(g.size+1),_),A},f.relocateComponent=function(d,p,m){if(!m.fixedNodeConstraint){var g=Number.POSITIVE_INFINITY,y=Number.NEGATIVE_INFINITY,v=Number.POSITIVE_INFINITY,x=Number.NEGATIVE_INFINITY;if(m.quality=="draft"){var b=!0,w=!1,S=void 0;try{for(var T=p.nodeIndexes[Symbol.iterator](),E;!(b=(E=T.next()).done);b=!0){var _=E.value,A=u(_,2),L=A[0],M=A[1],N=m.cy.getElementById(L);if(N){var k=N.boundingBox(),I=p.xCoords[M]-k.w/2,C=p.xCoords[M]+k.w/2,O=p.yCoords[M]-k.h/2,D=p.yCoords[M]+k.h/2;Iy&&(y=C),Ox&&(x=D)}}}catch(z){w=!0,S=z}finally{try{!b&&T.return&&T.return()}finally{if(w)throw S}}var P=d.x-(y+g)/2,F=d.y-(x+v)/2;p.xCoords=p.xCoords.map(function(z){return z+P}),p.yCoords=p.yCoords.map(function(z){return z+F})}else{Object.keys(p).forEach(function(z){var Y=p[z],Q=Y.getRect().x,X=Y.getRect().x+Y.getRect().width,ie=Y.getRect().y,j=Y.getRect().y+Y.getRect().height;Qy&&(y=X),iex&&(x=j)});var B=d.x-(y+g)/2,$=d.y-(x+v)/2;Object.keys(p).forEach(function(z){var Y=p[z];Y.setCenter(Y.getCenterX()+B,Y.getCenterY()+$)})}}},f.calcBoundingBox=function(d,p,m,g){for(var y=Number.MAX_SAFE_INTEGER,v=Number.MIN_SAFE_INTEGER,x=Number.MAX_SAFE_INTEGER,b=Number.MIN_SAFE_INTEGER,w=void 0,S=void 0,T=void 0,E=void 0,_=d.descendants().not(":parent"),A=_.length,L=0;Lw&&(y=w),vT&&(x=T),b{var u=l(548),h=l(140).CoSELayout,f=l(140).CoSENode,d=l(140).layoutBase.PointD,p=l(140).layoutBase.DimensionD,m=l(140).layoutBase.LayoutConstants,g=l(140).layoutBase.FDLayoutConstants,y=l(140).CoSEConstants,v=o(function(b,w){var S=b.cy,T=b.eles,E=T.nodes(),_=T.edges(),A=void 0,L=void 0,M=void 0,N={};b.randomize&&(A=w.nodeIndexes,L=w.xCoords,M=w.yCoords);var k=o(function(z){return typeof z=="function"},"isFn"),I=o(function(z,Y){return k(z)?z(Y):z},"optFn"),C=u.calcParentsWithoutChildren(S,T),O=o(function $(z,Y,Q,X){for(var ie=Y.length,j=0;j0){var se=void 0;se=Q.getGraphManager().add(Q.newGraph(),H),$(se,Z,Q,X)}}},"processChildrenList"),D=o(function(z,Y,Q){for(var X=0,ie=0,j=0;j0?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=X/ie:k(b.idealEdgeLength)?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=50:y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=b.idealEdgeLength,y.MIN_REPULSION_DIST=g.MIN_REPULSION_DIST=g.DEFAULT_EDGE_LENGTH/10,y.DEFAULT_RADIAL_SEPARATION=g.DEFAULT_EDGE_LENGTH)},"processEdges"),P=o(function(z,Y){Y.fixedNodeConstraint&&(z.constraints.fixedNodeConstraint=Y.fixedNodeConstraint),Y.alignmentConstraint&&(z.constraints.alignmentConstraint=Y.alignmentConstraint),Y.relativePlacementConstraint&&(z.constraints.relativePlacementConstraint=Y.relativePlacementConstraint)},"processConstraints");b.nestingFactor!=null&&(y.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=g.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=b.nestingFactor),b.gravity!=null&&(y.DEFAULT_GRAVITY_STRENGTH=g.DEFAULT_GRAVITY_STRENGTH=b.gravity),b.numIter!=null&&(y.MAX_ITERATIONS=g.MAX_ITERATIONS=b.numIter),b.gravityRange!=null&&(y.DEFAULT_GRAVITY_RANGE_FACTOR=g.DEFAULT_GRAVITY_RANGE_FACTOR=b.gravityRange),b.gravityCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_STRENGTH=g.DEFAULT_COMPOUND_GRAVITY_STRENGTH=b.gravityCompound),b.gravityRangeCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=g.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=b.gravityRangeCompound),b.initialEnergyOnIncremental!=null&&(y.DEFAULT_COOLING_FACTOR_INCREMENTAL=g.DEFAULT_COOLING_FACTOR_INCREMENTAL=b.initialEnergyOnIncremental),b.tilingCompareBy!=null&&(y.TILING_COMPARE_BY=b.tilingCompareBy),b.quality=="proof"?m.QUALITY=2:m.QUALITY=0,y.NODE_DIMENSIONS_INCLUDE_LABELS=g.NODE_DIMENSIONS_INCLUDE_LABELS=m.NODE_DIMENSIONS_INCLUDE_LABELS=b.nodeDimensionsIncludeLabels,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!b.randomize,y.ANIMATE=g.ANIMATE=m.ANIMATE=b.animate,y.TILE=b.tile,y.TILING_PADDING_VERTICAL=typeof b.tilingPaddingVertical=="function"?b.tilingPaddingVertical.call():b.tilingPaddingVertical,y.TILING_PADDING_HORIZONTAL=typeof b.tilingPaddingHorizontal=="function"?b.tilingPaddingHorizontal.call():b.tilingPaddingHorizontal,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!0,y.PURE_INCREMENTAL=!b.randomize,m.DEFAULT_UNIFORM_LEAF_NODE_SIZES=b.uniformNodeDimensions,b.step=="transformed"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!1),b.step=="enforced"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!1),b.step=="cose"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!0),b.step=="all"&&(b.randomize?y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0:y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!0),b.fixedNodeConstraint||b.alignmentConstraint||b.relativePlacementConstraint?y.TREE_REDUCTION_ON_INCREMENTAL=!1:y.TREE_REDUCTION_ON_INCREMENTAL=!0;var F=new h,B=F.newGraphManager();return O(B.addRoot(),u.getTopMostNodes(E),F,b),D(F,B,_),P(F,b),F.runLayout(),N},"coseLayout");a.exports={coseLayout:v}},212:(a,s,l)=>{var u=function(){function b(w,S){for(var T=0;T0)if(D){var B=d.getTopMostNodes(T.eles.nodes());if(k=d.connectComponents(E,T.eles,B),k.forEach(function(oe){var ke=oe.boundingBox();I.push({x:ke.x1+ke.w/2,y:ke.y1+ke.h/2})}),T.randomize&&k.forEach(function(oe){T.eles=oe,A.push(m(T))}),T.quality=="default"||T.quality=="proof"){var $=E.collection();if(T.tile){var z=new Map,Y=[],Q=[],X=0,ie={nodeIndexes:z,xCoords:Y,yCoords:Q},j=[];if(k.forEach(function(oe,ke){oe.edges().length==0&&(oe.nodes().forEach(function(Ie,Se){$.merge(oe.nodes()[Se]),Ie.isParent()||(ie.nodeIndexes.set(oe.nodes()[Se].id(),X++),ie.xCoords.push(oe.nodes()[0].position().x),ie.yCoords.push(oe.nodes()[0].position().y))}),j.push(ke))}),$.length>1){var J=$.boundingBox();I.push({x:J.x1+J.w/2,y:J.y1+J.h/2}),k.push($),A.push(ie);for(var Z=j.length-1;Z>=0;Z--)k.splice(j[Z],1),A.splice(j[Z],1),I.splice(j[Z],1)}}k.forEach(function(oe,ke){T.eles=oe,N.push(y(T,A[ke])),d.relocateComponent(I[ke],N[ke],T)})}else k.forEach(function(oe,ke){d.relocateComponent(I[ke],A[ke],T)});var H=new Set;if(k.length>1){var q=[],K=_.filter(function(oe){return oe.css("display")=="none"});k.forEach(function(oe,ke){var Ie=void 0;if(T.quality=="draft"&&(Ie=A[ke].nodeIndexes),oe.nodes().not(K).length>0){var Se={};Se.edges=[],Se.nodes=[];var Ue=void 0;oe.nodes().not(K).forEach(function(Pe){if(T.quality=="draft")if(!Pe.isParent())Ue=Ie.get(Pe.id()),Se.nodes.push({x:A[ke].xCoords[Ue]-Pe.boundingbox().w/2,y:A[ke].yCoords[Ue]-Pe.boundingbox().h/2,width:Pe.boundingbox().w,height:Pe.boundingbox().h});else{var _e=d.calcBoundingBox(Pe,A[ke].xCoords,A[ke].yCoords,Ie);Se.nodes.push({x:_e.topLeftX,y:_e.topLeftY,width:_e.width,height:_e.height})}else N[ke][Pe.id()]&&Se.nodes.push({x:N[ke][Pe.id()].getLeft(),y:N[ke][Pe.id()].getTop(),width:N[ke][Pe.id()].getWidth(),height:N[ke][Pe.id()].getHeight()})}),oe.edges().forEach(function(Pe){var _e=Pe.source(),me=Pe.target();if(_e.css("display")!="none"&&me.css("display")!="none")if(T.quality=="draft"){var W=Ie.get(_e.id()),fe=Ie.get(me.id()),ge=[],re=[];if(_e.isParent()){var he=d.calcBoundingBox(_e,A[ke].xCoords,A[ke].yCoords,Ie);ge.push(he.topLeftX+he.width/2),ge.push(he.topLeftY+he.height/2)}else ge.push(A[ke].xCoords[W]),ge.push(A[ke].yCoords[W]);if(me.isParent()){var ne=d.calcBoundingBox(me,A[ke].xCoords,A[ke].yCoords,Ie);re.push(ne.topLeftX+ne.width/2),re.push(ne.topLeftY+ne.height/2)}else re.push(A[ke].xCoords[fe]),re.push(A[ke].yCoords[fe]);Se.edges.push({startX:ge[0],startY:ge[1],endX:re[0],endY:re[1]})}else 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r={name:t.filename,buffer:t.input.slice(0,-1),position:t.position,line:t.line,column:t.position-t.lineStart};return r.snippet=Met(r),new $s(e,r)}o(ave,"generateError");function Gt(t,e){throw ave(t,e)}o(Gt,"throwError");function TC(t,e){t.onWarning&&t.onWarning.call(null,ave(t,e))}o(TC,"throwWarning");var Pye={YAML:o(function(e,r,n){var i,a,s;e.version!==null&&Gt(e,"duplication of %YAML directive"),n.length!==1&&Gt(e,"YAML directive accepts exactly one argument"),i=/^([0-9]+)\.([0-9]+)$/.exec(n[0]),i===null&&Gt(e,"ill-formed argument of the YAML directive"),a=parseInt(i[1],10),s=parseInt(i[2],10),a!==1&&Gt(e,"unacceptable YAML version of the document"),e.version=n[0],e.checkLineBreaks=s<2,s!==1&&s!==2&&TC(e,"unsupported YAML version of the document")},"handleYamlDirective"),TAG:o(function(e,r,n){var i,a;n.length!==2&&Gt(e,"TAG directive accepts exactly two arguments"),i=n[0],a=n[1],tve.test(i)||Gt(e,"ill-formed tag handle (first argument) of the TAG 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s=t.input.charCodeAt(++t.position);while(s!==0&&!qc(s));break}if(qc(s))break;for(r=t.position;s!==0&&!Vs(s);)s=t.input.charCodeAt(++t.position);i.push(t.input.slice(r,t.position))}s!==0&&fF(t),Bf.call(Pye,n)?Pye[n](t,n,i):TC(t,'unknown document directive "'+n+'"')}if(Ai(t,!0,-1),t.lineIndent===0&&t.input.charCodeAt(t.position)===45&&t.input.charCodeAt(t.position+1)===45&&t.input.charCodeAt(t.position+2)===45?(t.position+=3,Ai(t,!0,-1)):a&&Gt(t,"directives end mark is expected"),N1(t,t.lineIndent-1,wC,!1,!0),Ai(t,!0,-1),t.checkLineBreaks&&ztt.test(t.input.slice(e,t.position))&&TC(t,"non-ASCII line breaks are interpreted as content"),t.documents.push(t.result),t.position===t.lineStart&&CC(t)){t.input.charCodeAt(t.position)===46&&(t.position+=3,Ai(t,!0,-1));return}if(t.position"u"&&(r=e,e=null);var n=sve(t,r);if(typeof e!="function")return n;for(var i=0,a=n.length;i=55296&&r<=56319&&e+1=56320&&n<=57343)?(r-55296)*1024+n-56320+65536:r}o(kb,"codePointAt");function mve(t){var e=/^\n* /;return e.test(t)}o(mve,"needIndentIndicator");var gve=1,cF=2,yve=3,vve=4,L1=5;function Rrt(t,e,r,n,i,a,s,l){var u,h=0,f=null,d=!1,p=!1,m=n!==-1,g=-1,y=Lrt(kb(t,0))&&Drt(kb(t,t.length-1));if(e||s)for(u=0;u=65536?u+=2:u++){if(h=kb(t,u),!Ab(h))return L1;y=y&&$ye(h,f,l),f=h}else{for(u=0;u=65536?u+=2:u++){if(h=kb(t,u),h===Cb)d=!0,m&&(p=p||u-g-1>n&&t[g+1]!==" ",g=u);else if(!Ab(h))return L1;y=y&&$ye(h,f,l),f=h}p=p||m&&u-g-1>n&&t[g+1]!==" "}return!d&&!p?y&&!s&&!i(t)?gve:a===Sb?L1:cF:r>9&&mve(t)?L1:s?a===Sb?L1:cF:p?vve:yve}o(Rrt,"chooseScalarStyle");function Nrt(t,e,r,n,i){t.dump=function(){if(e.length===0)return t.quotingType===Sb?'""':"''";if(!t.noCompatMode&&(Trt.indexOf(e)!==-1||krt.test(e)))return t.quotingType===Sb?'"'+e+'"':"'"+e+"'";var a=t.indent*Math.max(1,r),s=t.lineWidth===-1?-1:Math.max(Math.min(t.lineWidth,40),t.lineWidth-a),l=n||t.flowLevel>-1&&r>=t.flowLevel;function u(h){return _rt(t,h)}switch(o(u,"testAmbiguity"),Rrt(e,l,t.indent,s,u,t.quotingType,t.forceQuotes&&!n,i)){case gve:return e;case cF:return"'"+e.replace(/'/g,"''")+"'";case yve:return"|"+Vye(e,t.indent)+Uye(zye(e,a));case vve:return">"+Vye(e,t.indent)+Uye(zye(Mrt(e,s),a));case L1:return'"'+Irt(e)+'"';default:throw new $s("impossible error: invalid scalar style")}}()}o(Nrt,"writeScalar");function Vye(t,e){var r=mve(t)?String(e):"",n=t[t.length-1]===` -`,i=n&&(t[t.length-2]===` -`||t===` -`),a=i?"+":n?"":"-";return r+a+` -`}o(Vye,"blockHeader");function Uye(t){return t[t.length-1]===` -`?t.slice(0,-1):t}o(Uye,"dropEndingNewline");function Mrt(t,e){for(var r=/(\n+)([^\n]*)/g,n=function(){var h=t.indexOf(` -`);return h=h!==-1?h:t.length,r.lastIndex=h,Hye(t.slice(0,h),e)}(),i=t[0]===` -`||t[0]===" ",a,s;s=r.exec(t);){var l=s[1],u=s[2];a=u[0]===" ",n+=l+(!i&&!a&&u!==""?` -`:"")+Hye(u,e),i=a}return n}o(Mrt,"foldString");function Hye(t,e){if(t===""||t[0]===" ")return t;for(var r=/ [^ ]/g,n,i=0,a,s=0,l=0,u="";n=r.exec(t);)l=n.index,l-i>e&&(a=s>i?s:l,u+=` -`+t.slice(i,a),i=a+1),s=l;return u+=` 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Check if previously processed */ -/*! - * Wait for document loaded before starting the execution - */ -/*! Bundled license information: - -dompurify/dist/purify.js: - (*! @license DOMPurify 3.1.6 | (c) Cure53 and other contributors | Released under the Apache license 2.0 and Mozilla Public License 2.0 | github.com/cure53/DOMPurify/blob/3.1.6/LICENSE *) - -lodash-es/lodash.js: - (** - * @license - * Lodash (Custom Build) - * Build: `lodash modularize exports="es" -o ./` - * Copyright OpenJS Foundation and other contributors - * Released under MIT license - * Based on Underscore.js 1.8.3 - * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors - *) - -cytoscape/dist/cytoscape.esm.mjs: - (*! - Embeddable Minimum Strictly-Compliant Promises/A+ 1.1.1 Thenable - Copyright (c) 2013-2014 Ralf S. Engelschall (http://engelschall.com) - Licensed under The MIT License (http://opensource.org/licenses/MIT) - *) - (*! - Event object based on jQuery events, MIT license - - https://jquery.org/license/ - https://tldrlegal.com/license/mit-license - https://github.com/jquery/jquery/blob/master/src/event.js - *) - (*! Bezier curve function generator. Copyright Gaetan Renaudeau. MIT License: http://en.wikipedia.org/wiki/MIT_License *) - (*! Runge-Kutta spring physics function generator. Adapted from Framer.js, copyright Koen Bok. MIT License: http://en.wikipedia.org/wiki/MIT_License *) - -js-yaml/dist/js-yaml.mjs: - (*! js-yaml 4.1.0 https://github.com/nodeca/js-yaml @license MIT *) -*/ -globalThis.mermaid = globalThis.__esbuild_esm_mermaid.default; From c219b42b8e8f38539023fe5f232a245065bf1cb7 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 24 Mar 2025 16:12:41 +0100 Subject: [PATCH 079/147] Convert stored formula to a string --- R/sampler.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/sampler.R b/R/sampler.R index 21fddfc28..e435ac55e 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -275,7 +275,7 @@ prepare.formula <- function(formula, data, model, j, ct, pred, task) { # store formula in `model` only when task is "train" if (task == "train") { - assign("formula", deparse(formula), envir = model) + assign("formula", paste(deparse(formula), collapse = ""), envir = model) } return(formula) From 536be5832396597e3ae6d3e69688d000dde99633 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 25 Mar 2025 09:51:41 +0100 Subject: [PATCH 080/147] Skip unknown categories in factors from the linear predictor during fill --- R/check.model.R | 10 +++--- R/mice.impute.logreg.R | 5 +-- R/mice.impute.norm.R | 8 +++-- R/mice.impute.pmm.R | 6 ++-- R/mice.impute.polr.R | 5 +-- R/mice.impute.polyreg.R | 4 +-- tests/testthat/test-mice.impute.logreg.R | 42 ++++++++++++++++++++++++ tests/testthat/test-tasks.R | 3 +- 8 files changed, 64 insertions(+), 19 deletions(-) diff --git a/R/check.model.R b/R/check.model.R index d8f129bd8..7e8c6cee8 100644 --- a/R/check.model.R +++ b/R/check.model.R @@ -22,13 +22,13 @@ check.model.match <- function(model, x, method) { } xnames <- model$xnames - # if (is.matrix(model$beta.dot)) mnames <- rownames(model$beta.dot) dnames <- colnames(x) - if (ncol(x) != length(xnames) || any(xnames != dnames)) { + notfound <- !xnames %in% dnames + if (any(notfound)) { stop(paste("Model-Data mismatch: ", deparse(formula), "\n", - " Model:", paste(xnames, collapse = " "), "\n", - " Data: ", paste(dnames, collapse = " "), "\n")) + "Not found in data: ", paste(xnames[notfound], collapse = " "), "\n")) } - return(TRUE) + notfound <- !dnames %in% xnames + return(!notfound) } diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index e9a6d9819..29c8b0f26 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -60,8 +60,9 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) - if (task == "fill" && check.model.match(model, x, method)) { - lp <- x[wy, , drop = FALSE] %*% model$beta.dot + if (task == "fill") { + cols <- check.model.match(model, x, method) + lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot return(logreg.draw(lp, levels(y))) } diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index e0a34db0a..cf9530973 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -42,9 +42,11 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, method <- "norm" if (is.null(wy)) wy <- !ry x <- cbind(1, as.matrix(x)) - - if (task == "fill" && check.model.match(model, x, method)) { - return(x[wy, ] %*% model$beta.dot + rnorm(sum(wy)) * model$sigma.dot) + if (task == "fill") { + cols <- check.model.match(model, x, method) + lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot + noise <- rnorm(sum(wy)) * model$sigma.dot + return(lp + noise) } parm <- .norm.draw(y, ry, x, ridge = ridge, ...) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 566254f0a..67ff6625f 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -198,9 +198,9 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # Add intercept column to x x <- cbind(1, as.matrix(x)) - # >>> Fill task: fill from stored model - if (task == "fill" && check.model.match(model, x, method)) { - yhatmis <- x[wy, ] %*% model$beta.dot + if (task == "fill") { + cols <- check.model.match(model, x, method) + yhatmis <- x[wy, cols, drop = FALSE] %*% model$beta.dot impy <- draw.neighbors.pmm(yhatmis, edges = model$edges, lookup = model$lookup, diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index 509667a0f..3def9be1c 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -75,8 +75,9 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, wy <- aug$wy w <- aug$w - if (task == "fill" && check.model.match(model, x, method)) { - impy <- polr.draw(x = x[wy, , drop = FALSE], + if (task == "fill") { + cols <- check.model.match(model, x, method) + impy <- polr.draw(x = x[wy, cols, drop = FALSE], beta = model$beta.dot, zeta = model$zeta.mis, levels = model$factor$labels) diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 4e754460a..388ed8417 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -79,9 +79,9 @@ mice.impute.polyreg <- function( w <- aug$w if (task == "fill") { - x <- x[wy, , drop = FALSE] x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) - check.model.match(model, x, method) + cols <- check.model.match(model, x, method) + x <- x[wy, cols, drop = FALSE] return(polyreg.draw(x = x, beta = model$beta.dot, levels = levels(y))) diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index c68b43d88..42dcbfc80 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -50,3 +50,45 @@ perfectPred <- tryCatch( test_that("Complete separation results in same class as well behaved case", { expect_true(all.equal(class(wellBehaved), class(perfectPred))) }) + +## Check how logreg works with logical fills + +set.seed(123) +df <- data.frame( + factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), + factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), + factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), + logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), + logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), + numeric1 = rnorm(20) +) +n <- prod(dim(df)) +na_count <- round(0.3 * n) +missing_idx <- arrayInd(sample(n, na_count), .dim = dim(df)) +for (i in seq_len(nrow(missing_idx))) { + df[missing_idx[i, 1], missing_idx[i, 2]] <- NA +} + +# convert logicals to factors to evade problems +df[] <- lapply(df, function(col) { + if (is.logical(col)) factor(col, levels = c(TRUE, FALSE)) else col +}) + +# there are loggedEvents +expect_warning(trained <- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) + +# now fill +newdata <- rbind(df[1:2, ], data.frame( + factor2 = NA, + factor3 = NA, + factor4 = NA, + logical1 = NA, + logical2 = NA, + numeric1 = NA +)) + +test_that("Filling logicals work when converted to factors", { + expect_silent(filled <- mice(newdata, tasks = "fill", models = trained$models, print = FALSE)) +}) + +# filled <- mice(newdata, tasks = "fill", models = trained$models) diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 810d53246..c3ed613cd 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -28,9 +28,8 @@ test_that("the procedure informs the user about a mismatch between model and dat newdata$age <- factor(newdata$age, levels = c(levels(newdata$age), "not_a_level")) newdata$age[1] <- "not_a_level" newdata$age[2] <- NA - expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") + expect_silent(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) newdata <- nhanes2 levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") }) - From 3d305fff31e6f2057e87573df003ff880599e675 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 10:17:33 +0100 Subject: [PATCH 081/147] Make sure that mice() does not convert logicals to factors --- R/design.R | 10 +++++++++- R/method.R | 21 ++++++++++----------- R/mice.impute.logreg.R | 17 +++++++++++++---- tests/testthat/test-mice.impute.logreg.R | 17 ++++++++++------- 4 files changed, 42 insertions(+), 23 deletions(-) diff --git a/R/design.R b/R/design.R index d9e9c87d7..dd9c9467b 100644 --- a/R/design.R +++ b/R/design.R @@ -1,4 +1,12 @@ obtain.design <- function(data, formula = ~.) { mf <- model.frame(formula, data = data, na.action = na.pass) + + # Convert logical variables to numeric to prevent dummy expansion + for (v in names(mf)) { + if (is.logical(mf[[v]])) { + mf[[v]] <- as.numeric(mf[[v]]) + } + } + model.matrix(formula, data = mf) -} +} \ No newline at end of file diff --git a/R/method.R b/R/method.R index 958505a25..bd89d76f6 100644 --- a/R/method.R +++ b/R/method.R @@ -170,24 +170,23 @@ overwrite.method <- function(method, blocks, tasks, models) { return(method) } - # assign methods based on type, # use method 1 if there is no single method within the block assign.method <- function(y) { if (is.numeric(y)) { - return(1) + return(1L) } - if (nlevels(y) == 2) { - return(2) + if (is.logical(y)) { + return(2L) } - if (is.ordered(y) && nlevels(y) > 2) { - return(4) + if (is.ordered(y) && nlevels(y) > 2L) { + return(4L) } - if (nlevels(y) > 2) { - return(3) + if (nlevels(y) == 2L) { + return(2L) } - if (is.logical(y)) { - return(2) + if (nlevels(y) > 2L) { + return(3L) } - 1 + return(1L) } diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index 29c8b0f26..e9151783c 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -91,12 +91,21 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, return(logreg.draw(lp, levels(y))) } -logreg.draw <- function(lp, levels) { +# logreg.draw <- function(lp, levels) { +# p <- 1 / (1 + exp(-lp)) +# vec <- (runif(nrow(p)) <= p) +# vec[vec] <- 1 +# if (!is.null(levels)) { +# vec <- factor(vec, c(0, 1), levels) +# } +# return(vec) +# } + +logreg.draw <- function(lp, levels = NULL) { p <- 1 / (1 + exp(-lp)) - vec <- (runif(nrow(p)) <= p) - vec[vec] <- 1 + vec <- runif(nrow(p)) <= p if (!is.null(levels)) { - vec <- factor(vec, c(0, 1), levels) + vec <- factor(vec, levels = c(FALSE, TRUE), labels = levels) } return(vec) } diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index 42dcbfc80..9cd69460c 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -69,10 +69,11 @@ for (i in seq_len(nrow(missing_idx))) { df[missing_idx[i, 1], missing_idx[i, 2]] <- NA } -# convert logicals to factors to evade problems -df[] <- lapply(df, function(col) { - if (is.logical(col)) factor(col, levels = c(TRUE, FALSE)) else col -}) +# Convert logicals to factors. +# This breaks consistency, so let's avoid this. +#df[] <- lapply(df, function(col) { +# if (is.logical(col)) factor(col, levels = c(TRUE, FALSE)) else col +#}) # there are loggedEvents expect_warning(trained <- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) @@ -87,8 +88,10 @@ newdata <- rbind(df[1:2, ], data.frame( numeric1 = NA )) -test_that("Filling logicals work when converted to factors", { - expect_silent(filled <- mice(newdata, tasks = "fill", models = trained$models, print = FALSE)) +test_that("df and newdata have same types before fill", { + expect_identical(sapply(df, class), sapply(newdata, class)) }) -# filled <- mice(newdata, tasks = "fill", models = trained$models) +test_that("Filling logicals work without converting to factors", { + expect_silent(filled <- mice(newdata, tasks = "fill", models = trained$models, print = FALSE)) +}) From fefa9cdf1520f77a0fae2c163dcaff12d89b0e36 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 13:18:43 +0100 Subject: [PATCH 082/147] Adapt logreg.draw() to support logical, factor and numeric output --- R/mice.impute.logreg.R | 44 +++++++++++++++--------- tests/testthat/test-mice.impute.logreg.R | 19 ++++++---- 2 files changed, 40 insertions(+), 23 deletions(-) diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index e9151783c..3f4e4bde5 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -49,6 +49,7 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, check.model.exists(model, task) method <- "logreg" if (is.null(wy)) wy <- !ry + n <- sum(ry) # augment data in order to evade perfect prediction aug <- augment(y, ry, x, wy) @@ -60,6 +61,14 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, x <- cbind(1, as.matrix(x)) + if (task == "fill") { + cols <- check.model.match(model, x, method) + lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot + return(logreg.draw(lp, + levels = model$factor$labels, + class = model$class, + ordered = isTRUE(model$ordered))) + } if (task == "fill") { cols <- check.model.match(model, x, method) lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot @@ -79,35 +88,36 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, if (task == "train") { model$setup <- list(method = method, - n = sum(ry), + n = n, task = task) model$beta.hat <- drop(beta) model$beta.dot <- drop(beta.star) model$factor <- list(labels = levels(y), quant = c(0, 1)) model$xnames <- colnames(x) + model$class <- class(y) + model$ordered <- is.ordered(y) } lp <- x[wy, , drop = FALSE] %*% beta.star - return(logreg.draw(lp, levels(y))) + return(logreg.draw(lp, levels(y), class(y), is.ordered(y))) } -# logreg.draw <- function(lp, levels) { -# p <- 1 / (1 + exp(-lp)) -# vec <- (runif(nrow(p)) <= p) -# vec[vec] <- 1 -# if (!is.null(levels)) { -# vec <- factor(vec, c(0, 1), levels) -# } -# return(vec) -# } - -logreg.draw <- function(lp, levels = NULL) { +logreg.draw <- function(lp, levels = NULL, class = NULL, ordered = FALSE) { + # supports logical, factor and numeric output p <- 1 / (1 + exp(-lp)) - vec <- runif(nrow(p)) <= p - if (!is.null(levels)) { - vec <- factor(vec, levels = c(FALSE, TRUE), labels = levels) + draws <- (runif(length(p)) <= p) * 1L + + if (!is.null(class)) { + if (class == "logical") { + return(as.logical(draws)) + } + if (class == "factor") { + return(factor(draws, levels = c(0, 1), labels = levels, ordered = ordered)) + } } - return(vec) + + # fallback, numeric + return(draws) } #' Imputation by logistic regression using the bootstrap diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index 9cd69460c..53916e150 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -56,8 +56,8 @@ test_that("Complete separation results in same class as well behaved case", { set.seed(123) df <- data.frame( factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), - factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), - factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), +# factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), +# factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), numeric1 = rnorm(20) @@ -75,14 +75,13 @@ for (i in seq_len(nrow(missing_idx))) { # if (is.logical(col)) factor(col, levels = c(TRUE, FALSE)) else col #}) -# there are loggedEvents -expect_warning(trained <- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) +expect_silent(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) # now fill newdata <- rbind(df[1:2, ], data.frame( factor2 = NA, - factor3 = NA, - factor4 = NA, +# factor3 = NA, +# factor4 = NA, logical1 = NA, logical2 = NA, numeric1 = NA @@ -92,6 +91,14 @@ test_that("df and newdata have same types before fill", { expect_identical(sapply(df, class), sapply(newdata, class)) }) +# fill0 <- mice(newdata, tasks = "fill", models = trained$models, m = 20, maxit = 0, print = FALSE, seed = 2) +# fill0$imp +# +# fill1 <- mice(newdata, tasks = "fill", models = trained$models, m = 20, maxit = 1, print = FALSE, seed = 2) +# fill1$imp + test_that("Filling logicals work without converting to factors", { expect_silent(filled <- mice(newdata, tasks = "fill", models = trained$models, print = FALSE)) + expect_identical(sapply(newdata, class), sapply(complete(filled), class)) }) + From f0b5bed6197f2d182ee0771e58bf4c4259c33171 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 13:31:26 +0100 Subject: [PATCH 083/147] Adapt polyreg.draw() to return factors --- R/mice.impute.polyreg.R | 79 +++++++++++++++--------- tests/testthat/test-mice.impute.logreg.R | 4 +- 2 files changed, 51 insertions(+), 32 deletions(-) diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 388ed8417..5ed5730cd 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -66,11 +66,9 @@ mice.impute.polyreg <- function( check.model.exists(model, task) method <- "polyreg" - if (is.null(wy)) { - wy <- !ry - } + if (is.null(wy)) wy <- !ry - # Augment data to evade issues with perfect prediction + # Augment data aug <- augment(y, ry, x, wy) x <- aug$x y <- aug$y @@ -82,18 +80,22 @@ mice.impute.polyreg <- function( x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) cols <- check.model.match(model, x, method) x <- x[wy, cols, drop = FALSE] - return(polyreg.draw(x = x, - beta = model$beta.dot, - levels = levels(y))) + return(polyreg.draw( + x = x, + beta = model$beta.dot, + levels = model$factor$labels, + class = model$class, + ordered = isTRUE(model$ordered) + )) } - # Escape estimation with same impute if the dependent does not vary + # Escape perfect prediction cat.has.all.obs <- table(y[ry]) == sum(ry) if (any(cat.has.all.obs)) { return(rep(levels(y)[cat.has.all.obs], sum(wy))) } - # Set hyper-parameters + # Set hyperparameters if (!missing(nnet.maxit)) maxit <- nnet.maxit if (!missing(nnet.MaxNWts)) MaxNWts <- nnet.MaxNWts MaxNWts_needed <- 100L + as.integer(ncol(x) * (length(levels(y)) - 1)) @@ -105,16 +107,17 @@ mice.impute.polyreg <- function( dots$Wts <- model$wts } - # Estimate model + # Fit multinomial model xy <- cbind.data.frame(y, x) fit <- do.call(nnet::multinom, c( list(formula(xy), - data = xy[ry, , drop = FALSE], weights = w[ry], + data = xy[ry, , drop = FALSE], + weights = w[ry], model = FALSE, trace = FALSE), dots )) - # Make names consistent + # Process beta coefficients x <- x[wy, , drop = FALSE] x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) beta <- coef(fit) @@ -125,38 +128,54 @@ mice.impute.polyreg <- function( } rownames(beta) <- gsub("`", "", rownames(beta)) - # Save for future use if (task == "train") { - model$setup <- list(method = method, - n = sum(ry), - task = task, - maxit = dots$maxit, - MaxNWts = dots$MaxNWts, - reltol = dots$reltol, - warmstart = warmstart) - model$result <- list(nWts = length(fit$wts), - value = fit$value, - convergence = fit$convergence) + model$setup <- list( + method = method, + n = sum(ry), + task = task, + maxit = dots$maxit, + MaxNWts = dots$MaxNWts, + reltol = dots$reltol, + warmstart = warmstart + ) + model$result <- list( + nWts = length(fit$wts), + value = fit$value, + convergence = fit$convergence + ) model$beta.dot <- beta if (warmstart) model$wts <- fit$wts model$factor <- list(labels = levels(y), quant = NULL) + model$class <- class(y) + model$ordered <- is.ordered(y) model$xnames <- colnames(x) } - # Draw imputations - return(polyreg.draw(x = x, - beta = beta, - levels = levels(y))) + # Return imputed values + polyreg.draw( + x = x, + beta = beta, + levels = levels(y), + class = class(y), + ordered = is.ordered(y) + ) } -polyreg.draw <- function(x, beta, levels) { +polyreg.draw <- function(x, beta, levels, class = NULL, ordered = FALSE) { if (nrow(x) == 0L) return(character(0)) lp <- x %*% beta p <- exp(lp) / rowSums(exp(lp) + 1) post <- cbind(1 - rowSums(p), p) + un <- rep(runif(nrow(x)), each = length(levels)) draws <- un > apply(post, 1L, cumsum) idx <- 1L + apply(draws, 2L, sum) - return(levels[idx]) -} + out <- levels[idx] + + if (!is.null(class) && class == "factor") { + out <- factor(out, levels = levels, ordered = ordered) + } + + return(out) +} diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index 53916e150..51648ffe6 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -56,7 +56,7 @@ test_that("Complete separation results in same class as well behaved case", { set.seed(123) df <- data.frame( factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), -# factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), + factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), # factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), @@ -80,7 +80,7 @@ expect_silent(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", # now fill newdata <- rbind(df[1:2, ], data.frame( factor2 = NA, -# factor3 = NA, + factor3 = NA, # factor4 = NA, logical1 = NA, logical2 = NA, From 3e36a1ddfca60a4580e68f031fa31a8ce114e06a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 13:43:26 +0100 Subject: [PATCH 084/147] Adapt polr.draw() to factor output --- R/mice.impute.polr.R | 52 +++++++++++++----------- tests/testthat/test-mice.impute.logreg.R | 6 +-- 2 files changed, 32 insertions(+), 26 deletions(-) diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index 3def9be1c..5195f75d1 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -77,10 +77,14 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, if (task == "fill") { cols <- check.model.match(model, x, method) - impy <- polr.draw(x = x[wy, cols, drop = FALSE], - beta = model$beta.dot, - zeta = model$zeta.mis, - levels = model$factor$labels) + impy <- polr.draw( + x = x[wy, cols, drop = FALSE], + beta = model$beta.dot, + zeta = model$zeta.mis, + levels = model$factor$labels, + class = model$class, + ordered = isTRUE(model$ordered) + ) return(impy) } @@ -100,8 +104,8 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, reltol = ifelse(is.null(reltol), 0.0001, reltol)) ) if (warmstart && task == "train" && - !is.null(model$beta.dot) && !is.null(model$zeta.mis)) { - dots$start <- c(model$beta.dot, model$zeta.mis) + !is.null(model$beta.dot) && !is.null(model$zeta.mis)) { + dots$start <- c(model$beta.dot, model$zeta.mis) } # Estimate ordered logistic (polr) model with polr @@ -131,29 +135,35 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, model$setup <- list(method = method, n = sum(ry), task = task, - maxit = dots$maxit, - reltol = dots$reltol, + maxit = dots$control$maxit, + reltol = dots$control$reltol, warmstart = warmstart) model$result <- list(value = fit$value, convergence = fit$convergence) model$beta.dot <- setNames(coef(fit), colnames(x)) model$zeta.mis <- fit$zeta model$factor <- list(labels = levels(y), quant = NULL) + model$class <- class(y) + model$ordered <- is.ordered(y) model$xnames <- colnames(x) } # Draw imputations if (execute == "polr") { - impy <- polr.draw(x = x[wy, , drop = FALSE], - beta = coef(fit), - zeta = fit$zeta, - levels = levels(y)) + impy <- polr.draw( + x = x[wy, , drop = FALSE], + beta = coef(fit), + zeta = fit$zeta, + levels = levels(y), + class = class(y), + ordered = is.ordered(y) + ) } return(impy) } -polr.draw <- function(x, beta, zeta, levels) { +polr.draw <- function(x, beta, zeta, levels, class = NULL, ordered = FALSE) { if (nrow(x) == 0L) return(character(0)) eta <- x %*% beta cumpr <- plogis(matrix(zeta, nrow(x), length(zeta), byrow = TRUE) - as.vector(eta)) @@ -161,16 +171,12 @@ polr.draw <- function(x, beta, zeta, levels) { un <- rep(runif(nrow(x)), each = length(levels)) draws <- un > apply(post, 1L, cumsum) idx <- 1L + apply(draws, 2L, sum) - return(levels[idx]) -} + out <- levels[idx] -safe_call <- function(fun, args) { - result <- try(suppressWarnings(do.call(fun, args)), silent = TRUE) - return(result) -} -safe_polr <- function(formula, data, ...) { - args <- list(formula = formula, data = data, ...) - safe_call(MASS::polr, args) -} + if (!is.null(class) && class == "ordered") { + out <- factor(out, levels = levels, ordered = ordered) + } + return(out) +} diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index 51648ffe6..86f049cc6 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -57,7 +57,7 @@ set.seed(123) df <- data.frame( factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), -# factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), + factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), numeric1 = rnorm(20) @@ -75,13 +75,13 @@ for (i in seq_len(nrow(missing_idx))) { # if (is.logical(col)) factor(col, levels = c(TRUE, FALSE)) else col #}) -expect_silent(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) +expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) # now fill newdata <- rbind(df[1:2, ], data.frame( factor2 = NA, factor3 = NA, -# factor4 = NA, + factor4 = NA, logical1 = NA, logical2 = NA, numeric1 = NA From 41678474f2ebd35544a71ea6e2e26eff44b92b3f Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 15:10:24 +0100 Subject: [PATCH 085/147] Move fill tests to test-tasks.R --- tests/testthat/test-mice.impute.logreg.R | 50 --------------------- tests/testthat/test-tasks.R | 56 ++++++++++++++++++++++++ 2 files changed, 56 insertions(+), 50 deletions(-) diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index 86f049cc6..8573297de 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -51,54 +51,4 @@ test_that("Complete separation results in same class as well behaved case", { expect_true(all.equal(class(wellBehaved), class(perfectPred))) }) -## Check how logreg works with logical fills - -set.seed(123) -df <- data.frame( - factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), - factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), - factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), - logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), - logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), - numeric1 = rnorm(20) -) -n <- prod(dim(df)) -na_count <- round(0.3 * n) -missing_idx <- arrayInd(sample(n, na_count), .dim = dim(df)) -for (i in seq_len(nrow(missing_idx))) { - df[missing_idx[i, 1], missing_idx[i, 2]] <- NA -} - -# Convert logicals to factors. -# This breaks consistency, so let's avoid this. -#df[] <- lapply(df, function(col) { -# if (is.logical(col)) factor(col, levels = c(TRUE, FALSE)) else col -#}) - -expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) - -# now fill -newdata <- rbind(df[1:2, ], data.frame( - factor2 = NA, - factor3 = NA, - factor4 = NA, - logical1 = NA, - logical2 = NA, - numeric1 = NA -)) - -test_that("df and newdata have same types before fill", { - expect_identical(sapply(df, class), sapply(newdata, class)) -}) - -# fill0 <- mice(newdata, tasks = "fill", models = trained$models, m = 20, maxit = 0, print = FALSE, seed = 2) -# fill0$imp -# -# fill1 <- mice(newdata, tasks = "fill", models = trained$models, m = 20, maxit = 1, print = FALSE, seed = 2) -# fill1$imp - -test_that("Filling logicals work without converting to factors", { - expect_silent(filled <- mice(newdata, tasks = "fill", models = trained$models, print = FALSE)) - expect_identical(sapply(newdata, class), sapply(complete(filled), class)) -}) diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index c3ed613cd..d26ecb850 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -33,3 +33,59 @@ test_that("the procedure informs the user about a mismatch between model and dat levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") }) + +## Check how categorical method handle fills for single row newdata + +set.seed(123) +df <- data.frame( + factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), + factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), + factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), + logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), + logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), + numeric1 = rnorm(20) +) +n <- prod(dim(df)) +na_count <- round(0.3 * n) +missing_idx <- arrayInd(sample(n, na_count), .dim = dim(df)) +for (i in seq_len(nrow(missing_idx))) { + df[missing_idx[i, 1], missing_idx[i, 2]] <- NA +} + +expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) + + +# single-row new data, wrong types +newdata_wrong <- data.frame( + factor2 = NA, + factor3 = NA, + factor4 = NA, + logical1 = NA, + logical2 = NA, + numeric1 = NA) + +# single-row new data, correct types +newdata_correct <- data.frame( + factor2 = factor(NA, levels = levels(df$factor2)), + factor3 = factor(NA, levels = levels(df$factor3)), + factor4 = factor(NA, levels = levels(df$factor4), ordered = TRUE), + logical1 = as.logical(NA), + logical2 = as.logical(NA), + numeric1 = as.numeric(NA)) + +test_that("df and newdata have same types before fill", { + expect_false(identical(sapply(df, class), sapply(newdata_wrong, class))) + expect_true(identical(sapply(df, class), sapply(newdata_correct, class))) +}) + +# fill0 <- mice(newdata_correct, tasks = "fill", models = trained$models, m = 20, maxit = 0, print = FALSE, seed = 2) +# fill0$imp +# +# fill1 <- mice(newdata_correct, tasks = "fill", models = trained$models, m = 20, maxit = 1, print = FALSE, seed = 2) +# fill1$imp + +test_that("Filling logicals work without converting to factors", { + expect_silent(filled <- mice(newdata_correct, tasks = "fill", models = trained$models, print = FALSE)) + expect_identical(sapply(newdata_correct, class), sapply(complete(filled), class)) +}) + From e6ae02872af3cde53dc753cbda7e338f6f393b1f Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 15:11:17 +0100 Subject: [PATCH 086/147] Ensure safer initialization imputations accounting for factors and logicals --- R/initialize.imp.R | 40 +++++++++++++++++++++++++++++++++------- 1 file changed, 33 insertions(+), 7 deletions(-) diff --git a/R/initialize.imp.R b/R/initialize.imp.R index 8f9dc75a0..011224a92 100644 --- a/R/initialize.imp.R +++ b/R/initialize.imp.R @@ -3,29 +3,55 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, imp <- vector("list", ncol(data)) names(imp) <- names(data) r <- !is.na(data) + for (h in visitSequence) { for (j in blocks[[h]]) { y <- data[, j] ry <- r[, j] & !ignore wy <- where[, j] - imp[[j]] <- as.data.frame(matrix(NA, nrow = sum(wy), ncol = m)) - dimnames(imp[[j]]) <- list(row.names(data)[wy], 1:m) + + # Determine correct NA type + na_type <- switch(class(y)[1], + "logical" = as.logical(NA), + "factor" = as.character(NA), + "ordered" = as.character(NA), + NA_real_ + ) + + # Initialize imp[[j]] with correct type + imp[[j]] <- as.data.frame( + matrix(na_type, nrow = sum(wy), ncol = m) + ) + dimnames(imp[[j]]) <- list(row.names(data)[wy], as.character(seq_len(m))) + if (method[h] != "") { for (i in seq_len(m)) { if (nmis[j] < nrow(data) && is.null(data.init)) { - imp[[j]][, i] <- mice.impute.sample(y, ry, wy = wy) + vec <- mice.impute.sample(y, ry, wy = wy) } else if (!is.null(data.init)) { - imp[[j]][, i] <- data.init[wy, j] + vec <- data.init[wy, j] } else { + # Type-safe fallback + n <- sum(wy) if (is.factor(y)) { - imp[[j]][, i] <- sample(levels(y), nrow(data), replace = TRUE) + vec <- sample(levels(y), n, replace = TRUE) + vec <- factor(vec, levels = levels(y), ordered = is.ordered(y)) + } else if (is.logical(y)) { + vec <- sample(c(TRUE, FALSE), n, replace = TRUE) } else { - imp[[j]][, i] <- rnorm(nrow(data)) + vec <- rnorm(n) } } + + # Final safety check: enforce type match with y + if (is.logical(y)) vec <- as.logical(vec) + if (is.factor(y)) vec <- factor(vec, levels = levels(y), ordered = is.ordered(y)) + + imp[[j]][, i] <- vec } } } } - imp + + return(imp) } From ee4cdb1e64cc03a845bfe4ee1a3bc3210e0dc8e0 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 15:37:52 +0100 Subject: [PATCH 087/147] Save model$class and model$ordered component for pmm --- R/mice.impute.pmm.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 67ff6625f..bb2610293 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -262,6 +262,8 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, nrow = nbins) model$factor <- list(labels = f$labels, quant = f$quant) model$xnames <- colnames(x) + model$class <- class(y) + model$ordered <- is.ordered(y) # Compute imputations from model impy <- draw.neighbors.pmm(yhatmis, From da420b5cbfa790a0da9fe3650289d2fc7714c389 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 26 Mar 2025 15:38:55 +0100 Subject: [PATCH 088/147] Add internal sanitize.vec() for logical, factor, ordered and numeric imputation vectors --- R/internal.R | 26 +++++++++++++++++++ ...{test-remove.lindep.R => test-internals.R} | 12 ++++++++- 2 files changed, 37 insertions(+), 1 deletion(-) rename tests/testthat/{test-remove.lindep.R => test-internals.R} (80%) diff --git a/R/internal.R b/R/internal.R index f2e2eea6c..5c937385c 100644 --- a/R/internal.R +++ b/R/internal.R @@ -157,3 +157,29 @@ backticks <- function(varname) { is.named.list <- function(x) { is.list(x) && !is.null(names(x)) && all(names(x) != "") } + + +sanitize.vec <- function(vec, y) { + # Insert at the end of any draw() or imputation function + # # Example for logreg.draw() + # vec <- logreg.draw(lp) + # vec <- sanitize.vec(vec, y) + + cls <- class(y)[1L] + + if (cls == "logical") { + return(as.logical(vec)) + } + + if (cls == "factor") { + return(factor(vec, levels = levels(y), ordered = is.ordered(y))) + } + + if (cls == "ordered") { + return(factor(vec, levels = levels(y), ordered = TRUE)) + } + + # default (numeric, character, etc.) + vec +} + diff --git a/tests/testthat/test-remove.lindep.R b/tests/testthat/test-internals.R similarity index 80% rename from tests/testthat/test-remove.lindep.R rename to tests/testthat/test-internals.R index 146c298c0..56d6c9061 100644 --- a/tests/testthat/test-remove.lindep.R +++ b/tests/testthat/test-internals.R @@ -1,4 +1,14 @@ -context("remove.lindep") +context("Internals: sanitize.vec()") + +x <- c(1, 0, 1) +test_that("converts to appropriate type", { + expect_is(mice:::sanitize.vec(x, y = c(TRUE, FALSE)), "logical") + expect_is(mice:::sanitize.vec(x, y = factor(c("a", "b", "a"))), "factor") + expect_is(mice:::sanitize.vec(x, y = ordered(c("low", "high"))), "ordered") + expect_is(mice:::sanitize.vec(x, y = rnorm(5)), "numeric") +}) + +context("Internals: remove.lindep()") set.seed(1) td <- matrix(rnorm(20), nrow = 5, ncol = 4) From 5884fbd3ab2129d1277e8a55d79c3f312301a390 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 27 Mar 2025 13:56:24 +0100 Subject: [PATCH 089/147] Define four classes: numeric, logical, factor, ordered --- R/initialize.imp.R | 2 +- R/mice.impute.logreg.R | 30 ++++++++++++++++-------------- R/mice.impute.pmm.R | 3 +-- R/mice.impute.polr.R | 16 +++++++--------- R/mice.impute.polyreg.R | 18 ++++++++---------- 5 files changed, 33 insertions(+), 36 deletions(-) diff --git a/R/initialize.imp.R b/R/initialize.imp.R index 011224a92..5189c5cf3 100644 --- a/R/initialize.imp.R +++ b/R/initialize.imp.R @@ -11,7 +11,7 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, wy <- where[, j] # Determine correct NA type - na_type <- switch(class(y)[1], + na_type <- switch(class(y)[1L], "logical" = as.logical(NA), "factor" = as.character(NA), "ordered" = as.character(NA), diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index 3f4e4bde5..84033fc36 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -66,13 +66,7 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot return(logreg.draw(lp, levels = model$factor$labels, - class = model$class, - ordered = isTRUE(model$ordered))) - } - if (task == "fill") { - cols <- check.model.match(model, x, method) - lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot - return(logreg.draw(lp, levels(y))) + class = model$class[1L])) } expr <- expression(glm.fit( @@ -94,15 +88,16 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, model$beta.dot <- drop(beta.star) model$factor <- list(labels = levels(y), quant = c(0, 1)) model$xnames <- colnames(x) - model$class <- class(y) - model$ordered <- is.ordered(y) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] } lp <- x[wy, , drop = FALSE] %*% beta.star - return(logreg.draw(lp, levels(y), class(y), is.ordered(y))) + return(logreg.draw(lp, + levels = levels(y), + class = if (is.ordered(y)) "ordered" else class(y)[1L])) } -logreg.draw <- function(lp, levels = NULL, class = NULL, ordered = FALSE) { +logreg.draw <- function(lp, levels = NULL, class = NULL) { # supports logical, factor and numeric output p <- 1 / (1 + exp(-lp)) draws <- (runif(length(p)) <= p) * 1L @@ -111,8 +106,11 @@ logreg.draw <- function(lp, levels = NULL, class = NULL, ordered = FALSE) { if (class == "logical") { return(as.logical(draws)) } - if (class == "factor") { - return(factor(draws, levels = c(0, 1), labels = levels, ordered = ordered)) + if (class %in% c("factor", "ordered")) { + return(factor(draws, + levels = c(0, 1), + labels = levels, + ordered = (class == "ordered"))) } } @@ -229,7 +227,11 @@ augment <- function(y, ry, x, wy, maxcat = 50) { xa <- rbind(x, d) # beware, concatenation of factors - ya <- if (is.factor(y)) as.factor(levels(y)[c(y, e)]) else c(y, e) + if (is.factor(y)) { + ya <- factor(levels(y)[c(y, e)], levels = levels(y), ordered = is.ordered(y)) + } else { + ya <- c(y, e) + } rya <- c(ry, rep.int(TRUE, nr)) wya <- c(wy, rep.int(FALSE, nr)) wa <- c(rep.int(1, length(y)), rep.int((p + 1) / nr, nr)) diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index bb2610293..a1c9d4004 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -262,8 +262,7 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, nrow = nbins) model$factor <- list(labels = f$labels, quant = f$quant) model$xnames <- colnames(x) - model$class <- class(y) - model$ordered <- is.ordered(y) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] # Compute imputations from model impy <- draw.neighbors.pmm(yhatmis, diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index 5195f75d1..7bd6dc7cc 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -82,8 +82,7 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, beta = model$beta.dot, zeta = model$zeta.mis, levels = model$factor$labels, - class = model$class, - ordered = isTRUE(model$ordered) + class = model$class[1L] ) return(impy) } @@ -110,6 +109,7 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, # Estimate ordered logistic (polr) model with polr # Fall back to multinom if polr fails + y <- droplevels(y) xy <- cbind.data.frame(y, x) execute <- "polr" fun <- MASS::polr @@ -143,8 +143,7 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, model$beta.dot <- setNames(coef(fit), colnames(x)) model$zeta.mis <- fit$zeta model$factor <- list(labels = levels(y), quant = NULL) - model$class <- class(y) - model$ordered <- is.ordered(y) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] model$xnames <- colnames(x) } @@ -155,15 +154,14 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, beta = coef(fit), zeta = fit$zeta, levels = levels(y), - class = class(y), - ordered = is.ordered(y) + class = if (is.ordered(y)) "ordered" else class(y)[1L] ) } return(impy) } -polr.draw <- function(x, beta, zeta, levels, class = NULL, ordered = FALSE) { +polr.draw <- function(x, beta, zeta, levels, class = NULL) { if (nrow(x) == 0L) return(character(0)) eta <- x %*% beta cumpr <- plogis(matrix(zeta, nrow(x), length(zeta), byrow = TRUE) - as.vector(eta)) @@ -173,8 +171,8 @@ polr.draw <- function(x, beta, zeta, levels, class = NULL, ordered = FALSE) { idx <- 1L + apply(draws, 2L, sum) out <- levels[idx] - if (!is.null(class) && class == "ordered") { - out <- factor(out, levels = levels, ordered = ordered) + if (!is.null(class) && class %in% c("factor", "ordered")) { + out <- factor(out, levels = levels, ordered = (class == "ordered")) } return(out) diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 5ed5730cd..a7396cb8d 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -84,9 +84,8 @@ mice.impute.polyreg <- function( x = x, beta = model$beta.dot, levels = model$factor$labels, - class = model$class, - ordered = isTRUE(model$ordered) - )) + class = model$class[1L]) + ) } # Escape perfect prediction @@ -108,6 +107,7 @@ mice.impute.polyreg <- function( } # Fit multinomial model + y <- droplevels(y) xy <- cbind.data.frame(y, x) fit <- do.call(nnet::multinom, c( list(formula(xy), @@ -146,8 +146,7 @@ mice.impute.polyreg <- function( model$beta.dot <- beta if (warmstart) model$wts <- fit$wts model$factor <- list(labels = levels(y), quant = NULL) - model$class <- class(y) - model$ordered <- is.ordered(y) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] model$xnames <- colnames(x) } @@ -156,12 +155,11 @@ mice.impute.polyreg <- function( x = x, beta = beta, levels = levels(y), - class = class(y), - ordered = is.ordered(y) + class = if (is.ordered(y)) "ordered" else class(y)[1L] ) } -polyreg.draw <- function(x, beta, levels, class = NULL, ordered = FALSE) { +polyreg.draw <- function(x, beta, levels, class = NULL) { if (nrow(x) == 0L) return(character(0)) lp <- x %*% beta p <- exp(lp) / rowSums(exp(lp) + 1) @@ -173,8 +171,8 @@ polyreg.draw <- function(x, beta, levels, class = NULL, ordered = FALSE) { out <- levels[idx] - if (!is.null(class) && class == "factor") { - out <- factor(out, levels = levels, ordered = ordered) + if (!is.null(class) && class %in% c("factor", "ordered")) { + out <- factor(out, levels = levels, ordered = (class == "ordered")) } return(out) From 5a89ddd4fc1e76130fedbeaa3e95f1ef3f085cfa Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 27 Mar 2025 15:58:48 +0100 Subject: [PATCH 090/147] Add scan.types() function to report the conformity of the (new) data with a given imputation model --- NAMESPACE | 1 + R/scan.types.R | 130 +++++++++++++++++++++++++++++++ man/scan.types.Rd | 27 +++++++ tests/testthat/test-scan.types.R | 50 ++++++++++++ 4 files changed, 208 insertions(+) create mode 100644 R/scan.types.R create mode 100644 man/scan.types.Rd create mode 100644 tests/testthat/test-scan.types.R diff --git a/NAMESPACE b/NAMESPACE index 1f7cc149e..951f6fe72 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -163,6 +163,7 @@ export(pool.syn) export(pool.table) export(quickpred) export(rbind) +export(scan) export(squeeze) export(stripplot) export(supports.transparent) diff --git a/R/scan.types.R b/R/scan.types.R new file mode 100644 index 000000000..16609f7ca --- /dev/null +++ b/R/scan.types.R @@ -0,0 +1,130 @@ +#' Scan variable types in new data and compare to trained models +#' +#' This function scans the variables in `data` and compares their types and structure +#' against the trained models. If `coerce = TRUE`, variables in `data` will be +#' coerced to match the type information in `models`, and the scan is rerun on the +#' coerced data. The modified data is attached as an attribute `"data"` to the result. +#' +#' @param data A data frame with new data to be filled +#' @param models A list of trained model objects as produced by `mice()` +#' @param coerce Logical, if TRUE attempt to coerce variables in `data` to match `models` +#' @param print Logical, if TRUE prints the resulting table. +#' @return A data frame with one row per variable, and diagnostic columns. If `coerce = TRUE`, +#' the result has an attribute `"data"` with the coerced data. +#' @export +scan.types <- function(data, models, coerce = FALSE, print = FALSE) { + orig.data <- data + vars.data <- names(orig.data) + vars.model <- names(models) + vars.all <- union(vars.data, vars.model) + + report <- data.frame( + variable = vars.all, + in_data = vars.all %in% vars.data, + in_model = vars.all %in% vars.model, + data_class = NA_character_, + model_class = NA_character_, + class_match = NA_character_, + levels_match = NA_character_, + levels_new_missing = NA_character_, + levels_extra = NA_character_, + distribution_match = NA_character_, + pred_match = NA_character_, + task = NA_character_, + stringsAsFactors = FALSE + ) + + for (i in seq_len(nrow(report))) { + j <- report$variable[i] + in_data <- isTRUE(report$in_data[i]) + in_model <- isTRUE(report$in_model[i]) + + x <- if (isTRUE(in_data) && j %in% names(orig.data)) orig.data[[j]] else NULL + mod <- if (in_model) models[[j]][[1]] else NULL + + report$data_class[i] <- if (!is.null(x)) { + if (inherits(x, "ordered")) "ordered" else class(x)[1] + } else NA + + if (!is.null(mod$class)) { + report$model_class[i] <- mod$class + } + + if (!is.null(x) && !is.null(mod$class)) { + match <- if (inherits(x, "ordered")) "ordered" else class(x)[1] + report$class_match[i] <- if (identical(match, mod$class)) "Y" else "N" + } + + if (!is.null(x) && is.factor(x) && !is.null(mod$factor$labels)) { + lvls.data <- levels(x) + lvls.model <- mod$factor$labels + report$levels_match[i] <- if (identical(lvls.data, lvls.model)) "Y" else "N" + report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" + report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" + } + + if (!is.null(x) && is.numeric(x) && !all(is.na(x))) { + q <- quantile(x, c(0.25, 0.75), na.rm = TRUE) + report$distribution_match[i] <- sprintf("Q1=%.2f, Q3=%.2f", q[1], q[2]) + } + + # predictor variable coverage + if (!is.null(mod$formula)) { + f <- as.formula(mod$formula) + needed <- all.vars(f[[3L]]) + present <- names(orig.data) + report$pred_match[i] <- if (all(needed %in% present)) "Y" else "N" + } + + report$task[i] <- if (!is.null(mod$setup$task)) { + mod$setup$task + } else if (in_data) { + "train" + } else { + "none" + } + } + + # Add can_fill column + report$can_fill <- vapply(seq_len(nrow(report)), function(i) { + if (isTRUE(report$in_model[i]) && + identical(report$class_match[i], "Y") && + !identical(report$levels_match[i], "N") && + identical(report$pred_match[i], "Y")) { + "Y" + } else { + "N" + } + }, character(1)) + + # If coerce = TRUE, try to coerce and rerun scan on coerced data + if (isTRUE(coerce)) { + coerced.data <- orig.data + for (i in seq_len(nrow(report))) { + j <- report$variable[i] + if (report$in_data[i] && report$in_model[i] && report$class_match[i] == "N") { + mod <- models[[j]][[1]] + x <- coerced.data[[j]] + target_class <- mod$class + target_levels <- mod$factor$labels + + if (!is.null(target_class)) { + if (target_class %in% c("factor", "ordered")) { + coerced.data[[j]] <- factor(x, levels = target_levels, ordered = (target_class == "ordered")) + } else if (target_class == "logical") { + coerced.data[[j]] <- as.logical(x) + } else if (target_class == "numeric") { + coerced.data[[j]] <- as.numeric(x) + } + } + } + } + result <- Recall(coerced.data, models, coerce = FALSE) + attr(result, "data") <- coerced.data + if (print) print(result) + return(result) + } + + if (print) print(report) + report +} diff --git a/man/scan.types.Rd b/man/scan.types.Rd new file mode 100644 index 000000000..5127066c3 --- /dev/null +++ b/man/scan.types.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/scan.types.R +\name{scan.types} +\alias{scan.types} +\title{Scan variable types in new data and compare to trained models} +\usage{ +scan.types(data, models, coerce = FALSE, print = FALSE) +} +\arguments{ +\item{data}{A data frame with new data to be filled} + +\item{models}{A list of trained model objects as produced by \code{mice()}} + +\item{coerce}{Logical, if TRUE attempt to coerce variables in \code{data} to match \code{models}} + +\item{print}{Logical, if TRUE prints the resulting table.} +} +\value{ +A data frame with one row per variable, and diagnostic columns. If \code{coerce = TRUE}, +the result has an attribute \code{"data"} with the coerced data. +} +\description{ +This function scans the variables in \code{data} and compares their types and structure +against the trained models. If \code{coerce = TRUE}, variables in \code{data} will be +coerced to match the type information in \code{models}, and the scan is rerun on the +coerced data. The modified data is attached as an attribute \code{"data"} to the result. +} diff --git a/tests/testthat/test-scan.types.R b/tests/testthat/test-scan.types.R new file mode 100644 index 000000000..d0127ef51 --- /dev/null +++ b/tests/testthat/test-scan.types.R @@ -0,0 +1,50 @@ +set.seed(123) +df <- data.frame( + factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), + factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), + factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), + logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), + logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), + numeric1 = rnorm(20) +) +n <- prod(dim(df)) +na_count <- round(0.3 * n) +missing_idx <- arrayInd(sample(n, na_count), .dim = dim(df)) +for (i in seq_len(nrow(missing_idx))) { + df[missing_idx[i, 1], missing_idx[i, 2]] <- NA +} + +expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) + +# single-row new data, correct typ +newdata <- make.data(models = trained$models, vars = names(df)) +result1 <- scan.types(data = newdata, models = trained$models, coerce = TRUE) + +test_that("scan.types() does not alter new data that has correct type", { + expect_true(identical(attr(result1, "data"), newdata)) +}) + +test_that("scan.types() sets can_fill to Y for correctly typed newdata", { + expect_true(length(result1$can_fill) > 0) + expect_true(all(result1$can_fill == "Y")) +}) + +# single-row new data, wrong types +newdata <- data.frame( + factor2 = NA, + factor3 = NA, + factor4 = NA, + logical1 = NA, + logical2 = NA, + numeric1 = NA) + +result2 <- scan.types(data = newdata, models = trained$models, coerce = TRUE) + +test_that("scan.types) coerces new data of wrong types to correct types", { + expect_false(identical(attr(result2, "data"), newdata)) + expect_true(identical(attr(result1, "data"), attr(result2, "data"))) +}) + +test_that("scan.types() sets can_fill to Y for coerced data", { + expect_true(all(result2$can_fill == "Y")) +}) From 757647dc7316e516f4bfd4888f2e971cace6f83b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 28 Mar 2025 10:00:34 +0100 Subject: [PATCH 091/147] Add make.data() for creating an NA data with the properties defined by trained models --- NAMESPACE | 3 ++- R/make.data.R | 47 +++++++++++++++++++++++++++++++++++++++++++++++ man/make.data.Rd | 30 ++++++++++++++++++++++++++++++ 3 files changed, 79 insertions(+), 1 deletion(-) create mode 100644 R/make.data.R create mode 100644 man/make.data.Rd diff --git a/NAMESPACE b/NAMESPACE index 951f6fe72..4df44eb51 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -91,6 +91,7 @@ export(lm.mids) export(mads) export(make.blocks) export(make.blots) +export(make.data) export(make.formulas) export(make.method) export(make.modeltype) @@ -163,7 +164,7 @@ export(pool.syn) export(pool.table) export(quickpred) export(rbind) -export(scan) +export(scan.types) export(squeeze) export(stripplot) export(supports.transparent) diff --git a/R/make.data.R b/R/make.data.R new file mode 100644 index 000000000..c9b5172c5 --- /dev/null +++ b/R/make.data.R @@ -0,0 +1,47 @@ +#' Construct a new data.frame from a trained model +#' +#' This function generates a data.frame with the correct structure (names, classes, +#' levels) based on a set of trained models, as produced by `mice()` with `tasks = "train"`. +#' +#' @param models A list of trained models from `mice()`. +#' @param n Number of rows to generate. Default is 1. +#' @param fill Value to fill the data with. Default is `NA`. +#' @param vars Optional character vector specifying the variable order. Default is `NULL`. +#' +#' @return A `data.frame` of `n` rows, where each column matches the type and levels +#' expected from the corresponding model. +#' @examples +#' trained <- mice(boys, tasks = "train", print = FALSE) +#' newdata <- make.data(trained$models, n = 5, vars = names(boys)) +#' str(newdata) +#' @export +make.data <- function(models, n = 1L, fill = NA, vars = NULL) { + stopifnot(is.list(models), is.numeric(n), length(n) == 1L) + + all_vars <- names(models) + vars <- if (is.null(vars)) all_vars else intersect(vars, all_vars) + + out <- vector("list", length(vars)) + names(out) <- vars + + for (j in vars) { + mod <- models[[j]][[1]] + cls <- mod$class + lvls <- mod$factor$labels + + if (cls == "factor") { + out[[j]] <- factor(rep(fill, n), levels = lvls) + } else if (cls == "ordered") { + out[[j]] <- factor(rep(fill, n), levels = lvls, ordered = TRUE) + } else if (cls == "logical") { + out[[j]] <- as.logical(rep(fill, n)) + } else if (cls == "numeric") { + out[[j]] <- as.numeric(rep(fill, n)) + } else { + warning(sprintf("Unknown class '%s' for variable '%s'", cls, j)) + out[[j]] <- rep(fill, n) + } + } + + as.data.frame(out) +} diff --git a/man/make.data.Rd b/man/make.data.Rd new file mode 100644 index 000000000..cd7f24dd8 --- /dev/null +++ b/man/make.data.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/make.data.R +\name{make.data} +\alias{make.data} +\title{Construct a new data.frame from a trained model} +\usage{ +make.data(models, n = 1L, fill = NA, vars = NULL) +} +\arguments{ +\item{models}{A list of trained models from \code{mice()}.} + +\item{n}{Number of rows to generate. Default is 1.} + +\item{fill}{Value to fill the data with. Default is \code{NA}.} + +\item{vars}{Optional character vector specifying the variable order. Default is \code{NULL}.} +} +\value{ +A \code{data.frame} of \code{n} rows, where each column matches the type and levels +expected from the corresponding model. +} +\description{ +This function generates a data.frame with the correct structure (names, classes, +levels) based on a set of trained models, as produced by \code{mice()} with \code{tasks = "train"}. +} +\examples{ +trained <- mice(boys, tasks = "train", print = FALSE) +newdata <- make.data(trained$models, n = 5, vars = names(boys)) +str(newdata) +} From 26d4e830483faa7420959ec0159f9cbf872e56f6 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 28 Mar 2025 10:02:07 +0100 Subject: [PATCH 092/147] Move tests for make.data() and scan.types() into test-scan.types.R --- tests/testthat/test-tasks.R | 56 ------------------------------------- 1 file changed, 56 deletions(-) diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index d26ecb850..c3ed613cd 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -33,59 +33,3 @@ test_that("the procedure informs the user about a mismatch between model and dat levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") }) - -## Check how categorical method handle fills for single row newdata - -set.seed(123) -df <- data.frame( - factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), - factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), - factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), - logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), - logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), - numeric1 = rnorm(20) -) -n <- prod(dim(df)) -na_count <- round(0.3 * n) -missing_idx <- arrayInd(sample(n, na_count), .dim = dim(df)) -for (i in seq_len(nrow(missing_idx))) { - df[missing_idx[i, 1], missing_idx[i, 2]] <- NA -} - -expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) - - -# single-row new data, wrong types -newdata_wrong <- data.frame( - factor2 = NA, - factor3 = NA, - factor4 = NA, - logical1 = NA, - logical2 = NA, - numeric1 = NA) - -# single-row new data, correct types -newdata_correct <- data.frame( - factor2 = factor(NA, levels = levels(df$factor2)), - factor3 = factor(NA, levels = levels(df$factor3)), - factor4 = factor(NA, levels = levels(df$factor4), ordered = TRUE), - logical1 = as.logical(NA), - logical2 = as.logical(NA), - numeric1 = as.numeric(NA)) - -test_that("df and newdata have same types before fill", { - expect_false(identical(sapply(df, class), sapply(newdata_wrong, class))) - expect_true(identical(sapply(df, class), sapply(newdata_correct, class))) -}) - -# fill0 <- mice(newdata_correct, tasks = "fill", models = trained$models, m = 20, maxit = 0, print = FALSE, seed = 2) -# fill0$imp -# -# fill1 <- mice(newdata_correct, tasks = "fill", models = trained$models, m = 20, maxit = 1, print = FALSE, seed = 2) -# fill1$imp - -test_that("Filling logicals work without converting to factors", { - expect_silent(filled <- mice(newdata_correct, tasks = "fill", models = trained$models, print = FALSE)) - expect_identical(sapply(newdata_correct, class), sapply(complete(filled), class)) -}) - From 100b8568709152219b27217ab69948a8c40e06bf Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 28 Mar 2025 11:12:04 +0100 Subject: [PATCH 093/147] Collect source code for scan.data(), make.data() and coerce.data() into data.R --- NAMESPACE | 3 +- R/data.R | 203 ++++++++++++++++++ R/make.data.R | 47 ---- R/scan.types.R | 130 ----------- man/coerce.data.Rd | 20 ++ man/make.data.Rd | 2 +- man/scan.data.Rd | 48 +++++ man/scan.types.Rd | 27 --- .../{test-scan.types.R => test-data.R} | 35 +-- tests/testthat/test-newdata.R | 8 + 10 files changed, 305 insertions(+), 218 deletions(-) create mode 100644 R/data.R delete mode 100644 R/make.data.R delete mode 100644 R/scan.types.R create mode 100644 man/coerce.data.Rd create mode 100644 man/scan.data.Rd delete mode 100644 man/scan.types.Rd rename tests/testthat/{test-scan.types.R => test-data.R} (56%) diff --git a/NAMESPACE b/NAMESPACE index 4df44eb51..81ed3a25d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -63,6 +63,7 @@ export(bwplot) export(cbind) export(cc) export(cci) +export(coerce.data) export(complete) export(construct.blocks) export(convergence) @@ -164,7 +165,7 @@ export(pool.syn) export(pool.table) export(quickpred) export(rbind) -export(scan.types) +export(scan.data) export(squeeze) export(stripplot) export(supports.transparent) diff --git a/R/data.R b/R/data.R new file mode 100644 index 000000000..800e5499b --- /dev/null +++ b/R/data.R @@ -0,0 +1,203 @@ +#' Scan data types and compare to model expectations +#' +#' This function compares the structure and type of `data` to what is expected +#' from the trained `models`. It reports differences in class, levels, and predictor +#' availability, useful to prepare for imputation or prediction. +#' +#' @param data A `data.frame` to be checked. +#' @param models A list of trained models from `mice()`. +#' @param print Logical, whether to print the resulting table. +#' +#' @return A `data.frame` with one row per variable (from either `data` or `models`) +#' and columns summarizing compatibility diagnostics. The following columns are included: +#' +#' \tabular{ll}{ +#' `variable` \tab Variable name \cr +#' `in_data` \tab Logical: whether variable is present in `data` \cr +#' `in_model` \tab Logical: whether a trained model is available \cr +#' `data_class` \tab Class of the variable in `data` (e.g., `"factor"`, `"ordered"`) \cr +#' `model_class` \tab model class according to the trained model \cr +#' `class_match` \tab `"Y"` if `data_class` matches `model_class`, `"N"` otherwise \cr +#' `levels_match` \tab `"Y"` if factor levels exactly match, `"N"` if they differ, "" if not applicable \cr +#' `pred_match` \tab `"Y"` if all predictors used by the model are present in `data`, `"N"` if any are missing, "" if unknown \cr +#' `can_fill` \tab `"Y"` if there is a model, if classes match, if levels match and if predictor match, other "" \cr +#' } +#' +#' @examples +#' # Train model on boys data +#' imp <- mice(boys, tasks = "train", m = 1, maxit = 1, print = FALSE) +#' +#' # Create a new dataset with missing values and mismatched types +#' data <- boys[1:3, ] +#' data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) # remove ordering +#' +#' # Run scan +#' scan.data(data, imp$models) +#' +#' @export +scan.data <- function(data, models, print = FALSE) { + orig.data <- data + vars.data <- names(orig.data) + vars.model <- names(models) + vars.all <- union(vars.data, vars.model) + + report <- data.frame( + variable = vars.all, + in_data = vars.all %in% vars.data, + in_model = vars.all %in% vars.model, + data_class = NA_character_, + model_class = NA_character_, + class_match = NA_character_, + levels_match = NA_character_, + levels_new_missing = NA_character_, + levels_extra = NA_character_, + distribution_match = NA_character_, + pred_match = NA_character_, + task = NA_character_, + stringsAsFactors = FALSE + ) + + for (i in seq_len(nrow(report))) { + j <- report$variable[i] + in_data <- isTRUE(report$in_data[i]) + in_model <- isTRUE(report$in_model[i]) + + x <- if (isTRUE(in_data) && j %in% names(orig.data)) orig.data[[j]] else NULL + mod <- if (in_model) models[[j]][[1]] else NULL + + report$data_class[i] <- if (!is.null(x)) { + if (inherits(x, "ordered")) "ordered" else class(x)[1] + } else NA + + if (!is.null(mod$class)) { + report$model_class[i] <- mod$class + } + + if (!is.null(x) && !is.null(mod$class)) { + match <- if (inherits(x, "ordered")) "ordered" else class(x)[1] + report$class_match[i] <- if (identical(match, mod$class)) "Y" else "N" + } + + if (!is.null(x) && is.factor(x) && !is.null(mod$factor$labels)) { + lvls.data <- levels(x) + lvls.model <- mod$factor$labels + report$levels_match[i] <- if (identical(lvls.data, lvls.model)) "Y" else "N" + report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" + report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" + } + + if (!is.null(x) && is.numeric(x) && !all(is.na(x))) { + q <- quantile(x, c(0.25, 0.75), na.rm = TRUE) + report$distribution_match[i] <- sprintf("Q1=%.2f, Q3=%.2f", q[1], q[2]) + } + + # predictor variable coverage + if (!is.null(mod$formula)) { + f <- as.formula(mod$formula) + needed <- all.vars(f[[3L]]) + present <- names(orig.data) + report$pred_match[i] <- if (all(needed %in% present)) "Y" else "N" + } + + report$task[i] <- if (!is.null(mod$setup$task)) { + mod$setup$task + } else if (in_data) { + "train" + } else { + "none" + } + } + + # Add can_fill column + report$can_fill <- vapply(seq_len(nrow(report)), function(i) { + if (isTRUE(report$in_model[i]) && + identical(report$class_match[i], "Y") && + !identical(report$levels_match[i], "N") && + identical(report$pred_match[i], "Y")) { + "Y" + } else { + "N" + } + }, character(1)) + + if (print) print(report) + report +} + +#' Construct a new data.frame from a trained model +#' +#' This function generates a data.frame with the correct structure (names, classes, +#' levels) based on a set of trained models, as produced by `mice()` with `tasks = "train"`. +#' +#' @param models A list of trained models from `mice()`. +#' @param n Number of rows to generate. Default is 1. +#' @param fill Value to fill the data with. Default is `NA`. +#' @param vars Optional character vector specifying the variable order. Default is `NULL`. +#' +#' @return A `data.frame` of `n` rows, where each column matches the type and levels +#' expected from the corresponding model. +#' @examples +#' trained <- mice(boys, tasks = "train", print = FALSE) +#' newdata <- make.data(trained$models, n = 5, vars = names(boys)) +#' str(newdata) +#' @export +make.data <- function(models, n = 1L, fill = NA, vars = NULL) { + stopifnot(is.list(models), is.numeric(n), length(n) == 1L) + + all_vars <- names(models) + vars <- if (is.null(vars)) all_vars else intersect(vars, all_vars) + + out <- vector("list", length(vars)) + names(out) <- vars + + for (j in vars) { + mod <- models[[j]][[1]] + cls <- mod$class + lvls <- mod$factor$labels + + if (cls == "factor") { + out[[j]] <- factor(rep(fill, n), levels = lvls) + } else if (cls == "ordered") { + out[[j]] <- factor(rep(fill, n), levels = lvls, ordered = TRUE) + } else if (cls == "logical") { + out[[j]] <- as.logical(rep(fill, n)) + } else if (cls == "numeric") { + out[[j]] <- as.numeric(rep(fill, n)) + } else { + warning(sprintf("Unknown class '%s' for variable '%s'", cls, j)) + out[[j]] <- rep(fill, n) + } + } + + as.data.frame(out) +} + +#' Coerce a data.frame to match model expectations +#' +#' This function coerces columns in `data` to match the type and levels +#' expected by the corresponding trained `models`. +#' +#' @param data A `data.frame` with input data. +#' @param models A list of trained models from `mice()`. +#' +#' @return A coerced `data.frame` that matches the class and levels from `models`. +#' @export +coerce.data <- function(data, models) { + data <- as.data.frame(data) + for (j in intersect(names(data), names(models))) { + mod <- models[[j]][[1]] + cls <- mod$class + lvls <- mod$factor$labels + + if (cls == "factor") { + data[[j]] <- factor(data[[j]], levels = lvls) + } else if (cls == "ordered") { + data[[j]] <- factor(data[[j]], levels = lvls, ordered = TRUE) + } else if (cls == "logical") { + data[[j]] <- as.logical(data[[j]]) + } else if (cls == "numeric") { + data[[j]] <- as.numeric(data[[j]]) + } + } + data +} diff --git a/R/make.data.R b/R/make.data.R deleted file mode 100644 index c9b5172c5..000000000 --- a/R/make.data.R +++ /dev/null @@ -1,47 +0,0 @@ -#' Construct a new data.frame from a trained model -#' -#' This function generates a data.frame with the correct structure (names, classes, -#' levels) based on a set of trained models, as produced by `mice()` with `tasks = "train"`. -#' -#' @param models A list of trained models from `mice()`. -#' @param n Number of rows to generate. Default is 1. -#' @param fill Value to fill the data with. Default is `NA`. -#' @param vars Optional character vector specifying the variable order. Default is `NULL`. -#' -#' @return A `data.frame` of `n` rows, where each column matches the type and levels -#' expected from the corresponding model. -#' @examples -#' trained <- mice(boys, tasks = "train", print = FALSE) -#' newdata <- make.data(trained$models, n = 5, vars = names(boys)) -#' str(newdata) -#' @export -make.data <- function(models, n = 1L, fill = NA, vars = NULL) { - stopifnot(is.list(models), is.numeric(n), length(n) == 1L) - - all_vars <- names(models) - vars <- if (is.null(vars)) all_vars else intersect(vars, all_vars) - - out <- vector("list", length(vars)) - names(out) <- vars - - for (j in vars) { - mod <- models[[j]][[1]] - cls <- mod$class - lvls <- mod$factor$labels - - if (cls == "factor") { - out[[j]] <- factor(rep(fill, n), levels = lvls) - } else if (cls == "ordered") { - out[[j]] <- factor(rep(fill, n), levels = lvls, ordered = TRUE) - } else if (cls == "logical") { - out[[j]] <- as.logical(rep(fill, n)) - } else if (cls == "numeric") { - out[[j]] <- as.numeric(rep(fill, n)) - } else { - warning(sprintf("Unknown class '%s' for variable '%s'", cls, j)) - out[[j]] <- rep(fill, n) - } - } - - as.data.frame(out) -} diff --git a/R/scan.types.R b/R/scan.types.R deleted file mode 100644 index 16609f7ca..000000000 --- a/R/scan.types.R +++ /dev/null @@ -1,130 +0,0 @@ -#' Scan variable types in new data and compare to trained models -#' -#' This function scans the variables in `data` and compares their types and structure -#' against the trained models. If `coerce = TRUE`, variables in `data` will be -#' coerced to match the type information in `models`, and the scan is rerun on the -#' coerced data. The modified data is attached as an attribute `"data"` to the result. -#' -#' @param data A data frame with new data to be filled -#' @param models A list of trained model objects as produced by `mice()` -#' @param coerce Logical, if TRUE attempt to coerce variables in `data` to match `models` -#' @param print Logical, if TRUE prints the resulting table. -#' @return A data frame with one row per variable, and diagnostic columns. If `coerce = TRUE`, -#' the result has an attribute `"data"` with the coerced data. -#' @export -scan.types <- function(data, models, coerce = FALSE, print = FALSE) { - orig.data <- data - vars.data <- names(orig.data) - vars.model <- names(models) - vars.all <- union(vars.data, vars.model) - - report <- data.frame( - variable = vars.all, - in_data = vars.all %in% vars.data, - in_model = vars.all %in% vars.model, - data_class = NA_character_, - model_class = NA_character_, - class_match = NA_character_, - levels_match = NA_character_, - levels_new_missing = NA_character_, - levels_extra = NA_character_, - distribution_match = NA_character_, - pred_match = NA_character_, - task = NA_character_, - stringsAsFactors = FALSE - ) - - for (i in seq_len(nrow(report))) { - j <- report$variable[i] - in_data <- isTRUE(report$in_data[i]) - in_model <- isTRUE(report$in_model[i]) - - x <- if (isTRUE(in_data) && j %in% names(orig.data)) orig.data[[j]] else NULL - mod <- if (in_model) models[[j]][[1]] else NULL - - report$data_class[i] <- if (!is.null(x)) { - if (inherits(x, "ordered")) "ordered" else class(x)[1] - } else NA - - if (!is.null(mod$class)) { - report$model_class[i] <- mod$class - } - - if (!is.null(x) && !is.null(mod$class)) { - match <- if (inherits(x, "ordered")) "ordered" else class(x)[1] - report$class_match[i] <- if (identical(match, mod$class)) "Y" else "N" - } - - if (!is.null(x) && is.factor(x) && !is.null(mod$factor$labels)) { - lvls.data <- levels(x) - lvls.model <- mod$factor$labels - report$levels_match[i] <- if (identical(lvls.data, lvls.model)) "Y" else "N" - report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" - report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" - } - - if (!is.null(x) && is.numeric(x) && !all(is.na(x))) { - q <- quantile(x, c(0.25, 0.75), na.rm = TRUE) - report$distribution_match[i] <- sprintf("Q1=%.2f, Q3=%.2f", q[1], q[2]) - } - - # predictor variable coverage - if (!is.null(mod$formula)) { - f <- as.formula(mod$formula) - needed <- all.vars(f[[3L]]) - present <- names(orig.data) - report$pred_match[i] <- if (all(needed %in% present)) "Y" else "N" - } - - report$task[i] <- if (!is.null(mod$setup$task)) { - mod$setup$task - } else if (in_data) { - "train" - } else { - "none" - } - } - - # Add can_fill column - report$can_fill <- vapply(seq_len(nrow(report)), function(i) { - if (isTRUE(report$in_model[i]) && - identical(report$class_match[i], "Y") && - !identical(report$levels_match[i], "N") && - identical(report$pred_match[i], "Y")) { - "Y" - } else { - "N" - } - }, character(1)) - - # If coerce = TRUE, try to coerce and rerun scan on coerced data - if (isTRUE(coerce)) { - coerced.data <- orig.data - for (i in seq_len(nrow(report))) { - j <- report$variable[i] - if (report$in_data[i] && report$in_model[i] && report$class_match[i] == "N") { - mod <- models[[j]][[1]] - x <- coerced.data[[j]] - target_class <- mod$class - target_levels <- mod$factor$labels - - if (!is.null(target_class)) { - if (target_class %in% c("factor", "ordered")) { - coerced.data[[j]] <- factor(x, levels = target_levels, ordered = (target_class == "ordered")) - } else if (target_class == "logical") { - coerced.data[[j]] <- as.logical(x) - } else if (target_class == "numeric") { - coerced.data[[j]] <- as.numeric(x) - } - } - } - } - result <- Recall(coerced.data, models, coerce = FALSE) - attr(result, "data") <- coerced.data - if (print) print(result) - return(result) - } - - if (print) print(report) - report -} diff --git a/man/coerce.data.Rd b/man/coerce.data.Rd new file mode 100644 index 000000000..dd494d893 --- /dev/null +++ b/man/coerce.data.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\name{coerce.data} +\alias{coerce.data} +\title{Coerce a data.frame to match model expectations} +\usage{ +coerce.data(data, models) +} +\arguments{ +\item{data}{A \code{data.frame} with input data.} + +\item{models}{A list of trained models from \code{mice()}.} +} +\value{ +A coerced \code{data.frame} that matches the class and levels from \code{models}. +} +\description{ +This function coerces columns in \code{data} to match the type and levels +expected by the corresponding trained \code{models}. +} diff --git a/man/make.data.Rd b/man/make.data.Rd index cd7f24dd8..ec2eef1ee 100644 --- a/man/make.data.Rd +++ b/man/make.data.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/make.data.R +% Please edit documentation in R/data.R \name{make.data} \alias{make.data} \title{Construct a new data.frame from a trained model} diff --git a/man/scan.data.Rd b/man/scan.data.Rd new file mode 100644 index 000000000..d0b905334 --- /dev/null +++ b/man/scan.data.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\name{scan.data} +\alias{scan.data} +\title{Scan data types and compare to model expectations} +\usage{ +scan.data(data, models, print = FALSE) +} +\arguments{ +\item{data}{A \code{data.frame} to be checked.} + +\item{models}{A list of trained models from \code{mice()}.} + +\item{print}{Logical, whether to print the resulting table.} +} +\value{ +A \code{data.frame} with one row per variable (from either \code{data} or \code{models}) +and columns summarizing compatibility diagnostics. The following columns are included: + +\tabular{ll}{ +\code{variable} \tab Variable name \cr +\code{in_data} \tab Logical: whether variable is present in \code{data} \cr +\code{in_model} \tab Logical: whether a trained model is available \cr +\code{data_class} \tab Class of the variable in \code{data} (e.g., \code{"factor"}, \code{"ordered"}) \cr +\code{model_class} \tab model class according to the trained model \cr +\code{class_match} \tab \code{"Y"} if \code{data_class} matches \code{model_class}, \code{"N"} otherwise \cr +\code{levels_match} \tab \code{"Y"} if factor levels exactly match, \code{"N"} if they differ, "" if not applicable \cr +\code{pred_match} \tab \code{"Y"} if all predictors used by the model are present in \code{data}, \code{"N"} if any are missing, "" if unknown \cr +\code{can_fill} \tab \code{"Y"} if there is a model, if classes match, if levels match and if predictor match, other "" \cr +} +} +\description{ +This function compares the structure and type of \code{data} to what is expected +from the trained \code{models}. It reports differences in class, levels, and predictor +availability, useful to prepare for imputation or prediction. +} +\examples{ +# Train model on boys data +imp <- mice(boys, tasks = "train", m = 1, maxit = 1, print = FALSE) + +# Create a new dataset with missing values and mismatched types +data <- boys[1:3, ] +data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) # remove ordering + +# Run scan +scan.data(data, imp$models) + +} diff --git a/man/scan.types.Rd b/man/scan.types.Rd deleted file mode 100644 index 5127066c3..000000000 --- a/man/scan.types.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/scan.types.R -\name{scan.types} -\alias{scan.types} -\title{Scan variable types in new data and compare to trained models} -\usage{ -scan.types(data, models, coerce = FALSE, print = FALSE) -} -\arguments{ -\item{data}{A data frame with new data to be filled} - -\item{models}{A list of trained model objects as produced by \code{mice()}} - -\item{coerce}{Logical, if TRUE attempt to coerce variables in \code{data} to match \code{models}} - -\item{print}{Logical, if TRUE prints the resulting table.} -} -\value{ -A data frame with one row per variable, and diagnostic columns. If \code{coerce = TRUE}, -the result has an attribute \code{"data"} with the coerced data. -} -\description{ -This function scans the variables in \code{data} and compares their types and structure -against the trained models. If \code{coerce = TRUE}, variables in \code{data} will be -coerced to match the type information in \code{models}, and the scan is rerun on the -coerced data. The modified data is attached as an attribute \code{"data"} to the result. -} diff --git a/tests/testthat/test-scan.types.R b/tests/testthat/test-data.R similarity index 56% rename from tests/testthat/test-scan.types.R rename to tests/testthat/test-data.R index d0127ef51..677cc5488 100644 --- a/tests/testthat/test-scan.types.R +++ b/tests/testthat/test-data.R @@ -1,3 +1,5 @@ +context("scan.data, make.data, coerce.data") + set.seed(123) df <- data.frame( factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), @@ -16,19 +18,25 @@ for (i in seq_len(nrow(missing_idx))) { expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) -# single-row new data, correct typ +# make single-row new data with correct type newdata <- make.data(models = trained$models, vars = names(df)) -result1 <- scan.types(data = newdata, models = trained$models, coerce = TRUE) -test_that("scan.types() does not alter new data that has correct type", { - expect_true(identical(attr(result1, "data"), newdata)) -}) +# run scan on newdata +result1 <- scan.data(data = newdata, models = trained$models) -test_that("scan.types() sets can_fill to Y for correctly typed newdata", { +test_that("scan.data() sets can_fill to Y for correctly typed newdata", { expect_true(length(result1$can_fill) > 0) expect_true(all(result1$can_fill == "Y")) }) +# coerce newdata (not needed here) +coerced1 <- coerce.data(data = newdata, models = trained$models) + +test_that("coerce.data() does not alter when it has correct type", { + expect_true(identical(coerced1, newdata)) +}) + + # single-row new data, wrong types newdata <- data.frame( factor2 = NA, @@ -38,13 +46,16 @@ newdata <- data.frame( logical2 = NA, numeric1 = NA) -result2 <- scan.types(data = newdata, models = trained$models, coerce = TRUE) +result2 <- scan.data(data = newdata, models = trained$models) -test_that("scan.types) coerces new data of wrong types to correct types", { - expect_false(identical(attr(result2, "data"), newdata)) - expect_true(identical(attr(result1, "data"), attr(result2, "data"))) +test_that("scan.data() reports that it cannot fill all variables", { + expect_false(all(result2$can_fill == "Y")) }) -test_that("scan.types() sets can_fill to Y for coerced data", { - expect_true(all(result2$can_fill == "Y")) +coerced2 <- coerce.data(data = newdata, models = trained$models) + +test_that("coerce.data() can coerces wrong to correct types", { + expect_false(identical(attr(result2, "data"), newdata)) + expect_true(identical(coerced1, coerced2)) }) + diff --git a/tests/testthat/test-newdata.R b/tests/testthat/test-newdata.R index 0ef6aa419..7088192cd 100644 --- a/tests/testthat/test-newdata.R +++ b/tests/testthat/test-newdata.R @@ -1,5 +1,13 @@ context("mice.mids: newdata") +# SvB 20250328 +# This file contains tests for the newdata argument in mice.mids +# using a newdata argument +# +# This method is superseded by the tasks = "train"/"fill" arguments +# in mice(), but is retained here for backwards compatibility, and +# for methods that do not support the tasks argument. + # Check that mice.mids correctly appends the newdata to the # existing mids object init0 <- mice(nhanes, maxit = 0, m = 1, print = FALSE, seed = 1) From a1070f6f6caf32e3118826ceac9ff82f96cd79a2 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 28 Mar 2025 16:21:29 +0100 Subject: [PATCH 094/147] Signal dev --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e654d2f44..efaa6defa 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: mice Type: Package -Version: 3.17.3 +Version: 3.17.3.9000 Title: Multivariate Imputation by Chained Equations Date: 2025-3-28 Authors@R: c(person("Stef", "van Buuren", role = c("aut","cre"), From 8f41c721bbab6530c2dc95fec0ff417271622d60 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 28 Mar 2025 16:35:18 +0100 Subject: [PATCH 095/147] Remove columns from scan.data() --- R/data.R | 32 ++++++++------------------------ 1 file changed, 8 insertions(+), 24 deletions(-) diff --git a/R/data.R b/R/data.R index 800e5499b..c54c5b1d3 100644 --- a/R/data.R +++ b/R/data.R @@ -45,15 +45,12 @@ scan.data <- function(data, models, print = FALSE) { variable = vars.all, in_data = vars.all %in% vars.data, in_model = vars.all %in% vars.model, - data_class = NA_character_, - model_class = NA_character_, - class_match = NA_character_, - levels_match = NA_character_, - levels_new_missing = NA_character_, - levels_extra = NA_character_, - distribution_match = NA_character_, - pred_match = NA_character_, - task = NA_character_, + data_class = "", + model_class = "", + class_match = "", + levels_match = "", + pred_match = "", + can_fill = "", stringsAsFactors = FALSE ) @@ -82,13 +79,8 @@ scan.data <- function(data, models, print = FALSE) { lvls.data <- levels(x) lvls.model <- mod$factor$labels report$levels_match[i] <- if (identical(lvls.data, lvls.model)) "Y" else "N" - report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" - report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" - } - - if (!is.null(x) && is.numeric(x) && !all(is.na(x))) { - q <- quantile(x, c(0.25, 0.75), na.rm = TRUE) - report$distribution_match[i] <- sprintf("Q1=%.2f, Q3=%.2f", q[1], q[2]) + # report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" + # report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" } # predictor variable coverage @@ -98,14 +90,6 @@ scan.data <- function(data, models, print = FALSE) { present <- names(orig.data) report$pred_match[i] <- if (all(needed %in% present)) "Y" else "N" } - - report$task[i] <- if (!is.null(mod$setup$task)) { - mod$setup$task - } else if (in_data) { - "train" - } else { - "none" - } } # Add can_fill column From 8a7b7f4f37bde1657a93feee462836bc88e5c2af Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 28 Mar 2025 23:10:42 +0100 Subject: [PATCH 096/147] Add class integer to coerce.data() --- R/data.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/data.R b/R/data.R index c54c5b1d3..dec0e605d 100644 --- a/R/data.R +++ b/R/data.R @@ -169,7 +169,7 @@ make.data <- function(models, n = 1L, fill = NA, vars = NULL) { coerce.data <- function(data, models) { data <- as.data.frame(data) for (j in intersect(names(data), names(models))) { - mod <- models[[j]][[1]] + mod <- models[[j]][[1L]] cls <- mod$class lvls <- mod$factor$labels @@ -181,6 +181,8 @@ coerce.data <- function(data, models) { data[[j]] <- as.logical(data[[j]]) } else if (cls == "numeric") { data[[j]] <- as.numeric(data[[j]]) + } else if (cls == "integer") { + data[[j]] <- as.integer(data[[j]]) } } data From 633a274308d9c0daba7c0684a8244599c5e14eca Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 31 Mar 2025 23:44:59 +0200 Subject: [PATCH 097/147] Convert scan.table() output into TRUE/FALSE --- R/data.R | 55 +++++++++++++++++++++----------------- man/scan.data.Rd | 21 ++++++++------- tests/testthat/test-data.R | 4 +-- 3 files changed, 43 insertions(+), 37 deletions(-) diff --git a/R/data.R b/R/data.R index dec0e605d..9f1663a6e 100644 --- a/R/data.R +++ b/R/data.R @@ -12,15 +12,15 @@ #' and columns summarizing compatibility diagnostics. The following columns are included: #' #' \tabular{ll}{ -#' `variable` \tab Variable name \cr -#' `in_data` \tab Logical: whether variable is present in `data` \cr -#' `in_model` \tab Logical: whether a trained model is available \cr -#' `data_class` \tab Class of the variable in `data` (e.g., `"factor"`, `"ordered"`) \cr -#' `model_class` \tab model class according to the trained model \cr -#' `class_match` \tab `"Y"` if `data_class` matches `model_class`, `"N"` otherwise \cr -#' `levels_match` \tab `"Y"` if factor levels exactly match, `"N"` if they differ, "" if not applicable \cr -#' `pred_match` \tab `"Y"` if all predictors used by the model are present in `data`, `"N"` if any are missing, "" if unknown \cr -#' `can_fill` \tab `"Y"` if there is a model, if classes match, if levels match and if predictor match, other "" \cr +#' `variable` \tab Variable name \cr +#' `in_data` \tab Logical: whether variable is present in `data` \cr +#' `in_model` \tab Logical: whether a trained model is available \cr +#' `data_class` \tab Class of the variable in `data` (e.g., `"factor"`, `"ordered"`) \cr +#' `model_class` \tab model class according to the trained model \cr +#' `class_match` \tab `TRUE` if `data_class` matches `model_class`, `FALSE` otherwise \cr +#' `levels_match` \tab `TRUE` if factor levels exactly match, `FALSE` if they differ, `NA` if not applicable \cr +#' `pred_match` \tab `TRUE` if all predictors used by the model are present in `data`, `FALSE` if any are missing, `NA` if unknown \cr +#' `can_fill` \tab `TRUE` if there is a model, if classes match, if levels match and if predictor match, otherwise `FALSE` \cr #' } #' #' @examples @@ -28,8 +28,9 @@ #' imp <- mice(boys, tasks = "train", m = 1, maxit = 1, print = FALSE) #' #' # Create a new dataset with missing values and mismatched types +#' # remove ordering #' data <- boys[1:3, ] -#' data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) # remove ordering +#' data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) #' #' # Run scan #' scan.data(data, imp$models) @@ -45,12 +46,12 @@ scan.data <- function(data, models, print = FALSE) { variable = vars.all, in_data = vars.all %in% vars.data, in_model = vars.all %in% vars.model, - data_class = "", - model_class = "", - class_match = "", - levels_match = "", - pred_match = "", - can_fill = "", + data_class = character(length(vars.all)), + model_class = character(length(vars.all)), + class_match = logical(length(vars.all)), + levels_match = logical(length(vars.all)), + pred_match = logical(length(vars.all)), + can_fill = logical(length(vars.all)), stringsAsFactors = FALSE ) @@ -72,15 +73,17 @@ scan.data <- function(data, models, print = FALSE) { if (!is.null(x) && !is.null(mod$class)) { match <- if (inherits(x, "ordered")) "ordered" else class(x)[1] - report$class_match[i] <- if (identical(match, mod$class)) "Y" else "N" + report$class_match[i] <- if (identical(match, mod$class)) TRUE else FALSE } if (!is.null(x) && is.factor(x) && !is.null(mod$factor$labels)) { lvls.data <- levels(x) lvls.model <- mod$factor$labels - report$levels_match[i] <- if (identical(lvls.data, lvls.model)) "Y" else "N" + report$levels_match[i] <- if (identical(lvls.data, lvls.model)) TRUE else FALSE # report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" # report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" + } else { + report$levels_match[i] <- NA } # predictor variable coverage @@ -88,21 +91,21 @@ scan.data <- function(data, models, print = FALSE) { f <- as.formula(mod$formula) needed <- all.vars(f[[3L]]) present <- names(orig.data) - report$pred_match[i] <- if (all(needed %in% present)) "Y" else "N" + report$pred_match[i] <- if (all(needed %in% present)) TRUE else FALSE } } # Add can_fill column report$can_fill <- vapply(seq_len(nrow(report)), function(i) { if (isTRUE(report$in_model[i]) && - identical(report$class_match[i], "Y") && - !identical(report$levels_match[i], "N") && - identical(report$pred_match[i], "Y")) { - "Y" + report$class_match[i] && + !isFALSE(report$levels_match[i]) && + report$pred_match[i]) { + TRUE } else { - "N" + FALSE } - }, character(1)) + }, NA) if (print) print(report) report @@ -147,6 +150,8 @@ make.data <- function(models, n = 1L, fill = NA, vars = NULL) { out[[j]] <- as.logical(rep(fill, n)) } else if (cls == "numeric") { out[[j]] <- as.numeric(rep(fill, n)) + } else if (cls == "integer") { + out[[j]] <- as.integer(rep(fill, n)) } else { warning(sprintf("Unknown class '%s' for variable '%s'", cls, j)) out[[j]] <- rep(fill, n) diff --git a/man/scan.data.Rd b/man/scan.data.Rd index d0b905334..21faf41c1 100644 --- a/man/scan.data.Rd +++ b/man/scan.data.Rd @@ -18,15 +18,15 @@ A \code{data.frame} with one row per variable (from either \code{data} or \code{ and columns summarizing compatibility diagnostics. The following columns are included: \tabular{ll}{ -\code{variable} \tab Variable name \cr -\code{in_data} \tab Logical: whether variable is present in \code{data} \cr -\code{in_model} \tab Logical: whether a trained model is available \cr -\code{data_class} \tab Class of the variable in \code{data} (e.g., \code{"factor"}, \code{"ordered"}) \cr -\code{model_class} \tab model class according to the trained model \cr -\code{class_match} \tab \code{"Y"} if \code{data_class} matches \code{model_class}, \code{"N"} otherwise \cr -\code{levels_match} \tab \code{"Y"} if factor levels exactly match, \code{"N"} if they differ, "" if not applicable \cr -\code{pred_match} \tab \code{"Y"} if all predictors used by the model are present in \code{data}, \code{"N"} if any are missing, "" if unknown \cr -\code{can_fill} \tab \code{"Y"} if there is a model, if classes match, if levels match and if predictor match, other "" \cr +\code{variable} \tab Variable name \cr +\code{in_data} \tab Logical: whether variable is present in \code{data} \cr +\code{in_model} \tab Logical: whether a trained model is available \cr +\code{data_class} \tab Class of the variable in \code{data} (e.g., \code{"factor"}, \code{"ordered"}) \cr +\code{model_class} \tab model class according to the trained model \cr +\code{class_match} \tab \code{TRUE} if \code{data_class} matches \code{model_class}, \code{FALSE} otherwise \cr +\code{levels_match} \tab \code{TRUE} if factor levels exactly match, \code{FALSE} if they differ, \code{NA} if not applicable \cr +\code{pred_match} \tab \code{TRUE} if all predictors used by the model are present in \code{data}, \code{FALSE} if any are missing, \code{NA} if unknown \cr +\code{can_fill} \tab \code{TRUE} if there is a model, if classes match, if levels match and if predictor match, otherwise \code{FALSE} \cr } } \description{ @@ -39,8 +39,9 @@ availability, useful to prepare for imputation or prediction. imp <- mice(boys, tasks = "train", m = 1, maxit = 1, print = FALSE) # Create a new dataset with missing values and mismatched types +# remove ordering data <- boys[1:3, ] -data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) # remove ordering +data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) # Run scan scan.data(data, imp$models) diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R index 677cc5488..c12bc5ab8 100644 --- a/tests/testthat/test-data.R +++ b/tests/testthat/test-data.R @@ -26,7 +26,7 @@ result1 <- scan.data(data = newdata, models = trained$models) test_that("scan.data() sets can_fill to Y for correctly typed newdata", { expect_true(length(result1$can_fill) > 0) - expect_true(all(result1$can_fill == "Y")) + expect_true(all(result1$can_fill)) }) # coerce newdata (not needed here) @@ -49,7 +49,7 @@ newdata <- data.frame( result2 <- scan.data(data = newdata, models = trained$models) test_that("scan.data() reports that it cannot fill all variables", { - expect_false(all(result2$can_fill == "Y")) + expect_false(all(result2$can_fill)) }) coerced2 <- coerce.data(data = newdata, models = trained$models) From c9b67ae1cd54784267a01f1b70d93a40d509a5de Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 1 Apr 2025 09:23:19 +0200 Subject: [PATCH 098/147] Add names of tasks vector when omitted; Check whether the data and model match to enable filling --- R/check.tasks.R | 45 ++++++++++++++++++++++++++++--------- R/mice.R | 2 +- tests/testthat/test-tasks.R | 20 ++++++----------- 3 files changed, 43 insertions(+), 24 deletions(-) diff --git a/R/check.tasks.R b/R/check.tasks.R index f71350688..4d41f6fa9 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -1,4 +1,9 @@ -check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { +check.tasks <- function(tasks, data, models = NULL, blocks = NULL, + skip.check.tasks = FALSE) { + if (skip.check.tasks) { + return(tasks) + } + # This function is called during initialization if (is.null(tasks)) { tasks <- "impute" @@ -13,7 +18,7 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { # 2. Expand tasks if it's a single value bv <- unique(unlist(blocks)) - if (length(tasks) == 1) { + if (length(tasks) == 1L) { tasks <- setNames(rep(tasks, length(bv)), bv) } @@ -23,7 +28,13 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { ") must match the number of variables in `blocks` (", length(bv),").") } - # 4. Check if all names in tasks exist in blocks + # 4. Check that tasks is a named vector + if (is.null(names(tasks))) { + # Attach names assuming same order as unique variables in blocks + names(tasks) <- bv + } + + # 5. Check if all names in tasks exist in blocks notFound <- !names(tasks) %in% bv if (any(notFound)) { stop(paste0( @@ -33,9 +44,9 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { )) } - # 5. Check if all tasks are valid + # 6. Check if all tasks are valid invalid_ops <- setdiff(unique(tasks), valid_tasks) - if (length(invalid_ops) > 0) { + if (length(invalid_ops) > 0L) { stop(paste0( "Invalid task(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", "Valid tasks are: ", paste(valid_tasks, collapse = ", "), ".\n", @@ -43,17 +54,17 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { )) } - # 6. Prevent "fill" if models is NULL + # 7. Prevent "fill" if models is NULL if ("fill" %in% tasks && is.null(models)) { stop("The task 'fill' requires a stored model, but `models` is NULL.\n", "Please provide a valid `models` object with a trained imputation model.") } - # 7. Ensure that all "fill" variables have a trained model in models + # 8. Ensure that all "fill" variables have a trained model in models if ("fill" %in% tasks && !is.null(models)) { fill_vars <- names(tasks[tasks == "fill"]) missing_models <- setdiff(fill_vars, ls(models)) - if (length(missing_models) > 0) { + if (length(missing_models) > 0L) { stop(paste0( "The following variables specified as 'fill' do not have stored models: ", paste(missing_models, collapse = ", "), ".\n", @@ -62,11 +73,11 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { } } - # 8. Ensure all variables in models exist in blocks + # 9. Ensure all variables in models exist in blocks if (!is.null(models)) { trained_vars <- ls(models) missing_from_data <- setdiff(trained_vars, bv) # Use block variable names - if (length(missing_from_data) > 0) { + if (length(missing_from_data) > 0L) { stop(paste0( "The following variables are present in `models` but missing from `data`: ", paste(missing_from_data, collapse = ", "), ".\n", @@ -75,5 +86,19 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL) { } } + # 10. Check whether the data match the models for filling + if ("fill" %in% tasks) { + fill_vars <- names(tasks[tasks == "fill"]) + scanned <- scan.data(data, models) + idx <- scanned$variable %in% fill_vars & !scanned$can_fill + if (any(idx)) { + stop(paste0( + "The following variables in `data` do not match the stored models for filling: ", + paste(scanned$variable[idx], collapse = ", "), ".\n", + "Use scan.data() to diagnose mismatch between data and models." + )) + } + } + return(tasks) } diff --git a/R/mice.R b/R/mice.R index e466ed807..0db430581 100644 --- a/R/mice.R +++ b/R/mice.R @@ -483,7 +483,7 @@ mice <- function(data, user.visitSequence = user.visitSequence, maxit = maxit ) - tasks <- check.tasks(tasks, data, models, blocks) + tasks <- check.tasks(tasks, data, models, blocks, skip.check.tasks = FALSE) store <- ifelse(length(unique(tasks)) == 1L, tasks[1L], "train") if (compact && store == "train") store <- "train_compact" diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index c3ed613cd..7737fc70b 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -1,21 +1,15 @@ context("tasks") -# We have to test the following cases: - -# - Does train-run setup with a factor variable produce imputations? YES -# - Does train-run setup with a factor variable produce imputations when the factor has fewer categories during running than training? YES -# - Does train-run setup with a factor variable produce imputations when the factor has more categories during running than training? - test_that("m filling recycles training models", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$bmi[[1]]$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 4, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) - expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2, m = 4, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) }) test_that("fully synthetic datasets can be created from completely observed variables", { dataset <- complete(mice(nhanes2, m = 1, maxit = 1, method = "pmm", print = FALSE)) - expect_silent(imp1 <- mice(dataset, m = 2, maxit = 1, task = "train", method = "pmm", print = FALSE)) + expect_silent(imp1 <- mice(dataset, m = 2, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) expect_false(is.null(imp1$models$age[[1]]$lookup)) expect_silent(imp2 <- mice(dataset, where = make.where(dataset, "all"), m = 2, maxit = 3, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) synt1 <- complete(imp2, 1) @@ -23,13 +17,13 @@ test_that("fully synthetic datasets can be created from completely observed vari }) test_that("the procedure informs the user about a mismatch between model and data", { - expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, task = "train", method = "pmm", print = FALSE)) + expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) newdata <- nhanes2 newdata$age <- factor(newdata$age, levels = c(levels(newdata$age), "not_a_level")) newdata$age[1] <- "not_a_level" newdata$age[2] <- NA - expect_silent(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) newdata <- nhanes2 levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" - expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, task = "fill", method = "pmm", models = imp1$models, print = FALSE), "Model-Data mismatch") + expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) }) From 58a1295dbd85cc6967f14685fc679486dab93fc7 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 6 Apr 2025 23:46:11 +0200 Subject: [PATCH 099/147] Merge branch imputation.models into branch trim.data --- .Rbuildignore | 2 +- .gitignore | 2 + DESCRIPTION | 3 +- NAMESPACE | 7 +- R/cbind.R | 34 ++ R/check.model.R | 34 ++ R/check.tasks.R | 104 ++++ R/complete.R | 5 + R/data.R | 194 +++++++ R/design.R | 10 +- R/edit.setup.R | 59 +- R/filter.R | 6 + R/imports.R | 4 +- R/initialize.imp.R | 40 +- R/internal.R | 26 + R/method.R | 81 ++- R/mice.R | 91 ++- R/mice.impute.2l.bin.R | 3 +- R/mice.impute.2l.lmer.R | 4 +- R/mice.impute.lasso.pmm.R | 2 +- R/mice.impute.logreg.R | 71 ++- R/mice.impute.norm.R | 43 +- R/mice.impute.pmm.R | 327 ++++++----- R/mice.impute.polr.R | 175 ++++-- R/mice.impute.polyreg.R | 150 +++-- R/mice.mids.R | 7 +- R/mids.R | 127 ++++- R/models.R | 185 +++++++ R/quantify.R | 34 ++ R/rbind.R | 15 + R/sampler.R | 130 +++-- R/tasks.R | 26 + _pkgdown.yml | 13 +- man/coerce.data.Rd | 20 + man/export.models.env.Rd | 42 ++ man/filter.mids.Rd | 2 + man/import.models.env.Rd | 52 ++ man/initialize.models.env.Rd | 69 +++ man/make.data.Rd | 30 + man/make.method.Rd | 18 + man/make.tasks.Rd | 55 ++ man/mice.Rd | 50 +- man/mice.impute.logreg.Rd | 13 +- man/mice.impute.norm.Rd | 29 +- man/mice.impute.pmm.Rd | 77 ++- man/mice.impute.polr.Rd | 71 ++- man/mice.impute.polyreg.Rd | 43 +- man/mids.Rd | 42 +- man/pmm.match.Rd | 49 -- man/scan.data.Rd | 49 ++ tests/testthat/test-data.R | 61 ++ ...{test-remove.lindep.R => test-internals.R} | 12 +- tests/testthat/test-mice.impute.logreg.R | 2 + tests/testthat/test-mice.impute.pmm.R | 1 + tests/testthat/test-models.R | 9 + tests/testthat/test-newdata.R | 8 + tests/testthat/test-quantify.R | 71 +++ tests/testthat/test-tasks.R | 29 + vignettes/.gitignore | 1 + vignettes/_imputation_models.qmd | 522 ++++++++++++++++++ vignettes/references.bib | 109 ++++ 61 files changed, 3033 insertions(+), 517 deletions(-) create mode 100644 R/check.model.R create mode 100644 R/check.tasks.R create mode 100644 R/data.R create mode 100644 R/models.R create mode 100644 R/quantify.R create mode 100644 R/tasks.R create mode 100644 man/coerce.data.Rd create mode 100644 man/export.models.env.Rd create mode 100644 man/import.models.env.Rd create mode 100644 man/initialize.models.env.Rd create mode 100644 man/make.data.Rd create mode 100644 man/make.tasks.Rd delete mode 100644 man/pmm.match.Rd create mode 100644 man/scan.data.Rd create mode 100644 tests/testthat/test-data.R rename tests/testthat/{test-remove.lindep.R => test-internals.R} (80%) create mode 100644 tests/testthat/test-models.R create mode 100644 tests/testthat/test-quantify.R create mode 100644 tests/testthat/test-tasks.R create mode 100644 vignettes/.gitignore create mode 100644 vignettes/_imputation_models.qmd create mode 100644 vignettes/references.bib diff --git a/.Rbuildignore b/.Rbuildignore index 6f9817985..486e80d98 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -11,7 +11,7 @@ cran-comments.Rmd changes_mice* ^data-raw$ .png -^script$ +^script/ ^CODE_OF_CONDUCT* ^mice\.Rproj$ vignettes/ diff --git a/.gitignore b/.gitignore index 788f95055..9ab84fc54 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ inst/doc *_cache script docs + +/.quarto/ diff --git a/DESCRIPTION b/DESCRIPTION index 0991fc780..9ce29e1b4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: mice Type: Package -Version: 3.17.3 +Version: 3.17.3.9000 Title: Multivariate Imputation by Chained Equations Date: 2025-3-28 Authors@R: c(person("Stef", "van Buuren", role = c("aut","cre"), @@ -104,3 +104,4 @@ LinkingTo: cpp11, Rcpp License: GPL (>= 2) RoxygenNote: 7.3.2 Roxygen: list(markdown = TRUE) +Config/Needs/website: rmarkdown diff --git a/NAMESPACE b/NAMESPACE index c47236259..dfef94704 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -47,7 +47,6 @@ S3method(with,mids) S3method(xyplot,mads) S3method(xyplot,mids) export(.norm.draw) -export(.pmm.match) export(D1) export(D2) export(D3) @@ -64,6 +63,7 @@ export(bwplot) export(cbind) export(cc) export(cci) +export(coerce.data) export(complete) export(construct.blocks) export(convergence) @@ -94,11 +94,13 @@ export(lm.mids) export(mads) export(make.blocks) export(make.blots) +export(make.data) export(make.formulas) export(make.method) export(make.modeltype) export(make.post) export(make.predictorMatrix) +export(make.tasks) export(make.visitSequence) export(make.where) export(matchindex) @@ -171,6 +173,7 @@ export(pool.table) export(quickpred) export(rbind) export(remove.lindep) +export(scan.data) export(squeeze) export(stripplot) export(supports.transparent) @@ -254,6 +257,7 @@ importFrom(stats,na.omit) importFrom(stats,na.pass) importFrom(stats,pchisq) importFrom(stats,pf) +importFrom(stats,plogis) importFrom(stats,predict) importFrom(stats,pt) importFrom(stats,qt) @@ -266,6 +270,7 @@ importFrom(stats,rgamma) importFrom(stats,rnorm) importFrom(stats,runif) importFrom(stats,sd) +importFrom(stats,setNames) importFrom(stats,spline) importFrom(stats,summary.glm) importFrom(stats,terms) diff --git a/R/cbind.R b/R/cbind.R index 820540519..6542d7558 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,6 +97,10 @@ cbind.mids <- function(x, y = NULL, ...) { post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames blots <- x$blots + tasks <- c(x$tasks, "impute") + names(tasks) <- c(names(x$tasks), tail(varnames, 1L)) + models <- x$models + store <- x$store ignore <- x$ignore # seed, lastSeedValue, number of iterations, chainMean and chainVar @@ -125,6 +129,9 @@ cbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, + tasks = tasks, + models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, @@ -231,6 +238,30 @@ cbind.mids.mids <- function(x, y, call) { names(post) <- varnames blots <- c(x$blots, y$blots) names(blots) <- blocknames + tasks <- c(x$tasks, y$tasks) + # FIXME: Assumes combined task names yields unique names as in colnames(data) + names(tasks) <- make.unique(c(names(x$tasks), names(y$tasks))) + + # Function to copy all objects from one environment to another + merge_envs <- function(target_env, source_env) { + for (name in ls(source_env, all.names = TRUE)) { + assign(name, get(name, envir = source_env), envir = target_env) + } + return(target_env) + } + if (!is.null(x$models) && !is.null(y$models)) { + models <- merge_envs(x$models, y$models) + } else if (!is.null(x$models)) { + models <- x$models + } else if (!is.null(y$models)) { + models <- y$models + } else { + models <- NULL + } + store <- x$store + if (y$store != store) { + store <- "train" + } ignore <- x$ignore # For the elements seed, lastSeedValue and iteration the values @@ -296,6 +327,9 @@ cbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, + tasks = tasks, + models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/check.model.R b/R/check.model.R new file mode 100644 index 000000000..7e8c6cee8 --- /dev/null +++ b/R/check.model.R @@ -0,0 +1,34 @@ +check.model.exists <- function(model, task) { + if (task == "impute") { + return() + } + if (is.null(model) || !is.environment(model)) { + stop("`model` must be an environment to store results persistently.") + } + return() +} + +check.model.match <- function(model, x, method) { + formula <- model$formula + if (!length(formula)) { + stop("No model stored in environment") + } + + mmeth <- model$setup$method + if (length(mmeth) && mmeth != method) { + stop(paste("Model-Method mismatch: ", deparse(formula), "\n", + " Model: ", mmeth, "\n", + " Method: ", method, "\n")) + } + + xnames <- model$xnames + dnames <- colnames(x) + notfound <- !xnames %in% dnames + if (any(notfound)) { + stop(paste("Model-Data mismatch: ", deparse(formula), "\n", + "Not found in data: ", paste(xnames[notfound], collapse = " "), "\n")) + } + + notfound <- !dnames %in% xnames + return(!notfound) +} diff --git a/R/check.tasks.R b/R/check.tasks.R new file mode 100644 index 000000000..4d41f6fa9 --- /dev/null +++ b/R/check.tasks.R @@ -0,0 +1,104 @@ +check.tasks <- function(tasks, data, models = NULL, blocks = NULL, + skip.check.tasks = FALSE) { + if (skip.check.tasks) { + return(tasks) + } + + # This function is called during initialization + if (is.null(tasks)) { + tasks <- "impute" + } + + valid_tasks <- c("impute", "train", "fill") + + # 1. Default blocks to individual variables if not provided + if (is.null(blocks)) { + blocks <- setNames(as.list(names(data)), names(data)) + } + + # 2. Expand tasks if it's a single value + bv <- unique(unlist(blocks)) + if (length(tasks) == 1L) { + tasks <- setNames(rep(tasks, length(bv)), bv) + } + + # 3. Check length + if (length(tasks) != length(bv)) { + stop("The length of `tasks` (", length(tasks), + ") must match the number of variables in `blocks` (", length(bv),").") + } + + # 4. Check that tasks is a named vector + if (is.null(names(tasks))) { + # Attach names assuming same order as unique variables in blocks + names(tasks) <- bv + } + + # 5. Check if all names in tasks exist in blocks + notFound <- !names(tasks) %in% bv + if (any(notFound)) { + stop(paste0( + "The following variables specified in `tasks` are not present in `blocks`: ", + paste(names(tasks)[notFound], collapse = ", "), ".\n", + "Ensure all specified variables match those in `blocks`." + )) + } + + # 6. Check if all tasks are valid + invalid_ops <- setdiff(unique(tasks), valid_tasks) + if (length(invalid_ops) > 0L) { + stop(paste0( + "Invalid task(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", + "Valid tasks are: ", paste(valid_tasks, collapse = ", "), ".\n", + "Please correct the `tasks` argument." + )) + } + + # 7. Prevent "fill" if models is NULL + if ("fill" %in% tasks && is.null(models)) { + stop("The task 'fill' requires a stored model, but `models` is NULL.\n", + "Please provide a valid `models` object with a trained imputation model.") + } + + # 8. Ensure that all "fill" variables have a trained model in models + if ("fill" %in% tasks && !is.null(models)) { + fill_vars <- names(tasks[tasks == "fill"]) + missing_models <- setdiff(fill_vars, ls(models)) + if (length(missing_models) > 0L) { + stop(paste0( + "The following variables specified as 'fill' do not have stored models: ", + paste(missing_models, collapse = ", "), ".\n", + "Ensure these variables were previously fitted before using 'fill'." + )) + } + } + + # 9. Ensure all variables in models exist in blocks + if (!is.null(models)) { + trained_vars <- ls(models) + missing_from_data <- setdiff(trained_vars, bv) # Use block variable names + if (length(missing_from_data) > 0L) { + stop(paste0( + "The following variables are present in `models` but missing from `data`: ", + paste(missing_from_data, collapse = ", "), ".\n", + "Ensure that all stored models correspond to variables in the dataset." + )) + } + } + + # 10. Check whether the data match the models for filling + if ("fill" %in% tasks) { + fill_vars <- names(tasks[tasks == "fill"]) + scanned <- scan.data(data, models) + idx <- scanned$variable %in% fill_vars & !scanned$can_fill + if (any(idx)) { + stop(paste0( + "The following variables in `data` do not match the stored models for filling: ", + paste(scanned$variable[idx], collapse = ", "), ".\n", + "Use scan.data() to diagnose mismatch between data and models." + )) + } + } + + return(tasks) +} diff --git a/R/complete.R b/R/complete.R index 2c1cfe242..d51922dea 100644 --- a/R/complete.R +++ b/R/complete.R @@ -84,6 +84,10 @@ complete.mids <- function(data, action = 1L, include = FALSE, mild = FALSE, order = c("last", "first"), ...) { if (!is.mids(data)) stop("'data' not of class 'mids'") + if (data$store == "train_compact") { + stop(paste("Cannot complete compact training object.\n", + "Set 'compact = FALSE' to preserve training data and imputations.")) + } order <- match.arg(order) m <- as.integer(data$m) @@ -166,3 +170,4 @@ single.complete <- function(data, where, imp, ell) { } data } + diff --git a/R/data.R b/R/data.R new file mode 100644 index 000000000..9f1663a6e --- /dev/null +++ b/R/data.R @@ -0,0 +1,194 @@ +#' Scan data types and compare to model expectations +#' +#' This function compares the structure and type of `data` to what is expected +#' from the trained `models`. It reports differences in class, levels, and predictor +#' availability, useful to prepare for imputation or prediction. +#' +#' @param data A `data.frame` to be checked. +#' @param models A list of trained models from `mice()`. +#' @param print Logical, whether to print the resulting table. +#' +#' @return A `data.frame` with one row per variable (from either `data` or `models`) +#' and columns summarizing compatibility diagnostics. The following columns are included: +#' +#' \tabular{ll}{ +#' `variable` \tab Variable name \cr +#' `in_data` \tab Logical: whether variable is present in `data` \cr +#' `in_model` \tab Logical: whether a trained model is available \cr +#' `data_class` \tab Class of the variable in `data` (e.g., `"factor"`, `"ordered"`) \cr +#' `model_class` \tab model class according to the trained model \cr +#' `class_match` \tab `TRUE` if `data_class` matches `model_class`, `FALSE` otherwise \cr +#' `levels_match` \tab `TRUE` if factor levels exactly match, `FALSE` if they differ, `NA` if not applicable \cr +#' `pred_match` \tab `TRUE` if all predictors used by the model are present in `data`, `FALSE` if any are missing, `NA` if unknown \cr +#' `can_fill` \tab `TRUE` if there is a model, if classes match, if levels match and if predictor match, otherwise `FALSE` \cr +#' } +#' +#' @examples +#' # Train model on boys data +#' imp <- mice(boys, tasks = "train", m = 1, maxit = 1, print = FALSE) +#' +#' # Create a new dataset with missing values and mismatched types +#' # remove ordering +#' data <- boys[1:3, ] +#' data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) +#' +#' # Run scan +#' scan.data(data, imp$models) +#' +#' @export +scan.data <- function(data, models, print = FALSE) { + orig.data <- data + vars.data <- names(orig.data) + vars.model <- names(models) + vars.all <- union(vars.data, vars.model) + + report <- data.frame( + variable = vars.all, + in_data = vars.all %in% vars.data, + in_model = vars.all %in% vars.model, + data_class = character(length(vars.all)), + model_class = character(length(vars.all)), + class_match = logical(length(vars.all)), + levels_match = logical(length(vars.all)), + pred_match = logical(length(vars.all)), + can_fill = logical(length(vars.all)), + stringsAsFactors = FALSE + ) + + for (i in seq_len(nrow(report))) { + j <- report$variable[i] + in_data <- isTRUE(report$in_data[i]) + in_model <- isTRUE(report$in_model[i]) + + x <- if (isTRUE(in_data) && j %in% names(orig.data)) orig.data[[j]] else NULL + mod <- if (in_model) models[[j]][[1]] else NULL + + report$data_class[i] <- if (!is.null(x)) { + if (inherits(x, "ordered")) "ordered" else class(x)[1] + } else NA + + if (!is.null(mod$class)) { + report$model_class[i] <- mod$class + } + + if (!is.null(x) && !is.null(mod$class)) { + match <- if (inherits(x, "ordered")) "ordered" else class(x)[1] + report$class_match[i] <- if (identical(match, mod$class)) TRUE else FALSE + } + + if (!is.null(x) && is.factor(x) && !is.null(mod$factor$labels)) { + lvls.data <- levels(x) + lvls.model <- mod$factor$labels + report$levels_match[i] <- if (identical(lvls.data, lvls.model)) TRUE else FALSE + # report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" + # report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" + } else { + report$levels_match[i] <- NA + } + + # predictor variable coverage + if (!is.null(mod$formula)) { + f <- as.formula(mod$formula) + needed <- all.vars(f[[3L]]) + present <- names(orig.data) + report$pred_match[i] <- if (all(needed %in% present)) TRUE else FALSE + } + } + + # Add can_fill column + report$can_fill <- vapply(seq_len(nrow(report)), function(i) { + if (isTRUE(report$in_model[i]) && + report$class_match[i] && + !isFALSE(report$levels_match[i]) && + report$pred_match[i]) { + TRUE + } else { + FALSE + } + }, NA) + + if (print) print(report) + report +} + +#' Construct a new data.frame from a trained model +#' +#' This function generates a data.frame with the correct structure (names, classes, +#' levels) based on a set of trained models, as produced by `mice()` with `tasks = "train"`. +#' +#' @param models A list of trained models from `mice()`. +#' @param n Number of rows to generate. Default is 1. +#' @param fill Value to fill the data with. Default is `NA`. +#' @param vars Optional character vector specifying the variable order. Default is `NULL`. +#' +#' @return A `data.frame` of `n` rows, where each column matches the type and levels +#' expected from the corresponding model. +#' @examples +#' trained <- mice(boys, tasks = "train", print = FALSE) +#' newdata <- make.data(trained$models, n = 5, vars = names(boys)) +#' str(newdata) +#' @export +make.data <- function(models, n = 1L, fill = NA, vars = NULL) { + stopifnot(is.list(models), is.numeric(n), length(n) == 1L) + + all_vars <- names(models) + vars <- if (is.null(vars)) all_vars else intersect(vars, all_vars) + + out <- vector("list", length(vars)) + names(out) <- vars + + for (j in vars) { + mod <- models[[j]][[1]] + cls <- mod$class + lvls <- mod$factor$labels + + if (cls == "factor") { + out[[j]] <- factor(rep(fill, n), levels = lvls) + } else if (cls == "ordered") { + out[[j]] <- factor(rep(fill, n), levels = lvls, ordered = TRUE) + } else if (cls == "logical") { + out[[j]] <- as.logical(rep(fill, n)) + } else if (cls == "numeric") { + out[[j]] <- as.numeric(rep(fill, n)) + } else if (cls == "integer") { + out[[j]] <- as.integer(rep(fill, n)) + } else { + warning(sprintf("Unknown class '%s' for variable '%s'", cls, j)) + out[[j]] <- rep(fill, n) + } + } + + as.data.frame(out) +} + +#' Coerce a data.frame to match model expectations +#' +#' This function coerces columns in `data` to match the type and levels +#' expected by the corresponding trained `models`. +#' +#' @param data A `data.frame` with input data. +#' @param models A list of trained models from `mice()`. +#' +#' @return A coerced `data.frame` that matches the class and levels from `models`. +#' @export +coerce.data <- function(data, models) { + data <- as.data.frame(data) + for (j in intersect(names(data), names(models))) { + mod <- models[[j]][[1L]] + cls <- mod$class + lvls <- mod$factor$labels + + if (cls == "factor") { + data[[j]] <- factor(data[[j]], levels = lvls) + } else if (cls == "ordered") { + data[[j]] <- factor(data[[j]], levels = lvls, ordered = TRUE) + } else if (cls == "logical") { + data[[j]] <- as.logical(data[[j]]) + } else if (cls == "numeric") { + data[[j]] <- as.numeric(data[[j]]) + } else if (cls == "integer") { + data[[j]] <- as.integer(data[[j]]) + } + } + data +} diff --git a/R/design.R b/R/design.R index 69400064d..8cb330a83 100644 --- a/R/design.R +++ b/R/design.R @@ -2,5 +2,13 @@ obtain.design <- function(data, formula = ~.) { # try out the following # formula <- update(formula, . ~ . - 1) mf <- model.frame(formula, data = data, na.action = na.pass) + + # Convert logical variables to numeric to prevent dummy expansion + for (v in names(mf)) { + if (is.logical(mf[[v]])) { + mf[[v]] <- as.numeric(mf[[v]]) + } + } + model.matrix(formula, data = mf) -} +} \ No newline at end of file diff --git a/R/edit.setup.R b/R/edit.setup.R index f63999563..1db7cf81d 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -1,9 +1,9 @@ -mice.edit.setup <- function(data, setup, - allow.na = FALSE, - remove.constant = TRUE, - remove.collinear = TRUE, - remove_collinear = TRUE, - ...) { +mice.edit.setup <- function(data, setup, tasks, + allow.na = FALSE, + remove.constant = TRUE, + remove.collinear = TRUE, + remove_collinear = TRUE, + ...) { # legacy handling if (!remove_collinear) remove.collinear <- FALSE @@ -18,7 +18,7 @@ mice.edit.setup <- function(data, setup, # FIXME: this function is not yet adapted to blocks if (ncol(pred) != nrow(pred) || length(meth) != nrow(pred) || - ncol(data) != nrow(pred)) { + ncol(data) != nrow(pred)) { return(setup) } @@ -28,28 +28,31 @@ mice.edit.setup <- function(data, setup, for (j in seq_len(ncol(data))) { if (!is.passive(meth[j])) { d.j <- data[, j] - v <- if (is.character(d.j)) NA else var(as.numeric(d.j), na.rm = TRUE) - constant <- if (allow.na) { - if (is.na(v)) FALSE else v < 1000 * .Machine$double.eps - } else { - is.na(v) || v < 1000 * .Machine$double.eps - } - didlog <- FALSE - if (constant && any(pred[, j] != 0) && remove.constant) { - out <- varnames[j] - pred[, j] <- 0 - updateLog(out = out, meth = "constant") - didlog <- TRUE - } - if (constant && meth[j] != "" && remove.constant) { - out <- varnames[j] - pred[j, ] <- 0 - if (!didlog) { + task <- unname(tasks[varnames[j]]) + if (task != "fill") { + v <- if (is.character(d.j)) NA else var(as.numeric(d.j), na.rm = TRUE) + constant <- if (allow.na) { + if (is.na(v)) FALSE else v < 1000 * .Machine$double.eps + } else { + is.na(v) || v < 1000 * .Machine$double.eps + } + didlog <- FALSE + if (constant && any(pred[, j] != 0) && remove.constant) { + out <- varnames[j] + pred[, j] <- 0 updateLog(out = out, meth = "constant") + didlog <- TRUE + } + if (constant && meth[j] != "" && remove.constant) { + out <- varnames[j] + pred[j, ] <- 0 + if (!didlog) { + updateLog(out = out, meth = "constant") + } + meth[j] <- "" + vis <- vis[vis != j] + post[j] <- "" } - meth[j] <- "" - vis <- vis[vis != j] - post[j] <- "" } } } @@ -61,6 +64,8 @@ mice.edit.setup <- function(data, setup, } else { droplist <- NULL } + # do not drop variables with task "fill" + droplist <- setdiff(droplist, names(tasks[tasks == "fill"])) if (length(droplist) > 0) { for (k in seq_along(droplist)) { j <- which(varnames %in% droplist[k]) diff --git a/R/filter.R b/R/filter.R index 9ff67b95c..5a86a5c8f 100644 --- a/R/filter.R +++ b/R/filter.R @@ -33,6 +33,8 @@ dplyr::filter #' \code{formulas} \tab Equals \code{.data$formulas}\cr #' \code{post} \tab Equals \code{.data$post}\cr #' \code{blots} \tab Equals \code{.data$blots}\cr +#' \code{tasks} \tab Equals \code{.data$tasks}\cr +#' \code{models} \tab Equals \code{.data$models}\cr #' \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr #' \code{seed} \tab Equals \code{.data$seed}\cr #' \code{iteration} \tab Equals \code{.data$iteration}\cr @@ -79,6 +81,8 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { formulas <- .data$formulas modeltype <- .data$modeltype blots <- .data$blots + tasks <- .data$tasks + models <- .data$models post <- .data$post seed <- .data$seed iteration <- .data$iteration @@ -120,6 +124,8 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { modeltype = modeltype, post = post, blots = blots, + tasks = tasks, + models = models, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/imports.R b/R/imports.R index 3b3f1f888..bd04c2d3a 100644 --- a/R/imports.R +++ b/R/imports.R @@ -18,10 +18,10 @@ #' formula gaussian getCall #' glm is.empty.model lm lm.fit #' median model.frame model.matrix -#' na.exclude na.omit na.pass +#' na.exclude na.omit na.pass plogis #' pf predict pt qt quantile quasibinomial #' rbinom rchisq reformulate rgamma rnorm runif -#' sd summary.glm terms update var vcov +#' sd setNames summary.glm terms update var vcov #' @importFrom tidyr complete #' @importFrom utils askYesNo flush.console hasName head install.packages #' methods packageDescription packageVersion diff --git a/R/initialize.imp.R b/R/initialize.imp.R index 8f9dc75a0..5189c5cf3 100644 --- a/R/initialize.imp.R +++ b/R/initialize.imp.R @@ -3,29 +3,55 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, imp <- vector("list", ncol(data)) names(imp) <- names(data) r <- !is.na(data) + for (h in visitSequence) { for (j in blocks[[h]]) { y <- data[, j] ry <- r[, j] & !ignore wy <- where[, j] - imp[[j]] <- as.data.frame(matrix(NA, nrow = sum(wy), ncol = m)) - dimnames(imp[[j]]) <- list(row.names(data)[wy], 1:m) + + # Determine correct NA type + na_type <- switch(class(y)[1L], + "logical" = as.logical(NA), + "factor" = as.character(NA), + "ordered" = as.character(NA), + NA_real_ + ) + + # Initialize imp[[j]] with correct type + imp[[j]] <- as.data.frame( + matrix(na_type, nrow = sum(wy), ncol = m) + ) + dimnames(imp[[j]]) <- list(row.names(data)[wy], as.character(seq_len(m))) + if (method[h] != "") { for (i in seq_len(m)) { if (nmis[j] < nrow(data) && is.null(data.init)) { - imp[[j]][, i] <- mice.impute.sample(y, ry, wy = wy) + vec <- mice.impute.sample(y, ry, wy = wy) } else if (!is.null(data.init)) { - imp[[j]][, i] <- data.init[wy, j] + vec <- data.init[wy, j] } else { + # Type-safe fallback + n <- sum(wy) if (is.factor(y)) { - imp[[j]][, i] <- sample(levels(y), nrow(data), replace = TRUE) + vec <- sample(levels(y), n, replace = TRUE) + vec <- factor(vec, levels = levels(y), ordered = is.ordered(y)) + } else if (is.logical(y)) { + vec <- sample(c(TRUE, FALSE), n, replace = TRUE) } else { - imp[[j]][, i] <- rnorm(nrow(data)) + vec <- rnorm(n) } } + + # Final safety check: enforce type match with y + if (is.logical(y)) vec <- as.logical(vec) + if (is.factor(y)) vec <- factor(vec, levels = levels(y), ordered = is.ordered(y)) + + imp[[j]][, i] <- vec } } } } - imp + + return(imp) } diff --git a/R/internal.R b/R/internal.R index 1e8e92d74..da6288b53 100644 --- a/R/internal.R +++ b/R/internal.R @@ -98,3 +98,29 @@ sweep_operator <- function(S, k) { is.named.list <- function(x) { is.list(x) && !is.null(names(x)) && all(names(x) != "") } + + +sanitize.vec <- function(vec, y) { + # Insert at the end of any draw() or imputation function + # # Example for logreg.draw() + # vec <- logreg.draw(lp) + # vec <- sanitize.vec(vec, y) + + cls <- class(y)[1L] + + if (cls == "logical") { + return(as.logical(vec)) + } + + if (cls == "factor") { + return(factor(vec, levels = levels(y), ordered = is.ordered(y))) + } + + if (cls == "ordered") { + return(factor(vec, levels = levels(y), ordered = TRUE)) + } + + # default (numeric, character, etc.) + vec +} + diff --git a/R/method.R b/R/method.R index bf1c68b01..bd89d76f6 100644 --- a/R/method.R +++ b/R/method.R @@ -12,6 +12,7 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), + tasks = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { method <- rep("", length(blocks)) names(method) <- names(blocks) @@ -28,30 +29,58 @@ make.method <- function(data, method[j] <- defaultMethod[k] } nimp <- nimp(where, blocks) - method[nimp == 0] <- "" + + # preserve old behaviour that sets method <- "" with impute task + names(method) <- names(blocks) + if (!is.null(tasks)) { + for (j in names(blocks)) { + vname <- blocks[[j]] + if (nimp[j] == 0L && "impute" == tasks[vname]) method[j] <- "" + } + } + + support <- c("pmm", "norm", "logreg", "polr", "polyreg") + # check whether methods support train and fill tasks + for (j in names(blocks)) { + vname <- blocks[[j]] + for (yname in vname) { + mj <- method[j] + task <- tasks[yname] + if (!is.null(task) && task %in% c("train", "fill") && !mj %in% support) { + stop(paste("Method", mj, "lacks support for train and fill tasks.")) + } + } + } + method } - -check.method <- function(method, data, where, blocks, defaultMethod) { +check.method <- function(method, data, where, blocks, tasks, defaultMethod) { if (is.null(method)) { return(make.method( data = data, where = where, blocks = blocks, + tasks = tasks, defaultMethod = defaultMethod )) } nimp <- nimp(where, blocks) - # expand user's imputation method to all visited columns + # expand user's imputation method to all visited blocks # single string supplied by user (implicit assumption of two columns) - if (length(method) == 1) { + if (length(method) == 1L) { if (is.passive(method)) { - stop("Cannot have a passive imputation method for every column.") + stop("Cannot have a passive imputation method for every block.") } method <- rep(method, length(blocks)) - method[nimp == 0] <- "" + names(method) <- names(blocks) + + # preserve old behaviour that sets method <- "" with impute task + for (j in names(blocks)) { + vname <- blocks[[j]] + if (nimp[j] == 0L && "impute" == tasks[vname]) method[j] <- "" + } } # check the length of the argument @@ -120,28 +149,44 @@ check.method <- function(method, data, where, blocks, defaultMethod) { ) } } - method[nimp == 0] <- "" + unlist(method) } +overwrite.method <- function(method, blocks, tasks, models) { + # for fill tasks, overwrite method with stored model method + if (length(models) == 0L) { + return(method) + } + for (h in names(method)) { + for (varname in blocks[[h]]) { + if (tasks[varname] %in% c("fill")) { + newmethod <- models[[varname]]$`1`$setup$method + if (is.null(newmethod)) next + method[h] <- newmethod + } + } + } + return(method) +} # assign methods based on type, # use method 1 if there is no single method within the block assign.method <- function(y) { if (is.numeric(y)) { - return(1) + return(1L) } - if (nlevels(y) == 2) { - return(2) + if (is.logical(y)) { + return(2L) } - if (is.ordered(y) && nlevels(y) > 2) { - return(4) + if (is.ordered(y) && nlevels(y) > 2L) { + return(4L) } - if (nlevels(y) > 2) { - return(3) + if (nlevels(y) == 2L) { + return(2L) } - if (is.logical(y)) { - return(2) + if (nlevels(y) > 2L) { + return(3L) } - 1 + return(1L) } diff --git a/R/mice.R b/R/mice.R index 96081c6a7..645d37e92 100644 --- a/R/mice.R +++ b/R/mice.R @@ -219,6 +219,27 @@ #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function #' called for block \code{blockname}. +#' @param tasks A character vector specifying the task to perform for +#' each imputation block. The available options are: +#' \describe{ +#' \item{"impute"}{Estimate parameters, generates imputations and store +#' the original data plus imputations, but not the imputation model +#' (classic MICE behavior).} +#' \item{"train" }{Estimate parameters, generate imputations and +#' store the original data, the imputations and the imputation model.} +#' \item{"fill"}{Apply a previously trained imputation model to fill +#' imputations, without re-estimating parameters.} +#' } +#' This argument can be specified as a named vector, where names correspond +#' to variables and values specify the task for each variable. If a +#' single value is provided, it applies to the variables in all blocks. The +#' length of the vector must match the number of variables present in the +#' blocks. The default is \code{"impute"}. +#' @param models A list that stores fitted imputation models. The models +#' can be used to impute missing values in new data. \code{models} is +#' only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +#' to fill in missing values in new data, where \code{trained} is the +#' \code{mids} object returned by \code{mice(..., tasks = 'train')}. #' @param post A vector of strings with length \code{ncol(data)} specifying #' expressions as strings. Each string is parsed and #' executed within the \code{sampler()} function to post-process @@ -245,6 +266,12 @@ #' are created by a simple random draw from the data. Note that specification of #' \code{data.init} will start all \code{m} Gibbs sampling streams from the same #' imputation. +#' @param compact A logical value indicating whether the resulting \code{mids} +#' object should be stored in compact form. Only relevant if \code{tasks = 'train'}. +#' If \code{isTRUE(compact)}, training data, imputations and other data-specific +#' elements are removed from the resulting \code{mids} object. The +#' \code{store} element of the will be changed from \code{"train"} to +#' \code{"train.compact"}. The default is \code{compact = FALSE}. #' @param \dots Named arguments that are passed down to the univariate imputation #' functions. #' @@ -294,7 +321,22 @@ #' complete(imp) #' #' # imputation on mixed data with a different method per column -#' mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +#' imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) +#' +#' # store all imputation models +#' imp1 <- mice(nhanes, tasks = "train", print = FALSE) +#' imp1$models$bmi[[1]] +#' +#' # Store model for `bmi`, estimate others as usual +#' tasks <- c("age" = "impute", "bmi" = "train", "hyp" = "impute", "chl" = "impute") +#' imp2 <- mice(nhanes, tasks = tasks, print = FALSE) +#' +#' # Inspects the stored model for imputation 1 for `bmi` +#' imp2$models$bmi[[1]] +#' +#' # Fill missing `bmi` values using pre-trained model +#' tasks <- c("age" = "impute", "bmi" = "fill", "hyp" = "impute", "chl" = "impute") +#' imp3 <- mice(nhanes, tasks = tasks, models = imp2$models, print = FALSE) #' #' \dontrun{ #' # example where we fit the imputation model on the train data @@ -329,28 +371,21 @@ mice <- function(data, formulas, modeltype = NULL, blots = NULL, + tasks = NULL, + models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, printFlag = TRUE, seed = NA, data.init = NULL, -# saveDetails = FALSE, + compact = FALSE, ...) { call <- match.call() check.deprecated(...) if (!is.na(seed)) set.seed(seed) - # # create details environment - # if (saveDetails) { - # details <- rlang::env( - # call = call, - # seed = seed, - # version = packageVersion("mice"), - # date = Sys.Date()) - # assign("details", details, envir = globalenv()) - # check form of data and m data <- check.dataform(data) m <- check.m(m) @@ -442,7 +477,7 @@ mice <- function(data, # check visitSequence, edit predictorMatrix for monotone user.visitSequence <- visitSequence visitSequence <- check.visitSequence(visitSequence, - data = data, where = where, blocks = blocks + data = data, where = where, blocks = blocks ) predictorMatrix <- mice.edit.predictorMatrix( predictorMatrix = predictorMatrix, @@ -450,9 +485,14 @@ mice <- function(data, user.visitSequence = user.visitSequence, maxit = maxit ) + tasks <- check.tasks(tasks, data, models, blocks, skip.check.tasks = FALSE) + store <- ifelse(length(unique(tasks)) == 1L, tasks[1L], "train") + if (compact && store == "train") store <- "train_compact" + method <- check.method( method = method, data = data, where = where, - blocks = blocks, defaultMethod = defaultMethod + blocks = blocks, tasks = tasks, + defaultMethod = defaultMethod ) post <- check.post(post, data) blots <- check.blots(blots, data, blocks) @@ -469,26 +509,30 @@ mice <- function(data, visitSequence = visitSequence, post = post ) - setup <- mice.edit.setup(data, setup, ...) + setup <- mice.edit.setup(data, setup, tasks, ...) method <- setup$method predictorMatrix <- setup$predictorMatrix visitSequence <- setup$visitSequence post <- setup$post + # Initialize models for "train" and "fill" blocks that are missing in models + models <- initialize.models.env(models, tasks, method, blocks, m) + method <- overwrite.method(method, blocks, tasks, models) + # initialize imputations - nmis <- apply(is.na(data), 2, sum) + nmis <- apply(is.na(data), 2L, sum) imp <- initialize.imp( data, m, ignore, where, blocks, visitSequence, method, nmis, data.init ) # and iterate... - from <- 1 - to <- from + maxit - 1 + from <- 1L + to <- from + maxit - 1L q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, + modeltype, blots, tasks, models, post, c(from, to), printFlag, ... ) @@ -511,19 +555,22 @@ mice <- function(data, modeltype = modeltype, post = post, blots = blots, + tasks = tasks, + models = models, ignore = ignore, seed = seed, iteration = q$iteration, lastSeedValue = get(".Random.seed", - envir = globalenv(), mode = "integer", - inherits = FALSE), + envir = globalenv(), mode = "integer", + inherits = FALSE), chainMean = q$chainMean, chainVar = q$chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents, + store = store) if (!is.null(midsobj$loggedEvents)) { warning("Number of logged events: ", nrow(midsobj$loggedEvents), - call. = FALSE + call. = FALSE ) } return(midsobj) diff --git a/R/mice.impute.2l.bin.R b/R/mice.impute.2l.bin.R index 3b1f62d2d..a061d8e93 100644 --- a/R/mice.impute.2l.bin.R +++ b/R/mice.impute.2l.bin.R @@ -68,8 +68,7 @@ mice.impute.2l.bin <- function(y, ry, x, type, suppressWarnings(fit <- try( lme4::glmer(formula(randmodel), data = data.frame(yobs, xobs), - family = binomial, ... - ), + family = binomial), silent = TRUE )) if (!is.null(attr(fit, "class"))) { diff --git a/R/mice.impute.2l.lmer.R b/R/mice.impute.2l.lmer.R index a1f2f2e5a..e48d39ffc 100644 --- a/R/mice.impute.2l.lmer.R +++ b/R/mice.impute.2l.lmer.R @@ -79,9 +79,7 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... ) suppressWarnings(fit <- try( lme4::lmer(formula(randmodel), - data = data.frame(yobs, xobs), - ... - ), + data = data.frame(yobs, xobs)), silent = TRUE )) if (inherits(fit, "try-error")) { diff --git a/R/mice.impute.lasso.pmm.R b/R/mice.impute.lasso.pmm.R index d921bf914..92ff74765 100644 --- a/R/mice.impute.lasso.pmm.R +++ b/R/mice.impute.lasso.pmm.R @@ -165,7 +165,7 @@ mice.impute.lasso.pmm <- function(y, ry, x, wy = NULL, ynum <- y if (is.factor(y)) { if (quantify) { - ynum <- quantify(y, ry, x) + ynum <- quantify(y, ry, x)[["ynum"]] } else { # as.integer() may not make sense for unordered factors ynum <- as.integer(y) diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index fe1a7782e..84033fc36 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -43,8 +43,13 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.logreg <- function(y, ry, x, wy = NULL, ...) { +mice.impute.logreg <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + ...) { + check.model.exists(model, task) + method <- "logreg" if (is.null(wy)) wy <- !ry + n <- sum(ry) # augment data in order to evade perfect prediction aug <- augment(y, ry, x, wy) @@ -54,30 +59,64 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, ...) { wy <- aug$wy w <- aug$w - # fit model x <- cbind(1, as.matrix(x)) + + if (task == "fill") { + cols <- check.model.match(model, x, method) + lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot + return(logreg.draw(lp, + levels = model$factor$labels, + class = model$class[1L])) + } + expr <- expression(glm.fit( x = x[ry, , drop = FALSE], y = y[ry], family = quasibinomial(link = logit), - weights = w[ry] - )) + weights = w[ry])) fit <- eval(expr) fit.sum <- summary.glm(fit) beta <- coef(fit) rv <- t(chol(sym(fit.sum$cov.unscaled))) beta.star <- beta + rv %*% rnorm(ncol(rv)) - # draw imputations - p <- 1 / (1 + exp(-(x[wy, , drop = FALSE] %*% beta.star))) - vec <- (runif(nrow(p)) <= p) - vec[vec] <- 1 - if (is.factor(y)) { - vec <- factor(vec, c(0, 1), levels(y)) + if (task == "train") { + model$setup <- list(method = method, + n = n, + task = task) + model$beta.hat <- drop(beta) + model$beta.dot <- drop(beta.star) + model$factor <- list(labels = levels(y), quant = c(0, 1)) + model$xnames <- colnames(x) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] } - vec + + lp <- x[wy, , drop = FALSE] %*% beta.star + return(logreg.draw(lp, + levels = levels(y), + class = if (is.ordered(y)) "ordered" else class(y)[1L])) } +logreg.draw <- function(lp, levels = NULL, class = NULL) { + # supports logical, factor and numeric output + p <- 1 / (1 + exp(-lp)) + draws <- (runif(length(p)) <= p) * 1L + + if (!is.null(class)) { + if (class == "logical") { + return(as.logical(draws)) + } + if (class %in% c("factor", "ordered")) { + return(factor(draws, + levels = c(0, 1), + labels = levels, + ordered = (class == "ordered"))) + } + } + + # fallback, numeric + return(draws) +} #' Imputation by logistic regression using the bootstrap #' @@ -146,7 +185,7 @@ augment <- function(y, ry, x, wy, maxcat = 50) { # This function will prevent augmented data beyond the min and # the max of the data # Input: - # x: numeric data.frame (n rows) + # x: numeric matrix (n rows) # y: factor or numeric vector (lengt n) # ry: logical vector (length n) # Output: @@ -185,10 +224,14 @@ augment <- function(y, ry, x, wy, maxcat = 50) { e <- rep(rep(icod, each = 2), p) dimnames(d) <- list(paste0("AUG", seq_len(nrow(d))), dimnames(x)[[2]]) - xa <- rbind.data.frame(x, d) + xa <- rbind(x, d) # beware, concatenation of factors - ya <- if (is.factor(y)) as.factor(levels(y)[c(y, e)]) else c(y, e) + if (is.factor(y)) { + ya <- factor(levels(y)[c(y, e)], levels = levels(y), ordered = is.ordered(y)) + } else { + ya <- c(y, e) + } rya <- c(ry, rep.int(TRUE, nr)) wya <- c(wy, rep.int(FALSE, nr)) wa <- c(rep.int(1, length(y)), rep.int((p + 1) / nr, nr)) diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 664e1d4e5..cf9530973 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -34,11 +34,36 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.norm <- function(y, ry, x, wy = NULL, ...) { +mice.impute.norm <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + ridge = 1e-05, + ...) { + check.model.exists(model, task) + method <- "norm" if (is.null(wy)) wy <- !ry x <- cbind(1, as.matrix(x)) - parm <- .norm.draw(y, ry, x, ...) - x[wy, ] %*% parm$beta + rnorm(sum(wy)) * parm$sigma + if (task == "fill") { + cols <- check.model.match(model, x, method) + lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot + noise <- rnorm(sum(wy)) * model$sigma.dot + return(lp + noise) + } + + parm <- .norm.draw(y, ry, x, ridge = ridge, ...) + + if (task == "train") { + model$setup <- list(method = method, + n = sum(ry), + task = task, + ridge = ridge) + model$beta.hat <- drop(parm$coef) + model$beta.dot <- drop(parm$beta) + model$sigma.hat <- parm$sigma.hat + model$sigma.dot <- parm$sigma + model$xnames <- colnames(x) + } + + return(x[wy, ] %*% parm$beta + rnorm(sum(wy)) * parm$sigma) } @@ -69,14 +94,16 @@ norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { return(.norm.draw(y, ry, x, rank.adjust = TRUE, ...)) } -###' @rdname norm.draw -###' @export +#' @rdname norm.draw +#' @export .norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { p <- estimice(x[ry, , drop = FALSE], y[ry], ...) - sigma.star <- sqrt(sum((p$r)^2) / rchisq(1, p$df)) + ssq <- sum((p$r)^2) + sigma.hat <- sqrt(ssq / p$df) + sigma.star <- sqrt(ssq / rchisq(1, p$df)) beta.star <- p$c + (t(chol(sym(p$v))) %*% rnorm(ncol(x))) * sigma.star - parm <- list(p$c, beta.star, sigma.star, p$ls.meth) - names(parm) <- c("coef", "beta", "sigma", "estimation") + parm <- list(p$c, beta.star, sigma.star, p$ls.meth, sigma.hat) + names(parm) <- c("coef", "beta", "sigma", "estimation", "sigma.hat") if (any(is.na(parm$coef)) & rank.adjust) { parm$coef[is.na(parm$coef)] <- 0 parm$beta[is.na(parm$beta)] <- 0 diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index bedece950..a1c9d4004 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -8,29 +8,52 @@ #' (\code{TRUE}) and missing values (\code{FALSE}) in \code{y}. #' @param x Numeric design matrix with \code{length(y)} rows with predictors for #' \code{y}. Matrix \code{x} may have no missing values. -#' @param exclude Dependent values to exclude from the imputation model -#' and the collection of donor values -#' @param quantify Logical. If \code{TRUE}, factor levels are replaced -#' by the first canonical variate before fitting the imputation model. -#' If false, the procedure reverts to the old behaviour and takes the -#' integer codes (which may lack a sensible interpretation). -#' Relevant only of \code{y} is a factor. -#' @param trim Scalar integer. Minimum number of observations required in a -#' category in order to be considered as a potential donor value. -#' Relevant only of \code{y} is a factor. #' @param wy Logical vector of length \code{length(y)}. A \code{TRUE} value #' indicates locations in \code{y} for which imputations are created. #' @param donors The size of the donor pool among which a draw is made. #' The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects -#' the closest match, but is not recommended. Values between 3L and 10L -#' provide the best results in most cases (Morris et al, 2015). -#' @param matchtype Type of matching distance. The default choice +#' the closest match, but is not recommended. Values between 5L and 10L +#' provide the best results (Morris et al, 2015). +#' For task \code{"train"}, the number of donors +#' is calculated internally based on the number of +#' observations in \code{yobs}. +#' @param matchtype Type of matching distance. The default (recommended) choice #' (\code{matchtype = 1L}) calculates the distance between #' the \emph{predicted} value of \code{yobs} and #' the \emph{drawn} values of \code{ymis} (called type-1 matching). #' Other choices are \code{matchtype = 0L} #' (distance between predicted values) and \code{matchtype = 2L} #' (distance between drawn values). +#' @param quantify Logical. If \code{TRUE}, factor levels are replaced +#' by the first canonical variate before fitting the imputation model. +#' If false, the procedure reverts to the old behaviour and takes the +#' integer codes (which may lack a sensible interpretation). +#' Relevant only of \code{y} is a factor. +#' @param exclude Dependent values to exclude from the imputation model +#' and the collection of donor values +#' @param trim Scalar integer. Minimum number of observations required in a +#' category in order to be considered as a potential donor value. +#' Relevant only of \code{y} is a factor. +#' @param task Character string. The task to be performed. Can +#' be \code{"impute"}, \code{"train"} or \code{"fill"}. +#' The default is \code{"impute"} (classic MICE). See \code{mice()} for +#' details. +#' @param model An environment created by a parent to store the imputation +#' model setup and estimates. The model is stored in the \code{mids} object +#' under tasks \code{"train"}, and is needed as input +#' for task \code{"fill"}. The object \code{model} is not used under +#' task \code{"impute"}. +#' @param mlocal Experimental. Number of random imputations per missing values +#' generated from a fitted model under task \code{"train"}. +#' The default is 1. The \code{mlocal} parameter is different from \code{m}, +#' the number of multiple imputations, because it generates repeated +#' imputations from a single model. The \code{mlocal} parameter is useful +#' for large samples to reduce the computational burden, but still awaits +#' support within the mice algorithm. +#' @param nbins The number of bins used to store the predictive mean matching +#' model. Under task \code{"train"}, the number of donors +#' is calculated internally based on the number of observations in \code{yobs} +#' and the number of unique predictive values. #' @param ridge The ridge penalty used in \code{.norm.draw()} to prevent #' problems with multicollinearity. The default is \code{ridge = 1e-05}, #' which means that 0.01 percent of the diagonal is added to the cross-product. @@ -39,18 +62,14 @@ #' reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher. #' @param use.matcher Logical. Set \code{use.matcher = TRUE} to specify #' the C function \code{matcher()}, the now deprecated matching function that -#' was default in versions -#' \code{2.22} (June 2014) to \code{3.11.7} (Oct 2020). Since version \code{3.12.0} -#' \code{mice()} uses the much faster \code{matchindex} C function. Use -#' the deprecated \code{matcher} function only for exact reproduction. +#' was default in versions of \code{mice} prior to \code{3.12.0}. #' @param \dots Other named arguments. #' @return Vector with imputed data, same type as \code{y}, and of length #' \code{sum(wy)} -#' @author Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn +#' @author Stef van Buuren #' @details #' Imputation of \code{y} by predictive mean matching, based on #' van Buuren (2012, p. 73). The procedure is as follows: -#' #' \enumerate{ #' \item{Calculate the cross-product matrix \eqn{S=X_{obs}'X_{obs}}.} #' \item{Calculate \eqn{V = (S+{diag}(S)\kappa)^{-1}}, with some small ridge @@ -146,131 +165,191 @@ #' # in addition, eliminate category 20 #' mice.impute.pmm(y, ry, x, trim = 2L, exclude = 20) #' -#' # to get old behavior: as.integer(y)) +#' # to get old behavior (before mice v3.16.4): as.integer(y)) #' mice.impute.pmm(y, ry, x, quantify = FALSE) #' @export -mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, - matchtype = 1L, exclude = NULL, - quantify = TRUE, trim = 1L, - ridge = 1e-05, use.matcher = FALSE, ...) { - if (is.null(wy)) { - wy <- !ry - } - - # Reformulate the imputation problem such that - # 1. the imputation model disregards records with excluded y-values - # 2. the donor set does not contain excluded y-values +mice.impute.pmm <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + exclude = NULL, trim = 1L, quantify = TRUE, + ridge = 1e-05, matchtype = 1L, + donors = 5L, nbins = NULL, use.matcher = FALSE, + mlocal = 1L, ...) +{ + check.model.exists(model, task) + method <- "pmm" + if (is.null(wy)) wy <- !ry - # Keep sparse categories out of the imputation model + # Remove excluded values and trim small categories if (is.factor(y)) { - active <- !ry | y %in% (levels(y)[table(y) >= trim]) - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] + freq <- table(y) + keep_levels <- names(freq[freq >= trim & !(names(freq) %in% exclude)]) + y <- factor(y, levels = keep_levels) + idx <- !ry | y %in% keep_levels + } else { + idx <- !ry | !(y %in% exclude) } - # Keep excluded values out of the imputation model - if (!is.null(exclude)) { - active <- !ry | !y %in% exclude - y <- y[active] - ry <- ry[active] - x <- x[active, , drop = FALSE] - wy <- wy[active] + if (any(!idx)) { + y <- y[idx] + ry <- ry[idx] + x <- x[idx, , drop = FALSE] + wy <- wy[idx] } + # Add intercept column to x x <- cbind(1, as.matrix(x)) - # quantify categories for factors - ynum <- y - if (is.factor(y)) { - if (quantify) { - ynum <- quantify(y, ry, x) - } else { - ynum <- as.integer(y) - } + if (task == "fill") { + cols <- check.model.match(model, x, method) + yhatmis <- x[wy, cols, drop = FALSE] %*% model$beta.dot + impy <- draw.neighbors.pmm(yhatmis, + edges = model$edges, + lookup = model$lookup, + mlocal = mlocal) + return(impy) } - # parameter estimation - parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) + # Quantify factor levels + f <- quantify(y, ry, x, quantify = quantify) + ynum <- f$ynum - if (matchtype == 0L) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$coef - yhatmis <- x[wy, , drop = FALSE] %*% parm$coef - } + # Predict ynum on observed data with linear model + parm <- .norm.draw(ynum, ry, x, ridge = ridge, ...) if (matchtype == 1L) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$coef - yhatmis <- x[wy, , drop = FALSE] %*% parm$beta + beta.hat <- drop(parm$coef) + beta.dot <- drop(parm$beta) + } else if (matchtype == 0L) { + beta.dot <- beta.hat <- drop(parm$coef) + } else if (matchtype == 2L) { + beta.dot <- beta.hat <- drop(parm$beta) } - if (matchtype == 2L) { - yhatobs <- x[ry, , drop = FALSE] %*% parm$beta - yhatmis <- x[wy, , drop = FALSE] %*% parm$beta + x_ry <- x[ry, , drop = FALSE] + x_wy <- x[wy, , drop = FALSE] + yhatobs <- as.vector(x_ry %*% beta.hat) + yhatmis <- x_wy %*% beta.dot + + # >>> Impute task: Impute values (classic MICE PMM) + if (task == "impute") { + if (use.matcher) { + idx <- matcher(yhatobs, yhatmis, k = donors) + } else { + idx <- matchindex(yhatobs, yhatmis, donors) + } + return(y[ry][idx]) } - if (use.matcher) { - idx <- matcher(yhatobs, yhatmis, k = donors) - } else { - idx <- matchindex(yhatobs, yhatmis, donors) + + # >>> Train task: Store model in environment + nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) + donors <- initialize.donors(donors, length(yhatobs)) + edges <- quantile(yhatobs, probs = seq(0, 1, length.out = nbins + 1L), + type = 7L, na.rm = TRUE) + lookup <- bin.yhat(yhatobs, ynum[ry], k = donors, edges = edges) + + # Store the imputation model in models environment + model$setup <- list(method = method, + n = length(yhatobs), + donors = donors, + matchtype = matchtype, + quantify = quantify, + exclude = exclude, + trim = trim, + task = task, + nbins = nbins, + ridge = ridge) + model$beta.hat <- beta.hat + model$beta.dot <- beta.dot + model$edges <- edges + model$lookup <- matrix((unquantify(lookup, f$quant, levels(y))), + nrow = nbins) + model$factor <- list(labels = f$labels, quant = f$quant) + model$xnames <- colnames(x) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] + + # Compute imputations from model + impy <- draw.neighbors.pmm(yhatmis, + edges = model$edges, + lookup = model$lookup, + mlocal = mlocal) + return(impy) +} + +# --- PMM helpers + +initialize.nbins <- function(nbins, n, nu) { + if (is.null(nbins)) { + nbins <- round(4 * log(n) + 1.5) } - return(y[ry][idx]) + # max nbin is number of unique yhat values, but ensure at least 2 bins + if (nbins > nu) { + nbins <- nu + } + nbins <- max(2L, nbins) + return(nbins) } -#' Finds an imputed value from matches in the predictive metric (deprecated) -#' -#' This function finds matches among the observed data in the predictive -#' mean metric. It selects the \code{donors} closest matches, randomly -#' samples one of the donors, and returns the observed value of the -#' match. -#' -#' This function is included for backward compatibility. It was -#' used up to \code{mice 2.21}. The current \code{mice.impute.pmm()} -#' function calls the faster \code{C} function \code{matcher} instead of -#' \code{.pmm.match()}. -#' -#' @aliases .pmm.match -#' @param z A scalar containing the predicted value for the current case -#' to be imputed. -#' @param yhat A vector containing the predicted values for all cases with an observed -#' outcome. -#' @param y A vector of \code{length(yhat)} elements containing the observed outcome -#' @param donors The size of the donor pool among which a draw is made. The default is -#' \code{donors = 5}. Setting \code{donors = 1} always selects the closest match. Values -#' between 3 and 10 provide the best results. Note: This setting was changed from -#' 3 to 5 in version 2.19, based on simulation work by Tim Morris (UCL). -#' @param \dots Other parameters (not used). -#' @return A scalar containing the observed value of the selected donor. -#' @author Stef van Buuren -#' @rdname pmm.match -#' @references -#' Schenker N & Taylor JMG (1996) Partially parametric techniques -#' for multiple imputation. \emph{Computational Statistics and Data Analysis}, 22, 425-446. -#' -#' Little RJA (1988) Missing-data adjustments in large surveys (with discussion). -#' \emph{Journal of Business Economics and Statistics}, 6, 287-301. -#' -#' @export -.pmm.match <- function(z, yhat = yhat, y = y, donors = 5, ...) { - d <- abs(yhat - z) - f <- d > 0 - a1 <- ifelse(any(f), min(d[f]), 1) - d <- d + runif(length(d), 0, a1 / 10^10) - if (donors == 1) { - return(y[which.min(d)]) +initialize.donors <- function(donors, n) { + if (is.null(donors)) { + donors <- round(n / 600 + 7) } - donors <- min(donors, length(d)) - donors <- max(donors, 1) - ds <- sort.int(d, partial = donors) - m <- sample(y[d <= ds[donors]], 1) - return(m) + donors <- max(1L, min(donors, n)) + return(donors) } -quantify <- function(y, ry, x) { - # replaces (reduced set of) categories by optimal scaling - yf <- factor(y[ry], exclude = NULL) - yd <- model.matrix(~ 0 + yf) - xd <- x[ry, , drop = FALSE] - cca <- cancor(yd, xd, xcenter = FALSE, ycenter = FALSE) - ynum <- as.integer(y) - ynum[ry] <- scale(as.vector(yd %*% cca$xcoef[, 2L])) - # plot(y[ry], ynum[ry]) - return(ynum) +bin.yhat <- function(yhat, y, k, edges) { + stopifnot(length(yhat) == length(y)) + + # Sort yhat and y together + sort_order <- order(yhat) + yhat_sorted <- yhat[sort_order] + y_sorted <- y[sort_order] + + # Assign values to bins + bin <- findInterval(yhat_sorted, vec = edges, all.inside = TRUE) + + # Split y_sorted by bins + values_list <- split(y_sorted, bin) + + # Fill lookup table with k potential donor values per bin + # If bin is empty, sample from entire y_sorted to avoid NA values + # If only one value is available, repeat it + # Watch out for odd sample(x) behavior when length(x) == 1 + # Sample with replacement if we fewer than k bin values + nbins <- length(edges) - 1L + lookup <- t(sapply(seq_len(nbins), function(b) { + values <- values_list[[as.character(b)]] + if (length(values) == 0L) { + sample(y_sorted, size = k, replace = TRUE) + } else if (length(values) == 1L) { + rep(values, k) + } else { + sample(values, size = k, replace = length(values) < k) + }})) + + return(lookup) } + +draw.neighbors.pmm <- function(yhat, edges, lookup, mlocal = 1L) { + # Bins are defined by edges[i] and edges[i+1] + n <- length(yhat) + nbins <- length(edges) - 1L + + # Result matrix: rows = number of queries, columns = mlocal draws per query + imputed_values <- matrix(NA_real_, nrow = n, ncol = mlocal) + + # Find the bin for each query value + bin <- findInterval(yhat, edges, rightmost.closed = TRUE, all.inside = TRUE) + + # Compute probability of selecting from left bin (smooth transition) + t0 <- edges[pmax(bin, 1L)] + t1 <- edges[pmin(bin + 1L, nbins)] + p_left <- ifelse(t1 > t0, (t1 - yhat) / (t1 - t0), 0.5) + + # Determine which bin to sample from + selected_bin <- ifelse(runif(n) < p_left, bin, pmin(bin + 1L, nbins)) + + # Vectorized sampling from lookup table + indices <- matrix(sample(1L:ncol(lookup), n * mlocal, replace = TRUE), nrow = n) + impy <- matrix(lookup[cbind(selected_bin, indices)], nrow = n, ncol = mlocal) + return(impy) +} + diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index ef354f18d..7bd6dc7cc 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -1,36 +1,33 @@ -#' Imputation of ordered data by polytomous regression +#' Imputation of categorical data by the ordered logistic (polr) model +#' +#' The function \code{mice.impute.polr()} imputes missing data in an +#' ordinal categorical variable using the proportional odds logistic +#' regression model (`polr`). This model, also known as the cumulative +#' link model, estimates cumulative probabilities using a set of +#' threshold parameters. The method assumes that the effect of predictor +#' variables is the same across all category transitions (proportional odds +#' assumption). #' -#' Imputes missing data in a categorical variable using polytomous regression #' @aliases mice.impute.polr #' @inheritParams mice.impute.pmm -#' @param nnet.maxit Tuning parameter for \code{nnet()}. -#' @param nnet.trace Tuning parameter for \code{nnet()}. -#' @param nnet.MaxNWts Tuning parameter for \code{nnet()}. -#' @param polr.to.loggedEvents A logical indicating whether each fallback +#' @inheritParams mice.impute.polyreg +#' @param polr.to.loggedEvents A logical indicating whether each fall-back #' to the \code{multinom()} function should be written to \code{loggedEvents}. #' The default is \code{FALSE}. #' @return Vector with imputed data, same type as \code{y}, and of length #' \code{sum(wy)} #' @details -#' The function \code{mice.impute.polr()} imputes for ordered categorical response -#' variables by the proportional odds logistic regression (polr) model. The -#' function repeatedly applies logistic regression on the successive splits. The -#' model is also known as the cumulative link model. -#' -#' By default, ordered factors with more than two levels are imputed by -#' \code{mice.impute.polr}. -#' #' The algorithm of \code{mice.impute.polr} uses the function \code{polr()} from #' the \code{MASS} package. #' #' In order to avoid bias due to perfect prediction, the algorithm augment the #' data according to the method of White, Daniel and Royston (2010). #' -#' The call to \code{polr} might fail, usually because the data are very sparse. +#' Calls to \code{polr} might fail if the data are very sparse. #' In that case, \code{multinom} is tried as a fallback. #' If the local flag \code{polr.to.loggedEvents} is set to TRUE, -#' a record is written -#' to the \code{loggedEvents} component of the \code{\link{mids}} object. +#' a record is written to the \code{loggedEvents} component of +#' the \code{\link{mids}} object. #' Use \code{mice(data, polr.to.loggedEvents = TRUE)} to set the flag. #' #' @note @@ -40,7 +37,7 @@ #' for \code{polr} in these versions were in fact handled by \code{multinom}. #' See \url{https://github.com/amices/mice/issues/206} for details. #' -#' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 +#' @author Stef van Buuren #' @seealso \code{\link{mice}}, \code{\link[nnet]{multinom}}, #' \code{\link[MASS]{polr}} #' @references @@ -49,10 +46,6 @@ #' Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical #' Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} #' -#' Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of -#' multiple imputation strategies for the statistical analysis of incomplete -#' data sets.} Dissertation. Rotterdam: Erasmus University. -#' #' White, I.R., Daniel, R. Royston, P. (2010). Avoiding bias due to perfect #' prediction in multiple imputation of incomplete categorical variables. #' \emph{Computational Statistics and Data Analysis}, 54, 2267-2275. @@ -62,52 +55,126 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.polr <- function(y, ry, x, wy = NULL, nnet.maxit = 100, - nnet.trace = FALSE, nnet.MaxNWts = 1500, - polr.to.loggedEvents = FALSE, ...) { - if (is.null(wy)) wy <- !ry +mice.impute.polr <- function(y, ry, x, wy = NULL, + task = "impute", model = NULL, + nnet.maxit = NULL, nnet.MaxNWts = NULL, + maxit = NULL, MaxNWts = NULL, reltol = NULL, + warmstart = FALSE, polr.to.loggedEvents = FALSE, + ...) { + check.model.exists(model, task) + method <- "polr" + if (is.null(wy)) { + wy <- !ry + } - # augment data to evade issues with perfect prediction - x <- as.matrix(x) + # Augment data to evade issues with perfect prediction aug <- augment(y, ry, x, wy) x <- aug$x y <- aug$y ry <- aug$ry wy <- aug$wy w <- aug$w - xy <- cbind.data.frame(y = y, x = x) - ## polr may fail on sparse data. We revert to multinom in such cases. - fit <- try( - suppressWarnings(MASS::polr(formula(xy), - data = xy[ry, , drop = FALSE], - weights = w[ry], - control = list(...) - )), - silent = TRUE + if (task == "fill") { + cols <- check.model.match(model, x, method) + impy <- polr.draw( + x = x[wy, cols, drop = FALSE], + beta = model$beta.dot, + zeta = model$zeta.mis, + levels = model$factor$labels, + class = model$class[1L] + ) + return(impy) + } + + # Escape estimation with same impute if the dependent does not vary + cat.has.all.obs <- table(y[ry]) == sum(ry) + if (any(cat.has.all.obs)) { + return(rep(levels(y)[cat.has.all.obs], sum(wy))) + } + + # Set hyper-parameters + dots <- list( + model = FALSE, + method = "logistic", + control = list( + trace = 0L, + maxit = ifelse(is.null(maxit), 100L, maxit), + reltol = ifelse(is.null(reltol), 0.0001, reltol)) ) + if (warmstart && task == "train" && + !is.null(model$beta.dot) && !is.null(model$zeta.mis)) { + dots$start <- c(model$beta.dot, model$zeta.mis) + } + + # Estimate ordered logistic (polr) model with polr + # Fall back to multinom if polr fails + y <- droplevels(y) + xy <- cbind.data.frame(y, x) + execute <- "polr" + fun <- MASS::polr + args <- c(list(formula = formula(xy), + data = xy[ry, , drop = FALSE]), + dots) + fit <- try(suppressWarnings(do.call(fun, args)), silent = TRUE) if (inherits(fit, "try-error")) { if (polr.to.loggedEvents) { updateLog(out = "polr falls back to multinom", frame = 6) } - fit <- nnet::multinom(formula(xy), - data = xy[ry, , drop = FALSE], - weights = w[ry], - maxit = nnet.maxit, trace = nnet.trace, - MaxNWts = nnet.MaxNWts, ... - ) + execute <- "multinom" + impy <- mice.impute.polyreg( + y = y, ry = ry, x = x, wy = wy, + task = task, model = model, + nnet.maxit = nnet.maxit, nnet.MaxNWts = nnet.MaxNWts, + maxit = maxit, MaxNWts = MaxNWts, reltol = reltol, + warmstart = warmstart, ...) } - post <- predict(fit, xy[wy, , drop = FALSE], type = "probs") - if (sum(wy) == 1) { - post <- matrix(post, nrow = 1, ncol = length(post)) + + # Save for future use + if (task == "train" && execute == "polr") { + model$setup <- list(method = method, + n = sum(ry), + task = task, + maxit = dots$control$maxit, + reltol = dots$control$reltol, + warmstart = warmstart) + model$result <- list(value = fit$value, + convergence = fit$convergence) + model$beta.dot <- setNames(coef(fit), colnames(x)) + model$zeta.mis <- fit$zeta + model$factor <- list(labels = levels(y), quant = NULL) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] + model$xnames <- colnames(x) } - fy <- as.factor(y) - nc <- length(levels(fy)) - un <- rep(runif(sum(wy)), each = nc) - if (is.vector(post)) { - post <- matrix(c(1 - post, post), ncol = 2) + + # Draw imputations + if (execute == "polr") { + impy <- polr.draw( + x = x[wy, , drop = FALSE], + beta = coef(fit), + zeta = fit$zeta, + levels = levels(y), + class = if (is.ordered(y)) "ordered" else class(y)[1L] + ) } - draws <- un > apply(post, 1, cumsum) - idx <- 1 + apply(draws, 2, sum) - levels(fy)[idx] + + return(impy) } + +polr.draw <- function(x, beta, zeta, levels, class = NULL) { + if (nrow(x) == 0L) return(character(0)) + eta <- x %*% beta + cumpr <- plogis(matrix(zeta, nrow(x), length(zeta), byrow = TRUE) - as.vector(eta)) + post <- t(apply(cumpr, 1L, function(x) diff(c(0, x, 1)))) + un <- rep(runif(nrow(x)), each = length(levels)) + draws <- un > apply(post, 1L, cumsum) + idx <- 1L + apply(draws, 2L, sum) + out <- levels[idx] + + if (!is.null(class) && class %in% c("factor", "ordered")) { + out <- factor(out, levels = levels, ordered = (class == "ordered")) + } + + return(out) +} + diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 4d3055f23..a7396cb8d 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -1,12 +1,21 @@ #' Imputation of unordered data by polytomous regression #' #' Imputes missing data in a categorical variable using polytomous regression +#' for unordered factors. #' #' @aliases mice.impute.polyreg #' @inheritParams mice.impute.pmm -#' @param nnet.maxit Tuning parameter for \code{nnet()}. -#' @param nnet.trace Tuning parameter for \code{nnet()}. -#' @param nnet.MaxNWts Tuning parameter for \code{nnet()}. +#' @param maxit Tuning parameter for \code{nnet()}. +#' @param MaxNWts Tuning parameter for \code{nnet()}. Internally, the procedure +#' computes the number of weights needed for the multinomial model as +#' 100 + \code{ncol(x)} times \code{length(levels(y)) - 1L)}. +#' Use \code{MaxNWts} to override this default if you get the +#' “too many weights” error. +#' @param nnet.maxit Legacy parameter. +#' @param nnet.MaxNWts Legacy parameter. +#' @param reltol Convergence parameter for \code{nnet()}. +#' @param warmstart Logical. If \code{TRUE}, the estimation process +#' uses weights from the previous iteration as warm starts. #' @return Vector with imputed data, same type as \code{y}, and of length #' \code{sum(wy)} #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 @@ -15,9 +24,6 @@ #' variables by the Bayesian polytomous regression model. See J.P.L. Brand #' (1999), Chapter 4, Appendix B. #' -#' By default, unordered factors with more than two levels are imputed by -#' \code{mice.impute.polyreg()}. -#' #' The method consists of the following steps: #' \enumerate{ #' \item Fit categorical response as a multinomial model @@ -51,14 +57,18 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.polyreg <- function(y, ry, x, wy = NULL, nnet.maxit = 100, - nnet.trace = FALSE, nnet.MaxNWts = 1500, ...) { - if (is.null(wy)) { - wy <- !ry - } +mice.impute.polyreg <- function( + y, ry, x, wy = NULL, + task = "impute", model = NULL, + nnet.maxit = NULL, nnet.MaxNWts = NULL, + maxit = NULL, MaxNWts = NULL, reltol = NULL, + warmstart = FALSE, ...) { + + check.model.exists(model, task) + method <- "polyreg" + if (is.null(wy)) wy <- !ry - # augment data to evade issues with perfect prediction - x <- as.matrix(x) + # Augment data aug <- augment(y, ry, x, wy) x <- aug$x y <- aug$y @@ -66,36 +76,104 @@ mice.impute.polyreg <- function(y, ry, x, wy = NULL, nnet.maxit = 100, wy <- aug$wy w <- aug$w - fy <- as.factor(y) - nc <- length(levels(fy)) - un <- rep(runif(sum(wy)), each = nc) - - xy <- cbind.data.frame(y = y, x = x) - - if (ncol(x) == 0L) { - xy <- data.frame(xy, int = 1) + if (task == "fill") { + x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) + cols <- check.model.match(model, x, method) + x <- x[wy, cols, drop = FALSE] + return(polyreg.draw( + x = x, + beta = model$beta.dot, + levels = model$factor$labels, + class = model$class[1L]) + ) } - # escape with same impute if the dependent does not vary + # Escape perfect prediction cat.has.all.obs <- table(y[ry]) == sum(ry) if (any(cat.has.all.obs)) { - return(rep(levels(fy)[cat.has.all.obs], sum(wy))) + return(rep(levels(y)[cat.has.all.obs], sum(wy))) } - fit <- nnet::multinom(formula(xy), - data = xy[ry, , drop = FALSE], weights = w[ry], - maxit = nnet.maxit, trace = nnet.trace, MaxNWts = nnet.MaxNWts, - ... - ) - post <- predict(fit, xy[wy, , drop = FALSE], type = "probs") - if (sum(wy) == 1) { - post <- matrix(post, nrow = 1, ncol = length(post)) + # Set hyperparameters + if (!missing(nnet.maxit)) maxit <- nnet.maxit + if (!missing(nnet.MaxNWts)) MaxNWts <- nnet.MaxNWts + MaxNWts_needed <- 100L + as.integer(ncol(x) * (length(levels(y)) - 1)) + dots <- list(...) + dots$maxit <- ifelse(is.null(maxit), 100L, maxit) + dots$MaxNWts <- ifelse(is.null(MaxNWts), MaxNWts_needed, MaxNWts) + dots$reltol <- ifelse(is.null(reltol), 0.0001, reltol) + if (warmstart && task == "train" && !is.null(model$wts)) { + dots$Wts <- model$wts + } + + # Fit multinomial model + y <- droplevels(y) + xy <- cbind.data.frame(y, x) + fit <- do.call(nnet::multinom, c( + list(formula(xy), + data = xy[ry, , drop = FALSE], + weights = w[ry], + model = FALSE, trace = FALSE), + dots + )) + + # Process beta coefficients + x <- x[wy, , drop = FALSE] + x <- cbind(`(Intercept)` = rep(1, nrow(x)), x) + beta <- coef(fit) + if (is.vector(beta)) { + beta <- matrix(beta, ncol = 1L) + } else { + beta <- t(beta) } - if (is.vector(post)) { - post <- matrix(c(1 - post, post), ncol = 2) + rownames(beta) <- gsub("`", "", rownames(beta)) + + if (task == "train") { + model$setup <- list( + method = method, + n = sum(ry), + task = task, + maxit = dots$maxit, + MaxNWts = dots$MaxNWts, + reltol = dots$reltol, + warmstart = warmstart + ) + model$result <- list( + nWts = length(fit$wts), + value = fit$value, + convergence = fit$convergence + ) + model$beta.dot <- beta + if (warmstart) model$wts <- fit$wts + model$factor <- list(labels = levels(y), quant = NULL) + model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] + model$xnames <- colnames(x) + } + + # Return imputed values + polyreg.draw( + x = x, + beta = beta, + levels = levels(y), + class = if (is.ordered(y)) "ordered" else class(y)[1L] + ) +} + +polyreg.draw <- function(x, beta, levels, class = NULL) { + if (nrow(x) == 0L) return(character(0)) + lp <- x %*% beta + p <- exp(lp) / rowSums(exp(lp) + 1) + post <- cbind(1 - rowSums(p), p) + + un <- rep(runif(nrow(x)), each = length(levels)) + draws <- un > apply(post, 1L, cumsum) + idx <- 1L + apply(draws, 2L, sum) + + out <- levels[idx] + + if (!is.null(class) && class %in% c("factor", "ordered")) { + out <- factor(out, levels = levels, ordered = (class == "ordered")) } - draws <- un > apply(post, 1, cumsum) - idx <- 1 + apply(draws, 2, sum) - levels(fy)[idx] + return(out) } diff --git a/R/mice.mids.R b/R/mice.mids.R index 75d51c2a9..6f9672ce2 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -112,8 +112,9 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { q <- sampler( obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, - obj$formulas, obj$modeltype, obj$blots, obj$post, - c(from, to), printFlag, ... + obj$formulas, obj$modeltype, obj$blots, + obj$tasks, obj$models, + obj$post, c(from, to), printFlag, ... ) imp <- q$imp @@ -167,6 +168,8 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { modeltype = obj$modeltype, post = obj$post, blots = obj$blots, + tasks = obj$tasks, + models = obj$models, ignore = obj$ignore, seed = obj$seed, iteration = sumIt, diff --git a/R/mids.R b/R/mids.R index 79ee110c4..f638adcc8 100644 --- a/R/mids.R +++ b/R/mids.R @@ -24,6 +24,7 @@ #' @param loggedEvents Calculated field #' @param version Calculated field #' @param date Calculated field +#' @param store Calculated field #' @return #' \code{mids()} returns a \code{mids} object. #' @@ -58,6 +59,9 @@ #' with commands for post-processing.} #' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} #' function.} +#' \item{\code{tasks}:}{A character vector of length \code{length(blocks)}.} +#' \item{\code{models}:}{The \code{models} list contains imputation model +#' estimates.} #' \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating #' the rows in \code{data} used to build the imputation model. (new in \code{mice 3.12.0})} #' \item{\code{seed}:}{The seed value of the solution.} @@ -71,18 +75,19 @@ #' \item{\code{chainVar}:}{An array with similar structure as #' \code{chainMean}, containing the variance of the imputed values.} #' \item{\code{loggedEvents}:}{A \code{data.frame} with five columns -#' containing warnings, corrective actions, and other inside info.} +#' containing warnings, corrective tasks, and other inside info.} #' \item{\code{version}:}{Version number of \code{mice} package that #' created the object.} #' \item{\code{date}:}{Date at which the object was created.} +#' \item{\code{store}:}{A string, indicating the type of mids object.} #' } #' #' @section LoggedEvents: #' The \code{loggedEvents} entry is a matrix with five columns containing a -#' record of automatic removal actions. It is \code{NULL} is no action was +#' record of automatic removal tasks. It is \code{NULL} is no record was #' made. At initialization the program removes constant variables, and #' removes variables to cause collinearity. -#' During iteration, the program does the following actions: +#' During iteration, the program does the following tasks: #' \itemize{ #' \item One or more variables that are linearly dependent are removed #' (for categorical data, a 'variable' corresponds to a dummy variable) @@ -135,6 +140,8 @@ #' formulas = list(a = a ~ b, b = b ~ a), #' post = NULL, #' blots = NULL, +#' tasks = NULL, +#' models = NULL, #' ignore = logical(nrow(data)), #' seed = 123, #' iteration = 1, @@ -159,6 +166,8 @@ mids <- function( modeltype = character(), post = character(), blots = list(), + tasks = character(), + models = new.env(), ignore = logical(), seed = integer(), iteration = integer(), @@ -170,32 +179,92 @@ mids <- function( chainVar = list(), loggedEvents = data.frame(), version = packageVersion("mice"), - date = Sys.Date()) { - obj <- list( - data = data, - imp = imp, - m = m, - where = where, - blocks = blocks, - call = call, - nmis = nmis, - method = method, - predictorMatrix = predictorMatrix, - visitSequence = visitSequence, - formulas = formulas, - modeltype = modeltype, - post = post, - blots = blots, - ignore = ignore, - seed = seed, - iteration = iteration, - lastSeedValue = lastSeedValue, - chainMean = chainMean, - chainVar = chainVar, - loggedEvents = loggedEvents, - version = packageVersion("mice"), - date = Sys.Date() - ) + date = Sys.Date(), + store = "impute") { + + if (store == "impute") { + obj <- list( + data = data, + imp = imp, + m = m, + where = where, + blocks = blocks, + call = call, + nmis = nmis, + method = method, + predictorMatrix = predictorMatrix, + visitSequence = visitSequence, + formulas = formulas, + modeltype = modeltype, + post = post, + blots = blots, + tasks = tasks, + ignore = ignore, + seed = seed, + iteration = iteration, + lastSeedValue = lastSeedValue, + chainMean = chainMean, + chainVar = chainVar, + loggedEvents = loggedEvents, + version = packageVersion("mice"), + date = Sys.Date(), + store = store) + } else if (store == "train") { + obj <- list( + data = data, + imp = imp, + m = m, + where = where, + blocks = blocks, + call = call, + nmis = nmis, + method = method, + predictorMatrix = predictorMatrix, + visitSequence = visitSequence, + formulas = formulas, + modeltype = modeltype, + post = post, + blots = blots, + tasks = tasks, + models = export.models.env(models), + ignore = ignore, + seed = seed, + iteration = iteration, + lastSeedValue = lastSeedValue, + chainMean = chainMean, + chainVar = chainVar, + loggedEvents = loggedEvents, + version = packageVersion("mice"), + date = Sys.Date(), + store = store) + } else if (store == "train_compact") { + obj <- list( + m = m, + blocks = blocks, + method = method, + blots = blots, + visitSequence = visitSequence, + iteration = iteration, + lastSeedValue = lastSeedValue, + tasks = tasks, + models = export.models.env(models), + store = store, + version = packageVersion("mice"), + date = Sys.Date(), + call = call) + } else if (store == "fill") { + obj <- list( + data = data, + imp = imp, + m = m, + where = where, + store = store, + version = packageVersion("mice"), + date = Sys.Date(), + call = call) + } else { + stop("store must be one of 'impute', 'train', 'train_compact', or 'fill'") + } class(obj) <- "mids" return(obj) } diff --git a/R/models.R b/R/models.R new file mode 100644 index 000000000..44a73c96c --- /dev/null +++ b/R/models.R @@ -0,0 +1,185 @@ +#' Initialize Models Environment +#' +#' Creates an environment structure for models based on specified task types. +#' It ensures that a nested environment is created for each variable in `tasks` +#' that is labeled as `"train"`, with sub-environments for each iteration +#' from `1` to `m`. +#' +#' @inheritParams mice +#' @param m An integer specifying the number of nested sub-environments to +#' create under each `"train"` variable. +#' +#' @return An environment containing model environments structured as: +#' \itemize{ +#' \item \code{models$varname} - An environment for each `"train"` variable. +#' \item \code{models$varname$i} - Nested environments for each iteration from `1` to `m`. +#' } +#' @keywords internal +initialize.models.env <- function(models = NULL, tasks, method, blocks, m) { + + # Import models into environment from a model list object + if (is.list(models)) { + models <- import.models.env(models) + } + + # Ensure `models` is an environment + if (is.null(models)) { + models <- new.env(parent = emptyenv()) + } + + # Identify variables that require models (i.e., "train" or "fill" tasks) + imported.models <- names(models) + model.vars <- names(tasks[tasks %in% c("train", "fill")]) + empty.methods <- character(0L) + for (h in names(blocks)) { + varnames <- blocks[[h]] + if (method[h] == "") { + empty.methods <- c(empty.methods, varnames) + } + } + model.vars <- setdiff(model.vars, c(imported.models, empty.methods)) + + for (varname in model.vars) { + if (tasks[varname] == "train") { + # Create an environment for the variable if it doesn't exist + if (!exists(varname, envir = models)) { + models[[varname]] <- new.env(parent = emptyenv()) + } + + # Create nested environments for `1:m` + for (i in seq_len(m)) { + if (!exists(as.character(i), envir = models[[varname]])) { + models[[varname]][[as.character(i)]] <- new.env(parent = emptyenv()) + } + } + } + } + + return(models) +} + +#' Convert Nested Environments to a List of m-Lists +#' +#' Recursively converts a three-level environment structure into a user-friendly +#' list where the imputation level is stored as a vector of length `m`. +#' +#' @param env The root environment containing task environments. +#' @param m The number of imputations (assumes all tasks have the same `m`). +#' @return A named list where: +#' \itemize{ +#' \item Each element corresponds to a task (e.g., "train" variables). +#' \item Each task is stored as a vector of `m` lists (one per imputation iteration). +#' \item Each list contains the objects stored for that imputation. +#' } +#' @examples +#' # Create a nested environment structure +#' models_env <- new.env() +#' models_env$a <- new.env() +#' models_env$a$`1` <- new.env() +#' models_env$a$`1`$model <- "Model A1" +#' models_env$a$`2` <- new.env() +#' models_env$a$`2`$model <- "Model A2" +#' models_env$b <- new.env() +#' models_env$b$`1` <- new.env() +#' models_env$b$`1`$model <- "Model B1" +#' +#' # Convert to a list +#' models_list <- mice:::export.models.env(models_env, m = 2) +#' print(models_list) +#' @keywords internal +export.models.env <- function(env, m = NULL) { + if (!is.environment(env)) stop("Input must be an environment") + + env_to_list <- function(env) { + obj_list <- as.list(env, all.names = TRUE) + for (name in names(obj_list)) { + if (is.environment(obj_list[[name]])) { + obj_list[[name]] <- env_to_list(obj_list[[name]]) + } + } + return(obj_list) + } + + # Convert first-level environment into a list + models_list <- env_to_list(env) + m <- ifelse(is.null(m), max(sapply(models_list, length)), m) + + # Restructure each task's models into a vector of m lists + for (varname in names(models_list)) { + task_models <- models_list[[varname]] + + # Initialize an empty list of length m + imputation_list <- vector("list", m) + + # Fill in models from the extracted task_models + for (i in seq_len(m)) { + iter_name <- as.character(i) + imputation_list[[i]] <- + if (iter_name %in% names(task_models)) { + task_models[[iter_name]] + } else { + list() + } + } + + # Replace with the m-length vector of lists + models_list[[varname]] <- imputation_list + } + + return(models_list) +} + +#' Convert a List of m-Lists Back to a Nested Environment +#' +#' Converts a structured list back into a nested environment where: +#' - The first level contains task names (e.g., "train" variables). +#' - The second level contains iteration indices (`1:m`). +#' - The third level contains stored objects within each iteration. +#' +#' @param models_list A list where: +#' - Each element corresponds to a task (e.g., "train" variables). +#' - Each task contains a vector of `m` lists (one per imputation iteration). +#' - Each list contains the stored objects for that iteration. +#' @return A nested environment structured as: +#' - `models_env$varname` (An environment for each task). +#' - `models_env$varname$i` (Nested environments for each iteration). +#' - Objects within each iteration are stored inside their respective environments. +#' +#' @examples +#' # Example list structure +#' models_list <- list( +#' a = list( +#' list(model = "Model A1"), +#' list(model = "Model A2") +#' ), +#' b = list( +#' list(model = "Model B1"), +#' list() # Empty list for missing iteration +#' ) +#' ) +#' +#' # Convert list to environment +#' models_env <- mice:::import.models.env(models_list) +#' print(ls(models_env)) # Should list "a" and "b" +#' print(ls(models_env$a)) # Should list "1" and "2" +#' print(models_env$a$`1`$model) # Should be "Model A1" +#' @keywords internal +import.models.env <- function(models_list) { + if (!is.list(models_list)) stop("Input must be a list") + + models_env <- new.env(parent = emptyenv()) + + for (varname in names(models_list)) { + models_env[[varname]] <- new.env(parent = emptyenv()) # Create first-level environment + + for (i in seq_along(models_list[[varname]])) { + iteration_data <- models_list[[varname]][[i]] + + if (length(iteration_data) > 0) { # Only create non-empty environments + models_env[[varname]][[as.character(i)]] <- list2env(iteration_data, parent = emptyenv()) + } + } + } + + return(models_env) +} diff --git a/R/quantify.R b/R/quantify.R new file mode 100644 index 000000000..2a561e85d --- /dev/null +++ b/R/quantify.R @@ -0,0 +1,34 @@ +quantify <- function(y, ry, x, quantify = TRUE) { + if (!is.factor(y)) { + return(list(ynum = y, + labels = NULL, + quant = NULL)) + } + if (!quantify) { + ynum <- as.integer(y) + return(list(ynum = ynum, + labels = levels(y), + quant = 1L:length(levels(y)))) + } + + # replace (reduced set of) categories by optimal scaling + yf <- factor(y[ry], exclude = NULL) + yd <- model.matrix(~ 0 + yf) + xd <- cbind(1, x[ry, , drop = FALSE]) + cca <- cancor(y = yd, x = xd, xcenter = FALSE, ycenter = FALSE) + oldlevels <- levels(y) + levels(y) <- as.vector(cca$ycoef[, 2L]) + ynum <- as.numeric(as.character(y)) + return(list(ynum = ynum, + labels = oldlevels, + quant = as.numeric(levels(y)))) +} + +unquantify <- function(ynum = NULL, quant = NULL, labels = NULL) { + if (is.null(labels)) return(ynum) + y <- factor(ynum, levels = quant, labels = labels) + if (anyNA(levels(y))) { + y <- droplevels(y, exclude = NA) + } + return(y) +} diff --git a/R/rbind.R b/R/rbind.R index f4abc8871..4a10f7f73 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -46,6 +46,9 @@ rbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots + tasks <- x$tasks + models <- x$models + store <- x$store predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -75,6 +78,9 @@ rbind.mids <- function(x, y = NULL, ...) { modeltype = modeltype, post = post, blots = blots, + tasks = tasks, + models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, @@ -125,6 +131,12 @@ rbind.mids.mids <- function(x, y, call) { formulas <- x$formulas modeltype <- x$modeltype blots <- x$blots + tasks <- x$tasks + models <- x$models + store <- x$store + if (y$store != store) { + store <- "train" + } ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -172,6 +184,9 @@ rbind.mids.mids <- function(x, y, call) { modeltype = modeltype, post = post, blots = blots, + tasks = tasks, + models = models, + store = store, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/sampler.R b/R/sampler.R index 363a2579b..cb5a721bb 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,7 +2,7 @@ # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - modeltype, blots, + modeltype, blots, tasks, models, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] @@ -45,6 +45,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, if (calltype == "formula") ff <- formulas[[h]] else ff <- NULL pred <- predictorMatrix[h, ] user <- blots[[h]] + key <- paste0(h, "_", i) # univariate/multivariate logic theMethod <- method[h] @@ -69,17 +70,23 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, # (repeated) univariate imputation - pred method if (univ) { for (j in b) { + # if m outruns m.train, recycle m.train + m.train <- length(models[[j]]) + mod <- (i - 1L) %% m.train + 1L imp[[j]][, i] <- sampler.univ( data = data, r = r, where = where, pred = pred, formula = ff, method = theMethod, + task = tasks[j], + model = models[[j]][[as.character(mod)]], yname = j, k = k, calltype = calltype, user = user, ignore = ignore, ... ) + # update data data[(!r[, j]) & where[, j], j] <- imp[[j]][(!r[, j])[where[, j]], i] @@ -111,14 +118,12 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, type = pred, ... )) } else { - stop("Cannot call function of type ", calltype, - call. = FALSE - ) + stop("Cannot call function of type ", calltype, call. = FALSE) } if (is.null(imputes)) { stop("No imputations from ", theMethod, - h, - call. = FALSE + h, + call. = FALSE ) } for (j in names(imputes)) { @@ -133,11 +138,8 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, for (j in b) { wy <- where[, j] ry <- r[, j] - imp[[j]][, i] <- model.frame( - as.formula(theMethod), - data[wy, ], - na.action = na.pass - ) + imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], + na.action = na.pass) data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] } } @@ -179,43 +181,23 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } -sampler.univ <- function(data, r, where, pred, formula, method, yname, k, - calltype = "pred", user, ignore, +sampler.univ <- function(data, r, where, pred, formula, method, task, model, + yname, k, calltype = "pred", user, ignore, trimmer = "lindep", ...) { j <- yname[1L] # nothing to impute - if (!any(where[, j])) { - return(numeric(0)) + if (all(!where[, j]) && task != "train") { + return(numeric(0)) } - if (calltype == "pred") { - vars <- colnames(data)[pred != 0] - xnames <- setdiff(vars, j) - if (length(xnames) > 0L) { - formula <- reformulate(backticks(xnames), response = backticks(j)) - formula <- update(formula, ". ~ . ") - } else { - formula <- as.formula(paste0(j, " ~ 1")) - } - } - - if (calltype == "formula") { - # move terms other than j from lhs to rhs - ymove <- setdiff(lhs(formula), j) - formula <- update(formula, paste(j, " ~ . ")) - if (length(ymove) > 0L) { - formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) - } - } - - # get the model matrix + # prepare formula and model matrix + formula <- prepare.formula(formula, data, model, j, calltype, pred, task) x <- obtain.design(data, formula) # expand pred vector to model matrix, remove intercept if (calltype == "pred") { type <- pred[labels(terms(formula))][attr(x, "assign")] - # xnames <- names(type) x <- x[, -1L, drop = FALSE] names(type) <- colnames(x) } @@ -247,6 +229,30 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, imputes <- data[wy, j] imputes[!iy] <- NA + # remove linear dependencies + if (task != "fill") { + keep <- trim.data( + y = data[, j], + ry = r[, j] & !ignore, + x = x, + trimmer = trimmer, ... + ) + } + + # store the names of the features + # xj <- unique(xnames[keep$cols]) + # print(xj) + + # set up univariate imputation method + # wy: entries we wish to impute (length(y) elements) + # iy: entries we will impute (sum(wy) elements) + wy <- complete.cases(x) & where[, j] + iy <- wy[where[, j]] + + # wipe out previous values + imputes <- data[wy, j] + imputes[!iy] <- NA + # here we go f <- paste("mice.impute", method, sep = ".") args <- c( @@ -255,10 +261,52 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, ry = keep$rows, x = x[, keep$cols, drop = FALSE], wy = wy, - type = type[keep$cols] - ), - user, list(...) - ) + type = type[keep$cols], + task = task, + model = model), + user, list(...)) imputes[iy] <- do.call(f, args = args) return(imputes) } + + +prepare.formula <- function(formula, data, model, j, ct, pred, task) { + # prepares the formula for univariate imputation + # saves (for "train") or retrieves (for "fill") the formula + + # for "fill", use the stored formula instead of recalculating + if (task == "fill") { + if (!exists("formula", envir = model)) { + stop("Error: No stored formula found in model for 'fill' task.") + } + formula <- get("formula", envir = model) + return(as.formula(formula)) + } + + if (ct == "pred") { + vars <- colnames(data)[pred != 0] + xnames <- setdiff(vars, j) + if (length(xnames) > 0L) { + formula <- reformulate(backticks(xnames), response = backticks(j)) + formula <- update(formula, ". ~ . ") + } else { + formula <- as.formula(paste0(j, " ~ 1")) + } + } + + if (ct == "formula") { + # move terms other than j from lhs to rhs + ymove <- setdiff(lhs(formula), j) + formula <- update(formula, paste(j, " ~ . ")) + if (length(ymove) > 0L) { + formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) + } + } + + # store formula in `model` only when task is "train" + if (task == "train") { + assign("formula", paste(deparse(formula), collapse = ""), envir = model) + } + + return(formula) +} \ No newline at end of file diff --git a/R/tasks.R b/R/tasks.R new file mode 100644 index 000000000..cecf02626 --- /dev/null +++ b/R/tasks.R @@ -0,0 +1,26 @@ +#' Creates a \code{tasks} argument +#' +#' This helper function creates a valid \code{tasks} vector. The +#' \code{tasks} vector is an argument to the \code{mice} function that +#' specifies the task for each column in the data. +#' @inheritParams mice +#' @return Character vector of \code{ncol(data)} elements +#' @seealso \code{\link{mice}} +#' @examples +#' make.tasks(nhanes2) +#' @export +make.tasks <- function(data, + tasks = "impute", + blocks = make.blocks(data)) { + bv <- unique(unlist(blocks)) + if (length(tasks) == 1L) { + tasks <- setNames(rep(tasks, length(bv)), bv) + } else { + if (length(tasks) != length(bv)) { + stop("length(tasks) does not match variables to be imputed", call. = FALSE) + } + names(tasks) <- bv + } + + return(tasks) +} diff --git a/_pkgdown.yml b/_pkgdown.yml index 785505ad7..386755431 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -47,6 +47,7 @@ reference: - make.modeltype - make.post - make.predictorMatrix + - make.tasks - make.visitSequence - make.where - construct.blocks @@ -125,7 +126,6 @@ reference: - estimice - norm.draw - .norm.draw - - .pmm.match - title: Multivariate amputation desc: | Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies. @@ -173,9 +173,8 @@ reference: - supports.transparent - version articles: -- title: General - navbar: ~ - contents: - - overview - - oldfriends - + - title: General + navbar: ~ + contents: + - overview + - oldfriends diff --git a/man/coerce.data.Rd b/man/coerce.data.Rd new file mode 100644 index 000000000..dd494d893 --- /dev/null +++ b/man/coerce.data.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\name{coerce.data} +\alias{coerce.data} +\title{Coerce a data.frame to match model expectations} +\usage{ +coerce.data(data, models) +} +\arguments{ +\item{data}{A \code{data.frame} with input data.} + +\item{models}{A list of trained models from \code{mice()}.} +} +\value{ +A coerced \code{data.frame} that matches the class and levels from \code{models}. +} +\description{ +This function coerces columns in \code{data} to match the type and levels +expected by the corresponding trained \code{models}. +} diff --git a/man/export.models.env.Rd b/man/export.models.env.Rd new file mode 100644 index 000000000..f422ff05b --- /dev/null +++ b/man/export.models.env.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{export.models.env} +\alias{export.models.env} +\title{Convert Nested Environments to a List of m-Lists} +\usage{ +export.models.env(env, m = NULL) +} +\arguments{ +\item{env}{The root environment containing task environments.} + +\item{m}{The number of imputations (assumes all tasks have the same \code{m}).} +} +\value{ +A named list where: +\itemize{ +\item Each element corresponds to a task (e.g., "train" variables). +\item Each task is stored as a vector of \code{m} lists (one per imputation iteration). +\item Each list contains the objects stored for that imputation. +} +} +\description{ +Recursively converts a three-level environment structure into a user-friendly +list where the imputation level is stored as a vector of length \code{m}. +} +\examples{ +# Create a nested environment structure +models_env <- new.env() +models_env$a <- new.env() +models_env$a$`1` <- new.env() +models_env$a$`1`$model <- "Model A1" +models_env$a$`2` <- new.env() +models_env$a$`2`$model <- "Model A2" +models_env$b <- new.env() +models_env$b$`1` <- new.env() +models_env$b$`1`$model <- "Model B1" + +# Convert to a list +models_list <- mice:::export.models.env(models_env, m = 2) +print(models_list) +} +\keyword{internal} diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index 61e419a03..bd5c9256a 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -44,6 +44,8 @@ The function constructs the elements of the filtered \code{mids} object as follo \code{formulas} \tab Equals \code{.data$formulas}\cr \code{post} \tab Equals \code{.data$post}\cr \code{blots} \tab Equals \code{.data$blots}\cr +\code{tasks} \tab Equals \code{.data$tasks}\cr +\code{models} \tab Equals \code{.data$models}\cr \code{ignore} \tab Select positions in \code{.data$ignore} for which \code{include == TRUE}\cr \code{seed} \tab Equals \code{.data$seed}\cr \code{iteration} \tab Equals \code{.data$iteration}\cr diff --git a/man/import.models.env.Rd b/man/import.models.env.Rd new file mode 100644 index 000000000..72d35a54e --- /dev/null +++ b/man/import.models.env.Rd @@ -0,0 +1,52 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{import.models.env} +\alias{import.models.env} +\title{Convert a List of m-Lists Back to a Nested Environment} +\usage{ +import.models.env(models_list) +} +\arguments{ +\item{models_list}{A list where: +\itemize{ +\item Each element corresponds to a task (e.g., "train" variables). +\item Each task contains a vector of \code{m} lists (one per imputation iteration). +\item Each list contains the stored objects for that iteration. +}} +} +\value{ +A nested environment structured as: +\itemize{ +\item \code{models_env$varname} (An environment for each task). +\item \code{models_env$varname$i} (Nested environments for each iteration). +\item Objects within each iteration are stored inside their respective environments. +} +} +\description{ +Converts a structured list back into a nested environment where: +\itemize{ +\item The first level contains task names (e.g., "train" variables). +\item The second level contains iteration indices (\code{1:m}). +\item The third level contains stored objects within each iteration. +} +} +\examples{ +# Example list structure +models_list <- list( + a = list( + list(model = "Model A1"), + list(model = "Model A2") + ), + b = list( + list(model = "Model B1"), + list() # Empty list for missing iteration + ) +) + +# Convert list to environment +models_env <- mice:::import.models.env(models_list) +print(ls(models_env)) # Should list "a" and "b" +print(ls(models_env$a)) # Should list "1" and "2" +print(models_env$a$`1`$model) # Should be "Model A1" +} +\keyword{internal} diff --git a/man/initialize.models.env.Rd b/man/initialize.models.env.Rd new file mode 100644 index 000000000..44a5ebaf4 --- /dev/null +++ b/man/initialize.models.env.Rd @@ -0,0 +1,69 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{initialize.models.env} +\alias{initialize.models.env} +\title{Initialize Models Environment} +\usage{ +initialize.models.env(models = NULL, tasks, method, blocks, m) +} +\arguments{ +\item{models}{A list that stores fitted imputation models. The models +can be used to impute missing values in new data. \code{models} is +only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +to fill in missing values in new data, where \code{trained} is the +\code{mids} object returned by \code{mice(..., tasks = 'train')}.} + +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + +\item{method}{Can be either a single string, or a vector of strings with +length \code{length(blocks)}, specifying the imputation method to be +used for each column in data. If specified as a single string, the same +method will be used for all blocks. The default imputation method (when no +argument is specified) depends on the measurement level of the target column, +as regulated by the \code{defaultMethod} argument. Columns that need +not be imputed have the empty method \code{""}. See details.} + +\item{blocks}{List of vectors with variable names per block. List elements +may be named to identify blocks. Variables within a block are +imputed by a multivariate imputation method +(see \code{method} argument). By default each variable is placed +into its own block, which is effectively +fully conditional specification (FCS) by univariate models +(variable-by-variable imputation). Only variables whose names appear in +\code{blocks} are imputed. The relevant columns in the \code{where} +matrix are set to \code{FALSE} of variables that are not block members. +A variable may appear in multiple blocks. In that case, it is +effectively re-imputed each time that it is visited.} + +\item{m}{An integer specifying the number of nested sub-environments to +create under each \code{"train"} variable.} +} +\value{ +An environment containing model environments structured as: +\itemize{ +\item \code{models$varname} - An environment for each \code{"train"} variable. +\item \code{models$varname$i} - Nested environments for each iteration from \code{1} to \code{m}. +} +} +\description{ +Creates an environment structure for models based on specified task types. +It ensures that a nested environment is created for each variable in \code{tasks} +that is labeled as \code{"train"}, with sub-environments for each iteration +from \code{1} to \code{m}. +} +\keyword{internal} diff --git a/man/make.data.Rd b/man/make.data.Rd new file mode 100644 index 000000000..ec2eef1ee --- /dev/null +++ b/man/make.data.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\name{make.data} +\alias{make.data} +\title{Construct a new data.frame from a trained model} +\usage{ +make.data(models, n = 1L, fill = NA, vars = NULL) +} +\arguments{ +\item{models}{A list of trained models from \code{mice()}.} + +\item{n}{Number of rows to generate. Default is 1.} + +\item{fill}{Value to fill the data with. Default is \code{NA}.} + +\item{vars}{Optional character vector specifying the variable order. Default is \code{NULL}.} +} +\value{ +A \code{data.frame} of \code{n} rows, where each column matches the type and levels +expected from the corresponding model. +} +\description{ +This function generates a data.frame with the correct structure (names, classes, +levels) based on a set of trained models, as produced by \code{mice()} with \code{tasks = "train"}. +} +\examples{ +trained <- mice(boys, tasks = "train", print = FALSE) +newdata <- make.data(trained$models, n = 5, vars = names(boys)) +str(newdata) +} diff --git a/man/make.method.Rd b/man/make.method.Rd index a4d9a843f..d54caa628 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -8,6 +8,7 @@ make.method( data, where = make.where(data), blocks = make.blocks(data), + tasks = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr") ) } @@ -37,6 +38,23 @@ matrix are set to \code{FALSE} of variables that are not block members. A variable may appear in multiple blocks. In that case, it is effectively re-imputed each time that it is visited.} +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + \item{defaultMethod}{A vector of length 4 containing the default imputation methods for 1) numeric data, 2) factor data with 2 levels, 3) factor data with > 2 unordered levels, and 4) factor data with > 2 diff --git a/man/make.tasks.Rd b/man/make.tasks.Rd new file mode 100644 index 000000000..4a8163d99 --- /dev/null +++ b/man/make.tasks.Rd @@ -0,0 +1,55 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/tasks.R +\name{make.tasks} +\alias{make.tasks} +\title{Creates a \code{tasks} argument} +\usage{ +make.tasks(data, tasks = "impute", blocks = make.blocks(data)) +} +\arguments{ +\item{data}{A data frame or a matrix containing the incomplete data. Missing +values are coded as \code{NA}.} + +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + +\item{blocks}{List of vectors with variable names per block. List elements +may be named to identify blocks. Variables within a block are +imputed by a multivariate imputation method +(see \code{method} argument). By default each variable is placed +into its own block, which is effectively +fully conditional specification (FCS) by univariate models +(variable-by-variable imputation). Only variables whose names appear in +\code{blocks} are imputed. The relevant columns in the \code{where} +matrix are set to \code{FALSE} of variables that are not block members. +A variable may appear in multiple blocks. In that case, it is +effectively re-imputed each time that it is visited.} +} +\value{ +Character vector of \code{ncol(data)} elements +} +\description{ +This helper function creates a valid \code{tasks} vector. The +\code{tasks} vector is an argument to the \code{mice} function that +specifies the task for each column in the data. +} +\examples{ +make.tasks(nhanes2) +} +\seealso{ +\code{\link{mice}} +} diff --git a/man/mice.Rd b/man/mice.Rd index 2db20a230..0ea098e69 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -18,12 +18,15 @@ mice( formulas, modeltype = NULL, blots = NULL, + tasks = NULL, + models = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, printFlag = TRUE, seed = NA, data.init = NULL, + compact = FALSE, ... ) } @@ -128,6 +131,29 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + +\item{models}{A list that stores fitted imputation models. The models +can be used to impute missing values in new data. \code{models} is +only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +to fill in missing values in new data, where \code{trained} is the +\code{mids} object returned by \code{mice(..., tasks = 'train')}.} + \item{post}{A vector of strings with length \code{ncol(data)} specifying expressions as strings. Each string is parsed and executed within the \code{sampler()} function to post-process @@ -160,6 +186,13 @@ are created by a simple random draw from the data. Note that specification of \code{data.init} will start all \code{m} Gibbs sampling streams from the same imputation.} +\item{compact}{A logical value indicating whether the resulting \code{mids} +object should be stored in compact form. Only relevant if \code{tasks = 'train'}. +If \code{isTRUE(compact)}, training data, imputations and other data-specific +elements are removed from the resulting \code{mids} object. The +\code{store} element of the will be changed from \code{"train"} to +\code{"train.compact"}. The default is \code{compact = FALSE}.} + \item{\dots}{Named arguments that are passed down to the univariate imputation functions.} } @@ -388,7 +421,22 @@ imp$imp$bmi complete(imp) # imputation on mixed data with a different method per column -mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm")) +imp0 <- mice(nhanes2, meth = c("sample", "pmm", "logreg", "norm"), print = FALSE) + +# store all imputation models +imp1 <- mice(nhanes, tasks = "train", print = FALSE) +imp1$models$bmi[[1]] + +# Store model for `bmi`, estimate others as usual +tasks <- c("age" = "impute", "bmi" = "train", "hyp" = "impute", "chl" = "impute") +imp2 <- mice(nhanes, tasks = tasks, print = FALSE) + +# Inspects the stored model for imputation 1 for `bmi` +imp2$models$bmi[[1]] + +# Fill missing `bmi` values using pre-trained model +tasks <- c("age" = "impute", "bmi" = "fill", "hyp" = "impute", "chl" = "impute") +imp3 <- mice(nhanes, tasks = tasks, models = imp2$models, print = FALSE) \dontrun{ # example where we fit the imputation model on the train data diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index d54aa8997..45ade3cc9 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -4,7 +4,7 @@ \alias{mice.impute.logreg} \title{Imputation by logistic regression} \usage{ -mice.impute.logreg(y, ry, x, wy = NULL, ...) +mice.impute.logreg(y, ry, x, wy = NULL, task = "impute", model = NULL, ...) } \arguments{ \item{y}{Vector to be imputed} @@ -20,6 +20,17 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} + +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} + \item{...}{Other named arguments.} } \value{ diff --git a/man/mice.impute.norm.Rd b/man/mice.impute.norm.Rd index 945ffce95..5375dae55 100644 --- a/man/mice.impute.norm.Rd +++ b/man/mice.impute.norm.Rd @@ -5,7 +5,16 @@ \alias{norm} \title{Imputation by Bayesian linear regression} \usage{ -mice.impute.norm(y, ry, x, wy = NULL, ...) +mice.impute.norm( + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + ridge = 1e-05, + ... +) } \arguments{ \item{y}{Vector to be imputed} @@ -21,6 +30,24 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} + +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} + +\item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent +problems with multicollinearity. The default is \code{ridge = 1e-05}, +which means that 0.01 percent of the diagonal is added to the cross-product. +Larger ridges may result in more biased estimates. For highly noisy data +(e.g. many junk variables), set \code{ridge = 1e-06} or even lower to +reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} + \item{...}{Other named arguments.} } \value{ diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 394f98a5a..702586781 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -10,13 +10,17 @@ mice.impute.pmm( ry, x, wy = NULL, - donors = 5L, - matchtype = 1L, + task = "impute", + model = NULL, exclude = NULL, - quantify = TRUE, trim = 1L, + quantify = TRUE, ridge = 1e-05, + matchtype = 1L, + donors = 5L, + nbins = NULL, use.matcher = FALSE, + mlocal = 1L, ... ) } @@ -34,32 +38,30 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} -\item{donors}{The size of the donor pool among which a draw is made. -The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects -the closest match, but is not recommended. Values between 3L and 10L -provide the best results in most cases (Morris et al, 2015).} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} -\item{matchtype}{Type of matching distance. The default choice -(\code{matchtype = 1L}) calculates the distance between -the \emph{predicted} value of \code{yobs} and -the \emph{drawn} values of \code{ymis} (called type-1 matching). -Other choices are \code{matchtype = 0L} -(distance between predicted values) and \code{matchtype = 2L} -(distance between drawn values).} +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} \item{exclude}{Dependent values to exclude from the imputation model and the collection of donor values} +\item{trim}{Scalar integer. Minimum number of observations required in a +category in order to be considered as a potential donor value. +Relevant only of \code{y} is a factor.} + \item{quantify}{Logical. If \code{TRUE}, factor levels are replaced by the first canonical variate before fitting the imputation model. If false, the procedure reverts to the old behaviour and takes the integer codes (which may lack a sensible interpretation). Relevant only of \code{y} is a factor.} -\item{trim}{Scalar integer. Minimum number of observations required in a -category in order to be considered as a potential donor value. -Relevant only of \code{y} is a factor.} - \item{ridge}{The ridge penalty used in \code{.norm.draw()} to prevent problems with multicollinearity. The default is \code{ridge = 1e-05}, which means that 0.01 percent of the diagonal is added to the cross-product. @@ -67,12 +69,38 @@ Larger ridges may result in more biased estimates. For highly noisy data (e.g. many junk variables), set \code{ridge = 1e-06} or even lower to reduce bias. For highly collinear data, set \code{ridge = 1e-04} or higher.} +\item{matchtype}{Type of matching distance. The default (recommended) choice +(\code{matchtype = 1L}) calculates the distance between +the \emph{predicted} value of \code{yobs} and +the \emph{drawn} values of \code{ymis} (called type-1 matching). +Other choices are \code{matchtype = 0L} +(distance between predicted values) and \code{matchtype = 2L} +(distance between drawn values).} + +\item{donors}{The size of the donor pool among which a draw is made. +The default is \code{donors = 5L}. Setting \code{donors = 1L} always selects +the closest match, but is not recommended. Values between 5L and 10L +provide the best results (Morris et al, 2015). +For task \code{"train"}, the number of donors +is calculated internally based on the number of +observations in \code{yobs}.} + +\item{nbins}{The number of bins used to store the predictive mean matching +model. Under task \code{"train"}, the number of donors +is calculated internally based on the number of observations in \code{yobs} +and the number of unique predictive values.} + \item{use.matcher}{Logical. Set \code{use.matcher = TRUE} to specify the C function \code{matcher()}, the now deprecated matching function that -was default in versions -\code{2.22} (June 2014) to \code{3.11.7} (Oct 2020). Since version \code{3.12.0} -\code{mice()} uses the much faster \code{matchindex} C function. Use -the deprecated \code{matcher} function only for exact reproduction.} +was default in versions of \code{mice} prior to \code{3.12.0}.} + +\item{mlocal}{Experimental. Number of random imputations per missing values +generated from a fitted model under task \code{"train"}. +The default is 1. The \code{mlocal} parameter is different from \code{m}, +the number of multiple imputations, because it generates repeated +imputations from a single model. The \code{mlocal} parameter is useful +for large samples to reduce the computational burden, but still awaits +support within the mice algorithm.} \item{\dots}{Other named arguments.} } @@ -86,7 +114,6 @@ Imputation by predictive mean matching \details{ Imputation of \code{y} by predictive mean matching, based on van Buuren (2012, p. 73). The procedure is as follows: - \enumerate{ \item{Calculate the cross-product matrix \eqn{S=X_{obs}'X_{obs}}.} \item{Calculate \eqn{V = (S+{diag}(S)\kappa)^{-1}}, with some small ridge @@ -167,7 +194,7 @@ mice.impute.pmm(y, ry, x, trim = 2L) # in addition, eliminate category 20 mice.impute.pmm(y, ry, x, trim = 2L, exclude = 20) -# to get old behavior: as.integer(y)) +# to get old behavior (before mice v3.16.4): as.integer(y)) mice.impute.pmm(y, ry, x, quantify = FALSE) } \references{ @@ -211,7 +238,7 @@ Other univariate imputation functions: \code{\link{mice.impute.ri}()} } \author{ -Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn +Stef van Buuren } \concept{univariate imputation functions} \keyword{datagen} diff --git a/man/mice.impute.polr.Rd b/man/mice.impute.polr.Rd index 6fb48a826..6575c1be3 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -2,16 +2,21 @@ % Please edit documentation in R/mice.impute.polr.R \name{mice.impute.polr} \alias{mice.impute.polr} -\title{Imputation of ordered data by polytomous regression} +\title{Imputation of categorical data by the ordered logistic (polr) model} \usage{ mice.impute.polr( y, ry, x, wy = NULL, - nnet.maxit = 100, - nnet.trace = FALSE, - nnet.MaxNWts = 1500, + task = "impute", + model = NULL, + nnet.maxit = NULL, + nnet.MaxNWts = NULL, + maxit = NULL, + MaxNWts = NULL, + reltol = NULL, + warmstart = FALSE, polr.to.loggedEvents = FALSE, ... ) @@ -30,13 +35,35 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} -\item{nnet.maxit}{Tuning parameter for \code{nnet()}.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} -\item{nnet.trace}{Tuning parameter for \code{nnet()}.} +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} -\item{nnet.MaxNWts}{Tuning parameter for \code{nnet()}.} +\item{nnet.maxit}{Legacy parameter.} -\item{polr.to.loggedEvents}{A logical indicating whether each fallback +\item{nnet.MaxNWts}{Legacy parameter.} + +\item{maxit}{Tuning parameter for \code{nnet()}.} + +\item{MaxNWts}{Tuning parameter for \code{nnet()}. Internally, the procedure +computes the number of weights needed for the multinomial model as +100 + \code{ncol(x)} times \code{length(levels(y)) - 1L)}. +Use \code{MaxNWts} to override this default if you get the +“too many weights” error.} + +\item{reltol}{Convergence parameter for \code{nnet()}.} + +\item{warmstart}{Logical. If \code{TRUE}, the estimation process +uses weights from the previous iteration as warm starts.} + +\item{polr.to.loggedEvents}{A logical indicating whether each fall-back to the \code{multinom()} function should be written to \code{loggedEvents}. The default is \code{FALSE}.} @@ -47,28 +74,26 @@ Vector with imputed data, same type as \code{y}, and of length \code{sum(wy)} } \description{ -Imputes missing data in a categorical variable using polytomous regression +The function \code{mice.impute.polr()} imputes missing data in an +ordinal categorical variable using the proportional odds logistic +regression model (\code{polr}). This model, also known as the cumulative +link model, estimates cumulative probabilities using a set of +threshold parameters. The method assumes that the effect of predictor +variables is the same across all category transitions (proportional odds +assumption). } \details{ -The function \code{mice.impute.polr()} imputes for ordered categorical response -variables by the proportional odds logistic regression (polr) model. The -function repeatedly applies logistic regression on the successive splits. The -model is also known as the cumulative link model. - -By default, ordered factors with more than two levels are imputed by -\code{mice.impute.polr}. - The algorithm of \code{mice.impute.polr} uses the function \code{polr()} from the \code{MASS} package. In order to avoid bias due to perfect prediction, the algorithm augment the data according to the method of White, Daniel and Royston (2010). -The call to \code{polr} might fail, usually because the data are very sparse. +Calls to \code{polr} might fail if the data are very sparse. In that case, \code{multinom} is tried as a fallback. If the local flag \code{polr.to.loggedEvents} is set to TRUE, -a record is written -to the \code{loggedEvents} component of the \code{\link{mids}} object. +a record is written to the \code{loggedEvents} component of +the \code{\link{mids}} object. Use \code{mice(data, polr.to.loggedEvents = TRUE)} to set the flag. } \note{ @@ -83,10 +108,6 @@ Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} -Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of -multiple imputation strategies for the statistical analysis of incomplete -data sets.} Dissertation. Rotterdam: Erasmus University. - White, I.R., Daniel, R. Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. \emph{Computational Statistics and Data Analysis}, 54, 2267-2275. @@ -123,7 +144,7 @@ Other univariate imputation functions: \code{\link{mice.impute.ri}()} } \author{ -Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 +Stef van Buuren } \concept{univariate imputation functions} \keyword{datagen} diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index 738e07649..9dd93005d 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -9,9 +9,14 @@ mice.impute.polyreg( ry, x, wy = NULL, - nnet.maxit = 100, - nnet.trace = FALSE, - nnet.MaxNWts = 1500, + task = "impute", + model = NULL, + nnet.maxit = NULL, + nnet.MaxNWts = NULL, + maxit = NULL, + MaxNWts = NULL, + reltol = NULL, + warmstart = FALSE, ... ) } @@ -29,11 +34,33 @@ model is fitted. The \code{ry} generally distinguishes the observed \item{wy}{Logical vector of length \code{length(y)}. A \code{TRUE} value indicates locations in \code{y} for which imputations are created.} -\item{nnet.maxit}{Tuning parameter for \code{nnet()}.} +\item{task}{Character string. The task to be performed. Can +be \code{"impute"}, \code{"train"} or \code{"fill"}. +The default is \code{"impute"} (classic MICE). See \code{mice()} for +details.} -\item{nnet.trace}{Tuning parameter for \code{nnet()}.} +\item{model}{An environment created by a parent to store the imputation +model setup and estimates. The model is stored in the \code{mids} object +under tasks \code{"train"}, and is needed as input +for task \code{"fill"}. The object \code{model} is not used under +task \code{"impute"}.} -\item{nnet.MaxNWts}{Tuning parameter for \code{nnet()}.} +\item{nnet.maxit}{Legacy parameter.} + +\item{nnet.MaxNWts}{Legacy parameter.} + +\item{maxit}{Tuning parameter for \code{nnet()}.} + +\item{MaxNWts}{Tuning parameter for \code{nnet()}. Internally, the procedure +computes the number of weights needed for the multinomial model as +100 + \code{ncol(x)} times \code{length(levels(y)) - 1L)}. +Use \code{MaxNWts} to override this default if you get the +“too many weights” error.} + +\item{reltol}{Convergence parameter for \code{nnet()}.} + +\item{warmstart}{Logical. If \code{TRUE}, the estimation process +uses weights from the previous iteration as warm starts.} \item{...}{Other named arguments.} } @@ -43,15 +70,13 @@ Vector with imputed data, same type as \code{y}, and of length } \description{ Imputes missing data in a categorical variable using polytomous regression +for unordered factors. } \details{ The function \code{mice.impute.polyreg()} imputes categorical response variables by the Bayesian polytomous regression model. See J.P.L. Brand (1999), Chapter 4, Appendix B. -By default, unordered factors with more than two levels are imputed by -\code{mice.impute.polyreg()}. - The method consists of the following steps: \enumerate{ \item Fit categorical response as a multinomial model diff --git a/man/mids.Rd b/man/mids.Rd index ee33b1cc7..b0fe71748 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -23,6 +23,8 @@ mids( modeltype = character(), post = character(), blots = list(), + tasks = character(), + models = new.env(), ignore = logical(), seed = integer(), iteration = integer(), @@ -32,7 +34,8 @@ mids( chainVar = list(), loggedEvents = data.frame(), version = packageVersion("mice"), - date = Sys.Date() + date = Sys.Date(), + store = "impute" ) \method{plot}{mids}( @@ -153,6 +156,29 @@ to pass down arguments to lower level imputation function. The entries of element \code{blots[[blockname]]} are passed down to the function called for block \code{blockname}.} +\item{tasks}{A character vector specifying the task to perform for +each imputation block. The available options are: +\describe{ +\item{"impute"}{Estimate parameters, generates imputations and store +the original data plus imputations, but not the imputation model +(classic MICE behavior).} +\item{"train" }{Estimate parameters, generate imputations and +store the original data, the imputations and the imputation model.} +\item{"fill"}{Apply a previously trained imputation model to fill +imputations, without re-estimating parameters.} +} +This argument can be specified as a named vector, where names correspond +to variables and values specify the task for each variable. If a +single value is provided, it applies to the variables in all blocks. The +length of the vector must match the number of variables present in the +blocks. The default is \code{"impute"}.} + +\item{models}{A list that stores fitted imputation models. The models +can be used to impute missing values in new data. \code{models} is +only returned if \code{tasks = 'train'}. Use \code{models = trained$models} +to fill in missing values in new data, where \code{trained} is the +\code{mids} object returned by \code{mice(..., tasks = 'train')}.} + \item{ignore}{A logical vector of \code{nrow(data)} elements indicating which rows are ignored when creating the imputation model. The default \code{NULL} includes all rows that have an observed value of the variable @@ -182,6 +208,8 @@ generator alone.} \item{date}{Calculated field} +\item{store}{Calculated field} + \item{x}{An object of class \code{mids}} \item{y}{A formula that specifies which variables, stream and iterations are plotted. @@ -258,6 +286,9 @@ identified by its name, so list names must correspond to block names.} with commands for post-processing.} \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} function.} +\item{\code{tasks}:}{A character vector of length \code{length(blocks)}.} +\item{\code{models}:}{The \code{models} list contains imputation model +estimates.} \item{\code{ignore}:}{A logical vector of length \code{nrow(data)} indicating the rows in \code{data} used to build the imputation model. (new in \code{mice 3.12.0})} \item{\code{seed}:}{The seed value of the solution.} @@ -271,20 +302,21 @@ Note that observed data are not present in this mean.} \item{\code{chainVar}:}{An array with similar structure as \code{chainMean}, containing the variance of the imputed values.} \item{\code{loggedEvents}:}{A \code{data.frame} with five columns -containing warnings, corrective actions, and other inside info.} +containing warnings, corrective tasks, and other inside info.} \item{\code{version}:}{Version number of \code{mice} package that created the object.} \item{\code{date}:}{Date at which the object was created.} +\item{\code{store}:}{A string, indicating the type of mids object.} } } \section{LoggedEvents}{ The \code{loggedEvents} entry is a matrix with five columns containing a -record of automatic removal actions. It is \code{NULL} is no action was +record of automatic removal tasks. It is \code{NULL} is no record was made. At initialization the program removes constant variables, and removes variables to cause collinearity. -During iteration, the program does the following actions: +During iteration, the program does the following tasks: \itemize{ \item One or more variables that are linearly dependent are removed (for categorical data, a 'variable' corresponds to a dummy variable) @@ -343,6 +375,8 @@ imp <- mids( formulas = list(a = a ~ b, b = b ~ a), post = NULL, blots = NULL, + tasks = NULL, + models = NULL, ignore = logical(nrow(data)), seed = 123, iteration = 1, diff --git a/man/pmm.match.Rd b/man/pmm.match.Rd deleted file mode 100644 index d4404115f..000000000 --- a/man/pmm.match.Rd +++ /dev/null @@ -1,49 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/mice.impute.pmm.R -\name{.pmm.match} -\alias{.pmm.match} -\title{Finds an imputed value from matches in the predictive metric (deprecated)} -\usage{ -.pmm.match(z, yhat = yhat, y = y, donors = 5, ...) -} -\arguments{ -\item{z}{A scalar containing the predicted value for the current case -to be imputed.} - -\item{yhat}{A vector containing the predicted values for all cases with an observed -outcome.} - -\item{y}{A vector of \code{length(yhat)} elements containing the observed outcome} - -\item{donors}{The size of the donor pool among which a draw is made. The default is -\code{donors = 5}. Setting \code{donors = 1} always selects the closest match. Values -between 3 and 10 provide the best results. Note: This setting was changed from -3 to 5 in version 2.19, based on simulation work by Tim Morris (UCL).} - -\item{\dots}{Other parameters (not used).} -} -\value{ -A scalar containing the observed value of the selected donor. -} -\description{ -This function finds matches among the observed data in the predictive -mean metric. It selects the \code{donors} closest matches, randomly -samples one of the donors, and returns the observed value of the -match. -} -\details{ -This function is included for backward compatibility. It was -used up to \code{mice 2.21}. The current \code{mice.impute.pmm()} -function calls the faster \code{C} function \code{matcher} instead of -\code{.pmm.match()}. -} -\references{ -Schenker N & Taylor JMG (1996) Partially parametric techniques -for multiple imputation. \emph{Computational Statistics and Data Analysis}, 22, 425-446. - -Little RJA (1988) Missing-data adjustments in large surveys (with discussion). -\emph{Journal of Business Economics and Statistics}, 6, 287-301. -} -\author{ -Stef van Buuren -} diff --git a/man/scan.data.Rd b/man/scan.data.Rd new file mode 100644 index 000000000..21faf41c1 --- /dev/null +++ b/man/scan.data.Rd @@ -0,0 +1,49 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\name{scan.data} +\alias{scan.data} +\title{Scan data types and compare to model expectations} +\usage{ +scan.data(data, models, print = FALSE) +} +\arguments{ +\item{data}{A \code{data.frame} to be checked.} + +\item{models}{A list of trained models from \code{mice()}.} + +\item{print}{Logical, whether to print the resulting table.} +} +\value{ +A \code{data.frame} with one row per variable (from either \code{data} or \code{models}) +and columns summarizing compatibility diagnostics. The following columns are included: + +\tabular{ll}{ +\code{variable} \tab Variable name \cr +\code{in_data} \tab Logical: whether variable is present in \code{data} \cr +\code{in_model} \tab Logical: whether a trained model is available \cr +\code{data_class} \tab Class of the variable in \code{data} (e.g., \code{"factor"}, \code{"ordered"}) \cr +\code{model_class} \tab model class according to the trained model \cr +\code{class_match} \tab \code{TRUE} if \code{data_class} matches \code{model_class}, \code{FALSE} otherwise \cr +\code{levels_match} \tab \code{TRUE} if factor levels exactly match, \code{FALSE} if they differ, \code{NA} if not applicable \cr +\code{pred_match} \tab \code{TRUE} if all predictors used by the model are present in \code{data}, \code{FALSE} if any are missing, \code{NA} if unknown \cr +\code{can_fill} \tab \code{TRUE} if there is a model, if classes match, if levels match and if predictor match, otherwise \code{FALSE} \cr +} +} +\description{ +This function compares the structure and type of \code{data} to what is expected +from the trained \code{models}. It reports differences in class, levels, and predictor +availability, useful to prepare for imputation or prediction. +} +\examples{ +# Train model on boys data +imp <- mice(boys, tasks = "train", m = 1, maxit = 1, print = FALSE) + +# Create a new dataset with missing values and mismatched types +# remove ordering +data <- boys[1:3, ] +data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) + +# Run scan +scan.data(data, imp$models) + +} diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R new file mode 100644 index 000000000..c12bc5ab8 --- /dev/null +++ b/tests/testthat/test-data.R @@ -0,0 +1,61 @@ +context("scan.data, make.data, coerce.data") + +set.seed(123) +df <- data.frame( + factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), + factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), + factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), + logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), + logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), + numeric1 = rnorm(20) +) +n <- prod(dim(df)) +na_count <- round(0.3 * n) +missing_idx <- arrayInd(sample(n, na_count), .dim = dim(df)) +for (i in seq_len(nrow(missing_idx))) { + df[missing_idx[i, 1], missing_idx[i, 2]] <- NA +} + +expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) + +# make single-row new data with correct type +newdata <- make.data(models = trained$models, vars = names(df)) + +# run scan on newdata +result1 <- scan.data(data = newdata, models = trained$models) + +test_that("scan.data() sets can_fill to Y for correctly typed newdata", { + expect_true(length(result1$can_fill) > 0) + expect_true(all(result1$can_fill)) +}) + +# coerce newdata (not needed here) +coerced1 <- coerce.data(data = newdata, models = trained$models) + +test_that("coerce.data() does not alter when it has correct type", { + expect_true(identical(coerced1, newdata)) +}) + + +# single-row new data, wrong types +newdata <- data.frame( + factor2 = NA, + factor3 = NA, + factor4 = NA, + logical1 = NA, + logical2 = NA, + numeric1 = NA) + +result2 <- scan.data(data = newdata, models = trained$models) + +test_that("scan.data() reports that it cannot fill all variables", { + expect_false(all(result2$can_fill)) +}) + +coerced2 <- coerce.data(data = newdata, models = trained$models) + +test_that("coerce.data() can coerces wrong to correct types", { + expect_false(identical(attr(result2, "data"), newdata)) + expect_true(identical(coerced1, coerced2)) +}) + diff --git a/tests/testthat/test-remove.lindep.R b/tests/testthat/test-internals.R similarity index 80% rename from tests/testthat/test-remove.lindep.R rename to tests/testthat/test-internals.R index 146c298c0..56d6c9061 100644 --- a/tests/testthat/test-remove.lindep.R +++ b/tests/testthat/test-internals.R @@ -1,4 +1,14 @@ -context("remove.lindep") +context("Internals: sanitize.vec()") + +x <- c(1, 0, 1) +test_that("converts to appropriate type", { + expect_is(mice:::sanitize.vec(x, y = c(TRUE, FALSE)), "logical") + expect_is(mice:::sanitize.vec(x, y = factor(c("a", "b", "a"))), "factor") + expect_is(mice:::sanitize.vec(x, y = ordered(c("low", "high"))), "ordered") + expect_is(mice:::sanitize.vec(x, y = rnorm(5)), "numeric") +}) + +context("Internals: remove.lindep()") set.seed(1) td <- matrix(rnorm(20), nrow = 5, ncol = 4) diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index c68b43d88..8573297de 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -50,3 +50,5 @@ perfectPred <- tryCatch( test_that("Complete separation results in same class as well behaved case", { expect_true(all.equal(class(wellBehaved), class(perfectPred))) }) + + diff --git a/tests/testthat/test-mice.impute.pmm.R b/tests/testthat/test-mice.impute.pmm.R index 33fbb74c6..ec7355db6 100644 --- a/tests/testthat/test-mice.impute.pmm.R +++ b/tests/testthat/test-mice.impute.pmm.R @@ -112,3 +112,4 @@ test_that("cancor with many junk variables does not crash", { expect_warning(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, maxit = 1, m = 1, seed = 1, print = FALSE)) }) + diff --git a/tests/testthat/test-models.R b/tests/testthat/test-models.R new file mode 100644 index 000000000..1317f975e --- /dev/null +++ b/tests/testthat/test-models.R @@ -0,0 +1,9 @@ +context("models") + +trained <- mice(nhanes2, m = 2, maxit = 1, print = FALSE, tasks = "train", seed = 1) +models_env <- import.models.env(trained$models) +models_list <- export.models.env(models_env) + +test_that("list and environment representation have equal size", { + expect_identical(object.size(trained$models), object.size(models_list)) +}) diff --git a/tests/testthat/test-newdata.R b/tests/testthat/test-newdata.R index 0ef6aa419..7088192cd 100644 --- a/tests/testthat/test-newdata.R +++ b/tests/testthat/test-newdata.R @@ -1,5 +1,13 @@ context("mice.mids: newdata") +# SvB 20250328 +# This file contains tests for the newdata argument in mice.mids +# using a newdata argument +# +# This method is superseded by the tasks = "train"/"fill" arguments +# in mice(), but is retained here for backwards compatibility, and +# for methods that do not support the tasks argument. + # Check that mice.mids correctly appends the newdata to the # existing mids object init0 <- mice(nhanes, maxit = 0, m = 1, print = FALSE, seed = 1) diff --git a/tests/testthat/test-quantify.R b/tests/testthat/test-quantify.R new file mode 100644 index 000000000..572485a86 --- /dev/null +++ b/tests/testthat/test-quantify.R @@ -0,0 +1,71 @@ +test_that("quantify() and unquantify() work correctly for factors", { + set.seed(123) + y <- factor(sample(c("A", "B", "C"), 10, replace = TRUE), levels = c("A", "B", "C")) + x <- matrix(rnorm(10 * 3), ncol = 3) + ry <- sample(c(TRUE), 10, replace = TRUE) + + # Quantify the factor (optimal scaling) + f <- mice:::quantify(y, ry, x) + ynum_quantified <- f$ynum + y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_quantified) + + # Integer coding + f <- mice:::quantify(y, ry, x, quantify = FALSE) + ynum_quantified <- f$ynum + y_reconstructed_integer <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_integer) + + # Test 1: Levels should remain in the original order + expect_equal(levels(y_reconstructed_quantified), levels(y)) + expect_equal(levels(y_reconstructed_integer), levels(y)) + + # Test 2: Factor reconstruction should match original + expect_equal(y_reconstructed_quantified, y) + expect_equal(y_reconstructed_integer, y) + + # Handle missing values, with extra level + y_with_na <- y + y_with_na[c(2, 5)] <- NA + ry[c(2,5)] <- FALSE + + f <- mice:::quantify(y_with_na, ry, x) + ynum_quantified_na <- f$ynum + y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, quant = f$quant, labels = f$labels) + expect_equal(y_with_na, y_reconstructed_na) + + expect_true(is.na(y_reconstructed_na[2])) + expect_true(is.na(y_reconstructed_na[5])) +}) + +test_that("quantify() and unquantify() work correctly for numeric variables", { + set.seed(123) + y <- rnorm(10) + x <- matrix(rnorm(10 * 3), ncol = 3) + ry <- sample(c(TRUE), 10, replace = TRUE) + + # Pass through a numeric variable + f <- mice:::quantify(y, ry, x) + ynum_quantified <- f$ynum + y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_quantified) + + # Pass through, integer coding + f <- mice:::quantify(y, ry, x, quantify = FALSE) + ynum_quantified <- f$ynum + y_reconstructed_integer <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + expect_equal(y, y_reconstructed_integer) + + # Handle missing values, with extra level + y_with_na <- y + y_with_na[c(2, 5)] <- NA + ry[c(2,5)] <- FALSE + + f <- mice:::quantify(y_with_na, ry, x) + ynum_quantified_na <- f$ynum + y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, quant = f$quant, labels = f$labels) + expect_equal(y_with_na, y_reconstructed_na) +}) + + + diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R new file mode 100644 index 000000000..7737fc70b --- /dev/null +++ b/tests/testthat/test-tasks.R @@ -0,0 +1,29 @@ +context("tasks") + +test_that("m filling recycles training models", { + expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) + expect_false(is.null(imp1$models$bmi[[1]]$lookup)) + expect_silent(imp2 <- mice(nhanes2, m = 4, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) +}) + +test_that("fully synthetic datasets can be created from completely observed variables", { + dataset <- complete(mice(nhanes2, m = 1, maxit = 1, method = "pmm", print = FALSE)) + expect_silent(imp1 <- mice(dataset, m = 2, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) + expect_false(is.null(imp1$models$age[[1]]$lookup)) + expect_silent(imp2 <- mice(dataset, where = make.where(dataset, "all"), m = 2, maxit = 3, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + synt1 <- complete(imp2, 1) + synt2 <- complete(imp2, 2) +}) + +test_that("the procedure informs the user about a mismatch between model and data", { + expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) + newdata <- nhanes2 + newdata$age <- factor(newdata$age, levels = c(levels(newdata$age), "not_a_level")) + newdata$age[1] <- "not_a_level" + newdata$age[2] <- NA + expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) + newdata <- nhanes2 + levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" + expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) +}) diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 000000000..075b2542a --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/vignettes/_imputation_models.qmd b/vignettes/_imputation_models.qmd new file mode 100644 index 000000000..1b0e8bb24 --- /dev/null +++ b/vignettes/_imputation_models.qmd @@ -0,0 +1,522 @@ +--- +title: "Imputation Models in MICE" +author: "Stef van Buuren" +date: "`r Sys.Date()`" +format: + html: + theme: sandstone + highlight-style: github + number-sections: true + embed-resources: true + toc: true + toc-depth: 3 +bibliography: references.bib +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +## Example + +Suppose you created a risk prediction model for a cohort of patients. The dataset contained missing values, which you imputed using MICE. Now, you want to implement the risk prediction model in clinical practice to assist decision-making for new patients. Since missing values are expected in the new patient data, how should you proceed? + +Clearly, you **cannot retrain the imputation model on the new patients**. The number of new patients may be too small to support proper training, and a new imputation model would likely produce different missing value estimates. These differences raise concerns about the consistency and comparability of the risk prediction model. + +A better approach is to apply the **same imputation model** trained on the original cohort to fill in missing values for new patients. Previously, doing so required manual workarounds that were inefficient and difficult to implement. This vignette introduces a new functionality that lets you **store the imputation model and apply it seamlessly to new patients**, ensuring consistency and reproducibility. + +## Installation + +The new functionality is **currently experimental** and available in the `"tasks"` branch of the `mice` repository. To install this branch from GitHub, use: + +```{r eval=FALSE} +remotes::install_github("amices/mice", ref = "imputation_models") +``` + +## MICE Architecture + +The MICE algorithm follows a **two-level modular architecture**. At the first level, `mice()`, the core function in the `mice` package [@vanbuuren2011; @vanbuuren2018], **orchestrates the imputation process** by managing data preprocessing, variable selection, and iterative imputation steps. At the second level, **MICE applies elementary imputation methods**—such as normal imputation and PMM—to generate missing values based on model-specific assumptions. + +Since storing and reusing imputation models requires both managing the overall imputation process and adjusting the underlying imputation methods, modifications were made at both levels. + +We begin by exploring the **first level** of the MICE architecture, where the new `tasks` and `models` arguments modify the imputation workflow. + +Next, we examine the **second level**, where elementary imputation functions—such as `mice.impute.norm()` and `mice.impute.pmm()`—have been extended to support `tasks` and `models`. + +## New `tasks` and `model` Arguments + +The `mice()` function orchestrates the imputation process by **managing the imputation model and generating imputed values**. It first validates the input data and user-specified settings. It then initializes the imputation model, iterates elementary imputation methods, and returns a `mids` object containing the original dataset, model specifications, and imputed values. + +The `mids` object is central to the multiple imputation workflow, supporting **pooling, diagnostics, and visualization**. + +To enhance imputation flexibility, `mice()` introduces two new arguments: `tasks` and `models`, which we detail in the following sections. + +### Tasks + +The `tasks` argument in `mice()` is a character vector specifying the task to perform for each variable during imputation. The available options are: + +- `tasks = "impute"`: Estimates parameters, generates imputations, and stores the original data along with imputations—but not the imputation model. This corresponds to **classic MICE behavior** and is the default setting. +- `tasks = "train"`: Estimates parameters, generates imputations, and stores the original data, imputations, and imputation model. `tasks = "train"` saves the imputation model along with the data, allowing for both inspection and reuse. +- `tasks = "fill"`: Applies a stored imputation model to new data, generating imputations without re-estimating parameters. + +All `tasks` options produce `mids` objects, which can be used for **pooling, diagnostics, and visualization** as in standard MICE workflows. + +Next, we show practical examples of how to use the `tasks` argument. + +#### Example: `tasks = "impute"` + +Setting `tasks = "impute"` returns a `mids` object identical to the classic MICE implementation: it contains imputed values but **does not store the imputation model**. The following code generates a `mids` object using the built-in `nhanes` dataset: + +```{r} +library(mice, warn.conflicts = FALSE) +imputed <- mice(nhanes, tasks = "impute", seed = 1, print = FALSE) +class(imputed) +``` + +As in classic MICE, specifying `tasks = "impute"` applies this setting to all variables by expanding to: + +```{r} +imputed$tasks +``` + +`tasks = "impute"` behaves exactly like the classic `mice()` function, returning a `mids` object with imputed values only—without storing the imputation model. + + +#### Example: `tasks = "train"` + +To also store the imputation model, use `tasks = "train"` instead of `tasks = "impute"`: + +```{r} +trained <- mice(nhanes, tasks = "train", seed = 1, print = FALSE) +class(trained) +``` + +This time, the `mids` object contains both the imputed values and the imputation model. To confirm the difference, inspect the `store` component: + +```{r} +trained$store +``` + +The new models component in the `mids` object stores the imputation model for each variable and each imputation. A `mids` object with `store == "train"` can later be used to generate imputations for new data. + +Before applying a stored model, let’s highlight two key differences between `store == "impute"` and `store == "train"`: + +- `store == "train"`: The `mids` object includes the trained imputation model in the `models` component. +- `store == "impute"`: No model is stored. + +Depending on the imputation method, the stored models may differ slightly. For parametric methods, storing the model requires saving formulas, estimated coefficients, and metadata such as factor levels. For non-parametric methods like PMM, storing the model involves saving observed values instead of estimated parameters. + +#### Example: `tasks = "fill"` + +Training creates a **transferable representation** of the imputation model, allowing it to be reused on new data **without altering the original model or re-estimating parameters**. To apply a stored model, use `tasks = "fill"` in `mice()`, along with the `models` argument to specify the trained model. + +The following code demonstrates how to use a trained model to fill missing values in new data: + +```{r} +newdata <- data.frame(age = c(2, 1), bmi = c(NA, NA), chl = c(NA, 190), hyp = c(NA, 1)) +filled <- mice(newdata, tasks = "fill", models = trained$models, seed = 1, print = FALSE) +filled$store +filled$data +filled$imp +complete(filled, 2) +``` + +Note the use of both `tasks = "fill"` (to apply the stored model) and `models = trained$models` (to provide the trained imputation model). + +- `filled$store` will be set to `"fill"` if all missing values are imputed using the stored model. +- `filled$data` holds the new dataset with missing values, while `filled$imp` stores the imputations. + +The `complete()` function can be used to extract the multiply-imputed datasets. The example displays the imputed values for the second imputation. + +### Compact Representation of the Imputation Model + +The `compact` argument controls the storage of the trained model. When `compact = TRUE`, the imputation model is stored in a **minimized form**, without retaining the training data used to estimate the model. This form retains all necessary parameters for generating imputed values while excluding training data, making it suitable for **exchange, distribution, and production**. + +```{r} +# Train a compact imputation model (no training data stored) +trained_compact <- mice(nhanes, tasks = "train", compact = TRUE, seed = 1, print = FALSE) +trained_compact$store + +# No training data is stored in the compact model +trained_compact$data +trained_compact$imp + +# But we can still use the compact model to fill missing values in new data +filled_compact <- mice(newdata, tasks = "fill", models = trained_compact$models, seed = 1, print = FALSE) +filled_compact$store +complete(filled_compact, 2) +``` + +Since compact models do not store training data, the `data` and `imp` components remain empty. + +```{r} +# Attempting to complete a compact model without training data will return an error +try(complete(trained_compact)) +``` + +Running `complete()` requires stored imputations to reconstruct datasets, which are unavailable in compact models. As a result, `complete()` does not work when `store == "train_compact"`. + +### Comparison of `tasks = "train"`, `"train_compact"`, and `"fill"` + +| Feature | `tasks = "train"` | `tasks = "train", compact = TRUE` | `tasks = "fill"` | +|--------------------------|------------------|----------------------------------|------------------| +| **Stores imputations?** | ✅ Yes | ❌ No | ✅ Yes (on new data) | +| **Stores imputation model?** | ✅ Yes | ✅ Yes | ❌ No | +| **Stores training data?** | ✅ Yes | ❌ No | ❌ No | +| **Can generate new imputations?** | ✅ Yes | ✅ Yes | ✅ Yes | +| **Requires `models` argument?** | ❌ No | ❌ No | ✅ Yes (to specify trained model) | +| **`complete()` works?** | ✅ Yes | ❌ No | ✅ Yes | +| **Typical use case** | Storing full imputation model and training data | Creating a lightweight imputation model for sharing or production | Applying a trained imputation model to new data | + +#### Explanation of Key Differences +- **`tasks = "train"`**: Stores **everything**, including the original dataset, the imputation model, and all imputations. +- **`tasks = "train", compact = TRUE`**: Stores **only the imputation model**, making it **lightweight** but preventing the use of `complete()`. +- **`tasks = "fill"`**: Uses a previously stored model to generate **new imputations** but does **not** store the model itself. + +### Train and Fill Subsets of Variables + +Imputation models can be trained on a **subset** of variables rather than the entire dataset. This allows missing values in those variables to be filled using the trained model, while other variables are imputed using standard MICE methods. + +The following fragment specifies a vector `tasks = c("bmi", "hyp")` to train the imputation model **only for `bmi` and `hyp`**: + +```{r} +tasks <- make.tasks(nhanes) +tasks[c("bmi", "hyp")] <- "train" +trained_subset <- mice(nhanes, tasks = tasks, seed = 1, print = FALSE) +names(trained_subset$models) +``` + +Now apply the subset model to fill missing values in `newdata`: + +```{r} +tasks[c("bmi", "hyp")] <- c("fill", "fill") +filled_subset <- mice(newdata, tasks = tasks, models = trained_subset$models, seed = 1, print = FALSE) +complete(filled_subset) +``` + +Row 2 was successfully imputed using the trained model for `bmi` and `hyp`. However, imputation fails for row 1 because `mice()` cannot build an imputation model for `chl` (which was not part of the trained subset). + +`mice()` checks each variable for constant or collinear values before building an imputation model. In this case, `chl` is constant (only one value exists), so `mice()` fails to construct an imputation model, leading to missing values propagating to `bmi` and `hyp`. However, variables explicitly set to `"fill"` (`bmi` and `hyp`) do not require a model to be built, so they bypass this check. + +There are two ways to address this issue: + +- Include `chl` in the training subset, effectively training models for all variables. +- Append additional records to `newdata` so that `mice()` can generate an on-the-fly imputation model for `chl`. This approach is shown below: + +```{r} +newdata_append <- rbind(newdata, nhanes[11:20, ]) +filled_subset <- mice(newdata_append, tasks = tasks, models = trained_subset$models, seed = 1, print = FALSE) +complete(filled_subset, 2)[1:2, ] +``` + +Here, 10 records from `nhanes` are appended to `newdata`. This allows `mice()` to train an imputation model for `chl` on the extra data, while still applying the trained model for `bmi` and `hyp`. + +Final observations: + +- When a combination of training and filling is used, the resulting mids object is stored with `store = "train"`. +- Appendix A contains the full specification of `mids` components for store modes `"impute"`, `"train"`, and `"fill"`. + +## Elementary Imputation Functions + +This section explains how `mice.impute.norm()`, `mice.impute.pmm()`, and other `mice.impute.*()` functions have been **adapted to support `tasks` and `models`**, allowing for reusable imputation models and enhanced flexibility in workflows. + +`mice.impute.*()` functions generate imputed values for **individual variables** (univariate imputation) or **groups of variables** (multivariate imputation). `mice()` automatically calls `mice.impute.*()` functions to handle variable-wise and block-wise imputation. + +This section is primarily relevant for developers who wish to extend the `mice` package with new imputation methods. + +### Normal Imputation + +The `mice.impute.norm()` function is responsible for the following tasks: + +1. Verify whether an imputation model exists before proceeding. +2. For `task = "fill"`: Retrieve the stored imputation model, generate imputed values, and return them. +3. Estimate imputation model parameters. +4. For `task = "train"`: Store the imputation model for later use. +5. Generate and return imputed values. + +#### Example: `mice.impute.norm()` + +Let us examine how these actions are implemented in `mice.impute.norm()` for normal imputation: + +```{r} +mice::mice.impute.norm +``` + +The function argument list introduces two new arguments: + +- `model`: An environment that contains the imputation model. +- `task`: Specifies the task to perform (`"impute"`, `"train"`, or `"fill"`). + +The function body clearly separates different tasks: + +1. Check model availability: `check.model.exists(model, task)` +2. Fill missing values `(task = "fill")`: Retrieve the stored model and generate imputations. +3. Estimate parameters: `.norm.draw(y, ry, x, ridge = ridge, ...)` estimates imputation parameters based on the available data. +4. Train and store model `(task = "train")`: Save the trained imputation model. +5. Return imputed values: `return(x[wy, ], ...)`. + +All `mice.impute.*()` functions follow this pattern. + +Model storage and retrieval: The `model` object is an environment that stores imputation parameters. During training, each iteration updates the imputation model by overwriting previous estimates with new ones. After the final iteration, the model is stored as a list in the `models` component of the `mids` object. When `mice()` imports a trained model, it reconstructs this list back into a nested environment—one for each variable and each imputation. + +### Predictive Mean Matching (PMM) + +The `mice.impute.pmm()` function performs the same core tasks as `mice.impute.norm()`. However, PMM is a **semi-parametric imputation method** [@little1988] that requires additional steps to ensure imputations remain consistent with the standard algorithm while enabling imputations without access to the original training dataset. + +To store the imputation model, we use **percentile binning**, which divides the linear predictor into equally sized bins and assigns donor values to each bin. + +#### Steps in `mice.impute.pmm()` + +1. Estimate the imputation model parameters. +2. Calculate the linear predictor for all cases. +3. For each case with a missing outcome: + - Identify the $k$ closest observed cases based on the linear predictor. + - Randomly select one of these $k$ donor values as the imputed value. + +While parameter estimation follows a similar approach to `mice.impute.norm()`, PMM requires an efficient way to store donor values. Instead of storing the full training dataset, we create **equally sized bins** based on the linear predictor. At each bin threshold, we store the $k$ closest donor values. + +When imputing a new case, the function: + +1. Identifies the bin it belongs to by locating its left and right bin thresholds. +2. Draws a donor value from the $k$ closest observed values within the left or right bin. +3. Weighs donor selection based on the case's **relative distance** to the bin thresholds. + +This approach is **memory-efficient**, handles **gaps in the linear predictor**, and allows for **fast and accurate imputations** based on the choice of bins ($t$) and donors ($k$). + +#### Model Storage for PMM + +The `mice.impute.pmm()` function stores the following components in the `model` object: + +- `edges`: A vector of length $t + 1$ defining the bin thresholds. +- `lookup`: A matrix of size $t \times k$ containing donor values for each bin. + +Appendix B describes four internal helper functions: + +- `initialize.nbins()`: Sets the number of bins $t$. +- `initialize.donors()`: Sets the number of donors $k$. +- `bin.yhat()`: Divides the linear predictor into bins using `edges`. +- `draw.neighbors.pmm()`: Selects imputed values from the `lookup` table. + +During training, all four functions are used. However, filling only needs `draw.neighbors.pmm()`. + +#### Experimental Aspects of $t$ and $k$ + +The methods for determining $t$ and $k$ in `initialize.nbins()` and `initialize.donors()` are **preliminary and based on limited simulations**. + +The default values for $t$ (typically 15–30 bins) and $k$ (typically 5–15 donors) were selected based on a small simulation study that varied only **sample size**. The study assumed an imputation model with approximately 10% explained variance. However, because optimal values for $t$ and $k$ likely depend on prediction error, these defaults may not be suitable for other models. Although some work is available in adaptive tuning [@schenker1996], optimizing $t$ and $k$ for different levels of prediction error remains an open research question. + +### Structure of the `models` Component + +When `tasks = "train"`, the `mice()` function stores the imputation model in the `models` component of the `mids` object. The `models` component is a list containing the **setup and estimates** of the imputation model for each variable and each repeated imputation. Since there are `m` imputations and `ncol(data)` variables, there can be **up to `m * ncol(data)` imputation models** in total. + +The `models` component is organized **by variable and imputation number**, allowing each variable's imputation process to be stored separately across imputations. This structure ensures that stored models can be reused for generating imputations on new data. + +The following diagram visualizes the hierarchical structure of the `models` component: + +```{mermaid} +graph TD; + A[mids] --> B[models] + B --> C[bmi] + B --> D[hyp] + B --> E[chl] + C --> F[Imputation 1] + C --> G[Imputation 2] + D --> H[Imputation 1] + D --> I[Imputation 2] + E --> J[Imputation 1] + E --> K[Imputation 2] +``` + +The names of the parts of the first imputation model for `bmi` are: + +```{r models-levels} +names(trained$models$bmi[[1]]) +``` + +In general, the names and types of stored objects depend on the imputation method. The `setup` object for the first imputation model for `bmi` can be accessed as follows: + +```{r} +unlist(trained$models$bmi[[1]]$setup) +``` + +It contains settings specific to the imputation model. We can retrieve the `formula` used in the model as: + +```{r} +trained$models$bmi[[1]]$formula +``` + +which shows that `bmi` is imputed using a linear combination of `age`, `hyp`, and `chl`. We obtain the least squares estimates for the regression weights by: + +```{r} +trained$models$bmi[[1]]$beta.hat +``` + +However, the actual imputation model does not use these weights directly; instead, it draws randomly from a distribution that accounts for parameter uncertainty [@rubin1987]. The drawn values can be accessed as: + +```{r} +trained$models$bmi[[1]]$beta.dot +``` + +For small samples or highly collinear predictors, `beta.hat` and `beta.dot` can differ substantially—a phenomenon sometimes called ‘bouncing betas’, where parameter estimates fluctuate across imputations. To ensure "proper imputation", MICE uses beta draws (`beta.dot`) rather than fixed estimates (`beta.hat`). + +### Additional Components for PMM + +For PMM, two additional objects are stored in `models`: + +- `edges`: A vector of length $t + 1$ containing the bin thresholds $\theta_j$. +- `lookup`: A $t × k$ matrix storing the donor values for each bin. + +The bin thresholds in `edges` are located on: + +```{r} +trained$models$bmi[[1]]$edges +``` + +The `lookup` object is a matrix, where each row represents a bin threshold $\theta_j$ that segments the linear predictor. Observations with $\hat y$ values falling outside the bin range ($\hat y \leq \theta_1$ or $\hat y > \theta_t$) are assigned to the first or last bin, respectively. The `lookup` table for the first imputation model for `bmi` can be accessed as: + +```{r} +trained$models$bmi[[1]]$lookup +``` + +The `lookup` object is used in `draw.neighbors.pmm()` to select donor values for imputations. + +### Sharing the `models` List Component + +The `models` list component in `mice()` allows users to store trained imputation models and apply them to new datasets, ensuring consistent missing data handling without re-estimating parameters. + +Beyond reusability, `models` enhances **transparency** into MICE’s mechanics, serving as a diagnostic tool for refining imputation strategies. By inspecting stored models, users can evaluate imputation decisions, identify patterns, and adjust settings accordingly. + +Additionally, `models` encapsulates all essential elements needed to **standardize and share imputation workflows** across datasets. When combined with a structured codebook, users can create **reproducible imputation modules**, enabling them to share, apply, and publish standardized missing data solutions in different studies and applications. + +## Methodological Considerations + +### Number of Imputations `m` Used in `"train"` and `"fill"` + +Since we did not specify $m_\text{train}$ (the number of imputations for training) and $m_\text{fill}$ (the number of imputations for filling), both steps default to `m = 5`. Although the user can set both independently, we **recommend $m_\text{train} = m_\text{fill}$** to ensure consistency. + +If $m_\text{train} < m_\text{fill}$, the `mice()` function will **automatically recycle** trained models to generate additional imputations, without warning the user. When recycling, the imputations may exhibit too little variability, particularly in small samples, potentially leading to **underestimated downstream variability**. + +If $m_\text{train} > m_\text{fill}$, the `mice()` function will discard the extra imputations. In general, increasing $m_\text{fill}$ reduces Monte Carlo error and improves the stability of the between-imputation variance estimate. + +Despite its statistical drawbacks, many users prefer $m_\text{fill} = 1$ for its simplicity, as working with a single dataset is often more convenient. However, as @dempster1983 [p. 8] caution: + +>>> Imputation is seductive because it can lull the user into the pleasurable state of believing that the data are complete after all. + +A single completed dataset may be a **convenient fiction**, useful for various purposes such as obtaining population estimates, developing an imputation model, or estimating the most likely version of the hidden data values. However, a single imputed dataset fails to account for uncertainty inherent to the missing data, leading to **biased estimates and overconfident inferences** in downstream decision making. + +### Full Fill vs. Partial Fill + +The simplest workflow, known as **full fill**, trains the imputation model on all columns in the dataset. A full fill is useful when data is split by rows (**horizontal partitioning**) or when standardized imputations are needed across datasets with the same structure. In such cases, a model can be trained on one dataset partition and then applied to new datasets containing different records with the same type of variables. A variation on this approach is to train the model on a subset of rows to generate imputations for the remaining rows to save computation time. Another use case occurs when some variables included in the trained model are missing in a new dataset, and the goal is to impute only those missing variables. Since the model does not need to be retrained, imputations for new data can be generated almost instantly. + +However, when data is split into different subsets of columns (**vertical partitioning**), the trained model may not cover all variables. In these cases, **partial fill** can be used, where supported variables are imputed along with additional imputations for variables absent from the original model. + +A common example occurs in **longitudinal studies**, where data is collected at multiple time points. A model trained on data from an earlier time point can be used to impute missing values before extending the model to include variables collected at later time points—without requiring retraining on the full dataset. Another example is the **integration of data from different sources** without directly merging them. Suppose source **A** contains variables $X$ and $Y$, while source **B** contains $X$ and $Z$. Instead of combining these datasets into a single table, an imputation model can be sequentially extended under the assumption of conditional independence $Y \perp Z \mid X$. + +The process proceeds as follows: + +1. Train a model on source **B** to impute $Z$ from $X$. +2. Use this model to impute $Z$ in source **A**. +3. Store the final model for $X, Y$ and $Z$ for future use. + +This approach removes the need to merge sources **A** and **B** into a single dataset. Analysts can share trained models instead of exchanging raw data, thereby improving efficiency, adaptability, and data privacy while still utilizing all available information. + +A key assumption in this approach is **conditional independence** $Y \perp Z \mid X$, meaning that once we account for $X$, there is no remaining association between $Y$ and $Z$. In practice, this assumption may not always hold. If an additional data source **C** containing both $Y$ and $Z$ is available, models trained on source **C** can be incorporated to capture the direct relationship between $Y$ and $Z$, improving the accuracy of imputations. + +### Store + +The `store` component of a `mids` object is automatically determined based on the `tasks` argument. It specifies which components are saved in the imputation model. + +If `tasks` is not specified (or set to `"impute"`), the default behavior is: + +```{r store-impute} +imputed <- mice(nhanes, seed = 1, print = FALSE) +imputed$store +imputed$tasks +imputed$models +``` + +Since `tasks = "impute"` does not train models, the models component remains NULL. The possible values of store are: + +- `impute`: When all tasks are `"impute"`, the `mids` object mimics the classic `mids` structure and does not store models. +- `train`: If one or more tasks include `"train"`, the `mids` object stores the models component for the subset of trained variables. +- `fill`: If all tasks are `"fill"`, the `mids` object does not store models. The setting `store = "fill"` assumes a fully trained model for all variables. + +Note: If additional variables exist in the new data, `mice()` will silently impute them using the trained model. This may produce unintended imputations if the new variables were not part of the training set. The resulting `mids` object will have `store = "train"`. + +Additionally, there is a special fourth `store` value: + +- `train_compact`: This value is assigned when `store = "train"` and `compact = TRUE`. The `train_compact` mode stores a minimized version of the imputation model without training data, making it suitable for production and sharing. + +## Conclusion + +This vignette introduced new functionality in `mice()` that enables storing and reusing imputation models, enhancing reproducibility, efficiency, and interoperability. By utilizing the `tasks` and `models` arguments, users can now train imputation models, apply them to new data, and share models across studies. The flexibility of **full fill** and **partial fill** workflows further supports diverse data structures, including **longitudinal data** and **multi-source datasets**. These enhancements streamline missing data workflows, reduce redundancy, and improve **data privacy** by allowing models to be shared instead of raw datasets. Future developments will refine model selection and optimization for various imputation scenarios. + +## References {.unnumbered} + +::: {#refs} +::: + +## APPENDIX A: Components of the `mids` object {.unnumbered} + +The following table summarizes the components saved by `mice()` for different `store` values. The `store` value is set to `"impute"`, `"train"`, or `"fill"` based on the `tasks` argument. `mice()` return `store == "impute"` by default, and `store == "fill"` if all variables are filled. In all other cases, the `store == "train"`. + +The table lists the components of the `mids` object for each `store` value. + +| Name | Description |I/O| Data Type |Impute|Train|Fill | +|---------------|----------------------------------------------|---|-------------------|-----|-----|-----| +| `predictorMatrix` | Specifies predictor set | I | `matrix` | YES | YES | NO | +| `formulas` | Formulae for imputation models | I | `list` | YES | YES | NO | +| `modeltype` | Form of imputation model | I | `character` | YES | YES | NO | +| `post` | Commands for post-processing | I | `character vector`| YES | YES | NO | +| `ignore` | Logical vector for ignored rows | I | `logical vector` | YES | YES | NO | +| `seed` | Seed value for reproducibility | I | `integer` | YES | YES | NO | +| `nmis` | Count of missing values per variable | O | `numeric vector` | YES | YES | NO | +| `chainMean` | Mean of imputed values | O | `array` | YES | YES | NO | +| `chainVar` | Variance of imputed values | O | `array` | YES | YES | NO | +| `loggedEvents` | Warnings and corrective tasks | O | `data.frame` | YES | YES | NO | +| `blocks` | Blocks of variables for imputation | I | `list` | YES | YES | NO | +| `method` | Imputation method per block | I | `character vector`| YES | YES | NO | +| `blots` | Extra arguments per block | I | `list` | YES | YES | NO | +| `visitSequence` | Order of block visits | I | `character vector`| YES | YES | NO | +| `iteration` | Last iteration number | O | `integer` | YES | YES | NO | +| `lastSeedValue` | Random number generator state | O | `integer` | YES | YES | NO | +| `tasks` | Specifies the imputation tasks | I | `character vector`| YES | YES | NO | +| `models` | Stores imputation model estimates | O | `list` | NO | YES | NO | +| `data` | Data to be imputed | I | `data.frame` | YES | YES | YES | +| `where` | Specifies where imputations occur | I | `matrix` | YES | YES | YES | +| `imp` | List of imputations per variable | O | `list` | YES | YES | YES | +| `m` | Number of imputations | I | `integer` | YES | YES | YES | +| `store` | Storage set | O | `character` | YES | YES | YES | +| `version` | Version number of `mice` package | O | `character` | YES | YES | YES | +| `date` | Date when the object was created | O | `character` | YES | YES | YES | +| `call` | Call that created the object | O | `call` | YES | YES | YES | + +The `mids` object `store` can be minimized from `"train"` to `"train_compact"` by setting `compact = TRUE`. The minimal representation `"train_compact"` saves the following components: `blocks`, `method`, `blots`, `visitSequence`, `iteration`, `lastSeedValue`, `tasks`, `models`, `m`, `store`, `version`, `date` and `call`. This feature is particularly useful if training data cannot be shared, or for production applications where memory efficiency is critical. The minimized model retains only the essential information required for generating imputed values, reducing memory usage while maintaining the core functionality of the imputation model. Note that some downstream functions, like `complete()` or `with()`, cannot support `store = "train_compact"`. + +## APPENDIX B: Computational details of binning in PMM {.unnumbered} + +### Initialization of the Number of Bins (`initialize.nbins`) {.unnumbered} + +The function `initialize.nbins()` determines an appropriate number of bins (`nbins`) to be used in PMM based on the sample size (`n`) and the number of unique values (`nu`) in the predicted or observed data. + +The default computation of `nbins` uses the relation `nbins = round(4 * log(n) + 1.5)`. This formula suggests that the number of bins grows logarithmically with the sample size (`n`), ensuring a reasonable bin width without excessive granularity. The coefficients (`4` and `1.5`) are chosen to scale the number of bins appropriately across different `n`. Since `nbins` represents a discretization of the data, it cannot exceed the number of unique values (`nu`) in the predicted or observed variable. If `nbins > nu`, the function sets `nbins = nu` to ensure that each unique value has its own bin. This adjustment is necessary to prevent empty bins and ensure that each unique value is represented in the binning process. The function enforces a lower bound of 2 bins to prevent degenerate cases where binning would be ineffective. + +For large samples of highly-correlated continuous data, the user can increase the set `nbins = 100` or higher to improve precision. + +### Initialization of the Number of Donors (`initialize.donors()`) {.unnumbered} + +The `initialize.donors()` function determines the number of donors `donors` for imputation based on the sample size (`n`). If `donors` is `NULL`, it is computed using `round(n / 600 + 7)`, ensuring a gradual increase as `n` grows. The computed value is then constrained using `max(1L, min(donors, n))`, which ensures at least one donor while preventing the donor count from exceeding `n`. + +Setting `donors = 1` (not recommended) selects the closest donor. Thus, different cases that are in the same bin will obtain the same imputed value from the left or right edge. Setting `donors = n` (not recommended) effectively samples from the marginal distribution, and weakens the relations between the data. The literature suggests values between 5 and 10 donors [@morris2014]. Because of binning, the optimal number of donors may actually need to be higher than 5 or 10 in order to reduce repetition of donors from the same bin. + +### Binning of the Linear Predictor (`bin.yhat()`) {.unnumbered} + +The `bin.yhat()` function divides the linear predictor into bins using the thresholds `edges`. It first sorts the linear predictor `yhat` and determines the bin index for each value based on the thresholds. The function uses the `findInterval()` function to assign each value to the corresponding bin. The bin index is calculated as `bin = findInterval(yhat, edges, left.open = TRUE)`. The `left.open = TRUE` argument ensures that values equal to the threshold are assigned to the left bin, consistent with the binning process. The function returns the bin index for each value. + +### Drawing Imputations (`draw.neighbors.pmm()`) {.unnumbered} + +The `draw.neighbors.pmm` function selects imputed values using predefined bins. Given predicted values (`yhat`), bin edges (`edges`), and a lookup table (`lookup`), the function assigns each `yhat` value to a bin using `findInterval`. If `yhat` is lower than the first bin or higher than the last bin, it selects the first or last bin. +To ensure smooth transitions between bins, the function calculates a probability weight based on the distance between `yhat` and the bin edges. Using a Bernoulli distribution, it probabilistically selects the left or right bin. Once the bin is selected, the function samples `mlocal` observed `y` values from the corresponding row in the lookup table. The result is an `n × mlocal` matrix containing imputed values. diff --git a/vignettes/references.bib b/vignettes/references.bib new file mode 100644 index 000000000..5460f49a8 --- /dev/null +++ b/vignettes/references.bib @@ -0,0 +1,109 @@ +@inproceedings{dempster1983, + Address = {New York}, + Author = {Dempster, A. P. and Rubin, D. B.}, + Booktitle = {Incomplete Data in Sample Surveys}, + Date-Added = {2011-01-25 08:31:11 +0100}, + Date-Modified = {2011-10-02 10:56:44 +0000}, + Pages = {3-10}, + Publisher = {Academic Press}, + Title = {Introduction}, + Volume = {2}, + Year = {1983}} + +@Article{kavelaars2022, +author = {Kavelaars, X. M. and {van Ginkel}, J. R. and {van Buuren}, S.}, +title = {Multiple imputation in data that grow over time: A comparison of three strategies}, +journal = {Multivariate Behavioral Research}, +volume = {57}, +number = {2-3}, +pages = {513--523}, +year = {2022}, +location = {}, +keywords = {}} + +@article{little1988, + Author = {Little, R. J. A.}, + Date-Modified = {2011-10-02 13:23:01 +0000}, + Journal = {Journal of Business Economics and Statistics}, + Number = {3}, + Pages = {287-301}, + Title = {Missing-data adjustments in large surveys (with discussion)}, + Volume = {6}, + Year = {1988}} + +@article{morris2014, +author = {Morris, T. P. and White, I. R. and Royston, P.}, +title = {Tuning multiple imputation by predictive mean matching and local residual draws}, +journal = {BMC Medical Reseach Methods}, +volume = {14}, +number = {}, +pages = {75}, +year = {2014}, +location = {}, +keywords = {}} + +@book{rubin1987, + Address = {New York}, + Author = {Rubin, D. B.}, + Date-Modified = {2011-10-02 14:41:28 +0000}, + Keywords = {Nonresponse}, + Publisher = {John Wiley \& Sons}, + Title = {Multiple Imputation for Nonresponse in Surveys}, + Year = {1987}} + +@article{schenker1996, + Author = {Schenker, N. and Taylor, J. M. G.}, + Journal = {Computational Statistics \& Data Analysis}, + Keywords = {Multiple imputation}, + Number = {4}, + Pages = {425-446}, + Title = {Partially parametric techniques for multiple imputation}, + Volume = {22}, + Year = {1996}} + +@Article{vanbuuren2007, +author = {{van Buuren}, S.}, +title = {Multiple imputation of discrete and continuous data by fully conditional specification}, +journal = {Statistical Methods in Medical Research}, +volume = {16}, +number = {3}, +pages = {219-242}, +year = {2007}, +abstract = {}, +location = {}, +keywords = {Multiple imputation; ERC}} + +@Article{vanbuuren2011, +author = {{van Buuren}, S. and Groothuis-Oudshoorn, K.}, +title = {{MICE}: Multivariate Imputation by Chained Equations in {R}}, +journal = {Journal of Statistical Software}, +volume = {45}, +number = {3}, +pages = {1–67}, +year = {2011}, +location = {}, +keywords = {ERC}} + +@Book{vanbuuren2018, +author = {{van Buuren}, S.}, +title = {Flexible Imputation of Missing Data. Second Edition}, +volume = {}, +pages = {}, +editor = {}, +publisher = {Chapman & Hall/CRC Press}, +address = {Boca Raton, FL}, +year = {2018}, +url = {https://stefvanbuuren.name/fimd/}, +abstract = {}} + +@Article{vink2015, +author = {Vink, G. and Lazendic, G. and {van Buuren}, S.}, +title = {Partioned predictive mean matching as a large data multilevel imputation technique}, +journal = {Psychological Test and Assessment Modeling}, +volume = {57}, +number = {4}, +pages = {577--594}, +year = {2015}, +abstract = {}, +location = {}, +keywords = {}} From db21a279d51554b414ff5c9c68ef8820dc419dd2 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 8 Apr 2025 10:39:49 +0200 Subject: [PATCH 100/147] Solve a problem with dropped factor levels --- R/quantify.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R/quantify.R b/R/quantify.R index 2a561e85d..7da410238 100644 --- a/R/quantify.R +++ b/R/quantify.R @@ -16,12 +16,12 @@ quantify <- function(y, ry, x, quantify = TRUE) { yd <- model.matrix(~ 0 + yf) xd <- cbind(1, x[ry, , drop = FALSE]) cca <- cancor(y = yd, x = xd, xcenter = FALSE, ycenter = FALSE) - oldlevels <- levels(y) - levels(y) <- as.vector(cca$ycoef[, 2L]) - ynum <- as.numeric(as.character(y)) + quant <- as.vector(cca$ycoef[, 2L]) + quant_expand <- quant[match(levels(y), levels(yf))] + ynum <- quant_expand[match(as.character(y), levels(y))] return(list(ynum = ynum, - labels = oldlevels, - quant = as.numeric(levels(y)))) + labels = levels(y), + quant = quant_expand)) } unquantify <- function(ynum = NULL, quant = NULL, labels = NULL) { From d4339e36a66b4636e820d024a68fba2a2140c1dd Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 14:01:21 +0200 Subject: [PATCH 101/147] Drop the restriction that all variables in the trained model should be in the newdata --- R/check.tasks.R | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/R/check.tasks.R b/R/check.tasks.R index 4d41f6fa9..854e1edc1 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -73,20 +73,7 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, } } - # 9. Ensure all variables in models exist in blocks - if (!is.null(models)) { - trained_vars <- ls(models) - missing_from_data <- setdiff(trained_vars, bv) # Use block variable names - if (length(missing_from_data) > 0L) { - stop(paste0( - "The following variables are present in `models` but missing from `data`: ", - paste(missing_from_data, collapse = ", "), ".\n", - "Ensure that all stored models correspond to variables in the dataset." - )) - } - } - - # 10. Check whether the data match the models for filling + # 9. Check whether the data match the models for filling if ("fill" %in% tasks) { fill_vars <- names(tasks[tasks == "fill"]) scanned <- scan.data(data, models) From 860fde8c58a59c81da8d1bf7200b365971d49f29 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 14:11:38 +0200 Subject: [PATCH 102/147] vars in make.data() can now also be used to select a subset of variables from the trained model --- R/data.R | 5 ++++- man/make.data.Rd | 5 ++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/R/data.R b/R/data.R index 9f1663a6e..a192e140f 100644 --- a/R/data.R +++ b/R/data.R @@ -119,7 +119,10 @@ scan.data <- function(data, models, print = FALSE) { #' @param models A list of trained models from `mice()`. #' @param n Number of rows to generate. Default is 1. #' @param fill Value to fill the data with. Default is `NA`. -#' @param vars Optional character vector specifying the variable order. Default is `NULL`. +#' @param vars Optional character vector specifying the variable order, or for +#' a selecting a subset of variable. The default returns all variables in +#' the order as they appear in `models`. Beware that taking a subset could lead +#' to an error if a predictor is undefined in the generated data. #' #' @return A `data.frame` of `n` rows, where each column matches the type and levels #' expected from the corresponding model. diff --git a/man/make.data.Rd b/man/make.data.Rd index ec2eef1ee..05b620221 100644 --- a/man/make.data.Rd +++ b/man/make.data.Rd @@ -13,7 +13,10 @@ make.data(models, n = 1L, fill = NA, vars = NULL) \item{fill}{Value to fill the data with. Default is \code{NA}.} -\item{vars}{Optional character vector specifying the variable order. Default is \code{NULL}.} +\item{vars}{Optional character vector specifying the variable order, or for +a selecting a subset of variable. The default returns all variables in +the order as they appear in \code{models}. Beware that taking a subset could lead +to an error if a predictor is undefined in the generated data.} } \value{ A \code{data.frame} of \code{n} rows, where each column matches the type and levels From 33a1880879c2e65f2871f424a031da2a5e24e8b9 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 14:32:40 +0200 Subject: [PATCH 103/147] Set call. = FALSE to all stop() calls in check.tasks() --- R/check.tasks.R | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/R/check.tasks.R b/R/check.tasks.R index 854e1edc1..5b9878436 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -25,7 +25,8 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, # 3. Check length if (length(tasks) != length(bv)) { stop("The length of `tasks` (", length(tasks), - ") must match the number of variables in `blocks` (", length(bv),").") + ") must match the number of variables in `blocks` (", length(bv),").", + call. = FALSE) } # 4. Check that tasks is a named vector @@ -41,7 +42,7 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, "The following variables specified in `tasks` are not present in `blocks`: ", paste(names(tasks)[notFound], collapse = ", "), ".\n", "Ensure all specified variables match those in `blocks`." - )) + ), call. = FALSE) } # 6. Check if all tasks are valid @@ -51,13 +52,14 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, "Invalid task(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", "Valid tasks are: ", paste(valid_tasks, collapse = ", "), ".\n", "Please correct the `tasks` argument." - )) + ), call. = FALSE) } # 7. Prevent "fill" if models is NULL if ("fill" %in% tasks && is.null(models)) { stop("The task 'fill' requires a stored model, but `models` is NULL.\n", - "Please provide a valid `models` object with a trained imputation model.") + "Please provide a valid `models` object with a trained imputation model.", + call. = FALSE) } # 8. Ensure that all "fill" variables have a trained model in models @@ -69,7 +71,7 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, "The following variables specified as 'fill' do not have stored models: ", paste(missing_models, collapse = ", "), ".\n", "Ensure these variables were previously fitted before using 'fill'." - )) + ), call. = FALSE) } } @@ -80,10 +82,10 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, idx <- scanned$variable %in% fill_vars & !scanned$can_fill if (any(idx)) { stop(paste0( - "The following variables in `data` do not match the stored models for filling: ", + "The following variables in `data` cannot be filled: ", paste(scanned$variable[idx], collapse = ", "), ".\n", "Use scan.data() to diagnose mismatch between data and models." - )) + ), call. = FALSE) } } From 61f516cd7e9a0b26929c0f8621067981f65abb76 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 14:34:00 +0200 Subject: [PATCH 104/147] Document scan.data(), coerce.data() and make.data() in imputation_models vignette --- vignettes/_imputation_models.qmd | 129 +++++++++++++++++++++++++------ 1 file changed, 107 insertions(+), 22 deletions(-) diff --git a/vignettes/_imputation_models.qmd b/vignettes/_imputation_models.qmd index 1b0e8bb24..5f2732316 100644 --- a/vignettes/_imputation_models.qmd +++ b/vignettes/_imputation_models.qmd @@ -32,7 +32,7 @@ A better approach is to apply the **same imputation model** trained on the origi The new functionality is **currently experimental** and available in the `"tasks"` branch of the `mice` repository. To install this branch from GitHub, use: -```{r eval=FALSE} +```{r install, eval=FALSE} remotes::install_github("amices/mice", ref = "imputation_models") ``` @@ -70,7 +70,7 @@ Next, we show practical examples of how to use the `tasks` argument. Setting `tasks = "impute"` returns a `mids` object identical to the classic MICE implementation: it contains imputed values but **does not store the imputation model**. The following code generates a `mids` object using the built-in `nhanes` dataset: -```{r} +```{r mice-impute} library(mice, warn.conflicts = FALSE) imputed <- mice(nhanes, tasks = "impute", seed = 1, print = FALSE) class(imputed) @@ -78,7 +78,7 @@ class(imputed) As in classic MICE, specifying `tasks = "impute"` applies this setting to all variables by expanding to: -```{r} +```{r imputed-tasks} imputed$tasks ``` @@ -89,14 +89,14 @@ imputed$tasks To also store the imputation model, use `tasks = "train"` instead of `tasks = "impute"`: -```{r} +```{r mice-train} trained <- mice(nhanes, tasks = "train", seed = 1, print = FALSE) class(trained) ``` This time, the `mids` object contains both the imputed values and the imputation model. To confirm the difference, inspect the `store` component: -```{r} +```{r trained-store} trained$store ``` @@ -113,10 +113,11 @@ Depending on the imputation method, the stored models may differ slightly. For p Training creates a **transferable representation** of the imputation model, allowing it to be reused on new data **without altering the original model or re-estimating parameters**. To apply a stored model, use `tasks = "fill"` in `mice()`, along with the `models` argument to specify the trained model. -The following code demonstrates how to use a trained model to fill missing values in new data: -```{r} -newdata <- data.frame(age = c(2, 1), bmi = c(NA, NA), chl = c(NA, 190), hyp = c(NA, 1)) +Later on we will show how to avoid this issue for more general cases. For now, let us alter `newdata$bmi` directly to ensure that the variable types match the imputation model: + +```{r mice-fill, error=TRUE} +newdata <- data.frame(age = c(2, 1), bmi = c(NA_real_, NA_real_), chl = c(NA, 190), hyp = c(NA, 1)) filled <- mice(newdata, tasks = "fill", models = trained$models, seed = 1, print = FALSE) filled$store filled$data @@ -135,7 +136,7 @@ The `complete()` function can be used to extract the multiply-imputed datasets. The `compact` argument controls the storage of the trained model. When `compact = TRUE`, the imputation model is stored in a **minimized form**, without retaining the training data used to estimate the model. This form retains all necessary parameters for generating imputed values while excluding training data, making it suitable for **exchange, distribution, and production**. -```{r} +```{r mice-compact} # Train a compact imputation model (no training data stored) trained_compact <- mice(nhanes, tasks = "train", compact = TRUE, seed = 1, print = FALSE) trained_compact$store @@ -152,7 +153,7 @@ complete(filled_compact, 2) Since compact models do not store training data, the `data` and `imp` components remain empty. -```{r} +```{r complete-compact} # Attempting to complete a compact model without training data will return an error try(complete(trained_compact)) ``` @@ -182,7 +183,7 @@ Imputation models can be trained on a **subset** of variables rather than the en The following fragment specifies a vector `tasks = c("bmi", "hyp")` to train the imputation model **only for `bmi` and `hyp`**: -```{r} +```{r make-tasks} tasks <- make.tasks(nhanes) tasks[c("bmi", "hyp")] <- "train" trained_subset <- mice(nhanes, tasks = tasks, seed = 1, print = FALSE) @@ -191,7 +192,7 @@ names(trained_subset$models) Now apply the subset model to fill missing values in `newdata`: -```{r} +```{r fill-subset} tasks[c("bmi", "hyp")] <- c("fill", "fill") filled_subset <- mice(newdata, tasks = tasks, models = trained_subset$models, seed = 1, print = FALSE) complete(filled_subset) @@ -206,7 +207,7 @@ There are two ways to address this issue: - Include `chl` in the training subset, effectively training models for all variables. - Append additional records to `newdata` so that `mice()` can generate an on-the-fly imputation model for `chl`. This approach is shown below: -```{r} +```{r newdata-append} newdata_append <- rbind(newdata, nhanes[11:20, ]) filled_subset <- mice(newdata_append, tasks = tasks, models = trained_subset$models, seed = 1, print = FALSE) complete(filled_subset, 2)[1:2, ] @@ -219,6 +220,90 @@ Final observations: - When a combination of training and filling is used, the resulting mids object is stored with `store = "train"`. - Appendix A contains the full specification of `mids` components for store modes `"impute"`, `"train"`, and `"fill"`. +## Ensuring Type Compatibility + +The following example illustrates a common pitfall when attempting to fill missing values in new data using `mice()`: + +```{r mice-fill-error, error=TRUE} +newdata <- data.frame(age = c(2, 1), bmi = c(NA, NA), chl = c(NA, 190), hyp = c(NA, 1)) +filled <- mice(newdata, tasks = "fill", models = trained$models, seed = 1, print = FALSE) +``` + +The error message may be surprising at first, but it results from standard R behavior: when a column contains only `NA` values, R infers its type as `logical`. However, the imputation model for `bmi` was trained on a numeric variable. The mismatch in types prevents the model from applying its learned parameters to the new data. To maintain transparency and reproducibility, the `mice()` function **does not attempt to guess or convert types automatically**. Instead, it explicitly halts and reports the mismatch, allowing the user to resolve it deliberately. + +### How to Resolve Type Mismatches + +Ensure that new data uses the **same types and structure** as the original training data. In the example above, the issue can be resolved by explicitly defining the `bmi` column as numeric, for instance using `NA_real_` instead of `NA`. + +More generally, three common issues can cause model-data mismatches during filling: + +- **Variable types**: If a variable in `newdata` has a different type than in the training data (e.g., logical instead of numeric), `mice()` will raise an error. + ➤ *Solution*: Convert variables to the expected types using `as.numeric()`, `as.factor()`, etc. + +- **Factor levels**: If a factor variable in `newdata` has different levels from those used in the training model, the filling process will fail. + ➤ *Solution*: Use `factor(..., levels = ...)` to match the training data levels exactly. + +- **Missing predictors**: If `newdata` omits variables used as predictors in the trained models, imputation will not proceed. + ➤ *Solution*: Ensure that all required predictor variables are present in `newdata`, even if their values are completely missing. + +You can automate these checks using `scan.data()` and align new data structures using `coerce.data()` or `make.data()`. + +### Using `scan.data()`, `coerce.data()`, and `make.data()` + +The `mice` package provides three helper functions to ensure that new data is compatible with trained imputation models: + +| Function | Purpose | +|------------------|-------------------------------------------------------------------------| +| `scan.data()` | Inspects a given dataset and reports mismatches with the trained models. Use this to diagnose potential issues before filling. | +| `coerce.data()` | Transforms a dataset to match the expected structure based on a trained model. Use this to fix detected type mismatches. | +| `make.data()` | Creates a skeleton `newdata` frame with the correct structure, types, and levels. Use this to start from scratch. | + +#### Example: Diagnosing and Fixing Type Mismatches + +Suppose we have a trained model `trained` and a problematic `newdata`: + +```{r} +# Problematic new data +newdata <- data.frame( + age = c(2, 1), + bmi = c(NA, NA), # implicit logical (should be numeric) + chl = c(NA, 190), + hyp = c(NA, 1) +) + +# Diagnose structure against trained models +scan.data(newdata, trained$models) +``` + +This diagnostic table shows that the `class_match` column for `bmi` is `FALSE`. It has class `logical` in the data and class `numeric` in the model. As a result, the last column `can_fill` is set to `FALSE`. + +#### Fix the Data with `coerce.data()` + +You can fix detected mismatches by coercing the data to the expected format: + +```{r} +newdata_fixed <- coerce.data(newdata, trained$models) + +# Check again +scan.data(newdata_fixed, trained$models) +``` + +Now all variables match in type and structure, and `can_fill` for `bmi` is changed to `TRUE`. + +#### Start from Scratch with `make.data()` + +If you don’t have any data yet, or want a fully clean starting point, use `make.data()`: + +```{r} +# Create a new empty row with correct types and levels +template <- make.data(models = trained$models) + +# View structure +str(template) +``` + +You can now safely populate `template` with new values, or directly use it with `tasks = "fill"`. + ## Elementary Imputation Functions This section explains how `mice.impute.norm()`, `mice.impute.pmm()`, and other `mice.impute.*()` functions have been **adapted to support `tasks` and `models`**, allowing for reusable imputation models and enhanced flexibility in workflows. @@ -241,7 +326,7 @@ The `mice.impute.norm()` function is responsible for the following tasks: Let us examine how these actions are implemented in `mice.impute.norm()` for normal imputation: -```{r} +```{r mice-impute-norm} mice::mice.impute.norm ``` @@ -316,7 +401,7 @@ The `models` component is organized **by variable and imputation number**, allow The following diagram visualizes the hierarchical structure of the `models` component: -```{mermaid} +```{mermaid models} graph TD; A[mids] --> B[models] B --> C[bmi] @@ -332,31 +417,31 @@ graph TD; The names of the parts of the first imputation model for `bmi` are: -```{r models-levels} +```{r models-bmi} names(trained$models$bmi[[1]]) ``` In general, the names and types of stored objects depend on the imputation method. The `setup` object for the first imputation model for `bmi` can be accessed as follows: -```{r} +```{r models-bmi-setup} unlist(trained$models$bmi[[1]]$setup) ``` It contains settings specific to the imputation model. We can retrieve the `formula` used in the model as: -```{r} +```{r models-bmi-formula} trained$models$bmi[[1]]$formula ``` which shows that `bmi` is imputed using a linear combination of `age`, `hyp`, and `chl`. We obtain the least squares estimates for the regression weights by: -```{r} +```{r models-bmi-betahat} trained$models$bmi[[1]]$beta.hat ``` However, the actual imputation model does not use these weights directly; instead, it draws randomly from a distribution that accounts for parameter uncertainty [@rubin1987]. The drawn values can be accessed as: -```{r} +```{r models-bmi-betadot} trained$models$bmi[[1]]$beta.dot ``` @@ -371,13 +456,13 @@ For PMM, two additional objects are stored in `models`: The bin thresholds in `edges` are located on: -```{r} +```{r models-bmi-edges} trained$models$bmi[[1]]$edges ``` The `lookup` object is a matrix, where each row represents a bin threshold $\theta_j$ that segments the linear predictor. Observations with $\hat y$ values falling outside the bin range ($\hat y \leq \theta_1$ or $\hat y > \theta_t$) are assigned to the first or last bin, respectively. The `lookup` table for the first imputation model for `bmi` can be accessed as: -```{r} +```{r models-bmi-lookup} trained$models$bmi[[1]]$lookup ``` From 048382faea899f88a2c09f73a36a87e64971e54d Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 15:21:55 +0200 Subject: [PATCH 105/147] Rename scan.data --> scan.newdata, coerce.data --> coerce.newdata and make.data --> make.newdata for improved clarity --- NAMESPACE | 6 +++--- R/check.tasks.R | 4 ++-- R/data.R | 10 +++++----- man/{coerce.data.Rd => coerce.newdata.Rd} | 6 +++--- man/{make.data.Rd => make.newdata.Rd} | 8 ++++---- man/{scan.data.Rd => scan.newdata.Rd} | 8 ++++---- tests/testthat/test-data.R | 20 +++++++++---------- vignettes/_imputation_models.qmd | 24 +++++++++++------------ 8 files changed, 43 insertions(+), 43 deletions(-) rename man/{coerce.data.Rd => coerce.newdata.Rd} (87%) rename man/{make.data.Rd => make.newdata.Rd} (86%) rename man/{scan.data.Rd => scan.newdata.Rd} (94%) diff --git a/NAMESPACE b/NAMESPACE index dfef94704..b0fa31fc1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -63,7 +63,7 @@ export(bwplot) export(cbind) export(cc) export(cci) -export(coerce.data) +export(coerce.newdata) export(complete) export(construct.blocks) export(convergence) @@ -94,10 +94,10 @@ export(lm.mids) export(mads) export(make.blocks) export(make.blots) -export(make.data) export(make.formulas) export(make.method) export(make.modeltype) +export(make.newdata) export(make.post) export(make.predictorMatrix) export(make.tasks) @@ -173,7 +173,7 @@ export(pool.table) export(quickpred) export(rbind) export(remove.lindep) -export(scan.data) +export(scan.newdata) export(squeeze) export(stripplot) export(supports.transparent) diff --git a/R/check.tasks.R b/R/check.tasks.R index 5b9878436..43ec5be33 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -78,13 +78,13 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, # 9. Check whether the data match the models for filling if ("fill" %in% tasks) { fill_vars <- names(tasks[tasks == "fill"]) - scanned <- scan.data(data, models) + scanned <- scan.newdata(data, models) idx <- scanned$variable %in% fill_vars & !scanned$can_fill if (any(idx)) { stop(paste0( "The following variables in `data` cannot be filled: ", paste(scanned$variable[idx], collapse = ", "), ".\n", - "Use scan.data() to diagnose mismatch between data and models." + "Use scan.newdata() to diagnose mismatch between data and models." ), call. = FALSE) } } diff --git a/R/data.R b/R/data.R index a192e140f..590b5b866 100644 --- a/R/data.R +++ b/R/data.R @@ -33,10 +33,10 @@ #' data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) #' #' # Run scan -#' scan.data(data, imp$models) +#' scan.newdata(data, imp$models) #' #' @export -scan.data <- function(data, models, print = FALSE) { +scan.newdata <- function(data, models, print = FALSE) { orig.data <- data vars.data <- names(orig.data) vars.model <- names(models) @@ -128,10 +128,10 @@ scan.data <- function(data, models, print = FALSE) { #' expected from the corresponding model. #' @examples #' trained <- mice(boys, tasks = "train", print = FALSE) -#' newdata <- make.data(trained$models, n = 5, vars = names(boys)) +#' newdata <- make.newdata(trained$models, n = 5, vars = names(boys)) #' str(newdata) #' @export -make.data <- function(models, n = 1L, fill = NA, vars = NULL) { +make.newdata <- function(models, n = 1L, fill = NA, vars = NULL) { stopifnot(is.list(models), is.numeric(n), length(n) == 1L) all_vars <- names(models) @@ -174,7 +174,7 @@ make.data <- function(models, n = 1L, fill = NA, vars = NULL) { #' #' @return A coerced `data.frame` that matches the class and levels from `models`. #' @export -coerce.data <- function(data, models) { +coerce.newdata <- function(data, models) { data <- as.data.frame(data) for (j in intersect(names(data), names(models))) { mod <- models[[j]][[1L]] diff --git a/man/coerce.data.Rd b/man/coerce.newdata.Rd similarity index 87% rename from man/coerce.data.Rd rename to man/coerce.newdata.Rd index dd494d893..5c75028a5 100644 --- a/man/coerce.data.Rd +++ b/man/coerce.newdata.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R -\name{coerce.data} -\alias{coerce.data} +\name{coerce.newdata} +\alias{coerce.newdata} \title{Coerce a data.frame to match model expectations} \usage{ -coerce.data(data, models) +coerce.newdata(data, models) } \arguments{ \item{data}{A \code{data.frame} with input data.} diff --git a/man/make.data.Rd b/man/make.newdata.Rd similarity index 86% rename from man/make.data.Rd rename to man/make.newdata.Rd index 05b620221..d66fd061c 100644 --- a/man/make.data.Rd +++ b/man/make.newdata.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R -\name{make.data} -\alias{make.data} +\name{make.newdata} +\alias{make.newdata} \title{Construct a new data.frame from a trained model} \usage{ -make.data(models, n = 1L, fill = NA, vars = NULL) +make.newdata(models, n = 1L, fill = NA, vars = NULL) } \arguments{ \item{models}{A list of trained models from \code{mice()}.} @@ -28,6 +28,6 @@ levels) based on a set of trained models, as produced by \code{mice()} with \cod } \examples{ trained <- mice(boys, tasks = "train", print = FALSE) -newdata <- make.data(trained$models, n = 5, vars = names(boys)) +newdata <- make.newdata(trained$models, n = 5, vars = names(boys)) str(newdata) } diff --git a/man/scan.data.Rd b/man/scan.newdata.Rd similarity index 94% rename from man/scan.data.Rd rename to man/scan.newdata.Rd index 21faf41c1..86844920c 100644 --- a/man/scan.data.Rd +++ b/man/scan.newdata.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R -\name{scan.data} -\alias{scan.data} +\name{scan.newdata} +\alias{scan.newdata} \title{Scan data types and compare to model expectations} \usage{ -scan.data(data, models, print = FALSE) +scan.newdata(data, models, print = FALSE) } \arguments{ \item{data}{A \code{data.frame} to be checked.} @@ -44,6 +44,6 @@ data <- boys[1:3, ] data$phb <- factor(data$phb, levels = levels(data$phb), ordered = FALSE) # Run scan -scan.data(data, imp$models) +scan.newdata(data, imp$models) } diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R index c12bc5ab8..39cda4a6c 100644 --- a/tests/testthat/test-data.R +++ b/tests/testthat/test-data.R @@ -1,4 +1,4 @@ -context("scan.data, make.data, coerce.data") +context("scan.newdata, make.newdata, coerce.newdata") set.seed(123) df <- data.frame( @@ -19,20 +19,20 @@ for (i in seq_len(nrow(missing_idx))) { expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) # make single-row new data with correct type -newdata <- make.data(models = trained$models, vars = names(df)) +newdata <- make.newdata(models = trained$models, vars = names(df)) # run scan on newdata -result1 <- scan.data(data = newdata, models = trained$models) +result1 <- scan.newdata(data = newdata, models = trained$models) -test_that("scan.data() sets can_fill to Y for correctly typed newdata", { +test_that("scan.newdata() sets can_fill to Y for correctly typed newdata", { expect_true(length(result1$can_fill) > 0) expect_true(all(result1$can_fill)) }) # coerce newdata (not needed here) -coerced1 <- coerce.data(data = newdata, models = trained$models) +coerced1 <- coerce.newdata(data = newdata, models = trained$models) -test_that("coerce.data() does not alter when it has correct type", { +test_that("coerce.newdata() does not alter when it has correct type", { expect_true(identical(coerced1, newdata)) }) @@ -46,15 +46,15 @@ newdata <- data.frame( logical2 = NA, numeric1 = NA) -result2 <- scan.data(data = newdata, models = trained$models) +result2 <- scan.newdata(data = newdata, models = trained$models) -test_that("scan.data() reports that it cannot fill all variables", { +test_that("scan.newdata() reports that it cannot fill all variables", { expect_false(all(result2$can_fill)) }) -coerced2 <- coerce.data(data = newdata, models = trained$models) +coerced2 <- coerce.newdata(data = newdata, models = trained$models) -test_that("coerce.data() can coerces wrong to correct types", { +test_that("coerce.newdata() can coerces wrong to correct types", { expect_false(identical(attr(result2, "data"), newdata)) expect_true(identical(coerced1, coerced2)) }) diff --git a/vignettes/_imputation_models.qmd b/vignettes/_imputation_models.qmd index 5f2732316..f5a6323a4 100644 --- a/vignettes/_imputation_models.qmd +++ b/vignettes/_imputation_models.qmd @@ -246,17 +246,17 @@ More generally, three common issues can cause model-data mismatches during filli - **Missing predictors**: If `newdata` omits variables used as predictors in the trained models, imputation will not proceed. ➤ *Solution*: Ensure that all required predictor variables are present in `newdata`, even if their values are completely missing. -You can automate these checks using `scan.data()` and align new data structures using `coerce.data()` or `make.data()`. +You can automate these checks using `scan.newdata()` and align new data structures using `coerce.newdata()` or `make.newdata()`. -### Using `scan.data()`, `coerce.data()`, and `make.data()` +### Using `scan.newdata()`, `coerce.newdata()`, and `make.newdata()` The `mice` package provides three helper functions to ensure that new data is compatible with trained imputation models: | Function | Purpose | |------------------|-------------------------------------------------------------------------| -| `scan.data()` | Inspects a given dataset and reports mismatches with the trained models. Use this to diagnose potential issues before filling. | -| `coerce.data()` | Transforms a dataset to match the expected structure based on a trained model. Use this to fix detected type mismatches. | -| `make.data()` | Creates a skeleton `newdata` frame with the correct structure, types, and levels. Use this to start from scratch. | +| `scan.newdata()` | Inspects a given dataset and reports mismatches with the trained models. Use this to diagnose potential issues before filling. | +| `coerce.newdata()` | Transforms a dataset to match the expected structure based on a trained model. Use this to fix detected type mismatches. | +| `make.newdata()` | Creates a skeleton `newdata` frame with the correct structure, types, and levels. Use this to start from scratch. | #### Example: Diagnosing and Fixing Type Mismatches @@ -272,31 +272,31 @@ newdata <- data.frame( ) # Diagnose structure against trained models -scan.data(newdata, trained$models) +scan.newdata(newdata, trained$models) ``` This diagnostic table shows that the `class_match` column for `bmi` is `FALSE`. It has class `logical` in the data and class `numeric` in the model. As a result, the last column `can_fill` is set to `FALSE`. -#### Fix the Data with `coerce.data()` +#### Fix the Data with `coerce.newdata()` You can fix detected mismatches by coercing the data to the expected format: ```{r} -newdata_fixed <- coerce.data(newdata, trained$models) +newdata_fixed <- coerce.newdata(newdata, trained$models) # Check again -scan.data(newdata_fixed, trained$models) +scan.newdata(newdata_fixed, trained$models) ``` Now all variables match in type and structure, and `can_fill` for `bmi` is changed to `TRUE`. -#### Start from Scratch with `make.data()` +#### Start from Scratch with `make.newdata()` -If you don’t have any data yet, or want a fully clean starting point, use `make.data()`: +If you don’t have any data yet, or want a fully clean starting point, use `make.newdata()`: ```{r} # Create a new empty row with correct types and levels -template <- make.data(models = trained$models) +template <- make.newdata(models = trained$models) # View structure str(template) From 1e393b943a6ff8d131fa60e2db26545268662a81 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 15:28:31 +0200 Subject: [PATCH 106/147] Update _pkgdown.yml to include several new functions --- _pkgdown.yml | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 386755431..b275dbf4d 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -5,6 +5,14 @@ template: params: bootswatch: lumen reference: +- title: Main imputation functions + desc: | + The workflow of multiple imputation is: multiply-impute the data, apply the complete-data model to each imputed data set, and pool the results to get to the final inference. The main functions for imputing the data are: + contents: + - mice + - mice.mids + - parlmice + - futuremice - title: Missing data exploration desc: | Functions to count and explore the structure of the missing data. @@ -22,14 +30,6 @@ reference: - fico - flux - fluxplot -- title: Main imputation functions - desc: | - The workflow of multiple imputation is: multiply-impute the data, apply the complete-data model to each imputed data set, and pool the results to get to the final inference. The main functions for imputing the data are: - contents: - - mice - - mice.mids - - parlmice - - futuremice - title: Elementary imputation functions desc: | The elementary imputation function is the workhorse that creates the actual imputations. Elementary functions are called through the `method` argument of `mice` function. Each function imputes one or more columns in the data. There are also `mice.impute.xxx` functions outside the `mice` package. @@ -38,21 +38,24 @@ reference: desc: | Specification of the imputation models can be made more convenient using the following set of helpers. contents: - - quickpred - - squeeze + - coerce.newdata + - construct.blocks - make.blocks - make.blots - make.formulas - make.method - make.modeltype + - make.newdata - make.post - make.predictorMatrix - make.tasks - make.visitSequence - make.where - - construct.blocks - name.blocks - name.formulas + - quickpred + - scan.newdata + - squeeze - title: Plots comparing observed to imputed/amputed data desc: | These plots contrast the observed data with the imputed/amputed data, usually with a blue/red distinction. @@ -123,9 +126,12 @@ reference: desc: | Several functions are dedicated to common low-level operations to generate the imputations: contents: + - correlar - estimice + - larspred - norm.draw - .norm.draw + - trim.data - title: Multivariate amputation desc: | Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies. From dd82db5b13731bb75eb1d0c8e1365f55a85cf172 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 9 Apr 2025 16:09:10 +0200 Subject: [PATCH 107/147] Replace imputation_models branch by dev branch --- vignettes/_imputation_models.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vignettes/_imputation_models.qmd b/vignettes/_imputation_models.qmd index f5a6323a4..a7c412cbc 100644 --- a/vignettes/_imputation_models.qmd +++ b/vignettes/_imputation_models.qmd @@ -30,10 +30,10 @@ A better approach is to apply the **same imputation model** trained on the origi ## Installation -The new functionality is **currently experimental** and available in the `"tasks"` branch of the `mice` repository. To install this branch from GitHub, use: +The new functionality is **currently experimental** and available in the `dev` branch of the `mice` repository. To install this branch from GitHub, use: ```{r install, eval=FALSE} -remotes::install_github("amices/mice", ref = "imputation_models") +remotes::install_github("amices/mice", ref = "dev") ``` ## MICE Architecture From ef620983bfa27440d8d6fdf341a9fd5f9afcac18 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 10 Apr 2025 13:56:29 +0200 Subject: [PATCH 108/147] Export quantify() and unquantify() to convert factors to numeric as vice versa by optimal scaling --- NAMESPACE | 2 + NEWS.md | 1 + R/quantify.R | 89 +++++++++++++++++++++++++++++++--- _pkgdown.yml | 2 + man/quantify.Rd | 59 ++++++++++++++++++++++ man/unquantify.Rd | 51 +++++++++++++++++++ tests/testthat/test-quantify.R | 34 ++++++++++++- 7 files changed, 231 insertions(+), 7 deletions(-) create mode 100644 man/quantify.Rd create mode 100644 man/unquantify.Rd diff --git a/NAMESPACE b/NAMESPACE index b0fa31fc1..4c679c916 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -170,6 +170,7 @@ export(pool.scalar) export(pool.scalar.syn) export(pool.syn) export(pool.table) +export(quantify) export(quickpred) export(rbind) export(remove.lindep) @@ -179,6 +180,7 @@ export(stripplot) export(supports.transparent) export(tidy) export(trim.data) +export(unquantify) export(version) export(xyplot) importFrom(Matrix,nearPD) diff --git a/NEWS.md b/NEWS.md index 85df067df..c86eca3f2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,4 @@ +* Exports `quantify()` and `unquantify()` for optimal scaling of factors to numeric representation * Adds a mechanism for filtering rows and selecting columns during the MICE iterations with univariate imputations. The method simplifies univariate imputation models by removing redundant predictors. The method is implemented in the top-level function `trim.data()`, which takes as input the design matrix `x`, the target variable `y` and the response `ry`, and returns a list of two logical vectors named `"rows"` (which filters rows of `x`) and `"cols"` (which selects columns of `x`). The user can choose among several low-level trimmers, including least angular regression, lasso, elastic net, and linear dependencies removal. It is also possible to specify your own low-level `mice.trim.mytrim()` function and call it from `mice()` using the `trimmer == "mytrim"` argument. The method is more robust and faster than `remove.lindep()` and can handle datasets with many variables. # mice 3.17.3 diff --git a/R/quantify.R b/R/quantify.R index 78f3724e0..106f7e808 100644 --- a/R/quantify.R +++ b/R/quantify.R @@ -1,3 +1,43 @@ +#' Quantify a factor for use in linear modeling +#' +#' This function replaces a factor variable by a numeric vector, +#' optionally using optimal scaling based on canonical correlation analysis. +#' It is used to transform categorical variables into a continuous scale +#' suitable for methods such as LARS or linear regression. +#' +#' If `quantify = TRUE`, the function derives an optimal numerical +#' representation of the factor levels based on their relationship with the +#' predictors `x`. If `quantify = FALSE`, the function simply maps the +#' factor levels to integers in their original order. +#' +#' If `y` is not a factor, the function returns `y` unchanged. +#' +#' @param y A response vector, possibly a factor. +#' @param ry A logical vector indicating the non-missing values of `y`. +#' @param x A numeric matrix of predictors. +#' @param quantify Logical. If `TRUE`, compute optimal scaling via canonical correlation analysis. +#' @return A list with the following components: +#' \describe{ +#' \item{ynum}{A numeric vector of the same length as `y` with the quantification applied.} +#' \item{labels}{Character vector of factor levels (or `NULL` if `y` is not a factor).} +#' \item{quant}{Numeric vector of quantification values for each level (or `NULL`).} +#' } +#' @author Stef van Buuren, 2025 +#' @seealso [unquantify()] +#' @examples +#' # Simple quantification +#' y <- factor(c("a", "b", "a", "c", "b", "c")) +#' ry <- !is.na(y) +#' x <- matrix(rnorm(length(y) * 2), ncol = 2) +#' quantify(y, ry, x) +#' +#' # Without optimal scaling +#' quantify(y, ry, x, quantify = FALSE) +#' +#' # y is numeric: returned unchanged +#' quantify(1:6, rep(TRUE, 6), x) +#' +#' @export quantify <- function(y, ry, x, quantify = TRUE) { if (!is.factor(y)) { return(list(ynum = y, @@ -24,12 +64,49 @@ quantify <- function(y, ry, x, quantify = TRUE) { quant = quant_expand)) } +#' Revert quantified variables back to factor representation +#' +#' This function reverses the transformation performed by [quantify()], +#' restoring the original factor levels from a numeric representation. +#' It works by assigning each value in `ynum` to the nearest quantification +#' value in `quant`, and then mapping that to the corresponding factor label. +#' If `ynum` contains `NA` values, these are preserved in the output. +#' +#' If `labels` is `NULL`, the function simply returns `ynum` unchanged. +#' +#' @param ynum A numeric vector created by [quantify()], typically containing scaled values. +#' @param quant A numeric vector of quantification values corresponding to the original factor levels. +#' @param labels A character vector of labels associated with the levels of the original factor. +#' @note This function is intended to be used after [quantify()] to revert the quantification. +#' The function may fail to produce the original factor levels if the quantification +#' values are not unique. +#' @return A factor vector with the levels specified by `labels`, or the numeric vector `ynum` if `labels` is `NULL`. +#' +#' @examples +#' set.seed(123) +#' y <- factor(c("low", "medium", "high", "high", "medium", "low")) +#' x <- matrix(runif(6), nrow = 6) +#' ry <- rep(TRUE, 6) +#' q <- quantify(y, ry, x) +#' unquantify(q$ynum, q$quant, q$labels) +#' +#' # Handle missing values +#' ymiss <- y +#' ymiss[c(1, 3)] <- NA +#' ry <- !is.na(ymiss) +#' q2 <- quantify(ymiss, ry, x) +#' unquantify(q2$ynum, q2$quant, q2$labels) +#' @seealso [quantify()] +#' @export unquantify <- function(ynum = NULL, quant = NULL, labels = NULL) { if (is.null(labels)) return(ynum) - y <- factor(ynum, levels = quant, labels = labels) - if (anyNA(levels(y))) { - y <- droplevels(y, exclude = NA) - } - return(y) -} + closest <- vapply(seq_along(ynum), function(i) { + y <- ynum[i] + if (is.na(y)) return(NA_character_) + i_match <- which.min(abs(y - quant)) + labels[i_match] + }, character(1)) + + factor(closest, levels = labels) +} diff --git a/_pkgdown.yml b/_pkgdown.yml index b275dbf4d..c396aca40 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -131,7 +131,9 @@ reference: - larspred - norm.draw - .norm.draw + - quantify - trim.data + - unquantify - title: Multivariate amputation desc: | Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies. diff --git a/man/quantify.Rd b/man/quantify.Rd new file mode 100644 index 000000000..7ffb24b35 --- /dev/null +++ b/man/quantify.Rd @@ -0,0 +1,59 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/quantify.R +\name{quantify} +\alias{quantify} +\title{Quantify a factor for use in linear modeling} +\usage{ +quantify(y, ry, x, quantify = TRUE) +} +\arguments{ +\item{y}{A response vector, possibly a factor.} + +\item{ry}{A logical vector indicating the non-missing values of \code{y}.} + +\item{x}{A numeric matrix of predictors.} + +\item{quantify}{Logical. If \code{TRUE}, compute optimal scaling via canonical correlation analysis.} +} +\value{ +A list with the following components: +\describe{ +\item{ynum}{A numeric vector of the same length as \code{y} with the quantification applied.} +\item{labels}{Character vector of factor levels (or \code{NULL} if \code{y} is not a factor).} +\item{quant}{Numeric vector of quantification values for each level (or \code{NULL}).} +} +} +\description{ +This function replaces a factor variable by a numeric vector, +optionally using optimal scaling based on canonical correlation analysis. +It is used to transform categorical variables into a continuous scale +suitable for methods such as LARS or linear regression. +} +\details{ +If \code{quantify = TRUE}, the function derives an optimal numerical +representation of the factor levels based on their relationship with the +predictors \code{x}. If \code{quantify = FALSE}, the function simply maps the +factor levels to integers in their original order. + +If \code{y} is not a factor, the function returns \code{y} unchanged. +} +\examples{ +# Simple quantification +y <- factor(c("a", "b", "a", "c", "b", "c")) +ry <- !is.na(y) +x <- matrix(rnorm(length(y) * 2), ncol = 2) +quantify(y, ry, x) + +# Without optimal scaling +quantify(y, ry, x, quantify = FALSE) + +# y is numeric: returned unchanged +quantify(1:6, rep(TRUE, 6), x) + +} +\seealso{ +\code{\link[=unquantify]{unquantify()}} +} +\author{ +Stef van Buuren, 2025 +} diff --git a/man/unquantify.Rd b/man/unquantify.Rd new file mode 100644 index 000000000..c16e4a51f --- /dev/null +++ b/man/unquantify.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/quantify.R +\name{unquantify} +\alias{unquantify} +\title{Revert quantified variables back to factor representation} +\usage{ +unquantify(ynum = NULL, quant = NULL, labels = NULL) +} +\arguments{ +\item{ynum}{A numeric vector created by \code{\link[=quantify]{quantify()}}, typically containing scaled values.} + +\item{quant}{A numeric vector of quantification values corresponding to the original factor levels.} + +\item{labels}{A character vector of labels associated with the levels of the original factor.} +} +\value{ +A factor vector with the levels specified by \code{labels}, or the numeric vector \code{ynum} if \code{labels} is \code{NULL}. +} +\description{ +This function reverses the transformation performed by \code{\link[=quantify]{quantify()}}, +restoring the original factor levels from a numeric representation. +It works by assigning each value in \code{ynum} to the nearest quantification +value in \code{quant}, and then mapping that to the corresponding factor label. +If \code{ynum} contains \code{NA} values, these are preserved in the output. +} +\details{ +If \code{labels} is \code{NULL}, the function simply returns \code{ynum} unchanged. +} +\note{ +This function is intended to be used after \code{\link[=quantify]{quantify()}} to revert the quantification. +The function may fail to produce the original factor levels if the quantification +values are not unique. +} +\examples{ +set.seed(123) +y <- factor(c("low", "medium", "high", "high", "medium", "low")) +x <- matrix(runif(6), nrow = 6) +ry <- rep(TRUE, 6) +q <- quantify(y, ry, x) +unquantify(q$ynum, q$quant, q$labels) + +# Handle missing values +ymiss <- y +ymiss[c(1, 3)] <- NA +ry <- !is.na(ymiss) +q2 <- quantify(ymiss, ry, x) +unquantify(q2$ynum, q2$quant, q2$labels) +} +\seealso{ +\code{\link[=quantify]{quantify()}} +} diff --git a/tests/testthat/test-quantify.R b/tests/testthat/test-quantify.R index 572485a86..45e5a8e8b 100644 --- a/tests/testthat/test-quantify.R +++ b/tests/testthat/test-quantify.R @@ -1,5 +1,5 @@ +set.seed(123) test_that("quantify() and unquantify() work correctly for factors", { - set.seed(123) y <- factor(sample(c("A", "B", "C"), 10, replace = TRUE), levels = c("A", "B", "C")) x <- matrix(rnorm(10 * 3), ncol = 3) ry <- sample(c(TRUE), 10, replace = TRUE) @@ -68,4 +68,36 @@ test_that("quantify() and unquantify() work correctly for numeric variables", { }) +test_that("quantify and unquantify handle small sample edge cases", { + # n = 1 per category (minimum viable case), + # but not enough to estimate a regression + y1 <- factor(c("low", "medium", "high")) + x1 <- matrix(runif(3), nrow = 3) + ry1 <- rep(TRUE, 3) + + q1 <- quantify(y1, ry1, x1) + y1_back <- unquantify(q1$ynum, q1$quant, q1$labels) + + expect_s3_class(y1_back, "factor") + expect_true(all(levels(y1_back) %in% levels(y1))) + expect_equal(length(y1_back), length(y1)) + # there are duplicate quant values, so the reconstructed factor + # will not be identical to the original factor + expect_false(identical(y1_back, y1)) + + # n = 2 per category + y2 <- factor(c("low", "low", "medium", "medium", "high", "high")) + x2 <- matrix(runif(12), nrow = 6) + ry2 <- rep(TRUE, 6) + + q2 <- quantify(y2, ry2, x2) + y2_back <- unquantify(q2$ynum, q2$quant, q2$labels) + + expect_s3_class(y2_back, "factor") + expect_equal(length(y2_back), length(y2)) + expect_true(all(levels(y2_back) %in% levels(y2))) + # there are no duplicate quant values, so the reconstructed factor + # will be identical to the original factor + expect_true(identical(y2_back, y2)) +}) From 40df790386ccc6910b9097d23639ca72837b38eb Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 16:38:23 +0200 Subject: [PATCH 109/147] Update vignette --- vignettes/_imputation_models.qmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/vignettes/_imputation_models.qmd b/vignettes/_imputation_models.qmd index a7c412cbc..f7578980a 100644 --- a/vignettes/_imputation_models.qmd +++ b/vignettes/_imputation_models.qmd @@ -20,6 +20,8 @@ knitr::opts_chunk$set( ) ``` +> This vignette describes a new feature in the `mice` package that is currently under development. The functionality may change in future releases, and the examples may not work as expected. Use at your own risk. + ## Example Suppose you created a risk prediction model for a cohort of patients. The dataset contained missing values, which you imputed using MICE. Now, you want to implement the risk prediction model in clinical practice to assist decision-making for new patients. Since missing values are expected in the new patient data, how should you proceed? From d249a31272ac30c3d990698b66c8b87d44f6a439 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 21:10:33 +0200 Subject: [PATCH 110/147] Add workflow to build vignettes/*.qmd and store as artifact --- .github/workflows/build-vignettes.yml | 36 +++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 .github/workflows/build-vignettes.yml diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml new file mode 100644 index 000000000..8fa6eb2de --- /dev/null +++ b/.github/workflows/build-vignettes.yml @@ -0,0 +1,36 @@ +name: Build Dev Vignettes + +on: + push: + branches: [dev] + workflow_dispatch: + +jobs: + build-vignettes: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-r@v2 + + - uses: r-lib/actions/setup-pandoc@v2 + + - name: Install Quarto + run: install.packages("quarto") + + - name: Render all .qmd vignettes + run: | + mkdir -p dev_html + for file in vignettes/*.qmd; do + quarto::quarto_render(file, output_dir = "dev_html") + done + shell: Rscript {0} + + - name: Upload vignettes as artifact + uses: actions/upload-artifact@v4 + with: + name: dev-vignettes + path: dev_html/*.html From 8b5e4d0895b48a5cc141256c5cbd1e5a59659727 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 21:13:10 +0200 Subject: [PATCH 111/147] Rename to imputation-models.qmd --- vignettes/{_imputation_models.qmd => imputation-models.qmd} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename vignettes/{_imputation_models.qmd => imputation-models.qmd} (100%) diff --git a/vignettes/_imputation_models.qmd b/vignettes/imputation-models.qmd similarity index 100% rename from vignettes/_imputation_models.qmd rename to vignettes/imputation-models.qmd From 4eeb646bb0ba7e58e5bf5e4bb1834565e1606937 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 21:15:34 +0200 Subject: [PATCH 112/147] Update .gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 9ab84fc54..ba11b3389 100644 --- a/.gitignore +++ b/.gitignore @@ -7,5 +7,5 @@ inst/doc *_cache script docs - +vignettes/*.html /.quarto/ From 95ce39237432bec7ed5fbe7d9a556a9fc175c08c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 21:33:23 +0200 Subject: [PATCH 113/147] Run install quarto in R, not bash --- .github/workflows/build-vignettes.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index 8fa6eb2de..949d3b8be 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -20,6 +20,7 @@ jobs: - name: Install Quarto run: install.packages("quarto") + shell: Rscript {0} - name: Render all .qmd vignettes run: | From 7dae7ad819684e684ece1ecf77699c63594f6500 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 21:42:12 +0200 Subject: [PATCH 114/147] Separate R and bash commands --- .github/workflows/build-vignettes.yml | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index 949d3b8be..37b5b87a0 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -22,12 +22,14 @@ jobs: run: install.packages("quarto") shell: Rscript {0} - - name: Render all .qmd vignettes + - name: Create output directory for rendered vignettes + run: mkdir -p dev_html + shell: bash + + - name: Render all .qmd vignettes to dev_html/ run: | - mkdir -p dev_html - for file in vignettes/*.qmd; do - quarto::quarto_render(file, output_dir = "dev_html") - done + files <- list.files("vignettes", pattern = "\\.qmd$", full.names = TRUE) + for (f in files) quarto::quarto_render(f, output_dir = "dev_html") shell: Rscript {0} - name: Upload vignettes as artifact From 499014b44501f76a8201da8e8667452904081683 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 22:01:53 +0200 Subject: [PATCH 115/147] Use bash quarto CLI to evade R --- .github/workflows/build-vignettes.yml | 31 ++++++++++++++++----------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index 37b5b87a0..c58c54ea6 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -1,4 +1,4 @@ -name: Build Dev Vignettes +name: Build Experimental Vignettes on: push: @@ -18,22 +18,27 @@ jobs: - uses: r-lib/actions/setup-pandoc@v2 - - name: Install Quarto - run: install.packages("quarto") - shell: Rscript {0} + - name: Install Quarto CLI + uses: quarto-dev/quarto-actions/setup@v2 + with: + version: "latest" + + - name: Check Quarto version (diagnostic) + run: quarto --version - - name: Create output directory for rendered vignettes - run: mkdir -p dev_html - shell: bash + - name: Install local R package (from dev branch) + run: R CMD INSTALL . - - name: Render all .qmd vignettes to dev_html/ + - name: Render all .qmd vignettes with Quarto CLI run: | - files <- list.files("vignettes", pattern = "\\.qmd$", full.names = TRUE) - for (f in files) quarto::quarto_render(f, output_dir = "dev_html") - shell: Rscript {0} + mkdir -p dev_html + for f in vignettes/*.qmd; do + echo "Rendering $f..." + quarto render "$f" --output-dir dev_html + done - - name: Upload vignettes as artifact + - name: Upload rendered HTML vignettes as artifact uses: actions/upload-artifact@v4 with: - name: dev-vignettes + name: experimental-vignettes path: dev_html/*.html From 41c34776c8a7157520095ebdd3b17ecde0352476 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 22:06:29 +0200 Subject: [PATCH 116/147] Remove latest from instal --- .github/workflows/build-vignettes.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index c58c54ea6..6038a9469 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -20,8 +20,6 @@ jobs: - name: Install Quarto CLI uses: quarto-dev/quarto-actions/setup@v2 - with: - version: "latest" - name: Check Quarto version (diagnostic) run: quarto --version From 1a950127bc6e31a839633ed008ea0540c5eafee1 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 22:10:57 +0200 Subject: [PATCH 117/147] Install R dependencies --- .github/workflows/build-vignettes.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index 6038a9469..0bae6280d 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -18,15 +18,17 @@ jobs: - uses: r-lib/actions/setup-pandoc@v2 + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: local::. + needs: vignette + - name: Install Quarto CLI uses: quarto-dev/quarto-actions/setup@v2 - name: Check Quarto version (diagnostic) run: quarto --version - - name: Install local R package (from dev branch) - run: R CMD INSTALL . - - name: Render all .qmd vignettes with Quarto CLI run: | mkdir -p dev_html From dc62507555d80e7c21398aa200f5b1b9d9daa4d9 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 6 May 2025 22:38:50 +0200 Subject: [PATCH 118/147] Enable dependencies cache --- .github/workflows/build-vignettes.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index 0bae6280d..f7d272f60 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -22,6 +22,7 @@ jobs: with: extra-packages: local::. needs: vignette + cache: true - name: Install Quarto CLI uses: quarto-dev/quarto-actions/setup@v2 From f260d153d93a7a21932f6cb81445436c7b5cbc69 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 7 May 2025 08:22:50 +0200 Subject: [PATCH 119/147] Upload entire folder --- .github/workflows/build-vignettes.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index f7d272f60..af9861833 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -42,4 +42,4 @@ jobs: uses: actions/upload-artifact@v4 with: name: experimental-vignettes - path: dev_html/*.html + path: dev_html/ From b97e4cb69aa8cfd402519d4eacb9a2f421b9a61c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 7 May 2025 08:28:26 +0200 Subject: [PATCH 120/147] Set output directory --- .github/workflows/build-vignettes.yml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-vignettes.yml b/.github/workflows/build-vignettes.yml index af9861833..d65093f6a 100644 --- a/.github/workflows/build-vignettes.yml +++ b/.github/workflows/build-vignettes.yml @@ -32,12 +32,15 @@ jobs: - name: Render all .qmd vignettes with Quarto CLI run: | - mkdir -p dev_html + mkdir -p "$PWD/dev_html" for f in vignettes/*.qmd; do echo "Rendering $f..." - quarto render "$f" --output-dir dev_html + quarto render "$f" --output-dir "$PWD/dev_html" done + - name: Show contents of dev_html + run: ls -lh dev_html + - name: Upload rendered HTML vignettes as artifact uses: actions/upload-artifact@v4 with: From 7fafc7b1ea8a59e4f5c21e3675ca9dcf1c7d8b32 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 7 May 2025 09:03:00 +0200 Subject: [PATCH 121/147] Clean up vignette Imputation Models --- vignettes/imputation-models.qmd | 5 +- vignettes/references.bib | 177 +++++++++++++++----------------- 2 files changed, 85 insertions(+), 97 deletions(-) diff --git a/vignettes/imputation-models.qmd b/vignettes/imputation-models.qmd index f7578980a..00aa5e088 100644 --- a/vignettes/imputation-models.qmd +++ b/vignettes/imputation-models.qmd @@ -1,7 +1,9 @@ --- title: "Imputation Models in MICE" author: "Stef van Buuren" -date: "`r Sys.Date()`" +date: today +bibliography: references.bib +link-citations: true format: html: theme: sandstone @@ -10,7 +12,6 @@ format: embed-resources: true toc: true toc-depth: 3 -bibliography: references.bib --- ```{r, include = FALSE} diff --git a/vignettes/references.bib b/vignettes/references.bib index 5460f49a8..7f06414a2 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -1,109 +1,96 @@ @inproceedings{dempster1983, - Address = {New York}, - Author = {Dempster, A. P. and Rubin, D. B.}, - Booktitle = {Incomplete Data in Sample Surveys}, - Date-Added = {2011-01-25 08:31:11 +0100}, - Date-Modified = {2011-10-02 10:56:44 +0000}, - Pages = {3-10}, - Publisher = {Academic Press}, - Title = {Introduction}, - Volume = {2}, - Year = {1983}} + author = {Dempster, A. P. and Rubin, D. B.}, + title = {Introduction}, + booktitle = {Incomplete Data in Sample Surveys}, + address = {New York}, + publisher = {Academic Press}, + volume = {2}, + pages = {3--10}, + year = {1983} +} -@Article{kavelaars2022, -author = {Kavelaars, X. M. and {van Ginkel}, J. R. and {van Buuren}, S.}, -title = {Multiple imputation in data that grow over time: A comparison of three strategies}, -journal = {Multivariate Behavioral Research}, -volume = {57}, -number = {2-3}, -pages = {513--523}, -year = {2022}, -location = {}, -keywords = {}} +@article{kavelaars2022, + author = {Kavelaars, X. M. and {van Ginkel}, J. R. and {van Buuren}, S.}, + title = {Multiple imputation in data that grow over time: A comparison of three strategies}, + journal = {Multivariate Behavioral Research}, + volume = {57}, + number = {2-3}, + pages = {513--523}, + year = {2022} +} @article{little1988, - Author = {Little, R. J. A.}, - Date-Modified = {2011-10-02 13:23:01 +0000}, - Journal = {Journal of Business Economics and Statistics}, - Number = {3}, - Pages = {287-301}, - Title = {Missing-data adjustments in large surveys (with discussion)}, - Volume = {6}, - Year = {1988}} + author = {Little, R. J. A.}, + title = {Missing-data adjustments in large surveys (with discussion)}, + journal = {Journal of Business Economics and Statistics}, + volume = {6}, + number = {3}, + pages = {287--301}, + year = {1988} +} @article{morris2014, -author = {Morris, T. P. and White, I. R. and Royston, P.}, -title = {Tuning multiple imputation by predictive mean matching and local residual draws}, -journal = {BMC Medical Reseach Methods}, -volume = {14}, -number = {}, -pages = {75}, -year = {2014}, -location = {}, -keywords = {}} + author = {Morris, T. P. and White, I. R. and Royston, P.}, + title = {Tuning multiple imputation by predictive mean matching and local residual draws}, + journal = {BMC Medical Research Methodology}, + volume = {14}, + pages = {75}, + year = {2014} +} @book{rubin1987, - Address = {New York}, - Author = {Rubin, D. B.}, - Date-Modified = {2011-10-02 14:41:28 +0000}, - Keywords = {Nonresponse}, - Publisher = {John Wiley \& Sons}, - Title = {Multiple Imputation for Nonresponse in Surveys}, - Year = {1987}} + author = {Rubin, D. B.}, + title = {Multiple Imputation for Nonresponse in Surveys}, + address = {New York}, + publisher = {John Wiley \& Sons}, + year = {1987} +} @article{schenker1996, - Author = {Schenker, N. and Taylor, J. M. G.}, - Journal = {Computational Statistics \& Data Analysis}, - Keywords = {Multiple imputation}, - Number = {4}, - Pages = {425-446}, - Title = {Partially parametric techniques for multiple imputation}, - Volume = {22}, - Year = {1996}} + author = {Schenker, N. and Taylor, J. M. G.}, + title = {Partially parametric techniques for multiple imputation}, + journal = {Computational Statistics \& Data Analysis}, + volume = {22}, + number = {4}, + pages = {425--446}, + year = {1996} +} -@Article{vanbuuren2007, -author = {{van Buuren}, S.}, -title = {Multiple imputation of discrete and continuous data by fully conditional specification}, -journal = {Statistical Methods in Medical Research}, -volume = {16}, -number = {3}, -pages = {219-242}, -year = {2007}, -abstract = {}, -location = {}, -keywords = {Multiple imputation; ERC}} +@article{vanbuuren2007, + author = {{van Buuren}, S.}, + title = {Multiple imputation of discrete and continuous data by fully conditional specification}, + journal = {Statistical Methods in Medical Research}, + volume = {16}, + number = {3}, + pages = {219--242}, + year = {2007} +} -@Article{vanbuuren2011, -author = {{van Buuren}, S. and Groothuis-Oudshoorn, K.}, -title = {{MICE}: Multivariate Imputation by Chained Equations in {R}}, -journal = {Journal of Statistical Software}, -volume = {45}, -number = {3}, -pages = {1–67}, -year = {2011}, -location = {}, -keywords = {ERC}} +@article{vanbuuren2011, + author = {{van Buuren}, S. and Groothuis-Oudshoorn, K.}, + title = {{MICE}: Multivariate Imputation by Chained Equations in {R}}, + journal = {Journal of Statistical Software}, + volume = {45}, + number = {3}, + pages = {1--67}, + year = {2011} +} -@Book{vanbuuren2018, -author = {{van Buuren}, S.}, -title = {Flexible Imputation of Missing Data. Second Edition}, -volume = {}, -pages = {}, -editor = {}, -publisher = {Chapman & Hall/CRC Press}, -address = {Boca Raton, FL}, -year = {2018}, -url = {https://stefvanbuuren.name/fimd/}, -abstract = {}} +@book{vanbuuren2018, + author = {{van Buuren}, S.}, + title = {Flexible Imputation of Missing Data. Second Edition}, + address = {Boca Raton, FL}, + publisher = {Chapman \& Hall/CRC Press}, + year = {2018}, + url = {https://stefvanbuuren.name/fimd/} +} -@Article{vink2015, -author = {Vink, G. and Lazendic, G. and {van Buuren}, S.}, -title = {Partioned predictive mean matching as a large data multilevel imputation technique}, -journal = {Psychological Test and Assessment Modeling}, -volume = {57}, -number = {4}, -pages = {577--594}, -year = {2015}, -abstract = {}, -location = {}, -keywords = {}} +@article{vink2015, + author = {Vink, G. and Lazendic, G. and {van Buuren}, S.}, + title = {Partitioned predictive mean matching as a large data multilevel imputation technique}, + journal = {Psychological Test and Assessment Modeling}, + volume = {57}, + number = {4}, + pages = {577--594}, + year = {2015} +} From b4c677c0a2c6c0b1dbbc911e6423da5960b48726 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 28 May 2025 08:55:33 +0200 Subject: [PATCH 122/147] Change export into S3method --- NAMESPACE | 10 +++++----- man/complete.mids.Rd | 2 +- man/filter.mids.Rd | 2 +- man/glance.mipo.Rd | 2 +- man/tidy.mipo.Rd | 2 +- man/xyplot.mads.Rd | 2 +- 6 files changed, 10 insertions(+), 10 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 349ddf5fd..3f109a14a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,11 +9,14 @@ S3method(cc,matrix) S3method(cc,mids) S3method(cci,default) S3method(cci,mids) +S3method(complete,mids) S3method(densityplot,mids) S3method(df.residual,lme) S3method(df.residual,mer) S3method(df.residual,mira) S3method(df.residual,multinom) +S3method(filter,mids) +S3method(glance,mipo) S3method(ic,data.frame) S3method(ic,default) S3method(ic,matrix) @@ -39,7 +42,9 @@ S3method(summary,mice.anova) S3method(summary,mids) S3method(summary,mipo) S3method(summary,mira) +S3method(tidy,mipo) S3method(with,mids) +S3method(xyplot,mads) S3method(xyplot,mids) export(.norm.draw) export(D1) @@ -60,7 +65,6 @@ export(cc) export(cci) export(coerce.newdata) export(complete) -export(complete.mids) export(construct.blocks) export(convergence) export(correlar) @@ -69,7 +73,6 @@ export(estimice) export(extractBS) export(fico) export(filter) -export(filter.mids) export(fix.coef) export(flux) export(fluxplot) @@ -77,7 +80,6 @@ export(futuremice) export(getfit) export(getqbar) export(glance) -export(glance.mipo) export(glm.mids) export(ibind) export(ic) @@ -177,12 +179,10 @@ export(squeeze) export(stripplot) export(supports.transparent) export(tidy) -export(tidy.mipo) export(trim.data) export(unquantify) export(version) export(xyplot) -export(xyplot.mads) importFrom(Matrix,nearPD) importFrom(Rcpp,evalCpp) importFrom(broom,glance) diff --git a/man/complete.mids.Rd b/man/complete.mids.Rd index 30f4590dd..2d015b3a6 100644 --- a/man/complete.mids.Rd +++ b/man/complete.mids.Rd @@ -5,7 +5,7 @@ \alias{complete} \title{Extracts the completed data from a \code{mids} object} \usage{ -complete.mids( +\method{complete}{mids}( data, action = 1L, include = FALSE, diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index 5d3e273f1..bd5c9256a 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -4,7 +4,7 @@ \alias{filter.mids} \title{Subset rows of a \code{mids} object} \usage{ -filter.mids(.data, ..., .preserve = FALSE) +\method{filter}{mids}(.data, ..., .preserve = FALSE) } \arguments{ \item{.data}{A \code{mids} object.} diff --git a/man/glance.mipo.Rd b/man/glance.mipo.Rd index 9a2add3b6..215ffc80a 100644 --- a/man/glance.mipo.Rd +++ b/man/glance.mipo.Rd @@ -4,7 +4,7 @@ \alias{glance.mipo} \title{Glance method to extract information from a \code{mipo} object} \usage{ -glance.mipo(x, ...) +\method{glance}{mipo}(x, ...) } \arguments{ \item{x}{An object with multiply-imputed models from \code{mice} (class: \code{mipo})} diff --git a/man/tidy.mipo.Rd b/man/tidy.mipo.Rd index 2410c3302..a10049baa 100644 --- a/man/tidy.mipo.Rd +++ b/man/tidy.mipo.Rd @@ -4,7 +4,7 @@ \alias{tidy.mipo} \title{Tidy method to extract results from a \code{mipo} object} \usage{ -tidy.mipo(x, conf.int = FALSE, conf.level = 0.95, ...) +\method{tidy}{mipo}(x, conf.int = FALSE, conf.level = 0.95, ...) } \arguments{ \item{x}{An object of class \code{mipo}} diff --git a/man/xyplot.mads.Rd b/man/xyplot.mads.Rd index 1bb0c6801..0713cf9b9 100644 --- a/man/xyplot.mads.Rd +++ b/man/xyplot.mads.Rd @@ -4,7 +4,7 @@ \alias{xyplot.mads} \title{Scatterplot of amputed and non-amputed data against weighted sum scores} \usage{ -xyplot.mads( +\method{xyplot}{mads}( x, data, which.pat = NULL, From 45c44e93b9e78de5e3437c75c48446623088116c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 28 May 2025 14:55:18 +0200 Subject: [PATCH 123/147] Merge parallel functionality (branch future.apply) into dev --- DESCRIPTION | 1 + NAMESPACE | 1 + NEWS.md | 25 ++ R/cbind.R | 32 +- R/complete.R | 15 +- R/edit.setup.R | 40 +-- R/futuremice.R | 23 +- R/initialize.imp.R | 14 + R/internal.R | 92 ++++- R/mice.R | 91 +++-- R/parlmice.R | 58 ++- R/sampler.R | 465 +++++++++++-------------- R/sampler.univ.R | 129 +++++++ _pkgdown.yml | 17 +- man/futuremice.Rd | 11 +- man/mice.Rd | 22 +- man/parlmice.Rd | 9 +- man/record.event.Rd | 46 +++ tests/testthat/test-as.mids.R | 11 +- tests/testthat/test-data.R | 2 +- tests/testthat/test-mice.impute.norm.R | 12 +- tests/testthat/test-mice.impute.pmm.R | 2 +- tests/testthat/test-parallel-sampler.R | 83 +++++ tests/testthat/test-rbind.R | 3 +- vignettes/developer-notes-complete.Rmd | 81 +++++ 25 files changed, 883 insertions(+), 402 deletions(-) create mode 100644 R/sampler.univ.R create mode 100644 man/record.event.Rd create mode 100644 tests/testthat/test-parallel-sampler.R create mode 100644 vignettes/developer-notes-complete.Rmd diff --git a/DESCRIPTION b/DESCRIPTION index a48077625..f921d095a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -66,6 +66,7 @@ Imports: Suggests: broom.mixed, future, + future.apply, furrr, haven, knitr, diff --git a/NAMESPACE b/NAMESPACE index 3f109a14a..8605228f1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -173,6 +173,7 @@ export(pool.table) export(quantify) export(quickpred) export(rbind) +export(record.event) export(remove.lindep) export(scan.newdata) export(squeeze) diff --git a/NEWS.md b/NEWS.md index f3111d413..9c3053d5c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,8 +1,33 @@ # mice 3.18.0.9000 +> **Experimental**: Native support for parallel imputation. + +- The `mice()` function now supports parallel execution of imputations via the new `parallel = TRUE` argument. When enabled, instead of sequentially calculating `m` imputations at a given iteration, the `m` chains are distributed across available CPU cores using the `future` and `future.apply` frameworks. +- Parallel imputation may significantly reduce runtime, especially for large datasets and many imputations (`m`), but does not pay-off for small datasets or few imputations. +- Parallel execution is implemented only in the `mice()` function, and does not affect the `mice.impute.*()` functions. + +- To activate parallel execution: + +``` +library(mice) +imp <- mice(data, parallel = TRUE) +``` + +- The default is `parallel = FALSE` for backward compatibility. +- The argument `n.core` specifies the number of CPU cores to use. If `n.core` is not specified (default) the actual number of cores used is calculated as minimum(number of available cores - 1, number of imputations). +- `printFlag = TRUE` prints iteration and imputation number only in sequential mode; parallel mode reports timing per iteration. +- Note: `mice()` will automatically select a parallel backend (default is `multisession`). To override, users may manually call `plan(...)` before running `mice()`. +- The `future` and `future.apply` packages must be installed to run parallel imputation. If not installed, `mice()` will throw an error and suggest installing the packages. +- The wrappers `parlmice()` and `futuremice()` are still functional, but now throw a warning that they will be deprecated in the future. Users are encouraged to use the new `parallel` argument in `mice()` instead. + +> **Experimental**: Saving and reusing models: See vignette + +> Other changes in this branch: + * Exports `quantify()` and `unquantify()` for optimal scaling of factors to numeric representation * Adds a mechanism for filtering rows and selecting columns during the MICE iterations with univariate imputations. The method simplifies univariate imputation models by removing redundant predictors. The method is implemented in the top-level function `trim.data()`, which takes as input the design matrix `x`, the target variable `y` and the response `ry`, and returns a list of two logical vectors named `"rows"` (which filters rows of `x`) and `"cols"` (which selects columns of `x`). The user can choose among several low-level trimmers, including least angular regression, lasso, elastic net, and linear dependencies removal. It is also possible to specify your own low-level `mice.trim.mytrim()` function and call it from `mice()` using the `trimmer == "mytrim"` argument. The method is more robust and faster than `remove.lindep()` and can handle datasets with many variables. + # mice 3.18.0 ### Major changes diff --git a/R/cbind.R b/R/cbind.R index a8537d6fb..3d7d8708f 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -18,8 +18,6 @@ cbind.mids <- function(x, y = NULL, ...) { y <- cbind.data.frame(y, dots) } - # Call is a vector, with first argument the mice statement - # and second argument the call to cbind.mids. call <- c(x$call, call) if (nrow(y) != nrow(x$data)) { @@ -32,13 +30,9 @@ cbind.mids <- function(x, y = NULL, ...) { varnames <- make.unique(colnames(data)) colnames(data) <- varnames - # where argument where <- cbind(x$where, matrix(FALSE, nrow = nrow(x$where), ncol = ncol(y))) colnames(where) <- varnames - # blocks: no renaming needed because all block definition will - # refer to varnames[1:ncol(x$data)] only, and are hence unique - # but we do need to rename duplicate block names yblocks <- vector("list", length = ncol(y)) blocks <- c(x$blocks, yblocks) xynames <- c(names(x$blocks), colnames(y)) @@ -50,31 +44,28 @@ cbind.mids <- function(x, y = NULL, ...) { m <- x$m - # count the number of missing data in y nmis <- c(x$nmis, colSums(is.na(y))) names(nmis) <- varnames - # imp: original data of y will be copied into the multiple imputed dataset, - # including the missing values of y. r <- (!is.na(y)) f <- function(j) { - m <- matrix(NA, - nrow = sum(!r[, j]), - ncol = x$m, - dimnames = list(row.names(y)[!r[, j]], seq_len(m)) + mtx <- matrix(NA, + nrow = sum(!r[, j]), + ncol = x$m, + dimnames = list(row.names(y)[!r[, j]], seq_len(m)) ) - as.data.frame(m) + as.data.frame(mtx) } - imp <- lapply(seq_len(ncol(y)), f) - imp <- c(x$imp, imp) + imp_y <- lapply(seq_len(ncol(y)), f) + + imp <- vector("list", length(varnames)) names(imp) <- varnames + imp[names(x$imp)] <- x$imp + imp[names(imp_y)] <- imp_y - # The imputation method for (columns in) y will be set to ''. method <- c(x$method, rep.int("", ncol(y))) names(method) <- blocknames - # The variable(s) in y are included in the predictorMatrix. - # y is not used as predictor as well as not imputed. predictorMatrix <- rbind( x$predictorMatrix, matrix(0, @@ -103,8 +94,6 @@ cbind.mids <- function(x, y = NULL, ...) { store <- x$store ignore <- x$ignore - # seed, lastSeedValue, number of iterations, chainMean and chainVar - # is taken as in mids object x. seed <- x$seed lastSeedValue <- x$lastSeedValue iteration <- x$iteration @@ -113,7 +102,6 @@ cbind.mids <- function(x, y = NULL, ...) { loggedEvents <- x$loggedEvents - ## save, and return midsobj <- mids( data = data, imp = imp, diff --git a/R/complete.R b/R/complete.R index d51922dea..21d3ce17b 100644 --- a/R/complete.R +++ b/R/complete.R @@ -154,17 +154,16 @@ single.complete <- function(data, where, imp, ell) { if (is.null(where)) { where <- is.na(data) } - idx <- seq_len(ncol(data))[apply(where, 2, any)] + idx <- intersect(seq_len(ncol(data)), match(names(imp), colnames(data))) for (j in idx) { - if (is.null(imp[[j]])) { - data[where[, j], j] <- NA + varname <- colnames(data)[j] + if (is.null(imp[[varname]])) { + data[where[, varname], varname] <- NA } else { - if (sum(where[, j]) == nrow(imp[[j]])) { - # assume equal length - data[where[, j], j] <- imp[[j]][, ell] + if (sum(where[, varname]) == nrow(imp[[varname]])) { + data[where[, varname], varname] <- imp[[varname]][, ell] } else { - # index by rowname - data[as.numeric(rownames(imp[[j]])), j] <- imp[[j]][, ell] + data[as.numeric(rownames(imp[[varname]])), varname] <- imp[[varname]][, ell] } } } diff --git a/R/edit.setup.R b/R/edit.setup.R index 4b2d4272a..7bca67fcb 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -4,20 +4,16 @@ mice.edit.setup <- function(data, setup, tasks, remove.constant = TRUE, remove.collinear = TRUE, remove_collinear = TRUE, + logenv = NULL, ...) { # legacy handling if (!remove_collinear) remove.collinear <- FALSE - # edits the imputation model setup - # When it detec constant or collinear variables, write in loggedEvents - # and continues imputation with reduced model - pred <- setup$predictorMatrix meth <- setup$method vis <- setup$visitSequence post <- setup$post - # FIXME: this function is not yet adapted to blocks if (ncol(pred) != nrow(pred) || length(meth) != nrow(pred) || ncol(data) != nrow(pred)) { return(setup) @@ -25,7 +21,6 @@ mice.edit.setup <- function(data, setup, tasks, varnames <- colnames(data) - # remove constant variables but leave passive variables untouched for (j in seq_len(ncol(data))) { if (!is.passive(meth[j])) { d.j <- data[, j] @@ -37,22 +32,16 @@ mice.edit.setup <- function(data, setup, tasks, } else { is.na(v) || v < 1000 * .Machine$double.eps } - didlog <- FALSE if (constant && any(pred[, j] != 0) && remove.constant) { - out <- varnames[j] pred[, j] <- 0 - updateLog(out = out, meth = "constant") - didlog <- TRUE + record.event(out = varnames[j], meth = "constant", logenv = logenv) } if (constant && meth[j] != "" && remove.constant) { - out <- varnames[j] pred[j, ] <- 0 - if (!didlog) { - updateLog(out = out, meth = "constant") - } meth[j] <- "" vis <- vis[vis != j] post[j] <- "" + record.event(out = varnames[j], meth = "constant", logenv = logenv) } } } @@ -60,38 +49,35 @@ mice.edit.setup <- function(data, setup, tasks, ## remove collinear variables ispredictor <- apply(pred != 0, 2, any) - if (any(ispredictor)) { - droplist <- find.collinear(data[, ispredictor, drop = FALSE], ...) + droplist <- if (any(ispredictor)) { + find.collinear(data[, ispredictor, drop = FALSE], logenv = logenv, ...) } else { - droplist <- NULL + NULL } + # do not drop variables with task "fill" droplist <- setdiff(droplist, names(tasks[tasks == "fill"])) + if (length(droplist) > 0) { for (k in seq_along(droplist)) { j <- which(varnames %in% droplist[k]) - didlog <- FALSE + if (any(pred[, j] != 0) && remove.collinear) { - # remove as predictor - out <- varnames[j] pred[, j] <- 0 - updateLog(out = out, meth = "collinear") - didlog <- TRUE + record.event(out = varnames[j], meth = "collinear", logenv = logenv) } + if (meth[j] != "" && remove.collinear) { - out <- varnames[j] pred[j, ] <- 0 - if (!didlog) { - updateLog(out = out, meth = "collinear") - } meth[j] <- "" vis <- vis[vis != j] post[j] <- "" + record.event(out = varnames[j], meth = "collinear", logenv = logenv) } } } - if (all(pred == 0L) && didlog) { + if (all(pred == 0L)) { stop("`mice` detected constant and/or collinear variables. No predictors were left after their removal.") } diff --git a/R/futuremice.R b/R/futuremice.R index 4142e8935..9217c1fbc 100644 --- a/R/futuremice.R +++ b/R/futuremice.R @@ -1,5 +1,14 @@ #' Wrapper function that runs MICE in parallel #' +#' @description +#' **Deprecated**: This function is deprecated as of `mice 3.18.0`. Please use +#' \code{mice(..., parallel = TRUE)} instead, which integrates native support +#' for parallel imputation via the \pkg{future} and \pkg{future.apply} frameworks. +#' +#' This wrapper is kept for backward compatibility and was based on the +#' \pkg{furrr} package, using \code{future_map()} to distribute imputations +#' across multiple R sessions. The output is combined via \code{\link{ibind}}. +#' #' This is a wrapper function for \code{\link{mice}}, using multiple cores to #' execute \code{\link{mice}} in parallel. As a result, the imputation #' procedure can be sped up, which may be useful in general. By default, @@ -46,7 +55,7 @@ #' The default \code{multisession} resolves futures asynchronously (in parallel) #' in separate \code{R} sessions running in the background. See #' \code{\link[future]{plan}} for more information on future plans. -#' @param packages A character vector with additional packages to be used in +#' @param packages A character vector with additional packages to be used in #' \code{mice} (e.g., for using external imputation functions). #' @param globals A character string with additional functions to be exported to #' each future (e.g., user-written imputation functions). @@ -78,8 +87,14 @@ #' #' @export futuremice <- function(data, m = 5, parallelseed = NA, n.core = NULL, seed = NA, - use.logical = TRUE, future.plan = "multisession", + use.logical = TRUE, future.plan = "multisession", packages = NULL, globals = NULL, ...) { + warning( + "'futuremice()' is deprecated as of mice 3.18.0. ", + "Please use 'mice(..., parallel = TRUE)' instead.", + call. = FALSE + ) + # check if packages available install.on.demand("parallelly", ...) install.on.demand("furrr", ...) @@ -136,7 +151,7 @@ futuremice <- function(data, m = 5, parallelseed = NA, n.core = NULL, seed = NA, } parallelseed <- get( ".Random.seed", - envir = globalenv(), + envir = globalenv(), mode = "integer", inherits = FALSE ) @@ -149,7 +164,7 @@ futuremice <- function(data, m = 5, parallelseed = NA, n.core = NULL, seed = NA, # begin future imps <- furrr::future_map( - n.imp.core, + n.imp.core, function(x) { mice(data = data, m = x, diff --git a/R/initialize.imp.R b/R/initialize.imp.R index 5189c5cf3..ffc76e58d 100644 --- a/R/initialize.imp.R +++ b/R/initialize.imp.R @@ -53,5 +53,19 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, } } + # Ensure imp[[j]] exists for any j used in where or blocks + vars_needed <- union(colnames(where)[colSums(where) > 0], unique(unlist(blocks))) + for (j in vars_needed) { + if (is.null(imp[[j]])) { + if (j %in% colnames(where)) { + wy <- where[, j] + } else { + wy <- rep(FALSE, nrow(data)) + } + imp[[j]] <- as.data.frame(matrix(NA, nrow = sum(wy), ncol = m)) + dimnames(imp[[j]]) <- list(row.names(data)[wy], as.character(seq_len(m))) + } + } + return(imp) } diff --git a/R/internal.R b/R/internal.R index da6288b53..1ec651ddd 100644 --- a/R/internal.R +++ b/R/internal.R @@ -5,22 +5,40 @@ keep.in.model <- function(y, ry, x, wy) { impute.with.na <- function(x, wy) !complete.cases(x) & wy +check.df <- function(x, y, ry) { + # If needed, writes the df warning message to the log + df <- sum(ry) - ncol(x) - 1 + mess <- paste("df set to 1. # observed cases:", sum(ry), " # predictors:", ncol(x) + 1) + if (df < 1 && sum(ry) > 0) { + record.event(out = mess, frame = 4) + } +} + ## make list of collinear variables to remove -find.collinear <- function(x, threshold = 0.999, ...) { +find.collinear <- function(x, threshold = 0.999, logenv = NULL, ...) { nvar <- ncol(x) x <- data.matrix(x) r <- !is.na(x) nr <- apply(r, 2, sum, na.rm = TRUE) ord <- order(nr, decreasing = TRUE) - xo <- x[, ord, drop = FALSE] ## SvB 24mar2011 + xo <- x[, ord, drop = FALSE] varnames <- dimnames(xo)[[2]] + z <- suppressWarnings(cor(xo, use = "pairwise.complete.obs")) hit <- outer(seq_len(nvar), seq_len(nvar), "<") & (abs(z) >= threshold) out <- apply(hit, 2, any, na.rm = TRUE) + + if (any(out)) { + record.event( + out = paste(paste(varnames[out], collapse = ", ")), + meth = "collinear", + logenv = logenv + ) + } + return(varnames[out]) } - updateLog <- function(out = NULL, meth = NULL, frame = 1) { # find structures defined a mice() level pos_state <- ma_exists("state", frame)$pos @@ -46,6 +64,74 @@ updateLog <- function(out = NULL, meth = NULL, frame = 1) { return() } +#' Record an event in the imputation log +#' +#' Records a logged event during the imputation process. This function is the +#' modern replacement for `updateLog()`, supporting both sequential and +#' parallel execution through an explicit logging environment. +#' +#' @param out A character string describing the event or the affected variable. +#' @param meth Optional method name associated with the event. If `NULL`, the method +#' from the current logging state will be used. +#' @param logenv Optional logging environment to store log entries and the current state. +#' If not supplied, the function attempts to log using `updateLog()` as fallback. +#' @param frame Stack frame level used in fallback mode when `logenv` is not available. +#' Default is `2`. +#' +#' @return This function is called for its side effects. It returns `invisible(NULL)`. +#' +#' @details +#' If `logenv` is provided and contains a valid logging state (`logenv$state`), +#' the function appends a log entry to `logenv$log` and sets `logenv$state$log <- TRUE`. +#' This is compatible with parallel execution using the `future` framework. +#' +#' When `logenv` is not provided, `record.event()` calls `mice:::updateLog()` using the legacy +#' frame-based mechanism. +#' +#' @section Advanced: +#' This function is intended primarily for package authors and developers of +#' custom imputation methods that integrate with the `mice` framework. It replaces +#' the internal `updateLog()` function to support structured, parallel-safe logging. +#' +#' @seealso [mice()] +#' +#' @export +record.event <- function(out = NULL, meth = NULL, frame = 1, logenv = NULL) { + # If no logenv passed explicitly, try .logenv from globalenv + if (is.null(logenv)) { + logenv <- tryCatch(get(".logenv", envir = .GlobalEnv), error = function(e) NULL) + } + + if (is.environment(logenv) && exists("state", envir = logenv, inherits = FALSE)) { + s <- get("state", envir = logenv, inherits = FALSE) + + if (!exists("log", envir = logenv, inherits = FALSE)) { + logenv$log <- data.frame( + it = integer(), im = integer(), dep = character(), + meth = character(), out = character(), + stringsAsFactors = FALSE + ) + } + + new_entry <- data.frame( + it = s$it, + im = s$im, + dep = s$dep, + meth = if (is.null(meth)) s$meth else meth, + out = if (is.null(out)) "" else out, + stringsAsFactors = FALSE + ) + + logenv$log <- rbind(logenv$log, new_entry) + s$log <- TRUE + assign("state", s, envir = logenv) + } else { + # Fallback: original behavior + updateLog(out = out, meth = meth, frame = frame + 1) + } + + invisible(NULL) +} sym <- function(x) { (x + t(x)) / 2 diff --git a/R/mice.R b/R/mice.R index cfa9a6877..bd13c154c 100644 --- a/R/mice.R +++ b/R/mice.R @@ -130,6 +130,15 @@ #' to turn off this behavior by specifying the #' argument \code{auxiliary = FALSE}. #' +#' If `parallel = TRUE`, the function uses the \pkg{future} package to +#' distribute the `m` imputations across multiple cores using `multisession`. +#' The number of workers defaults to one fewer than the number of available +#' cores, but is capped at `m`. Parallel workers are initialized at the start +#' of the function and cleaned up afterward using `on.exit()`. +#' +#' To use parallel computation, you must install the \pkg{future} and +#' \pkg{future.apply} packages. +#' #' @param data A data frame or a matrix containing the incomplete data. Missing #' values are coded as \code{NA}. #' @param m Number of multiple imputations. The default is \code{m=5}. @@ -272,18 +281,28 @@ #' elements are removed from the resulting \code{mids} object. The #' \code{store} element of the will be changed from \code{"train"} to #' \code{"train.compact"}. The default is \code{compact = FALSE}. +#' @param parallel Logical. Whether to perform imputations in parallel. +#' Default is `FALSE`. If `TRUE`, imputations over `m` datasets are +#' run in parallel using the \pkg{future} and \pkg{future.apply} packages. +#' @param n.core Optional integer. Specifies the maximum number of CPU cores +#' to use for parallel computation. If `NULL`, uses `min(m, availableCores() - 1)`. +#' The number of workers is always capped at `m`, the number of imputations. #' @param \dots Named arguments that are passed down to the univariate imputation #' functions. #' #' @return Returns an S3 object of class \code{\link[=mids-class]{mids}} #' (multiply imputed data set) +#' #' @author Stef van Buuren \email{stef.vanbuuren@@tno.nl}, Karin #' Groothuis-Oudshoorn \email{c.g.m.oudshoorn@@utwente.nl}, 2000-2010, with #' contributions of Alexander Robitzsch, Gerko Vink, Shahab Jolani, #' Roel de Jong, Jason Turner, Lisa Doove, #' John Fox, Frank E. Harrell, and Peter Malewski. +#' #' @seealso \code{\link[=mids-class]{mids}}, \code{\link{with.mids}}, -#' \code{\link{set.seed}}, \code{\link{complete}} +#' \code{\link{set.seed}}, \code{\link{complete}}, +#' \code{\link[future]{plan}}, \code{\link[future.apply]{future_lapply}} +#' #' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: #' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of #' Statistical Software}, \bold{45}(3), 1-67. @@ -380,6 +399,8 @@ mice <- function(data, seed = NA, data.init = NULL, compact = FALSE, + parallel = FALSE, + n.core = NULL, ...) { call <- match.call() check.deprecated(...) @@ -390,49 +411,67 @@ mice <- function(data, data <- check.dataform(data) m <- check.m(m) + # Set up parallel backend if requested + if (parallel) { + if (!requireNamespace("future.apply", quietly = TRUE)) { + stop("Please install the 'future.apply' package to use parallel execution.") + } + + available <- future::availableCores() + cores <- if (is.null(n.core)) { + min(m, max(1L, available - 1L)) + } else { + min(m, n.core) + } + + # Capture and restore old plan + old_plan <- future::plan() + on.exit(future::plan(old_plan), add = TRUE) + + # Set plan only if not already set + if (inherits(old_plan, "sequential") || inherits(old_plan, "default")) { + future::plan(future::multisession, workers = cores) + } else { + if ("workers" %in% names(formals(future::plan))) { + try(future::plan(workers = cores), silent = TRUE) + } + } + } + # determine input combination: predictorMatrix, blocks, formulas mp <- missing(predictorMatrix) mb <- missing(blocks) mf <- missing(formulas) - # case A if (mp & mb & mf) { - # blocks lead blocks <- make.blocks(colnames(data)) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") } - # case B + if (!mp & mb & mf) { - # predictorMatrix leads predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- make.blocks(colnames(predictorMatrix), partition = "scatter") formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") } - # case C if (mp & !mb & mf) { - # blocks leads blocks <- check.blocks(blocks, data) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") } - # case D if (mp & mb & !mf) { - # formulas leads formulas <- check.formulas(formulas, data) blocks <- construct.blocks(formulas) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") } - # case E if (!mp & !mb & mf) { - # predictor leads blocks <- check.blocks(blocks, data) z <- check.predictorMatrix(predictorMatrix, data, blocks) predictorMatrix <- z$predictorMatrix @@ -441,9 +480,7 @@ mice <- function(data, calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") } - # case F if (!mp & mb & !mf) { - # formulas lead formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- construct.blocks(formulas, predictorMatrix) @@ -453,18 +490,14 @@ mice <- function(data, calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") } - # case G if (mp & !mb & !mf) { - # blocks lead blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, blocks) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") } - # case H if (!mp & !mb & !mf) { - # blocks lead blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data, blocks) @@ -474,7 +507,6 @@ mice <- function(data, chk <- check.cluster(data, predictorMatrix) where <- check.where(where, data, blocks) - # check visitSequence, edit predictorMatrix for monotone user.visitSequence <- visitSequence visitSequence <- check.visitSequence(visitSequence, data = data, where = where, blocks = blocks @@ -498,18 +530,16 @@ mice <- function(data, blots <- check.blots(blots, data, blocks) ignore <- check.ignore(ignore, data) - # data frame for storing the event log - state <- list(it = 0, im = 0, dep = "", meth = "", log = FALSE) - loggedEvents <- data.frame(it = 0, im = 0, dep = "", meth = "", out = "") + logenv <- new.env(parent = emptyenv()) + logenv$state <- list(it = 0, im = 0, dep = "", meth = "", log = FALSE) - # edit imputation setup setup <- list( method = method, predictorMatrix = predictorMatrix, visitSequence = visitSequence, post = post ) - setup <- mice.edit.setup(data, setup, tasks, user.visitSequence, ...) + setup <- mice.edit.setup(data, setup, tasks, user.visitSequence, ..., logenv = logenv) method <- setup$method predictorMatrix <- setup$predictorMatrix visitSequence <- setup$visitSequence @@ -533,13 +563,18 @@ mice <- function(data, data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, calltype, blots, tasks, models, - post, c(from, to), printFlag, ... - ) + post, c(from, to), printFlag, ..., + parallel = parallel, logenv = logenv) - if (!state$log) loggedEvents <- NULL - if (state$log) row.names(loggedEvents) <- seq_len(nrow(loggedEvents)) + # Extract logged events from logenv if available + if (exists("log", envir = logenv) && nrow(logenv$log) > 0) { + loggedEvents <- logenv$log + logenv$state$log <- TRUE + } else { + loggedEvents <- NULL + logenv$state$log <- FALSE + } - ## save, and return midsobj <- mids( data = data, imp = q$imp, diff --git a/R/parlmice.R b/R/parlmice.R index e02464fff..4572546a7 100644 --- a/R/parlmice.R +++ b/R/parlmice.R @@ -1,7 +1,12 @@ #' Wrapper function that runs MICE in parallel #' +#' @description +#' **Deprecated**: This function is deprecated as of `mice 3.18.0`. Use +#' \code{mice(..., parallel = TRUE)} instead, which integrates parallel +#' functionality natively using the \pkg{future} framework. +#' #' This function is included for backward compatibility. The function -#' is superseded by \code{\link{futuremice}}. +#' was superseded by \code{\link{futuremice}}, which is also deprecated. #' #' This function relies on package \code{\link{parallel}}, which is a base #' package for R versions 2.14.0 and later. We have chosen to use parallel function @@ -68,79 +73,70 @@ #' @export parlmice <- function(data, m = 5, seed = NA, cluster.seed = NA, n.core = NULL, n.imp.core = NULL, cl.type = "PSOCK", ...) { - .Deprecated("futuremice") - # check form of data and m + warning( + "'parlmice()' is deprecated as of mice 3.18.0. ", + "Please use 'mice(..., parallel = TRUE)' instead.", + call. = FALSE + ) + data <- check.dataform(data) m <- check.m(m) - # check if data complete if (sum(is.na(data)) == 0) { stop("Data has no missing values") } - # check if arguments match CPU specifications if (!is.null(n.core)) { if (n.core > parallel::detectCores()) { - stop("Number of cores specified is greater than the number of logical cores in your CPU") + stop("n.core exceeds available logical CPU cores") } } - # determine course of action when not all arguments specified if (!is.null(n.core) & is.null(n.imp.core)) { n.imp.core <- m - warning(paste("Number of imputations per core not specified: n.imp.core = m =", m, "has been used")) + warning(paste("n.imp.core not specified. Using n.imp.core = m =", m)) } if (is.null(n.core) & !is.null(n.imp.core)) { n.core <- parallel::detectCores() - 1 - warning(paste("Number of cores not specified. Based on your machine a value of n.core =", parallel::detectCores() - 1, "is chosen")) + warning(paste("n.core not specified. Using n.core =", n.core)) } if (is.null(n.core) & is.null(n.imp.core)) { specs <- match.cluster(n.core = parallel::detectCores() - 1, m = m) n.core <- specs$cores n.imp.core <- specs$imps } - if (!is.na(seed)) { - if (n.core > 1) { - warning("Be careful; the specified seed is equal for all imputations. Please consider specifying cluster.seed instead.") - } + if (!is.na(seed) && n.core > 1) { + warning("Using the same seed across streams. Consider using cluster.seed for distinct streams.") } - # create arguments to export to cluster args <- match.call(mice, expand.dots = TRUE) args[[1]] <- NULL args$m <- n.imp.core - # make computing cluster cl <- parallel::makeCluster(n.core, type = cl.type) parallel::clusterExport(cl, - varlist = c( - "data", "m", "seed", "cluster.seed", - "n.core", "n.imp.core", "cl.type", - ls(parent.frame()) - ), - envir = environment() - ) - parallel::clusterExport(cl, - varlist = "do.call" - ) + varlist = c("data", "m", "seed", "cluster.seed", + "n.core", "n.imp.core", "cl.type", + ls(parent.frame())), + envir = environment()) + parallel::clusterExport(cl, varlist = "do.call") parallel::clusterEvalQ(cl, library(mice)) + if (!is.na(cluster.seed)) { parallel::clusterSetRNGStream(cl, cluster.seed) } - # generate imputations - imps <- parallel::parLapply(cl = cl, X = 1:n.core, function(x) do.call(mice, as.list(args), envir = environment())) + imps <- parallel::parLapply(cl = cl, X = 1:n.core, + function(x) do.call(mice, as.list(args), envir = environment())) parallel::stopCluster(cl) - # postprocess clustered imputation into a mids object imp <- imps[[1]] if (length(imps) > 1) { for (i in 2:length(imps)) { imp <- ibind(imp, imps[[i]]) } } - # let imputation matrix correspond to grand m - for (i in 1:length(imp$imp)) { + for (i in seq_along(imp$imp)) { colnames(imp$imp[[i]]) <- 1:imp$m } @@ -156,5 +152,5 @@ match.cluster <- function(n.core, m) { imps = rep(imps, each = n.core) ) which <- out[out[, "results"] == m, ] - which[order(which$cores, decreasing = T), ][1, 2:3] + which[order(which$cores, decreasing = TRUE), ][1, 2:3] } diff --git a/R/sampler.R b/R/sampler.R index 64a90eaba..dfc20261c 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -1,9 +1,9 @@ -# The sampler controls the actual Gibbs sampling iteration scheme. -# This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, calltype, blots, tasks, models, - post, fromto, printFlag, ...) { + post, fromto, printFlag, ..., + parallel = FALSE, future.packages = c("stats", "dplyr"), + logenv = logenv) { from <- fromto[1] to <- fromto[2] maxit <- to - from + 1 @@ -12,301 +12,254 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, # set up array for convergence checking chainMean <- chainVar <- initialize.chain(names(data), maxit, m) + # Initialize logged events + loggedEvents <- data.frame(it = integer(), im = integer(), dep = character(), + meth = character(), out = character(), + stringsAsFactors = FALSE) + state <- list(it = 0, im = 0, dep = "", meth = "", log = FALSE) + ## THE MAIN LOOP: GIBBS SAMPLER ## if (maxit < 1) iteration <- 0 if (maxit >= 1) { - if (printFlag) { + if (!parallel && printFlag) { cat("\n iter imp variable") } + for (k in from:to) { # begin k loop : main iteration loop iteration <- k - for (i in seq_len(m)) { - # begin i loop: repeated imputation loop - if (printFlag) { - cat("\n ", iteration, " ", i) - } + if (parallel) { + t0 <- Sys.time() + } - # prepare the i'th imputation - # do not overwrite any observed data - for (h in visitSequence) { - for (j in blocks[[h]]) { - y <- data[, j] - ry <- r[, j] - wy <- where[, j] - data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] + if (!parallel) { + # single-threaded processing + for (i in seq_len(m)) { + # begin i loop: repeated imputation loop + if (printFlag) { + cat("\n ", iteration, " ", i) } - } - - # impute block-by-block - for (h in visitSequence) { - ct <- calltype[[h]] - b <- blocks[[h]] - if (ct == "formula") ff <- formulas[[h]] else ff <- NULL - pred <- predictorMatrix[h, ] - user <- blots[[h]] - key <- paste0(h, "_", i) - # univariate/multivariate logic - theMethod <- method[h] - empt <- theMethod == "" - univ <- !empt && !is.passive(theMethod) && - !handles.format(paste0("mice.impute.", theMethod)) - mult <- !empt && !is.passive(theMethod) && - handles.format(paste0("mice.impute.", theMethod)) - pass <- !empt && is.passive(theMethod) && length(blocks[[h]]) == 1 - if (printFlag & !empt) cat(" ", b) - - ## store current state - oldstate <- get("state", pos = parent.frame()) - newstate <- list( - it = k, im = i, - dep = h, - meth = theMethod, - log = oldstate$log - ) - assign("state", newstate, pos = parent.frame(), inherits = TRUE) - - # (repeated) univariate imputation - pred method - if (univ) { - for (j in b) { - # if m outruns m.train, recycle m.train - m.train <- length(models[[j]]) - mod <- (i - 1L) %% m.train + 1L - imp[[j]][, i] <- - sampler.univ( - data = data, r = r, where = where, - pred = pred, formula = ff, - method = theMethod, - task = tasks[j], - model = models[[j]][[as.character(mod)]], - yname = j, k = k, - calltype = ct, - user = user, ignore = ignore, - ... - ) - - # update data - data[(!r[, j]) & where[, j], j] <- - imp[[j]][(!r[, j])[where[, j]], i] - - # optional post-processing - cmd <- post[j] - if (cmd != "") { - eval(parse(text = cmd)) - data[(!r[, j]) & where[, j], j] <- - imp[[j]][(!r[, j])[where[, j]], i] - } + # prepare the i'th imputation + # do not overwrite any observed data + for (h in visitSequence) { + for (j in blocks[[h]]) { + y <- data[, j] + ry <- r[, j] + wy <- where[, j] + data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] } } - # multivariate imputation - pred and formula - if (mult) { - mis <- !r - mis[, setdiff(colnames(data), b)] <- FALSE - data[mis] <- NA + result <- one.cycle(data, imp, r, where, i, k, visitSequence, + blocks, method, calltype, formulas, + predictorMatrix, blots, + tasks, models, + post, ignore, printFlag, ...) + data <- result$data + imp <- result$imp + } + k2 <- k - from + 1L + stat <- get.chain.stats(data, imp, chainMean, chainVar, k2, m, blocks, visitSequence) + chainMean <- stat$chainMean + chainVar <- stat$chainVar - fm <- paste("mice.impute", theMethod, sep = ".") - if (ct == "formula") { - imputes <- do.call(fm, args = list( - data = data, - formula = ff, ... - )) - } else if (ct == "pred") { - imputes <- do.call(fm, args = list( - data = data, - type = pred, ... - )) - } else { - stop("Cannot call function of type ", ct, call. = FALSE) - } - if (is.null(imputes)) { - stop("No imputations from ", theMethod, - h, - call. = FALSE - ) - } - for (j in names(imputes)) { - imp[[j]][, i] <- imputes[[j]] - data[!r[, j], j] <- imp[[j]][, i] + } else { + # parallel processing with future.apply + results_i <- future.apply::future_lapply(seq_len(m), function(i) { + data_i <- data + imp_i <- imp + + for (h in visitSequence) { + for (j in blocks[[h]]) { + y <- data_i[, j] + ry <- r[, j] + wy <- where[, j] + data_i[(!ry) & wy, j] <- imp_i[[j]][(!ry)[wy], i] } } - # passive imputation - # applies to all rows, so no ignore needed - if (pass) { - for (j in b) { - wy <- where[, j] - ry <- r[, j] - imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], - na.action = na.pass) - data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] + result <- one.cycle(data_i, imp_i, r, where, i, k, visitSequence, + blocks, method, calltype, formulas, + predictorMatrix, blots, + tasks, models, + post, ignore, printFlag = FALSE, ...) + + data_i <- result$data + imp_i <- result$imp + + mean_i <- initialize.chain(names(data), 1, 1)[[1]] + var_i <- initialize.chain(names(data), 1, 1)[[1]] + + for (h in visitSequence) { + for (j in blocks[[h]]) { + if (!is.factor(data[, j])) { + var_i[j] <- var(imp_i[[j]][, i], na.rm = TRUE) + mean_i[j] <- mean(imp_i[[j]][, i], na.rm = TRUE) + } else { + nc <- as.integer(factor(imp_i[[j]][, i], levels = levels(data[, j]))) + var_i[j] <- var(nc, na.rm = TRUE) + mean_i[j] <- mean(nc, na.rm = TRUE) + } } } - } # end h loop (blocks) - } # end i loop (imputation number) - # store means and sd of m imputes - k2 <- k - from + 1L - if (length(visitSequence) > 0L) { - for (h in visitSequence) { - for (j in blocks[[h]]) { - if (!is.factor(data[, j])) { - chainVar[j, k2, ] <- apply(imp[[j]], 2L, var, na.rm = TRUE) - chainMean[j, k2, ] <- colMeans(as.matrix(imp[[j]]), na.rm = TRUE) - } - if (is.factor(data[, j])) { - for (mm in seq_len(m)) { - nc <- as.integer(factor(imp[[j]][, mm], levels = levels(data[, j]))) - chainVar[j, k2, mm] <- var(nc, na.rm = TRUE) - chainMean[j, k2, mm] <- mean(nc, na.rm = TRUE) - } + log_i <- if (exists("loggedEvents", inherits = FALSE)) get("loggedEvents", inherits = FALSE) else NULL + list(imp = imp_i, mean = mean_i, var = var_i, log = log_i) + }, + future.packages = future.packages, + future.globals = list(initialize.chain = initialize.chain, + one.cycle = one.cycle, + get.chain.stats = get.chain.stats), + future.seed = TRUE) + + t1 <- Sys.time() + if (printFlag) cat(sprintf("\n iter %d (%s)", k, format(round(difftime(t1, t0), 2)))) + + k2 <- k - from + 1L + for (i in seq_len(m)) { + imp_i <- results_i[[i]]$imp + for (j in names(imp_i)) { + imp[[j]][, i] <- imp_i[[j]][, i] + } + mean_i <- results_i[[i]]$mean + var_i <- results_i[[i]]$var + for (j in names(mean_i)) { + if (j %in% dimnames(chainMean)[[1]]) { + chainMean[j, k2, i] <- mean_i[[j]] + chainVar[j, k2, i] <- var_i[[j]] } } + if (!is.null(results_i[[i]]$log)) { + loggedEvents <- rbind(loggedEvents, results_i[[i]]$log) + state$log <- TRUE + } } } - } # end main iteration + } # end k loop : main iteration loop - if (printFlag) { - r <- get("loggedEvents", parent.frame(1)) - ridge.used <- any(grepl("A ridge penalty", r$out)) - if (ridge.used) { + if (!parallel && printFlag) { + if (state$log && any(grepl("A ridge penalty", loggedEvents$out))) { cat("\n * Please inspect the loggedEvents \n") } else { cat("\n") } } } + list(iteration = maxit, imp = imp, chainMean = chainMean, chainVar = chainVar) } +one.cycle <- function(data, imp, r, where, i, k, visitSequence, + blocks, method, calltype, formulas, predictorMatrix, + blots, tasks, models, post, ignore, printFlag, ...) { + # this function makes one pass through the data + + # impute block-by-block + for (h in visitSequence) { + ct <- calltype[[h]] + b <- blocks[[h]] + ff <- if (ct == "formula") formulas[[h]] else NULL + pred <- predictorMatrix[h, ] + user <- blots[[h]] + + # univariate/multivariate logic + theMethod <- method[h] + empt <- theMethod == "" + univ <- !empt && !is.passive(theMethod) && + !handles.format(paste0("mice.impute.", theMethod)) + mult <- !empt && !is.passive(theMethod) && + handles.format(paste0("mice.impute.", theMethod)) + pass <- !empt && is.passive(theMethod) && length(blocks[[h]]) == 1 + if (printFlag & !empt) cat(" ", b) + + # (repeated) univariate imputation - pred method + if (univ) { + for (j in b) { + # if m outruns m.train, recycle m.train + m.train <- length(models[[j]]) + mod <- (i - 1L) %% m.train + 1L + imp[[j]][, i] <- + sampler.univ( + data = data, r = r, where = where, + pred = pred, formula = ff, + method = theMethod, + task = tasks[j], + model = models[[j]][[as.character(mod)]], + yname = j, k = k, + calltype = ct, + user = user, ignore = ignore, + ... + ) -sampler.univ <- function(data, r, where, pred, formula, method, task, model, - yname, k, calltype = "pred", user, ignore, - trimmer = "lindep", ...) { - j <- yname[1L] - - # nothing to impute - if (all(!where[, j]) && task != "train") { - return(numeric(0)) - } - - # prepare formula and model matrix - formula <- prepare.formula(formula, data, model, j, calltype, pred, task) - x <- obtain.design(data, formula) - - # expand pred vector to model matrix, remove intercept - if (calltype == "pred") { - type <- pred[labels(terms(formula))][attr(x, "assign")] - x <- x[, -1L, drop = FALSE] - names(type) <- colnames(x) - } - if (calltype == "formula") { - x <- x[, -1L, drop = FALSE] - type <- rep(1L, length = ncol(x)) - names(type) <- colnames(x) - } - - # select the features to feed into the imputation method - keep <- trim.data( - y = data[, j], - ry = r[, j] & !ignore, - x = x, - trimmer = trimmer, ... - ) - - # store the names of the features - # xj <- unique(xnames[keep$cols]) - # print(xj) - - # set up univariate imputation method - # wy: entries we wish to impute (length(y) elements) - # iy: entries we will impute (sum(wy) elements) - wy <- complete.cases(x) & where[, j] - iy <- wy[where[, j]] - - # wipe out previous values - imputes <- data[wy, j] - imputes[!iy] <- NA - - # remove linear dependencies - if (task != "fill") { - keep <- trim.data( - y = data[, j], - ry = r[, j] & !ignore, - x = x, - trimmer = trimmer, ... - ) - } - - # store the names of the features - # xj <- unique(xnames[keep$cols]) - # print(xj) - - # set up univariate imputation method - # wy: entries we wish to impute (length(y) elements) - # iy: entries we will impute (sum(wy) elements) - wy <- complete.cases(x) & where[, j] - iy <- wy[where[, j]] - - # wipe out previous values - imputes <- data[wy, j] - imputes[!iy] <- NA - - # here we go - f <- paste("mice.impute", method, sep = ".") - args <- c( - list( - y = data[, j], - ry = keep$rows, - x = x[, keep$cols, drop = FALSE], - wy = wy, - type = type[keep$cols], - task = task, - model = model), - user, list(...)) - imputes[iy] <- do.call(f, args = args) - return(imputes) -} + # update data + data[(!r[, j]) & where[, j], j] <- + imp[[j]][(!r[, j])[where[, j]], i] + # optional post-processing + cmd <- post[j] + if (cmd != "") { + eval(parse(text = cmd)) + data[(!r[, j]) & where[, j], j] <- + imp[[j]][(!r[, j])[where[, j]], i] + } + } + } -prepare.formula <- function(formula, data, model, j, ct, pred, task) { - # prepares the formula for univariate imputation - # saves (for "train") or retrieves (for "fill") the formula + # multivariate imputation - pred and formula + if (mult) { + mis <- !r + mis[, setdiff(colnames(data), b)] <- FALSE + data[mis] <- NA + + fm <- paste("mice.impute", theMethod, sep = ".") + imputes <- switch(ct, + formula = do.call(fm, list(data = data, formula = ff, ...)), + pred = do.call(fm, list(data = data, type = pred, ...)), + stop("Cannot call function of type ", ct)) + + # Abort if imputes is NULL + if (is.null(imputes)) { + stop("No imputations from ", theMethod, h) + } - # for "fill", use the stored formula instead of recalculating - if (task == "fill") { - if (!exists("formula", envir = model)) { - stop("Error: No stored formula found in model for 'fill' task.") + # Update imputations and data + for (j in names(imputes)) { + imp[[j]][, i] <- imputes[[j]] + data[!r[, j], j] <- imp[[j]][, i] + } } - formula <- get("formula", envir = model) - return(as.formula(formula)) - } - - if (ct == "pred") { - vars <- colnames(data)[pred != 0] - xnames <- setdiff(vars, j) - if (length(xnames) > 0L) { - formula <- reformulate(backticks(xnames), response = backticks(j)) - formula <- update(formula, ". ~ . ") - } else { - formula <- as.formula(paste0(j, " ~ 1")) + # passive imputation + # applies to all rows, so no ignore needed + if (pass) { + for (j in b) { + wy <- where[, j] + ry <- r[, j] + imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], + na.action = na.pass) + data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] + } } - } + } # end h loop (blocks) - if (ct == "formula") { - # move terms other than j from lhs to rhs - ymove <- setdiff(lhs(formula), j) - formula <- update(formula, paste(j, " ~ . ")) - if (length(ymove) > 0L) { - formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) - } - } + list(data = data, imp = imp) +} - # store formula in `model` only when task is "train" - if (task == "train") { - assign("formula", paste(deparse(formula), collapse = ""), envir = model) +get.chain.stats <- function(data, imp, chainMean, chainVar, k2, m, blocks, visitSequence) { + # Updates mean and variance of imputed values + for (h in visitSequence) { + for (j in blocks[[h]]) { + for (i in seq_len(m)) { + if (!is.factor(data[, j])) { + chainVar[j, k2, i] <- var(imp[[j]][, i], na.rm = TRUE) + chainMean[j, k2, i] <- mean(imp[[j]][, i], na.rm = TRUE) + } else { + nc <- as.integer(factor(imp[[j]][, i], levels = levels(data[, j]))) + chainVar[j, k2, i] <- var(nc, na.rm = TRUE) + chainMean[j, k2, i] <- mean(nc, na.rm = TRUE) + } + } + } } - - return(formula) + list(chainMean = chainMean, chainVar = chainVar) } diff --git a/R/sampler.univ.R b/R/sampler.univ.R new file mode 100644 index 000000000..bc0856696 --- /dev/null +++ b/R/sampler.univ.R @@ -0,0 +1,129 @@ +sampler.univ <- function(data, r, where, pred, formula, method, task, model, + yname, k, calltype = "pred", user, ignore, + trimmer = "lindep", ...) { + j <- yname[1L] + + # nothing to impute + if (all(!where[, j]) && task != "train") { + return(numeric(0)) + } + + # prepare formula and model matrix + formula <- prepare.formula(formula, data, model, j, calltype, pred, task) + x <- obtain.design(data, formula) + + # expand pred vector to model matrix, remove intercept + if (calltype == "pred") { + type <- pred[labels(terms(formula))][attr(x, "assign")] + x <- x[, -1L, drop = FALSE] + names(type) <- colnames(x) + } + if (calltype == "formula") { + x <- x[, -1L, drop = FALSE] + type <- rep(1L, length = ncol(x)) + names(type) <- colnames(x) + } + + # select the features to feed into the imputation method + keep <- trim.data( + y = data[, j], + ry = r[, j] & !ignore, + x = x, + trimmer = trimmer, ... + ) + + # store the names of the features + # xj <- unique(xnames[keep$cols]) + # print(xj) + + # set up univariate imputation method + # wy: entries we wish to impute (length(y) elements) + # iy: entries we will impute (sum(wy) elements) + wy <- complete.cases(x) & where[, j] + iy <- wy[where[, j]] + + # wipe out previous values + imputes <- data[wy, j] + imputes[!iy] <- NA + + # remove linear dependencies + if (task != "fill") { + keep <- trim.data( + y = data[, j], + ry = r[, j] & !ignore, + x = x, + trimmer = trimmer, ... + ) + } + + # store the names of the features + # xj <- unique(xnames[keep$cols]) + # print(xj) + + # set up univariate imputation method + # wy: entries we wish to impute (length(y) elements) + # iy: entries we will impute (sum(wy) elements) + wy <- complete.cases(x) & where[, j] + iy <- wy[where[, j]] + + # wipe out previous values + imputes <- data[wy, j] + imputes[!iy] <- NA + + # here we go + f <- paste("mice.impute", method, sep = ".") + args <- c( + list( + y = data[, j], + ry = keep$rows, + x = x[, keep$cols, drop = FALSE], + wy = wy, + type = type[keep$cols], + task = task, + model = model), + user, list(...)) + imputes[iy] <- do.call(f, args = args) + return(imputes) +} + + +prepare.formula <- function(formula, data, model, j, ct, pred, task) { + # prepares the formula for univariate imputation + # saves (for "train") or retrieves (for "fill") the formula + + # for "fill", use the stored formula instead of recalculating + if (task == "fill") { + if (!exists("formula", envir = model)) { + stop("Error: No stored formula found in model for 'fill' task.") + } + formula <- get("formula", envir = model) + return(as.formula(formula)) + } + + if (ct == "pred") { + vars <- colnames(data)[pred != 0] + xnames <- setdiff(vars, j) + if (length(xnames) > 0L) { + formula <- reformulate(backticks(xnames), response = backticks(j)) + formula <- update(formula, ". ~ . ") + } else { + formula <- as.formula(paste0(j, " ~ 1")) + } + } + + if (ct == "formula") { + # move terms other than j from lhs to rhs + ymove <- setdiff(lhs(formula), j) + formula <- update(formula, paste(j, " ~ . ")) + if (length(ymove) > 0L) { + formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) + } + } + + # store formula in `model` only when task is "train" + if (task == "train") { + assign("formula", paste(deparse(formula), collapse = ""), envir = model) + } + + return(formula) +} diff --git a/_pkgdown.yml b/_pkgdown.yml index 42f3249aa..a1a7b0a59 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,5 +1,7 @@ title: mice url: https://amices.org/mice/ +development: + mode: auto template: bootstrap: 5 params: @@ -129,10 +131,12 @@ reference: - estimice - larspred - norm.draw - - .norm.draw - quantify + - record.event - trim.data - unquantify + - .norm.draw + - .pmm.match - title: Multivariate amputation desc: | Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies. @@ -180,8 +184,9 @@ reference: - supports.transparent - version articles: - - title: General - navbar: ~ - contents: - - overview - - oldfriends +- title: General + navbar: ~ + contents: + - overview + - oldfriends + - developer-notes-complete diff --git a/man/futuremice.Rd b/man/futuremice.Rd index cd8a6d15f..8f9751364 100644 --- a/man/futuremice.Rd +++ b/man/futuremice.Rd @@ -56,13 +56,20 @@ each future (e.g., user-written imputation functions).} A mids object as defined by \code{\link{mids-class}} } \description{ +\strong{Deprecated}: This function is deprecated as of \verb{mice 3.18.0}. Please use +\code{mice(..., parallel = TRUE)} instead, which integrates native support +for parallel imputation via the \pkg{future} and \pkg{future.apply} frameworks. + +This wrapper is kept for backward compatibility and was based on the +\pkg{furrr} package, using \code{future_map()} to distribute imputations +across multiple R sessions. The output is combined via \code{\link{ibind}}. + This is a wrapper function for \code{\link{mice}}, using multiple cores to execute \code{\link{mice}} in parallel. As a result, the imputation procedure can be sped up, which may be useful in general. By default, \code{\link{futuremice}} distributes the number of imputations \code{m} about equally over the cores. -} -\details{ + This function relies on package \code{\link[furrr]{furrr}}, which is a package for R versions 3.2.0 and later. We have chosen to use furrr function \code{future_map} to allow the use of \code{futuremice} on Mac, Linux and diff --git a/man/mice.Rd b/man/mice.Rd index 2006c6bda..fb0a47c55 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -27,6 +27,8 @@ mice( seed = NA, data.init = NULL, compact = FALSE, + parallel = FALSE, + n.core = NULL, ... ) } @@ -193,6 +195,14 @@ elements are removed from the resulting \code{mids} object. The \code{store} element of the will be changed from \code{"train"} to \code{"train.compact"}. The default is \code{compact = FALSE}.} +\item{parallel}{Logical. Whether to perform imputations in parallel. +Default is \code{FALSE}. If \code{TRUE}, imputations over \code{m} datasets are +run in parallel using the \pkg{future} and \pkg{future.apply} packages.} + +\item{n.core}{Optional integer. Specifies the maximum number of CPU cores +to use for parallel computation. If \code{NULL}, uses \code{min(m, availableCores() - 1)}. +The number of workers is always capped at \code{m}, the number of imputations.} + \item{\dots}{Named arguments that are passed down to the univariate imputation functions.} } @@ -353,6 +363,15 @@ be added as main effects to the \code{formulas}, which will act as supplementary covariates in the imputation model. It is possible to turn off this behavior by specifying the argument \code{auxiliary = FALSE}. + +If \code{parallel = TRUE}, the function uses the \pkg{future} package to +distribute the \code{m} imputations across multiple cores using \code{multisession}. +The number of workers defaults to one fewer than the number of available +cores, but is capped at \code{m}. Parallel workers are initialized at the start +of the function and cleaned up afterward using \code{on.exit()}. + +To use parallel computation, you must install the \pkg{future} and +\pkg{future.apply} packages. } \section{Functions}{ @@ -507,7 +526,8 @@ data sets.} Dissertation. Rotterdam: Erasmus University. \code{\link{pool}}, \code{\link{complete}}, \code{\link{ampute}} \code{\link[=mids-class]{mids}}, \code{\link{with.mids}}, -\code{\link{set.seed}}, \code{\link{complete}} +\code{\link{set.seed}}, \code{\link{complete}}, +\code{\link[future]{plan}}, \code{\link[future.apply]{future_lapply}} } \author{ \strong{Maintainer}: Stef van Buuren \email{stef.vanbuuren@tno.nl} diff --git a/man/parlmice.Rd b/man/parlmice.Rd index 84dd6c00e..675ae2759 100644 --- a/man/parlmice.Rd +++ b/man/parlmice.Rd @@ -44,10 +44,13 @@ generally benefit from much faster cluster computation if \code{type} is set to A mids object as defined by \code{\link{mids-class}} } \description{ +\strong{Deprecated}: This function is deprecated as of \verb{mice 3.18.0}. Use +\code{mice(..., parallel = TRUE)} instead, which integrates parallel +functionality natively using the \pkg{future} framework. + This function is included for backward compatibility. The function -is superseded by \code{\link{futuremice}}. -} -\details{ +was superseded by \code{\link{futuremice}}, which is also deprecated. + This function relies on package \code{\link{parallel}}, which is a base package for R versions 2.14.0 and later. We have chosen to use parallel function \code{parLapply} to allow the use of \code{parlmice} on Mac, Linux and Windows diff --git a/man/record.event.Rd b/man/record.event.Rd new file mode 100644 index 000000000..b1ceea66b --- /dev/null +++ b/man/record.event.Rd @@ -0,0 +1,46 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/internal.R +\name{record.event} +\alias{record.event} +\title{Record an event in the imputation log} +\usage{ +record.event(out = NULL, meth = NULL, frame = 1, logenv = NULL) +} +\arguments{ +\item{out}{A character string describing the event or the affected variable.} + +\item{meth}{Optional method name associated with the event. If \code{NULL}, the method +from the current logging state will be used.} + +\item{frame}{Stack frame level used in fallback mode when \code{logenv} is not available. +Default is \code{2}.} + +\item{logenv}{Optional logging environment to store log entries and the current state. +If not supplied, the function attempts to log using \code{updateLog()} as fallback.} +} +\value{ +This function is called for its side effects. It returns \code{invisible(NULL)}. +} +\description{ +Records a logged event during the imputation process. This function is the +modern replacement for \code{updateLog()}, supporting both sequential and +parallel execution through an explicit logging environment. +} +\details{ +If \code{logenv} is provided and contains a valid logging state (\code{logenv$state}), +the function appends a log entry to \code{logenv$log} and sets \code{logenv$state$log <- TRUE}. +This is compatible with parallel execution using the \code{future} framework. + +When \code{logenv} is not provided, \code{record.event()} calls \code{mice:::updateLog()} using the legacy +frame-based mechanism. +} +\section{Advanced}{ + +This function is intended primarily for package authors and developers of +custom imputation methods that integrate with the \code{mice} framework. It replaces +the internal \code{updateLog()} function to support structured, parallel-safe logging. +} + +\seealso{ +\code{\link[=mice]{mice()}} +} diff --git a/tests/testthat/test-as.mids.R b/tests/testthat/test-as.mids.R index b7f695e40..548920c92 100644 --- a/tests/testthat/test-as.mids.R +++ b/tests/testthat/test-as.mids.R @@ -62,10 +62,17 @@ test_that("complete() reproduces the original data", { # works with dplyr library(dplyr) +nhanes3 <- nhanes +rownames(nhanes3) <- LETTERS[1:nrow(nhanes3)] +imp <- mice(nhanes3, m = 2, maxit = 1, print = FALSE) + +X <- complete(imp, action = "long", include = TRUE) + X3 <- X %>% group_by(hyp) %>% mutate(chlm = mean(chl, na.rm = TRUE)) -test_that("handles grouped_df", { - expect_silent(as.mids(X3)) +test_that("collinearity is logged during as.mids()", { + mids_obj <- suppressWarnings(as.mids(X3)) + expect_true(any(grepl("collinear", mids_obj$loggedEvents$meth))) }) diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R index 39cda4a6c..604ab7c54 100644 --- a/tests/testthat/test-data.R +++ b/tests/testthat/test-data.R @@ -16,7 +16,7 @@ for (i in seq_len(nrow(missing_idx))) { df[missing_idx[i, 1], missing_idx[i, 2]] <- NA } -expect_warning(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) +expect_silent(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) # make single-row new data with correct type newdata <- make.newdata(models = trained$models, vars = names(df)) diff --git a/tests/testthat/test-mice.impute.norm.R b/tests/testthat/test-mice.impute.norm.R index 8227db8ad..80805aef0 100644 --- a/tests/testthat/test-mice.impute.norm.R +++ b/tests/testthat/test-mice.impute.norm.R @@ -64,9 +64,9 @@ test_that("Correct estimation method used", { # TEST 3: correct imputation model # ##################################### -expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) -expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) -expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_silent(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_silent(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_silent(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) test_that("Imputations are equal", { expect_equal(imp.qr$imp, imp.svd$imp) @@ -78,9 +78,9 @@ test_that("Imputations are equal", { ##################################### # test on faulty imputation model (exactly singular system) -expect_warning(imp.qr <- mice(mammalsleep, ls.meth = "qr", seed = 123, print = FALSE)) -expect_warning(imp.svd <- mice(mammalsleep, ls.meth = "svd", seed = 123, print = FALSE)) -expect_warning(imp.ridge <- mice(mammalsleep, ls.meth = "ridge", seed = 123, print = FALSE)) +expect_silent(imp.qr <- mice(mammalsleep, ls.meth = "qr", seed = 123, print = FALSE)) +expect_silent(imp.svd <- mice(mammalsleep, ls.meth = "svd", seed = 123, print = FALSE)) +expect_silent(imp.ridge <- mice(mammalsleep, ls.meth = "ridge", seed = 123, print = FALSE)) test_that("Imputations are not equal", { expect_false(identical(imp.qr$imp, imp.svd$imp)) diff --git a/tests/testthat/test-mice.impute.pmm.R b/tests/testthat/test-mice.impute.pmm.R index ec7355db6..396abc867 100644 --- a/tests/testthat/test-mice.impute.pmm.R +++ b/tests/testthat/test-mice.impute.pmm.R @@ -109,7 +109,7 @@ data3$j25 <- rnorm(nrow(data3)) test_that("cancor with many junk variables does not crash", { - expect_warning(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, + expect_silent(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, maxit = 1, m = 1, seed = 1, print = FALSE)) }) diff --git a/tests/testthat/test-parallel-sampler.R b/tests/testthat/test-parallel-sampler.R new file mode 100644 index 000000000..5da49d04a --- /dev/null +++ b/tests/testthat/test-parallel-sampler.R @@ -0,0 +1,83 @@ +test_that("sampler() works identically in sequential and parallel modes", { + skip_if_not_installed("future.apply") + + # Sample data + data <- nhanes + m <- 2 + maxit <- 2 + + # Minimal setup + method <- rep("pmm", ncol(data)) + names(method) <- names(data) + blocks <- mice:::make.blocks(names(data)) + where <- is.na(data) + ignore <- rep(FALSE, nrow(data)) + predictorMatrix <- make.predictorMatrix(data = data, blocks = blocks) + formulas <- make.formulas(data, blocks) + calltype <- make.calltype(NULL, predictorMatrix, formulas, "pred") + blots <- vector("list", length(blocks)) + names(blots) <- names(blocks) + tasks <- check.tasks(tasks = NULL, data, models = NULL, blocks, skip.check.tasks = FALSE) + models <- NULL + post <- rep("", ncol(data)) + names(post) <- names(data) + visitSequence <- seq_along(blocks) + imp_init <- mice:::initialize.imp(data, m, ignore, where, blocks, visitSequence, method, nmis = colSums(where), data.init = NULL) + fromto <- c(1, maxit) + + # Run sampler in sequential mode + out_seq <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, + visitSequence, predictorMatrix, formulas, calltype, + blots, tasks, models, + post, fromto, printFlag = FALSE, parallel = FALSE) + + # Reset imputations + imp_init <- mice:::initialize.imp(data, m, ignore, where, blocks, visitSequence, method, nmis = colSums(where), data.init = NULL) + + # Run sampler in parallel mode + future::plan("multisession") + out_par <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, + visitSequence, predictorMatrix, formulas, calltype, + blots, tasks, models, + post, fromto, printFlag = FALSE, parallel = TRUE) + future::plan("sequential") + + # Compare output structure and content + expect_equal(dim(out_seq$chainMean), dim(out_par$chainMean)) + expect_equal(dim(out_seq$chainVar), dim(out_par$chainVar)) + expect_true(any(!is.na(out_par$chainMean))) + expect_true(any(!is.na(out_par$chainVar))) +}) + +test_that("sampler() collects loggedEvents in parallel mode", { + skip_if_not_installed("future.apply") + data <- nhanes + m <- 2 + method <- rep("pmm", ncol(data)) + names(method) <- names(data) + blocks <- mice:::make.blocks(names(data)) + where <- is.na(data) + ignore <- rep(FALSE, nrow(data)) + predictorMatrix <- make.predictorMatrix(data, blocks = blocks) + formulas <- make.formulas(data, blocks) + calltype <- make.calltype(NULL, predictorMatrix, formulas, "pred") + blots <- vector("list", length(blocks)) + names(blots) <- names(blocks) + tasks <- check.tasks(tasks = NULL, data, models = NULL, blocks, skip.check.tasks = FALSE) + models <- NULL + post <- rep("", ncol(data)) + names(post) <- names(data) + visitSequence <- seq_along(blocks) + imp_init <- mice:::initialize.imp(data, m, ignore, where, blocks, visitSequence, method, nmis = colSums(where), data.init = NULL) + fromto <- c(1, 1) + + future::plan("multisession") + out <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, + visitSequence, predictorMatrix, formulas, calltype, + blots, tasks, models, + post, fromto, printFlag = FALSE, parallel = TRUE) + future::plan("sequential") + + expect_true(is.data.frame(out$loggedEvents) || is.null(out$loggedEvents)) +}) + diff --git a/tests/testthat/test-rbind.R b/tests/testthat/test-rbind.R index ba7134e19..ac1ea0289 100644 --- a/tests/testthat/test-rbind.R +++ b/tests/testthat/test-rbind.R @@ -5,8 +5,9 @@ test_that("Constant variables are not imputed by default", { expect_equal(sum(is.na(complete(imp1))), 6L) }) +expect_silent(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE)) test_that("Constant variables are imputed for remove.constant = FALSE", { - expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, + expect_silent(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE, trimmer = "lindep")) expect_equal(sum(is.na(complete(imp1b))), 0L) }) diff --git a/vignettes/developer-notes-complete.Rmd b/vignettes/developer-notes-complete.Rmd new file mode 100644 index 000000000..3ac65571c --- /dev/null +++ b/vignettes/developer-notes-complete.Rmd @@ -0,0 +1,81 @@ +--- +title: "Developer Note: Robustness of `complete.mids()`" +author: "Stef van Buuren" +date: "`r Sys.Date()`" +format: gfm +--- + +## Overview + +This vignette provides implementation guidance and explains a subtle but important assumption in the interaction between the `imp` object (list of imputed values) and the `complete.mids()` function in the `mice` package. + +Historically, `complete.mids()` assumed that `imp[[j]]` existed only for variables that: + +- Contain missing values in the original data, and +- Were actually imputed (i.e., had `method[j] != ""`). + +This assumption generally held because `imp` was sparse — only containing entries for imputed variables — and `complete()` typically only acted on those. + +## Why the Assumption No Longer Holds + +Recent improvements in `mice` introduce new features that change these assumptions: + +- **Block-based imputation** allows blocks of variables to be processed together. +- **Flexible `where` masking** lets users request imputations for observed data, e.g., for simulation or perturbation. +- **Parallel Gibbs sampling** promotes full and explicit initialisation of the `imp` structure. + +As a result: + +- A variable may now appear in `blocks`, or be targeted in `where`, even if it is fully observed. +- `imp[[j]]` may not be initialized for such variables unless explicitly handled. + +## The Problem + +In this new setting, `complete.mids()` might attempt to access `imp[[j]]` for a variable that is: + +- Not missing, +- Not imputed, +- Not represented in `imp`. + +This results in a subscript error: + +``` +Error in imp[[j]]: subscript out of bounds +``` + +## The Solution + +We patch `initialize.imp()` and `single.complete()` to ensure: + +- Every variable that appears in `blocks` or `where` has an `imp[[j]]` entry. +- `imp[[j]]` is allowed to be empty (i.e., zero-row matrix), so it is safe to index. +- `single.complete()` uses `intersect()` to safely loop only over variables with defined imputations. + +This ensures compatibility with historical use cases **and** supports advanced, user-defined configurations. + +## Developer Guidance + +- Always use variable names (not numeric indices) when accessing `where[, j]` and `imp[[j]]`. +- In developer tools, consider `colnames(where)` and `names(imp)` as primary references. +- When modifying `blocks` or `visitSequence`, ensure `imp[[j]]` is initialized defensively. + +## Final Tip + +The patch was minimal but crucial. You can inspect the safety of an imputation object with: + +```r +setdiff(colnames(data$where)[colSums(data$where) > 0], names(data$imp)) +``` + +This identifies variables targeted for imputation that are missing from `imp`. + +## Related Patch + +- `initialize.imp()` now ensures all variables in `where` and `blocks` have a corresponding `imp[[j]]`. +- `single.complete()` now uses: + +```r +idx <- intersect(seq_len(ncol(data)), match(names(imp), colnames(data))) +``` + +to safely loop only over imputed variables. From 5c26ed514a308037664d3b3da75730149510f451 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 29 May 2025 13:08:24 +0200 Subject: [PATCH 124/147] Update site --- _pkgdown.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index a1a7b0a59..ab2acacfc 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -41,6 +41,7 @@ reference: Specification of the imputation models can be made more convenient using the following set of helpers. contents: - coerce.newdata + - construct.blocks - make.blocks - make.blots - make.calltype @@ -136,7 +137,6 @@ reference: - trim.data - unquantify - .norm.draw - - .pmm.match - title: Multivariate amputation desc: | Amputation is the inverse of imputation, starting with a complete dataset, and creating missing data pattern according to the posited missing data mechanism. Amputation is useful for simulation studies. From 3d743eb167010bd0183fb9f4cdc0d11c84c3bdfe Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 29 May 2025 13:09:25 +0200 Subject: [PATCH 125/147] Update ma_exists() and record.event() --- R/internal.R | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/R/internal.R b/R/internal.R index 1ec651ddd..0bd6c777b 100644 --- a/R/internal.R +++ b/R/internal.R @@ -113,7 +113,7 @@ record.event <- function(out = NULL, meth = NULL, frame = 1, logenv = NULL) { ) } - new_entry <- data.frame( + state.entry <- data.frame( it = s$it, im = s$im, dep = s$dep, @@ -122,7 +122,7 @@ record.event <- function(out = NULL, meth = NULL, frame = 1, logenv = NULL) { stringsAsFactors = FALSE ) - logenv$log <- rbind(logenv$log, new_entry) + logenv$log <- rbind(logenv$log, state.entry) s$log <- TRUE assign("state", s, envir = logenv) } else { @@ -140,28 +140,31 @@ sym <- function(x) { # This helper function was copied from # https://github.com/alexanderrobitzsch/miceadds/blob/master/R/ma_exists.R -ma_exists <- function(x, pos, n_index = 1:8) { +ma_exists <- function( x, pos, n_index = 1:8) +{ n_index <- n_index + 1 is_there <- exists(x, where = pos) obj <- NULL - if (is_there) { + nn <- 0 + if (is_there){ obj <- get(x, pos) } - if (!is_there) { - for (nn in n_index) { + if (!is_there){ + for (nn in n_index){ pos <- parent.frame(n = nn) is_there <- exists(x, where = pos) - if (is_there) { + if (is_there){ obj <- get(x, pos) break } } } #--- output - res <- list(is_there = is_there, obj = obj, pos = pos) + res <- list(is_there = is_there, obj = obj, pos = pos, n = nn) return(res) } + backticks <- function(varname) { sprintf("`%s`", varname) } From ed004fa5c42d8bda9898ca685a973754e01142dc Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 29 May 2025 18:56:54 +0200 Subject: [PATCH 126/147] Restore the loggedEvents data frame and newstate object for compatibility with miceadds --- NEWS.md | 5 +- R/edit.setup.R | 17 ++-- R/internal.R | 57 +++++++++--- R/mice.R | 25 +++--- R/sampler.R | 112 +++++++++++++++++------- man/mice.Rd | 51 +++++++++-- tests/testthat/test-anova.R | 1 + tests/testthat/test-data.R | 2 +- tests/testthat/test-internals.R | 5 +- tests/testthat/test-mice.impute.norm.R | 12 +-- tests/testthat/test-mice.impute.pmm.R | 2 +- tests/testthat/test-parallel-miceadds.R | 23 +++++ tests/testthat/test-rbind.R | 4 +- 13 files changed, 230 insertions(+), 86 deletions(-) create mode 100644 tests/testthat/test-parallel-miceadds.R diff --git a/NEWS.md b/NEWS.md index 9c3053d5c..1fd4b65a8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -27,6 +27,9 @@ imp <- mice(data, parallel = TRUE) * Exports `quantify()` and `unquantify()` for optimal scaling of factors to numeric representation * Adds a mechanism for filtering rows and selecting columns during the MICE iterations with univariate imputations. The method simplifies univariate imputation models by removing redundant predictors. The method is implemented in the top-level function `trim.data()`, which takes as input the design matrix `x`, the target variable `y` and the response `ry`, and returns a list of two logical vectors named `"rows"` (which filters rows of `x`) and `"cols"` (which selects columns of `x`). The user can choose among several low-level trimmers, including least angular regression, lasso, elastic net, and linear dependencies removal. It is also possible to specify your own low-level `mice.trim.mytrim()` function and call it from `mice()` using the `trimmer == "mytrim"` argument. The method is more robust and faster than `remove.lindep()` and can handle datasets with many variables. +### Minor changes + +* Changed argument # mice 3.18.0 @@ -51,7 +54,7 @@ This change in behavior ensures greater consistency at the end of each iteration The new behavior works well for simple cases. However, for more complex situations — especially when passive variables depend on other passive variables — it is recommended to manually specify a `visitSequence` that updates each passive variable immediately after one of its right-hand side predictors changes. (#699) -* **Adds the `calltype` argument to `mice()` for mixing `predictorMatrix` and `formulas` specifications** per variable-block. The `calltype` argument allows the user to specify some variables (or blocks of variables) by the `formulas` argument, and other variables by `predictorMatrix` argument. (Note: This argument was called `calltype` in version 3.17.1). +* **Adds the `calltype` argument to `mice()` for mixing `predictorMatrix` and `formulas` specifications** per variable-block. The `calltype` argument allows the user to specify some variables (or blocks of variables) by the `formulas` argument, and other variables by `predictorMatrix` argument. (Note: This argument was called `modeltype` in version 3.17.1). `calltype` is a character vector of `length(blocks)` elements that indicates how the imputation model is specified. Entries can one of two values: `"pred"` or `"formula"`. If `calltype = "pred"`, the predictors of the imputation model for the block are specified by the corresponding row of the `predictorMatrix`. If `calltype = "formula"` the imputation model is specified by relevant entry in `formulas`. The default depends on the presence of the `formulas` argument. If `formulas` is present, then `mice()` sets `calltype = "formula"` for any block for which a `formula` is specified. Otherwise, `calltype = "pred"`. diff --git a/R/edit.setup.R b/R/edit.setup.R index 7bca67fcb..a572e5ef7 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -4,7 +4,6 @@ mice.edit.setup <- function(data, setup, tasks, remove.constant = TRUE, remove.collinear = TRUE, remove_collinear = TRUE, - logenv = NULL, ...) { # legacy handling if (!remove_collinear) remove.collinear <- FALSE @@ -34,14 +33,17 @@ mice.edit.setup <- function(data, setup, tasks, } if (constant && any(pred[, j] != 0) && remove.constant) { pred[, j] <- 0 - record.event(out = varnames[j], meth = "constant", logenv = logenv) + updateLog(out = varnames[j], meth = "constant", frame = 1) + didlog <- TRUE } if (constant && meth[j] != "" && remove.constant) { pred[j, ] <- 0 meth[j] <- "" vis <- vis[vis != j] post[j] <- "" - record.event(out = varnames[j], meth = "constant", logenv = logenv) + if (!didlog) { + updateLog(out = varnames[j], meth = "constant", frame = 1) + } } } } @@ -50,7 +52,7 @@ mice.edit.setup <- function(data, setup, tasks, ## remove collinear variables ispredictor <- apply(pred != 0, 2, any) droplist <- if (any(ispredictor)) { - find.collinear(data[, ispredictor, drop = FALSE], logenv = logenv, ...) + find.collinear(data[, ispredictor, drop = FALSE], ...) } else { NULL } @@ -64,7 +66,8 @@ mice.edit.setup <- function(data, setup, tasks, if (any(pred[, j] != 0) && remove.collinear) { pred[, j] <- 0 - record.event(out = varnames[j], meth = "collinear", logenv = logenv) + updateLog(out = varnames[j], meth = "collinear", frame = 1) + didlog <- TRUE } if (meth[j] != "" && remove.collinear) { @@ -72,7 +75,9 @@ mice.edit.setup <- function(data, setup, tasks, meth[j] <- "" vis <- vis[vis != j] post[j] <- "" - record.event(out = varnames[j], meth = "collinear", logenv = logenv) + if (!didlog) { + updateLog(out = varnames[j], meth = "collinear", frame = 1) + } } } } diff --git a/R/internal.R b/R/internal.R index 0bd6c777b..56b860462 100644 --- a/R/internal.R +++ b/R/internal.R @@ -15,32 +15,31 @@ check.df <- function(x, y, ry) { } ## make list of collinear variables to remove -find.collinear <- function(x, threshold = 0.999, logenv = NULL, ...) { +find.collinear <- function(x, threshold = 0.999, ...) { nvar <- ncol(x) x <- data.matrix(x) r <- !is.na(x) nr <- apply(r, 2, sum, na.rm = TRUE) ord <- order(nr, decreasing = TRUE) - xo <- x[, ord, drop = FALSE] + xo <- x[, ord, drop = FALSE] ## SvB 24mar2011 varnames <- dimnames(xo)[[2]] - z <- suppressWarnings(cor(xo, use = "pairwise.complete.obs")) hit <- outer(seq_len(nvar), seq_len(nvar), "<") & (abs(z) >= threshold) out <- apply(hit, 2, any, na.rm = TRUE) + # Inform user if (any(out)) { - record.event( + updateLog( out = paste(paste(varnames[out], collapse = ", ")), meth = "collinear", - logenv = logenv + frame = 2 ) } return(varnames[out]) } -updateLog <- function(out = NULL, meth = NULL, frame = 1) { - # find structures defined a mice() level +updateLog <- function(out = NULL, meth = NULL, msg = NULL, fn = NULL, frame = 1) { pos_state <- ma_exists("state", frame)$pos pos_loggedEvents <- ma_exists("loggedEvents", frame)$pos @@ -48,22 +47,52 @@ updateLog <- function(out = NULL, meth = NULL, frame = 1) { r <- get("loggedEvents", pos_loggedEvents) rec <- data.frame( - it = s$it, - im = s$im, - dep = s$dep, + it = s$it, + im = s$im, + dep = s$dep, meth = if (is.null(meth)) s$meth else meth, - out = if (is.null(out)) "" else out + out = if (is.null(out)) "" else out, + msg = if (is.null(msg)) NA_character_ else msg, + fn = if (is.null(fn)) as.character(sys.call(-1)[1]) else as.character(fn), + stringsAsFactors = FALSE ) if (s$log) { - rec <- rbind(r, rec) + r <- rbind(r, rec) } + s$log <- TRUE assign("state", s, pos = pos_state, inherits = TRUE) - assign("loggedEvents", rec, pos = pos_loggedEvents, inherits = TRUE) - return() + assign("loggedEvents", r, pos = pos_loggedEvents, inherits = TRUE) + + invisible(NULL) } +# updateLog <- function(out = NULL, meth = NULL, frame = 1) { +# # find structures defined a mice() level +# pos_state <- ma_exists("state", frame)$pos +# pos_loggedEvents <- ma_exists("loggedEvents", frame)$pos +# +# s <- get("state", pos_state) +# r <- get("loggedEvents", pos_loggedEvents) +# +# rec <- data.frame( +# it = s$it, +# im = s$im, +# dep = s$dep, +# meth = if (is.null(meth)) s$meth else meth, +# out = if (is.null(out)) "" else out +# ) +# +# if (s$log) { +# rec <- rbind(r, rec) +# } +# s$log <- TRUE +# assign("state", s, pos = pos_state, inherits = TRUE) +# assign("loggedEvents", rec, pos = pos_loggedEvents, inherits = TRUE) +# return() +# } + #' Record an event in the imputation log #' #' Records a logged event during the imputation process. This function is the diff --git a/R/mice.R b/R/mice.R index bd13c154c..71f87ddac 100644 --- a/R/mice.R +++ b/R/mice.R @@ -130,6 +130,8 @@ #' to turn off this behavior by specifying the #' argument \code{auxiliary = FALSE}. #' +#' @section Parallel computation: +#' #' If `parallel = TRUE`, the function uses the \pkg{future} package to #' distribute the `m` imputations across multiple cores using `multisession`. #' The number of workers defaults to one fewer than the number of available @@ -139,6 +141,10 @@ #' To use parallel computation, you must install the \pkg{future} and #' \pkg{future.apply} packages. #' +#' @section Logged Events: +#' +#' The `mids` object produced by `mice()` contains an element `loggedEvents`. +#' #' @param data A data frame or a matrix containing the incomplete data. Missing #' values are coded as \code{NA}. #' @param m Number of multiple imputations. The default is \code{m=5}. @@ -530,8 +536,9 @@ mice <- function(data, blots <- check.blots(blots, data, blocks) ignore <- check.ignore(ignore, data) - logenv <- new.env(parent = emptyenv()) - logenv$state <- list(it = 0, im = 0, dep = "", meth = "", log = FALSE) + loggedEvents <- data.frame(it = 0L, im = 0L, dep = "", meth = "", out = "", + msg = NA_character_, fn = NA_character_, stringsAsFactors = FALSE) + state <- list(it = 0L, im = 0L, dep = "", meth = "", log = TRUE) setup <- list( method = method, @@ -539,7 +546,7 @@ mice <- function(data, visitSequence = visitSequence, post = post ) - setup <- mice.edit.setup(data, setup, tasks, user.visitSequence, ..., logenv = logenv) + setup <- mice.edit.setup(data, setup, tasks, user.visitSequence, ...) method <- setup$method predictorMatrix <- setup$predictorMatrix visitSequence <- setup$visitSequence @@ -564,15 +571,13 @@ mice <- function(data, visitSequence, predictorMatrix, formulas, calltype, blots, tasks, models, post, c(from, to), printFlag, ..., - parallel = parallel, logenv = logenv) + parallel = parallel) - # Extract logged events from logenv if available - if (exists("log", envir = logenv) && nrow(logenv$log) > 0) { - loggedEvents <- logenv$log - logenv$state$log <- TRUE - } else { + if (!state$log || nrow(loggedEvents) == 1L) { loggedEvents <- NULL - logenv$state$log <- FALSE + } else { + loggedEvents <- loggedEvents[-1L, ] + row.names(loggedEvents) <- seq_len(nrow(loggedEvents)) } midsobj <- mids( diff --git a/R/sampler.R b/R/sampler.R index dfc20261c..3d10723c6 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -2,8 +2,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, calltype, blots, tasks, models, post, fromto, printFlag, ..., - parallel = FALSE, future.packages = c("stats", "dplyr"), - logenv = logenv) { + parallel = FALSE, future.packages = NULL, future.seed = TRUE) { from <- fromto[1] to <- fromto[2] maxit <- to - from + 1 @@ -12,12 +11,6 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, # set up array for convergence checking chainMean <- chainVar <- initialize.chain(names(data), maxit, m) - # Initialize logged events - loggedEvents <- data.frame(it = integer(), im = integer(), dep = character(), - meth = character(), out = character(), - stringsAsFactors = FALSE) - state <- list(it = 0, im = 0, dep = "", meth = "", log = FALSE) - ## THE MAIN LOOP: GIBBS SAMPLER ## if (maxit < 1) iteration <- 0 if (maxit >= 1) { @@ -52,10 +45,10 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } result <- one.cycle(data, imp, r, where, i, k, visitSequence, - blocks, method, calltype, formulas, - predictorMatrix, blots, - tasks, models, - post, ignore, printFlag, ...) + blocks, method, calltype, formulas, + predictorMatrix, blots, + tasks, models, + post, ignore, printFlag, ...) data <- result$data imp <- result$imp } @@ -67,9 +60,14 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } else { # parallel processing with future.apply results_i <- future.apply::future_lapply(seq_len(m), function(i) { + emit_worker_log <- function(log_entry, file) { + saveRDS(log_entry, file = file) + } + data_i <- data imp_i <- imp + # loop over blocks for (h in visitSequence) { for (j in blocks[[h]]) { y <- data_i[, j] @@ -80,17 +78,15 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } result <- one.cycle(data_i, imp_i, r, where, i, k, visitSequence, - blocks, method, calltype, formulas, - predictorMatrix, blots, - tasks, models, - post, ignore, printFlag = FALSE, ...) + blocks, method, calltype, formulas, + predictorMatrix, blots, + tasks, models, + post, ignore, printFlag = FALSE, ...) data_i <- result$data imp_i <- result$imp - mean_i <- initialize.chain(names(data), 1, 1)[[1]] var_i <- initialize.chain(names(data), 1, 1)[[1]] - for (h in visitSequence) { for (j in blocks[[h]]) { if (!is.factor(data[, j])) { @@ -104,14 +100,32 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } } - log_i <- if (exists("loggedEvents", inherits = FALSE)) get("loggedEvents", inherits = FALSE) else NULL - list(imp = imp_i, mean = mean_i, var = var_i, log = log_i) + # Create a log record + log.entry <- data.frame( + it = k, im = i, dep = "cycle", meth = NA_character_, + out = "success", msg = "I1001", fn = "one.cycle", + stringsAsFactors = FALSE + ) + + logfile <- file.path(tempdir(), sprintf("log_it%02d_im%02d.rds", k, i)) + emit_worker_log(log.entry, logfile) + + list(imp = imp_i, mean = mean_i, var = var_i) }, future.packages = future.packages, future.globals = list(initialize.chain = initialize.chain, one.cycle = one.cycle, get.chain.stats = get.chain.stats), - future.seed = TRUE) + future.seed = future.seed) + + # Combine with existing log if needed + new.loggedEvents <- collect_logs() + if (!is.null(new.loggedEvents)) { + pos.loggedEvents <- parent.frame(1L) + loggedEvents <- get("loggedEvents", envir = pos.loggedEvents) + loggedEvents <- rbind(loggedEvents, new.loggedEvents) + assign("loggedEvents", loggedEvents, envir = pos.loggedEvents) + } t1 <- Sys.time() if (printFlag) cat(sprintf("\n iter %d (%s)", k, format(round(difftime(t1, t0), 2)))) @@ -130,29 +144,25 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, chainVar[j, k2, i] <- var_i[[j]] } } - if (!is.null(results_i[[i]]$log)) { - loggedEvents <- rbind(loggedEvents, results_i[[i]]$log) - state$log <- TRUE - } } } } # end k loop : main iteration loop - if (!parallel && printFlag) { - if (state$log && any(grepl("A ridge penalty", loggedEvents$out))) { - cat("\n * Please inspect the loggedEvents \n") - } else { - cat("\n") - } - } + # if (!parallel && printFlag) { + # if (state$log && any(grepl("A ridge penalty", loggedEvents$out))) { + # cat("\n * Please inspect the loggedEvents \n") + # } else { + # cat("\n") + # } + # } } list(iteration = maxit, imp = imp, chainMean = chainMean, chainVar = chainVar) } one.cycle <- function(data, imp, r, where, i, k, visitSequence, - blocks, method, calltype, formulas, predictorMatrix, - blots, tasks, models, post, ignore, printFlag, ...) { + blocks, method, calltype, formulas, predictorMatrix, + blots, tasks, models, post, ignore, printFlag, ...) { # this function makes one pass through the data # impute block-by-block @@ -176,6 +186,8 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, # (repeated) univariate imputation - pred method if (univ) { for (j in b) { + # miceadds support + newstate <- list(it = k, im = i, dep = j, meth = theMethod) # if m outruns m.train, recycle m.train m.train <- length(models[[j]]) mod <- (i - 1L) %% m.train + 1L @@ -208,6 +220,8 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, # multivariate imputation - pred and formula if (mult) { + # miceadds support + newstate <- list(it = k, im = i, dep = b, meth = theMethod) mis <- !r mis[, setdiff(colnames(data), b)] <- FALSE data[mis] <- NA @@ -233,6 +247,8 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, # applies to all rows, so no ignore needed if (pass) { for (j in b) { + # miceadds support + newstate <- list(it = k, im = i, dep = b, meth = theMethod) wy <- where[, j] ry <- r[, j] imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], @@ -263,3 +279,31 @@ get.chain.stats <- function(data, imp, chainMean, chainVar, k2, m, blocks, visit } list(chainMean = chainMean, chainVar = chainVar) } + +collect_logs <- function(path = tempdir(), pattern = "^log_it\\d+_im\\d+\\.rds$", + remove = TRUE, verbose = FALSE) { + log_files <- list.files(path, pattern = pattern, full.names = TRUE) + + if (length(log_files) == 0L) { + if (verbose) message("No log files found.") + return(NULL) + } + + logs <- lapply(log_files, function(file) { + tryCatch( + readRDS(file), + error = function(e) { + if (verbose) warning("Failed to read log file: ", file) + NULL + } + ) + }) + + logs <- do.call(rbind, logs[!vapply(logs, is.null, logical(1))]) + + if (remove) { + file.remove(log_files) + } + + logs +} diff --git a/man/mice.Rd b/man/mice.Rd index fb0a47c55..42ff75a1f 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -363,15 +363,6 @@ be added as main effects to the \code{formulas}, which will act as supplementary covariates in the imputation model. It is possible to turn off this behavior by specifying the argument \code{auxiliary = FALSE}. - -If \code{parallel = TRUE}, the function uses the \pkg{future} package to -distribute the \code{m} imputations across multiple cores using \code{multisession}. -The number of workers defaults to one fewer than the number of available -cores, but is capped at \code{m}. Parallel workers are initialized at the start -of the function and cleaned up afterward using \code{on.exit()}. - -To use parallel computation, you must install the \pkg{future} and -\pkg{future.apply} packages. } \section{Functions}{ @@ -428,6 +419,48 @@ depend on the operating system. See the discussion in the "R Installation and Administration" guide for further information. } +\section{Parallel computation}{ + + +If \code{parallel = TRUE}, the function uses the \pkg{future} package to +distribute the \code{m} imputations across multiple cores using \code{multisession}. +The number of workers defaults to one fewer than the number of available +cores, but is capped at \code{m}. Parallel workers are initialized at the start +of the function and cleaned up afterward using \code{on.exit()}. + +To use parallel computation, you must install the \pkg{future} and +\pkg{future.apply} packages. +} + +\section{Logging environment}{ + + +A new environment \code{logenv} is created at the start of the \code{mice()} function +to hold internal state information for logging and diagnostics. This environment +is isolated from the global environment by using \code{new.env(parent = emptyenv())}. + +The \code{logenv$state} object is a list with the following components: + +\describe{ +\item{\code{it}}{Current iteration index (integer).} +\item{\code{im}}{Current imputation index (integer).} +\item{\code{dep}}{Name of the dependent (target) variable currently being imputed (character).} +\item{\code{meth}}{Imputation method used for the current variable (character).} +\item{\code{log}}{A string with the logged message.} +} + +This internal object may be accessed by helper functions during iteration +to collect messages on the progress of the imputation process. + +After the iterations have ended, \code{logenv$state} is written to the \code{loggedEvents} +element of the \code{mids} object. + +\emph{Breaking change}: The \code{state} object and the \code{updateLog()} function have been +replaced with a more structured logging approach using the \code{record.event()}. +This allows for better integration with parallel processing and avoids +potential issues with global state management. +} + \examples{ # do default multiple imputation on a numeric matrix imp <- mice(nhanes) diff --git a/tests/testthat/test-anova.R b/tests/testthat/test-anova.R index 560c2c119..e0b5513f4 100644 --- a/tests/testthat/test-anova.R +++ b/tests/testthat/test-anova.R @@ -36,3 +36,4 @@ test_that("runs tests for the Cox model", { expect_error(D3(m2, m1)) expect_silent(anova(m3, m2, m1)) }) + diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R index 604ab7c54..1046a82ec 100644 --- a/tests/testthat/test-data.R +++ b/tests/testthat/test-data.R @@ -16,7 +16,7 @@ for (i in seq_len(nrow(missing_idx))) { df[missing_idx[i, 1], missing_idx[i, 2]] <- NA } -expect_silent(trained <<- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) +expect_warning(trained <- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) # make single-row new data with correct type newdata <- make.newdata(models = trained$models, vars = names(df)) diff --git a/tests/testthat/test-internals.R b/tests/testthat/test-internals.R index 56d6c9061..90389dfd5 100644 --- a/tests/testthat/test-internals.R +++ b/tests/testthat/test-internals.R @@ -18,8 +18,9 @@ y <- td[, 1] ry <- rep(TRUE, 5) # data frame for storing the event log -state <- list(it = 0, im = 0, dep = "y", meth = "test", log = FALSE) -loggedEvents <- data.frame(it = 0, im = 0, dep = "", meth = "", out = "") +state <- list(it = 0, im = 0, dep = "y", meth = "test", log = TRUE) +loggedEvents <- data.frame(it = 0L, im = 0L, dep = "", meth = "", out = "", + msg = NA_character_, fn = NA_character_, stringsAsFactors = FALSE) fr <- 2 diff --git a/tests/testthat/test-mice.impute.norm.R b/tests/testthat/test-mice.impute.norm.R index 80805aef0..8227db8ad 100644 --- a/tests/testthat/test-mice.impute.norm.R +++ b/tests/testthat/test-mice.impute.norm.R @@ -64,9 +64,9 @@ test_that("Correct estimation method used", { # TEST 3: correct imputation model # ##################################### -expect_silent(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) -expect_silent(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) -expect_silent(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) test_that("Imputations are equal", { expect_equal(imp.qr$imp, imp.svd$imp) @@ -78,9 +78,9 @@ test_that("Imputations are equal", { ##################################### # test on faulty imputation model (exactly singular system) -expect_silent(imp.qr <- mice(mammalsleep, ls.meth = "qr", seed = 123, print = FALSE)) -expect_silent(imp.svd <- mice(mammalsleep, ls.meth = "svd", seed = 123, print = FALSE)) -expect_silent(imp.ridge <- mice(mammalsleep, ls.meth = "ridge", seed = 123, print = FALSE)) +expect_warning(imp.qr <- mice(mammalsleep, ls.meth = "qr", seed = 123, print = FALSE)) +expect_warning(imp.svd <- mice(mammalsleep, ls.meth = "svd", seed = 123, print = FALSE)) +expect_warning(imp.ridge <- mice(mammalsleep, ls.meth = "ridge", seed = 123, print = FALSE)) test_that("Imputations are not equal", { expect_false(identical(imp.qr$imp, imp.svd$imp)) diff --git a/tests/testthat/test-mice.impute.pmm.R b/tests/testthat/test-mice.impute.pmm.R index 396abc867..ec7355db6 100644 --- a/tests/testthat/test-mice.impute.pmm.R +++ b/tests/testthat/test-mice.impute.pmm.R @@ -109,7 +109,7 @@ data3$j25 <- rnorm(nrow(data3)) test_that("cancor with many junk variables does not crash", { - expect_silent(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, + expect_warning(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, maxit = 1, m = 1, seed = 1, print = FALSE)) }) diff --git a/tests/testthat/test-parallel-miceadds.R b/tests/testthat/test-parallel-miceadds.R new file mode 100644 index 000000000..a95588ddf --- /dev/null +++ b/tests/testthat/test-parallel-miceadds.R @@ -0,0 +1,23 @@ +library(mice) +library(miceadds) + +#-- simulate data +set.seed(1) +N <- 100 +x <- stats::rnorm(N) +z <- 0.5*x + stats::rnorm(N, sd=.7) +y <- stats::rnorm(N, mean=.3*x - .2*z, sd=1 ) +dat <- data.frame(x,z,y) +dat[ seq(1,N,3), c("x","y") ] <- NA +dat[ seq(1,N,4), "z" ] <- NA + +#-- use imputation methods from miceadds +method <- c("x" = "rlm", "z" = "lm", "y" = "lqs") + +#-- impute data - single threaded +set.seed(1) +expect_silent(imps <- mice::mice(dat, method = method, maxit = 2, print = FALSE)) + +#-- impute data - parallel +set.seed(1) +expect_silent(impp <- mice::mice(dat, method = method, maxit = 2, parallel = TRUE, future.packages = "miceadds", print = FALSE)) diff --git a/tests/testthat/test-rbind.R b/tests/testthat/test-rbind.R index ac1ea0289..6dfff3909 100644 --- a/tests/testthat/test-rbind.R +++ b/tests/testthat/test-rbind.R @@ -5,9 +5,9 @@ test_that("Constant variables are not imputed by default", { expect_equal(sum(is.na(complete(imp1))), 6L) }) -expect_silent(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE)) +expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE)) test_that("Constant variables are imputed for remove.constant = FALSE", { - expect_silent(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, + expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE, trimmer = "lindep")) expect_equal(sum(is.na(complete(imp1b))), 0L) }) From 3e5cbace587d29ec7b6d84ad993fbca968a62cdf Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 29 May 2025 18:57:15 +0200 Subject: [PATCH 127/147] Inactive vignette --- vignettes/{imputation-models.qmd => _imputation-models.qmd} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename vignettes/{imputation-models.qmd => _imputation-models.qmd} (100%) diff --git a/vignettes/imputation-models.qmd b/vignettes/_imputation-models.qmd similarity index 100% rename from vignettes/imputation-models.qmd rename to vignettes/_imputation-models.qmd From 4d2ee7beecbcff2c2368f0f4e62c55b07caca4c8 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 29 May 2025 19:08:13 +0200 Subject: [PATCH 128/147] Update expected result for miceadds --- man/mice.Rd | 27 ++----------------------- tests/testthat/test-parallel-miceadds.R | 2 +- 2 files changed, 3 insertions(+), 26 deletions(-) diff --git a/man/mice.Rd b/man/mice.Rd index 42ff75a1f..a7005cccf 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -432,33 +432,10 @@ To use parallel computation, you must install the \pkg{future} and \pkg{future.apply} packages. } -\section{Logging environment}{ +\section{Logged Events}{ -A new environment \code{logenv} is created at the start of the \code{mice()} function -to hold internal state information for logging and diagnostics. This environment -is isolated from the global environment by using \code{new.env(parent = emptyenv())}. - -The \code{logenv$state} object is a list with the following components: - -\describe{ -\item{\code{it}}{Current iteration index (integer).} -\item{\code{im}}{Current imputation index (integer).} -\item{\code{dep}}{Name of the dependent (target) variable currently being imputed (character).} -\item{\code{meth}}{Imputation method used for the current variable (character).} -\item{\code{log}}{A string with the logged message.} -} - -This internal object may be accessed by helper functions during iteration -to collect messages on the progress of the imputation process. - -After the iterations have ended, \code{logenv$state} is written to the \code{loggedEvents} -element of the \code{mids} object. - -\emph{Breaking change}: The \code{state} object and the \code{updateLog()} function have been -replaced with a more structured logging approach using the \code{record.event()}. -This allows for better integration with parallel processing and avoids -potential issues with global state management. +The \code{mids} object produced by \code{mice()} contains an element \code{loggedEvents}. } \examples{ diff --git a/tests/testthat/test-parallel-miceadds.R b/tests/testthat/test-parallel-miceadds.R index a95588ddf..e14a5f0e7 100644 --- a/tests/testthat/test-parallel-miceadds.R +++ b/tests/testthat/test-parallel-miceadds.R @@ -20,4 +20,4 @@ expect_silent(imps <- mice::mice(dat, method = method, maxit = 2, print = FALSE) #-- impute data - parallel set.seed(1) -expect_silent(impp <- mice::mice(dat, method = method, maxit = 2, parallel = TRUE, future.packages = "miceadds", print = FALSE)) +expect_no_error(impp <- mice::mice(dat, method = method, maxit = 2, parallel = TRUE, future.packages = "miceadds", print = FALSE)) From 3a7f7527b309ac7ba0cb65bc8a33ee2b83a2bd78 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 30 May 2025 09:39:39 +0200 Subject: [PATCH 129/147] Change calltype --> calltypes (plural) when it is a vector --- NAMESPACE | 2 +- NEWS.md | 4 +- R/{modeltype.R => calltypes.R} | 56 ++++++++++----------- R/cbind.R | 12 ++--- R/filter.R | 4 +- R/ibind.R | 2 +- R/mice.R | 32 ++++++------ R/mice.mids.R | 4 +- R/mids.R | 6 +-- R/rbind.R | 8 +-- R/sampler.R | 18 +++---- _pkgdown.yml | 2 +- man/{make.calltype.Rd => make.calltypes.Rd} | 36 ++++++------- man/mice.Rd | 12 ++--- man/mids.Rd | 12 ++--- tests/testthat/test-parallel-sampler.R | 10 ++-- 16 files changed, 110 insertions(+), 110 deletions(-) rename R/{modeltype.R => calltypes.R} (56%) rename man/{make.calltype.Rd => make.calltypes.Rd} (64%) diff --git a/NAMESPACE b/NAMESPACE index 8605228f1..ca9532357 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -94,7 +94,7 @@ export(lm.mids) export(mads) export(make.blocks) export(make.blots) -export(make.calltype) +export(make.calltypes) export(make.formulas) export(make.method) export(make.newdata) diff --git a/NEWS.md b/NEWS.md index 1fd4b65a8..3c58950c5 100644 --- a/NEWS.md +++ b/NEWS.md @@ -54,9 +54,9 @@ This change in behavior ensures greater consistency at the end of each iteration The new behavior works well for simple cases. However, for more complex situations — especially when passive variables depend on other passive variables — it is recommended to manually specify a `visitSequence` that updates each passive variable immediately after one of its right-hand side predictors changes. (#699) -* **Adds the `calltype` argument to `mice()` for mixing `predictorMatrix` and `formulas` specifications** per variable-block. The `calltype` argument allows the user to specify some variables (or blocks of variables) by the `formulas` argument, and other variables by `predictorMatrix` argument. (Note: This argument was called `modeltype` in version 3.17.1). +* **Adds the `calltypes` argument to `mice()` for mixing `predictorMatrix` and `formulas` specifications** per variable-block. The `calltypes` argument allows the user to specify some variables (or blocks of variables) by the `formulas` argument, and other variables by `predictorMatrix` argument. (Note: This argument was called `modeltype` in version 3.17.1). - `calltype` is a character vector of `length(blocks)` elements that indicates how the imputation model is specified. Entries can one of two values: `"pred"` or `"formula"`. If `calltype = "pred"`, the predictors of the imputation model for the block are specified by the corresponding row of the `predictorMatrix`. If `calltype = "formula"` the imputation model is specified by relevant entry in `formulas`. The default depends on the presence of the `formulas` argument. If `formulas` is present, then `mice()` sets `calltype = "formula"` for any block for which a `formula` is specified. Otherwise, `calltype = "pred"`. + `calltypes` is a character vector of `length(blocks)` elements that indicates how the imputation model is specified. Entries can one of two values: `"pred"` or `"formula"`. If `calltypes = "pred"`, the predictors of the imputation model for the block are specified by the corresponding row of the `predictorMatrix`. If `calltypes = "formula"` the imputation model is specified by relevant entry in `formulas`. The default depends on the presence of the `formulas` argument. If `formulas` is present, then `mice()` sets `calltypes = "formula"` for any block for which a `formula` is specified. Otherwise, `calltypes = "pred"`. * **Introduces an optimized `matchindex` C++ function** to improve speed of predictive mean matching (#695) diff --git a/R/modeltype.R b/R/calltypes.R similarity index 56% rename from R/modeltype.R rename to R/calltypes.R index 161cbac08..147261d1f 100644 --- a/R/modeltype.R +++ b/R/calltypes.R @@ -1,21 +1,21 @@ -#' Create calltype of the imputation model +#' Create calltypes of the imputation model #' -#' The helper `make.calltype()` creates a vector that identifies per block if +#' The helper `make.calltypes()` creates a vector that identifies per block if #' the imputation model is taken from `predictorMatrix` or `formulas`. The #' function is used internally by `mice()`. #' -#' @param calltype A character vector of length equal to the number of blocks in \code{predictorMatrix}. +#' @param calltypes A character vector of length equal to the number of blocks in \code{predictorMatrix}. #' Each element specifies how the imputation model for the corresponding block is defined. -#' Valid values are \code{"pred"} and \code{"formula"}. If \code{NULL}, the calltype +#' Valid values are \code{"pred"} and \code{"formula"}. If \code{NULL}, the calltypes #' will be `"pred"` for all blocks, unless `priority` is `"formula"`. #' @param predictorMatrix A matrix specifying the predictors for each block. Each row corresponds #' to a block, and each column corresponds to a variable. Non-zero entries indicate that the variable #' is used as a predictor for the block. #' @param formulas A list of formulas, where each element corresponds to a block in \code{predictorMatrix}. -#' If a formula is provided for a block, the corresponding \code{calltype} entry is set to \code{"formula"}. -#' If \code{NULL}, formulas are not used to modify \code{calltype}. -#' @param priority A character string specifying the default value for \code{calltype} when it is \code{NULL}. -#' Defaults to \code{"pred"}. If \code{priority == "formula"}, the calltype will be `"formula"` +#' If a formula is provided for a block, the corresponding \code{calltypes} entry is set to \code{"formula"}. +#' If \code{NULL}, formulas are not used to modify \code{calltypes}. +#' @param priority A character string specifying the default value for \code{calltypes} when it is \code{NULL}. +#' Defaults to \code{"pred"}. If \code{priority == "formula"}, the calltypes will be `"formula"` #' for blocks found in `formulas` with a matching name. #' #' @return A character vector of length equal to the number of rows in \code{predictorMatrix}. @@ -28,8 +28,8 @@ #' dimnames = list(c("block1", "block2", "block3"), c("x1", "x2", "y"))) #' predictorMatrix[1, 3] <- 0 #' -#' # Case 1: No calltype or formulas specified -#' make.calltype(NULL, predictorMatrix, NULL) +#' # Case 1: No calltypes or formulas specified +#' make.calltypes(NULL, predictorMatrix, NULL) #' #' # Case 2: Formulas provided #' formulas <- list( @@ -37,39 +37,39 @@ #' y ~ x1 + x2, #' NULL #' ) -#' make.calltype(NULL, predictorMatrix, formulas) +#' make.calltypes(NULL, predictorMatrix, formulas) #' -#' # Case 3: Custom calltype -#' calltype <- c("pred", "formula", "pred") -#' make.calltype(calltype, predictorMatrix, NULL) +#' # Case 3: Custom calltypes +#' calltypes <- c("pred", "formula", "pred") +#' make.calltypes(calltypes, predictorMatrix, NULL) #' #' @export -make.calltype <- function(calltype, predictorMatrix, formulas, priority = "pred") { - # Validate calltype length - if (!is.null(calltype) && length(calltype) != nrow(predictorMatrix)) { - stop("Length of calltype must match the number of blocks in predictorMatrix.") +make.calltypes <- function(calltypes, predictorMatrix, formulas, priority = "pred") { + # Validate calltypes length + if (!is.null(calltypes) && length(calltypes) != nrow(predictorMatrix)) { + stop("Length of calltypes must match the number of blocks in predictorMatrix.") } - # Default calltype setup - if (is.null(calltype)) { - calltype <- rep("pred", nrow(predictorMatrix)) + # Default calltypes setup + if (is.null(calltypes)) { + calltypes <- rep("pred", nrow(predictorMatrix)) } - names(calltype) <- dimnames(predictorMatrix)[[1L]] + names(calltypes) <- dimnames(predictorMatrix)[[1L]] - # Adjust calltype based on formulas + # Adjust calltypes based on formulas if (priority == "formula") { - for (name in names(calltype)) { + for (name in names(calltypes)) { if (!is.null(formulas[[name]])) { - calltype[name] <- "formula" + calltypes[name] <- "formula" } } } # Validate entries valid_calltypes <- c("pred", "formula") - if (!all(calltype %in% valid_calltypes)) { - stop("All entries in calltype must be one of: ", paste(valid_calltypes, collapse = ", ")) + if (!all(calltypes %in% valid_calltypes)) { + stop("All entries in calltypes must be one of: ", paste(valid_calltypes, collapse = ", ")) } - return(calltype) + return(calltypes) } diff --git a/R/cbind.R b/R/cbind.R index 3d7d8708f..97cb0a3c0 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -39,8 +39,8 @@ cbind.mids <- function(x, y = NULL, ...) { blocknames <- make.unique(xynames) names(blocknames) <- xynames names(blocks) <- blocknames - calltype <- c(x$calltype, rep("pred", ncol(y))) - names(calltype) <- blocknames + calltypes <- c(x$calltypes, rep("pred", ncol(y))) + names(calltypes) <- blocknames m <- x$m @@ -114,7 +114,7 @@ cbind.mids <- function(x, y = NULL, ...) { predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, @@ -168,8 +168,8 @@ cbind.mids.mids <- function(x, y, call) { blocknames <- make.unique(xynames) names(blocknames) <- xynames names(blocks) <- blocknames - calltype <- c(x$calltype, y$calltype) - names(calltype) <- blocknames + calltypes <- c(x$calltypes, y$calltypes) + names(calltypes) <- blocknames m <- x$m nmis <- c(x$nmis, y$nmis) @@ -312,7 +312,7 @@ cbind.mids.mids <- function(x, y, call) { predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, diff --git a/R/filter.R b/R/filter.R index 457aea87f..f69829e36 100644 --- a/R/filter.R +++ b/R/filter.R @@ -79,7 +79,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { predictorMatrix <- .data$predictorMatrix visitSequence <- .data$visitSequence formulas <- .data$formulas - calltype <- .data$calltype + calltypes <- .data$calltypes blots <- .data$blots tasks <- .data$tasks models <- .data$models @@ -121,7 +121,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, diff --git a/R/ibind.R b/R/ibind.R index 20ba12b27..a33f74d48 100644 --- a/R/ibind.R +++ b/R/ibind.R @@ -97,7 +97,7 @@ ibind <- function(x, y) { predictorMatrix = x$predictorMatrix, visitSequence = visitSequence, formulas = x$formulas, - calltype = x$calltype, + calltypes = x$calltypes, post = x$post, blots = x$blots, ignore = x$ignore, diff --git a/R/mice.R b/R/mice.R index 71f87ddac..a1ff28e4d 100644 --- a/R/mice.R +++ b/R/mice.R @@ -218,18 +218,18 @@ #' The \code{formulas} argument is an alternative to the #' \code{predictorMatrix} argument that allows for more flexibility in #' specifying imputation models, e.g., for specifying interaction terms. -#' @param calltype A character vector of \code{length(block)} elements +#' @param calltypes A character vector of \code{length(block)} elements #' that indicates how the imputation model is specified. Entries can #' one of two values: \code{"pred"} or \code{"formula"}. If -#' \code{calltype = "pred"}, the predictors of the imputation +#' \code{calltypes = "pred"}, the predictors of the imputation #' model for the block are specified by the corresponding row of the -#' \code{predictorMatrix}. If \code{calltype = "formula"} the +#' \code{predictorMatrix}. If \code{calltypes = "formula"} the #' imputation model is specified by relevant entry in #' \code{formulas}. The default depends on the presence of the #' \code{formulas} argument. If \code{formulas} is present, then #' \code{mice()} sets -#' \code{calltype = "formula"} for any block -#' for which a formula is specified. Otherwise, \code{calltype = "pred"}. +#' \code{calltypes = "formula"} for any block +#' for which a formula is specified. Otherwise, \code{calltypes = "pred"}. #' @param blots A named \code{list} of \code{alist}'s that can be used #' to pass down arguments to lower level imputation function. The entries #' of element \code{blots[[blockname]]} are passed down to the function @@ -394,7 +394,7 @@ mice <- function(data, blocks, visitSequence = NULL, formulas, - calltype = NULL, + calltypes = NULL, blots = NULL, tasks = NULL, models = NULL, @@ -453,28 +453,28 @@ mice <- function(data, blocks <- make.blocks(colnames(data)) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "pred") } if (!mp & mb & mf) { predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- make.blocks(colnames(predictorMatrix), partition = "scatter") formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "pred") } if (mp & !mb & mf) { blocks <- check.blocks(blocks, data) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "pred") } if (mp & mb & !mf) { formulas <- check.formulas(formulas, data) blocks <- construct.blocks(formulas) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "formula") } if (!mp & !mb & mf) { @@ -483,7 +483,7 @@ mice <- function(data, predictorMatrix <- z$predictorMatrix blocks <- z$blocks formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "pred") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "pred") } if (!mp & mb & !mf) { @@ -493,21 +493,21 @@ mice <- function(data, predictorMatrix <- make.predictorMatrix(data, blocks = blocks, predictorMatrix = predictorMatrix) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "formula") } if (mp & !mb & !mf) { blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, blocks) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "formula") } if (!mp & !mb & !mf) { blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data, blocks) - calltype <- make.calltype(calltype, predictorMatrix, formulas, "formula") + calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "formula") } chk <- check.cluster(data, predictorMatrix) @@ -569,7 +569,7 @@ mice <- function(data, q <- sampler( data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - calltype, blots, tasks, models, + calltypes, blots, tasks, models, post, c(from, to), printFlag, ..., parallel = parallel) @@ -592,7 +592,7 @@ mice <- function(data, predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, diff --git a/R/mice.mids.R b/R/mice.mids.R index e8c89053f..a10d57c22 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -112,7 +112,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { q <- sampler( obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, - obj$formulas, obj$calltype, obj$blots, + obj$formulas, obj$calltypes, obj$blots, obj$tasks, obj$models, obj$post, c(from, to), printFlag, ... ) @@ -165,7 +165,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { predictorMatrix = obj$predictorMatrix, visitSequence = obj$visitSequence, formulas = obj$formulas, - calltype = obj$calltype, + calltypes = obj$calltypes, post = obj$post, blots = obj$blots, tasks = obj$tasks, diff --git a/R/mids.R b/R/mids.R index 3e135042f..66db9f5e1 100644 --- a/R/mids.R +++ b/R/mids.R @@ -163,7 +163,7 @@ mids <- function( predictorMatrix = matrix(), visitSequence = character(), formulas = list(), - calltype = character(), + calltypes = character(), post = character(), blots = list(), tasks = character(), @@ -195,7 +195,7 @@ mids <- function( predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, @@ -222,7 +222,7 @@ mids <- function( predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, diff --git a/R/rbind.R b/R/rbind.R index 48e70b1f3..eb10e6a44 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -44,7 +44,7 @@ rbind.mids <- function(x, y = NULL, ...) { method <- x$method post <- x$post formulas <- x$formulas - calltype <- x$calltype + calltypes <- x$calltypes blots <- x$blots tasks <- x$tasks models <- x$models @@ -75,7 +75,7 @@ rbind.mids <- function(x, y = NULL, ...) { predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, @@ -129,7 +129,7 @@ rbind.mids.mids <- function(x, y, call) { method <- x$method post <- x$post formulas <- x$formulas - calltype <- x$calltype + calltypes <- x$calltypes blots <- x$blots tasks <- x$tasks models <- x$models @@ -181,7 +181,7 @@ rbind.mids.mids <- function(x, y, call) { predictorMatrix = predictorMatrix, visitSequence = visitSequence, formulas = formulas, - calltype = calltype, + calltypes = calltypes, post = post, blots = blots, tasks = tasks, diff --git a/R/sampler.R b/R/sampler.R index 3d10723c6..763622aad 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -1,6 +1,6 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, visitSequence, predictorMatrix, formulas, - calltype, blots, tasks, models, + calltypes, blots, tasks, models, post, fromto, printFlag, ..., parallel = FALSE, future.packages = NULL, future.seed = TRUE) { from <- fromto[1] @@ -45,7 +45,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } result <- one.cycle(data, imp, r, where, i, k, visitSequence, - blocks, method, calltype, formulas, + blocks, method, calltypes, formulas, predictorMatrix, blots, tasks, models, post, ignore, printFlag, ...) @@ -78,7 +78,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } result <- one.cycle(data_i, imp_i, r, where, i, k, visitSequence, - blocks, method, calltype, formulas, + blocks, method, calltypes, formulas, predictorMatrix, blots, tasks, models, post, ignore, printFlag = FALSE, ...) @@ -161,15 +161,15 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } one.cycle <- function(data, imp, r, where, i, k, visitSequence, - blocks, method, calltype, formulas, predictorMatrix, + blocks, method, calltypes, formulas, predictorMatrix, blots, tasks, models, post, ignore, printFlag, ...) { # this function makes one pass through the data # impute block-by-block for (h in visitSequence) { - ct <- calltype[[h]] + calltype <- calltypes[[h]] b <- blocks[[h]] - ff <- if (ct == "formula") formulas[[h]] else NULL + ff <- if (calltype == "formula") formulas[[h]] else NULL pred <- predictorMatrix[h, ] user <- blots[[h]] @@ -199,7 +199,7 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, task = tasks[j], model = models[[j]][[as.character(mod)]], yname = j, k = k, - calltype = ct, + calltype = calltype, user = user, ignore = ignore, ... ) @@ -227,10 +227,10 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, data[mis] <- NA fm <- paste("mice.impute", theMethod, sep = ".") - imputes <- switch(ct, + imputes <- switch(calltype, formula = do.call(fm, list(data = data, formula = ff, ...)), pred = do.call(fm, list(data = data, type = pred, ...)), - stop("Cannot call function of type ", ct)) + stop("Cannot call function of type ", calltype)) # Abort if imputes is NULL if (is.null(imputes)) { diff --git a/_pkgdown.yml b/_pkgdown.yml index ab2acacfc..dbb53115b 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -44,7 +44,7 @@ reference: - construct.blocks - make.blocks - make.blots - - make.calltype + - make.calltypes - make.formulas - make.method - make.newdata diff --git a/man/make.calltype.Rd b/man/make.calltypes.Rd similarity index 64% rename from man/make.calltype.Rd rename to man/make.calltypes.Rd index d865b5fc8..21115633f 100644 --- a/man/make.calltype.Rd +++ b/man/make.calltypes.Rd @@ -1,15 +1,15 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modeltype.R -\name{make.calltype} -\alias{make.calltype} -\title{Create calltype of the imputation model} +% Please edit documentation in R/calltypes.R +\name{make.calltypes} +\alias{make.calltypes} +\title{Create calltypes of the imputation model} \usage{ -make.calltype(calltype, predictorMatrix, formulas, priority = "pred") +make.calltypes(calltypes, predictorMatrix, formulas, priority = "pred") } \arguments{ -\item{calltype}{A character vector of length equal to the number of blocks in \code{predictorMatrix}. +\item{calltypes}{A character vector of length equal to the number of blocks in \code{predictorMatrix}. Each element specifies how the imputation model for the corresponding block is defined. -Valid values are \code{"pred"} and \code{"formula"}. If \code{NULL}, the calltype +Valid values are \code{"pred"} and \code{"formula"}. If \code{NULL}, the calltypes will be \code{"pred"} for all blocks, unless \code{priority} is \code{"formula"}.} \item{predictorMatrix}{A matrix specifying the predictors for each block. Each row corresponds @@ -17,11 +17,11 @@ to a block, and each column corresponds to a variable. Non-zero entries indicate is used as a predictor for the block.} \item{formulas}{A list of formulas, where each element corresponds to a block in \code{predictorMatrix}. -If a formula is provided for a block, the corresponding \code{calltype} entry is set to \code{"formula"}. -If \code{NULL}, formulas are not used to modify \code{calltype}.} +If a formula is provided for a block, the corresponding \code{calltypes} entry is set to \code{"formula"}. +If \code{NULL}, formulas are not used to modify \code{calltypes}.} -\item{priority}{A character string specifying the default value for \code{calltype} when it is \code{NULL}. -Defaults to \code{"pred"}. If \code{priority == "formula"}, the calltype will be \code{"formula"} +\item{priority}{A character string specifying the default value for \code{calltypes} when it is \code{NULL}. +Defaults to \code{"pred"}. If \code{priority == "formula"}, the calltypes will be \code{"formula"} for blocks found in \code{formulas} with a matching name.} } \value{ @@ -30,7 +30,7 @@ Each element is either \code{"pred"} or \code{"formula"}, indicating how the imp is specified for the corresponding block. } \description{ -The helper \code{make.calltype()} creates a vector that identifies per block if +The helper \code{make.calltypes()} creates a vector that identifies per block if the imputation model is taken from \code{predictorMatrix} or \code{formulas}. The function is used internally by \code{mice()}. } @@ -40,8 +40,8 @@ predictorMatrix <- matrix(1, nrow = 3, ncol = 3, dimnames = list(c("block1", "block2", "block3"), c("x1", "x2", "y"))) predictorMatrix[1, 3] <- 0 -# Case 1: No calltype or formulas specified -make.calltype(NULL, predictorMatrix, NULL) +# Case 1: No calltypes or formulas specified +make.calltypes(NULL, predictorMatrix, NULL) # Case 2: Formulas provided formulas <- list( @@ -49,10 +49,10 @@ formulas <- list( y ~ x1 + x2, NULL ) -make.calltype(NULL, predictorMatrix, formulas) +make.calltypes(NULL, predictorMatrix, formulas) -# Case 3: Custom calltype -calltype <- c("pred", "formula", "pred") -make.calltype(calltype, predictorMatrix, NULL) +# Case 3: Custom calltypes +calltypes <- c("pred", "formula", "pred") +make.calltypes(calltypes, predictorMatrix, NULL) } diff --git a/man/mice.Rd b/man/mice.Rd index a7005cccf..a59527d85 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -16,7 +16,7 @@ mice( blocks, visitSequence = NULL, formulas, - calltype = NULL, + calltypes = NULL, blots = NULL, tasks = NULL, models = NULL, @@ -115,18 +115,18 @@ The \code{formulas} argument is an alternative to the \code{predictorMatrix} argument that allows for more flexibility in specifying imputation models, e.g., for specifying interaction terms.} -\item{calltype}{A character vector of \code{length(block)} elements +\item{calltypes}{A character vector of \code{length(block)} elements that indicates how the imputation model is specified. Entries can one of two values: \code{"pred"} or \code{"formula"}. If -\code{calltype = "pred"}, the predictors of the imputation +\code{calltypes = "pred"}, the predictors of the imputation model for the block are specified by the corresponding row of the -\code{predictorMatrix}. If \code{calltype = "formula"} the +\code{predictorMatrix}. If \code{calltypes = "formula"} the imputation model is specified by relevant entry in \code{formulas}. The default depends on the presence of the \code{formulas} argument. If \code{formulas} is present, then \code{mice()} sets -\code{calltype = "formula"} for any block -for which a formula is specified. Otherwise, \code{calltype = "pred"}.} +\code{calltypes = "formula"} for any block +for which a formula is specified. Otherwise, \code{calltypes = "pred"}.} \item{blots}{A named \code{list} of \code{alist}'s that can be used to pass down arguments to lower level imputation function. The entries diff --git a/man/mids.Rd b/man/mids.Rd index 31a4c0a90..5a95f84d0 100644 --- a/man/mids.Rd +++ b/man/mids.Rd @@ -20,7 +20,7 @@ mids( predictorMatrix = matrix(), visitSequence = character(), formulas = list(), - calltype = character(), + calltypes = character(), post = character(), blots = list(), tasks = character(), @@ -131,18 +131,18 @@ The \code{formulas} argument is an alternative to the \code{predictorMatrix} argument that allows for more flexibility in specifying imputation models, e.g., for specifying interaction terms.} -\item{calltype}{A character vector of \code{length(block)} elements +\item{calltypes}{A character vector of \code{length(block)} elements that indicates how the imputation model is specified. Entries can one of two values: \code{"pred"} or \code{"formula"}. If -\code{calltype = "pred"}, the predictors of the imputation +\code{calltypes = "pred"}, the predictors of the imputation model for the block are specified by the corresponding row of the -\code{predictorMatrix}. If \code{calltype = "formula"} the +\code{predictorMatrix}. If \code{calltypes = "formula"} the imputation model is specified by relevant entry in \code{formulas}. The default depends on the presence of the \code{formulas} argument. If \code{formulas} is present, then \code{mice()} sets -\code{calltype = "formula"} for any block -for which a formula is specified. Otherwise, \code{calltype = "pred"}.} +\code{calltypes = "formula"} for any block +for which a formula is specified. Otherwise, \code{calltypes = "pred"}.} \item{post}{A vector of strings with length \code{ncol(data)} specifying expressions as strings. Each string is parsed and diff --git a/tests/testthat/test-parallel-sampler.R b/tests/testthat/test-parallel-sampler.R index 5da49d04a..fc32b8546 100644 --- a/tests/testthat/test-parallel-sampler.R +++ b/tests/testthat/test-parallel-sampler.R @@ -14,7 +14,7 @@ test_that("sampler() works identically in sequential and parallel modes", { ignore <- rep(FALSE, nrow(data)) predictorMatrix <- make.predictorMatrix(data = data, blocks = blocks) formulas <- make.formulas(data, blocks) - calltype <- make.calltype(NULL, predictorMatrix, formulas, "pred") + calltypes <- make.calltypes(NULL, predictorMatrix, formulas, "pred") blots <- vector("list", length(blocks)) names(blots) <- names(blocks) tasks <- check.tasks(tasks = NULL, data, models = NULL, blocks, skip.check.tasks = FALSE) @@ -27,7 +27,7 @@ test_that("sampler() works identically in sequential and parallel modes", { # Run sampler in sequential mode out_seq <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, - visitSequence, predictorMatrix, formulas, calltype, + visitSequence, predictorMatrix, formulas, calltypes, blots, tasks, models, post, fromto, printFlag = FALSE, parallel = FALSE) @@ -37,7 +37,7 @@ test_that("sampler() works identically in sequential and parallel modes", { # Run sampler in parallel mode future::plan("multisession") out_par <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, - visitSequence, predictorMatrix, formulas, calltype, + visitSequence, predictorMatrix, formulas, calltypes, blots, tasks, models, post, fromto, printFlag = FALSE, parallel = TRUE) future::plan("sequential") @@ -60,7 +60,7 @@ test_that("sampler() collects loggedEvents in parallel mode", { ignore <- rep(FALSE, nrow(data)) predictorMatrix <- make.predictorMatrix(data, blocks = blocks) formulas <- make.formulas(data, blocks) - calltype <- make.calltype(NULL, predictorMatrix, formulas, "pred") + calltypes <- make.calltypes(NULL, predictorMatrix, formulas, "pred") blots <- vector("list", length(blocks)) names(blots) <- names(blocks) tasks <- check.tasks(tasks = NULL, data, models = NULL, blocks, skip.check.tasks = FALSE) @@ -73,7 +73,7 @@ test_that("sampler() collects loggedEvents in parallel mode", { future::plan("multisession") out <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, - visitSequence, predictorMatrix, formulas, calltype, + visitSequence, predictorMatrix, formulas, calltypes, blots, tasks, models, post, fromto, printFlag = FALSE, parallel = TRUE) future::plan("sequential") From fc5cfda9719469518b1b0e89873585587f2e62d6 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 3 Jun 2025 16:03:42 +0200 Subject: [PATCH 130/147] Restore didlog logic --- R/edit.setup.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/edit.setup.R b/R/edit.setup.R index a572e5ef7..542f010b6 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -31,6 +31,7 @@ mice.edit.setup <- function(data, setup, tasks, } else { is.na(v) || v < 1000 * .Machine$double.eps } + didlog <- FALSE if (constant && any(pred[, j] != 0) && remove.constant) { pred[, j] <- 0 updateLog(out = varnames[j], meth = "constant", frame = 1) @@ -63,6 +64,7 @@ mice.edit.setup <- function(data, setup, tasks, if (length(droplist) > 0) { for (k in seq_along(droplist)) { j <- which(varnames %in% droplist[k]) + didlog <- FALSE if (any(pred[, j] != 0) && remove.collinear) { pred[, j] <- 0 @@ -82,7 +84,7 @@ mice.edit.setup <- function(data, setup, tasks, } } - if (all(pred == 0L)) { + if (all(pred == 0L) && didlog) { stop("`mice` detected constant and/or collinear variables. No predictors were left after their removal.") } From 2bad8d622bec3241e5edb808ad3aed128aadc514 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 11:57:18 +0200 Subject: [PATCH 131/147] Document tasks-aware method assignment in make.method() (#745) Clarifies that make.method() assigns a method to every variable by type, regardless of whether the current data have missing values. Under the default "impute" task, variables with nothing to impute are still set to "" (preserving prior behaviour). Under "train" and "fill" tasks the method is retained so that a model fitted on complete training data can be re-used on deployment data that do have missing values. Also points users to mids$nmis and colSums(is.na(data)) for checking which variables currently have missing values. Co-Authored-By: Claude Sonnet 4.6 --- R/method.R | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/R/method.R b/R/method.R index 6be7ba5d6..4f5c22fb2 100644 --- a/R/method.R +++ b/R/method.R @@ -2,7 +2,24 @@ #' #' This helper function creates a valid \code{method} vector. The #' \code{method} vector is an argument to the \code{mice} function that -#' specifies the method for each block. +#' specifies the imputation method for each block. +#' +#' A method is assigned to every variable whose type can be imputed, +#' regardless of whether the current data contain missing values. This is +#' intentional: the same \code{mice()} setup can be re-used across tasks +#' (see the \code{tasks} argument). For example, a model trained on complete +#' data (\code{task = "train"}) retains its method so that it can later be +#' applied to new data that do have missing values (\code{task = "fill"}). +#' +#' Under the default \code{task = "impute"}, variables with nothing to impute +#' according to the \code{where} matrix receive an empty string \code{""}, +#' preserving the behaviour of earlier versions. Under \code{"train"} and +#' \code{"fill"} tasks the method is kept regardless of missingness in the +#' current data. +#' +#' To find out which variables have missing values in the current data, use +#' \code{mids$nmis} after running \code{mice()}, or +#' \code{colSums(is.na(data))} beforehand. #' @inheritParams mice #' @return Vector of \code{length(blocks)} element with method names #' @seealso \code{\link{mice}} From 5d81e22b0bdcbd2f8689447952a724abad37ad9b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 12:12:22 +0200 Subject: [PATCH 132/147] Fix dfcom = 1 for clmm models in pool() (#748) Root cause: get.dfcom() fell back to length(stats::residuals(model)) to estimate the number of observations when stats::df.residual() returned nothing. For clmm (ordinal package), both df.residual() and residuals() are defined but return empty vectors. This caused nobs = 0, and the floor max(0 - p, 1) = 1 was silently used as dfcom. Consequence: pool() produced grossly wrong degrees of freedom (dfcom = 1 instead of e.g. 28), which propagated to incorrect p-values and confidence intervals. Fix: replace the length(residuals()) call with stats::nobs(), which clmm implements correctly. The residuals() fallback is kept for models that implement neither df.residual() nor nobs(). The same pattern is applied to the broom <= 0.5.6 fallback in get.glanced(). Co-Authored-By: Claude Sonnet 4.6 --- R/get.df.R | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/R/get.df.R b/R/get.df.R index 18eceefa4..09a137b85 100644 --- a/R/get.df.R +++ b/R/get.df.R @@ -18,9 +18,13 @@ get.dfcom <- function(model, dfcom = NULL) { } # other model: n - p - nobs <- tryCatch(length(stats::residuals(model)), - error = function(e) NULL) - if (!is.null(nobs)) { + # prefer nobs() over length(residuals()) since some models (e.g. clmm) return + # empty residuals but have a valid nobs method + nobs <- tryCatch(stats::nobs(model), error = function(e) NULL) + if (is.null(nobs) || length(nobs) == 0L) { + nobs <- tryCatch(length(stats::residuals(model)), error = function(e) NULL) + } + if (!is.null(nobs) && nobs > 0L) { return(as.numeric(max(nobs - length(stats::coef(model)), 1))) } @@ -36,9 +40,15 @@ get.glanced <- function(object) { glanced <- try(data.frame(summary(getfit(object), type = "glance")), silent = TRUE) if (inherits(glanced, "data.frame")) { # nobs is needed for pool.r.squared - # broom <= 0.5.6 does not supply it + # broom <= 0.5.6 does not supply it; use nobs() rather than + # length(residuals()) since some models (e.g. clmm) return empty residuals if (!"nobs" %in% colnames(glanced)) { - glanced$nobs <- length(stats::residuals(object$analyses[[1]])) + m1 <- object$analyses[[1]] + nobs <- tryCatch(stats::nobs(m1), error = function(e) NULL) + if (is.null(nobs) || length(nobs) == 0L) { + nobs <- length(stats::residuals(m1)) + } + glanced$nobs <- nobs } } else { glanced <- NULL From 03c11d72bfac487ca9ac683c9c767fdc5b17421c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 12:16:25 +0200 Subject: [PATCH 133/147] Apply Air formatting --- R/D1.R | 3 +- R/D3.R | 37 +- R/ampute.R | 133 ++++-- R/ampute.continuous.R | 53 ++- R/ampute.discrete.R | 18 +- R/anova.R | 3 +- R/as.R | 42 +- R/auxiliary.R | 15 +- R/blocks.R | 45 +- R/blots.R | 8 +- R/bwplot.mads.R | 105 +++-- R/bwplot.mids.R | 72 ++-- R/calltypes.R | 16 +- R/cbind.R | 72 ++-- R/check.R | 7 +- R/check.deprecated.R | 6 +- R/check.model.R | 24 +- R/check.tasks.R | 87 ++-- R/complete.R | 43 +- R/convergence.R | 40 +- R/correlar.R | 27 +- R/data.R | 46 ++- R/densityplot.mids.R | 95 +++-- R/design.R | 2 +- R/edit.setup.R | 33 +- R/filter.R | 3 +- R/fix.coef.R | 11 +- R/flux.R | 63 ++- R/formula.R | 58 ++- R/futuremice.R | 60 ++- R/get.df.R | 16 +- R/getfit.R | 8 +- R/ibind.R | 15 +- R/initialize.imp.R | 37 +- R/install.on.demand.R | 8 +- R/internal.R | 54 ++- R/larspred.R | 21 +- R/lm.R | 26 +- R/mads.R | 17 +- R/mcar.R | 226 +++++++--- R/mdc.R | 20 +- R/method.R | 94 +++-- R/mice.R | 148 ++++--- R/mice.impute.2l.bin.R | 64 ++- R/mice.impute.2l.lmer.R | 63 ++- R/mice.impute.2l.norm.R | 58 ++- R/mice.impute.2l.pan.R | 71 +++- R/mice.impute.2lonly.mean.R | 15 +- R/mice.impute.2lonly.norm.R | 9 +- R/mice.impute.2lonly.pmm.R | 43 +- R/mice.impute.cart.R | 40 +- R/mice.impute.jomoImpute.R | 19 +- R/mice.impute.lasso.logreg.R | 7 +- R/mice.impute.lasso.norm.R | 7 +- R/mice.impute.lasso.pmm.R | 39 +- R/mice.impute.lasso.select.logreg.R | 25 +- R/mice.impute.lasso.select.norm.R | 25 +- R/mice.impute.lda.R | 4 +- R/mice.impute.logreg.R | 64 ++- R/mice.impute.midastouch.R | 51 ++- R/mice.impute.mnar.logreg.R | 24 +- R/mice.impute.mnar.norm.R | 24 +- R/mice.impute.mpmm.R | 12 +- R/mice.impute.norm.R | 38 +- R/mice.impute.norm.boot.R | 4 +- R/mice.impute.norm.nob.R | 4 +- R/mice.impute.norm.predict.R | 4 +- R/mice.impute.panImpute.R | 19 +- R/mice.impute.pmm.R | 91 ++-- R/mice.impute.polr.R | 82 ++-- R/mice.impute.polyreg.R | 64 ++- R/mice.impute.quadratic.R | 45 +- R/mice.impute.rf.R | 46 ++- R/mice.impute.ri.R | 7 +- R/mice.impute.sample.R | 4 +- R/mice.mids.R | 73 +++- R/mice.theme.R | 8 +- R/mids.R | 113 +++-- R/mids2mplus.R | 54 ++- R/mids2spss.R | 9 +- R/mipo.R | 71 ++-- R/mira.R | 10 +- R/models.R | 21 +- R/nimp.R | 4 +- R/parlmice.R | 40 +- R/parse.ums.R | 30 +- R/pool.R | 14 +- R/pool.compare.R | 36 +- R/pool.r.squared.R | 30 +- R/pool.scalar.R | 20 +- R/pool.table.R | 37 +- R/pool.vector.R | 54 ++- R/post.R | 4 +- R/predict_mi.R | 161 ++++---- R/predictorMatrix.R | 84 ++-- R/print.R | 12 +- R/quantify.R | 34 +- R/quickpred.R | 29 +- R/rbind.R | 13 +- R/rm.whitespace.R | 6 +- R/sampler.R | 301 ++++++++++---- R/sampler.univ.R | 38 +- R/squeeze.R | 7 +- R/stripplot.mids.R | 77 ++-- R/summary.R | 35 +- R/tasks.R | 9 +- R/tidiers.R | 30 +- R/trim.data.R | 69 +++- R/validate.arguments.R | 10 +- R/visitSequence.R | 22 +- R/where.R | 9 +- R/with.R | 19 +- R/xyplot.mads.R | 30 +- R/xyplot.mids.R | 63 ++- data-raw/R/brandsma.R | 3 +- data-raw/R/employee.R | 44 +- data-raw/R/nmar_demo_data.R | 1 - tests/testthat/test-D1.R | 29 +- tests/testthat/test-D3.R | 43 +- tests/testthat/test-ampute.R | 389 +++++++++++++----- tests/testthat/test-anova.R | 1 - tests/testthat/test-as.mids.R | 19 +- tests/testthat/test-blocks.R | 35 +- tests/testthat/test-blots.R | 18 +- tests/testthat/test-cbind.R | 39 +- tests/testthat/test-check.visitSequence.R | 18 +- tests/testthat/test-data.R | 20 +- tests/testthat/test-filter.R | 1 - tests/testthat/test-formulas.R | 1 - tests/testthat/test-internals.R | 12 +- tests/testthat/test-mice-initialize.R | 206 ++++++++-- tests/testthat/test-mice.R | 241 +++++++---- tests/testthat/test-mice.impute.2l.bin.R | 20 +- tests/testthat/test-mice.impute.2l.lmer.R | 22 +- tests/testthat/test-mice.impute.2l.norm.R | 11 +- tests/testthat/test-mice.impute.2lonly.norm.R | 32 +- .../testthat/test-mice.impute.lasso.logreg.R | 38 +- tests/testthat/test-mice.impute.lasso.norm.R | 40 +- tests/testthat/test-mice.impute.logreg.R | 2 - tests/testthat/test-mice.impute.mpmm.R | 4 +- tests/testthat/test-mice.impute.norm.R | 51 ++- tests/testthat/test-mice.impute.panImpute.R | 11 +- tests/testthat/test-mice.impute.pmm.R | 43 +- tests/testthat/test-models.R | 9 +- tests/testthat/test-newdata.R | 11 +- tests/testthat/test-parallel-miceadds.R | 25 +- tests/testthat/test-parallel-sampler.R | 125 +++++- tests/testthat/test-parlmice.R | 5 +- tests/testthat/test-pool.R | 54 ++- tests/testthat/test-predict_mi.R | 46 ++- tests/testthat/test-quantify.R | 46 ++- tests/testthat/test-quickpred.R | 82 ++-- tests/testthat/test-rbind.R | 54 ++- tests/testthat/test-tasks.R | 107 ++++- tests/testthat/test-tidiers.R | 9 +- 155 files changed, 4726 insertions(+), 1916 deletions(-) diff --git a/R/D1.R b/R/D1.R index 49ecee0d4..a4d20eda8 100644 --- a/R/D1.R +++ b/R/D1.R @@ -41,7 +41,8 @@ D1 <- function(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL) { # legacy handling if (!missing(df.com)) { - warning("argument df.com is deprecated; please use dfcom instead.", + warning( + "argument df.com is deprecated; please use dfcom instead.", call. = FALSE ) dfcom <- df.com diff --git a/R/D3.R b/R/D3.R index 4c885bcea..ec64f23da 100644 --- a/R/D3.R +++ b/R/D3.R @@ -47,7 +47,8 @@ #' @export D3 <- function(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL) { if (!missing(df.com)) { - warning("argument df.com is deprecated; please use dfcom instead.", + warning( + "argument df.com is deprecated; please use dfcom instead.", call. = FALSE ) dfcom <- df.com @@ -77,28 +78,34 @@ D3 <- function(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL) { # For each imputed dataset, calculate the deviance between the two # models as fitted - dev1.M <- -2 * lapply(fit1, glance) %>% - bind_rows() %>% - pull(.data$logLik) - dev0.M <- -2 * lapply(fit0, glance) %>% - bind_rows() %>% - pull(.data$logLik) + dev1.M <- -2 * + lapply(fit1, glance) %>% + bind_rows() %>% + pull(.data$logLik) + dev0.M <- -2 * + lapply(fit0, glance) %>% + bind_rows() %>% + pull(.data$logLik) # For each imputed dataset, calculate the deviance between the two # models with coefficients restricted to qbar mds1 <- lapply(fit1, fix.coef, beta = qbar1) - dev1.L <- -2 * lapply(mds1, glance) %>% - bind_rows() %>% - pull(.data$logLik) + dev1.L <- -2 * + lapply(mds1, glance) %>% + bind_rows() %>% + pull(.data$logLik) mds0 <- lapply(fit0, fix.coef, beta = qbar0) - dev0.L <- -2 * lapply(mds0, glance) %>% - bind_rows() %>% - pull(.data$logLik) + dev0.L <- -2 * + lapply(mds0, glance) %>% + bind_rows() %>% + pull(.data$logLik) deviances <- list( - dev1.M = dev1.M, dev0.M = dev0.M, - dev1.L = dev1.L, dev0.L = dev0.L + dev1.M = dev1.M, + dev0.M = dev0.M, + dev1.L = dev1.L, + dev0.L = dev0.L ) # scaled deviance, as fitted diff --git a/R/ampute.R b/R/ampute.R index 4171c1d0b..ad673f230 100644 --- a/R/ampute.R +++ b/R/ampute.R @@ -201,10 +201,20 @@ #' ) #' my_mads_boys$amp #' @export -ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, - mech = "MAR", weights = NULL, std = TRUE, cont = TRUE, - type = NULL, odds = NULL, - bycases = TRUE, run = TRUE) { +ampute <- function( + data, + prop = 0.5, + patterns = NULL, + freq = NULL, + mech = "MAR", + weights = NULL, + std = TRUE, + cont = TRUE, + type = NULL, + odds = NULL, + bycases = TRUE, + run = TRUE +) { if (is.null(data)) { stop("Argument data is missing, with no default", call. = FALSE) } @@ -219,12 +229,14 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, data <- data.frame(data) if (any(vapply(data, Negate(is.numeric), logical(1))) && mech != "MCAR") { data <- as.data.frame(sapply(data, as.numeric)) - warning("Data is made numeric internally, because the calculation of weights requires numeric data", + warning( + "Data is made numeric internally, because the calculation of weights requires numeric data", call. = FALSE ) } if (prop < 0 || prop > 100) { - stop("Proportion of missingness should be a value between 0 and 1 (for a proportion) or between 1 and 100 (for a percentage)", + stop( + "Proportion of missingness should be a value between 0 and 1 (for a proportion) or between 1 and 100 (for a percentage)", call. = FALSE ) } else if (prop > 1) { @@ -233,9 +245,16 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, if (is.null(patterns)) { patterns <- ampute.default.patterns(n = ncol(data)) } else if (is.vector(patterns) && (length(patterns) / ncol(data)) %% 1 == 0) { - patterns <- matrix(patterns, nrow = length(patterns) / ncol(data), byrow = TRUE) + patterns <- matrix( + patterns, + nrow = length(patterns) / ncol(data), + byrow = TRUE + ) if (nrow(patterns) == 1 && all(patterns[1, ] %in% 1)) { - stop("One pattern with merely ones results to no amputation at all, the procedure is therefore stopped", call. = FALSE) + stop( + "One pattern with merely ones results to no amputation at all, the procedure is therefore stopped", + call. = FALSE + ) } } else if (is.vector(patterns)) { stop("Length of pattern vector does not match #variables", call. = FALSE) @@ -254,7 +273,13 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, } else { freq <- c(freq, rep.int(0.2, nrow(patterns) - length(freq))) } - warning(paste("Length of vector with relative frequencies does not match #patterns and is therefore changed to", freq), call. = FALSE) + warning( + paste( + "Length of vector with relative frequencies does not match #patterns and is therefore changed to", + freq + ), + call. = FALSE + ) } if (sum(freq) != 1) { freq <- recalculate.freq(freq = freq) @@ -286,13 +311,17 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, warning("Mechanism should contain merely MCAR, MAR or MNAR", call. = FALSE) } else if (length(mech) > 1) { mech <- mech[1] - warning("Mechanism should contain merely MCAR, MAR or MNAR. First element is used", + warning( + "Mechanism should contain merely MCAR, MAR or MNAR. First element is used", call. = FALSE ) } # Check if there is a pattern with merely zeroos if (!is.null(check.pat[["row.zero"]]) && mech == "MAR") { - stop(paste("Patterns object contains merely zeros and this kind of pattern is not possible when mechanism is MAR"), + stop( + paste( + "Patterns object contains merely zeros and this kind of pattern is not possible when mechanism is MAR" + ), call. = FALSE ) } @@ -306,7 +335,11 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, } if (mech != "MCAR" && !is.null(weights)) { if (is.vector(weights) && (length(weights) / ncol(data)) %% 1 == 0) { - weights <- matrix(weights, nrow = length(weights) / ncol(data), byrow = TRUE) + weights <- matrix( + weights, + nrow = length(weights) / ncol(data), + byrow = TRUE + ) } else if (is.vector(weights)) { stop("Length of weight vector does not match #variables", call. = FALSE) } else if (!is.matrix(weights) && !is.data.frame(weights)) { @@ -329,13 +362,13 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, stop("The objects patterns and weights are not matching", call. = FALSE) } - if (!is.vector(cont)) { cont <- as.vector(cont) warning("Continuous should contain merely TRUE or FALSE", call. = FALSE) } else if (length(cont) > 1) { cont <- cont[1] - warning("Continuous should contain merely TRUE or FALSE. First element is used", + warning( + "Continuous should contain merely TRUE or FALSE. First element is used", call. = FALSE ) } @@ -344,13 +377,15 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, } if (cont && !is.null(odds)) { odds <- NULL - warning("Odds matrix is not used when continuous probabilities (cont == TRUE) are specified", + warning( + "Odds matrix is not used when continuous probabilities (cont == TRUE) are specified", call. = FALSE ) } if (!cont && !is.null(type)) { type <- NULL - warning("Type is not used when discrete probabilities (cont == FALSE) are specified", + warning( + "Type is not used when discrete probabilities (cont == FALSE) are specified", call. = FALSE ) } @@ -358,16 +393,17 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, type <- ampute.default.type(patterns = patterns.new) } if (any(!type %in% c("LEFT", "MID", "TAIL", "RIGHT"))) { - stop("Type should contain LEFT, MID, TAIL or RIGHT", - call. = FALSE - ) + stop("Type should contain LEFT, MID, TAIL or RIGHT", call. = FALSE) } if (!is.vector(type)) { type <- as.vector(type) warning("Type should be a vector of strings", call. = FALSE) } else if (!length(type) %in% c(1, nrow(patterns), nrow(patterns.new))) { type <- type[1] - warning("Type should either have length 1 or length equal to #patterns, first element is used for all patterns", call. = FALSE) + warning( + "Type should either have length 1 or length equal to #patterns, first element is used for all patterns", + call. = FALSE + ) } if (mech != "MCAR" && !is.null(odds) && !is.matrix(odds)) { if (nrow(patterns.new) == 1 && is.vector(odds)) { @@ -401,14 +437,26 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, # Because 0 and 1 will be used for missingness, # the numbering of the patterns will start from 2 P <- sample.int( - n = nrow(patterns.new), size = nrow(data), - replace = TRUE, prob = freq - ) + 1 + n = nrow(patterns.new), + size = nrow(data), + replace = TRUE, + prob = freq + ) + + 1 # Check whether cases are assigned to all patterns - non.used.patterns <- c(2:(nrow(patterns.new) + 1))[!c(2:(nrow(patterns.new) + 1)) %in% unique(P)] + non.used.patterns <- c(2:(nrow(patterns.new) + 1))[ + !c(2:(nrow(patterns.new) + 1)) %in% unique(P) + ] if (length(non.used.patterns) > 0) { - warning(paste0("No records are assigned to patterns ", toString(non.used.patterns - 1), ". These patterns will not be generated. Consider reducing the number of patterns or increasing the dataset size."), call. = FALSE) + warning( + paste0( + "No records are assigned to patterns ", + toString(non.used.patterns - 1), + ". These patterns will not be generated. Consider reducing the number of patterns or increasing the dataset size." + ), + call. = FALSE + ) } # Calculate missingness according MCAR or calculate weighted sum scores @@ -469,7 +517,8 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, amp = data.in, cand = P - 1, scores = scores, - data = as.data.frame(data)) + data = as.data.frame(data) + ) return(result) } @@ -493,7 +542,9 @@ sumscores <- function(P, data, std, weights, patterns) { return(length(unique(x)) == 1) } # shangzhi-hong, Feb 2020, #216 - if (nrow(candidates) > 1 && !(any(apply(candidates, 2, length_unique)))) { + if ( + nrow(candidates) > 1 && !(any(apply(candidates, 2, length_unique))) + ) { candidates <- scale(candidates) } } @@ -520,7 +571,8 @@ recalculate.prop <- function(prop, n, k, patterns, freq) { numeric(1) ) if (sum(cases) > n) { - stop("Proportion of missing cells is too large in combination with the desired number of missing variables", + stop( + "Proportion of missing cells is too large in combination with the desired number of missing variables", call. = FALSE ) } else { @@ -547,7 +599,14 @@ check.patterns <- function(patterns, freq, prop) { row.one <- c() for (h in seq_len(nrow(patterns))) { if (any(!patterns[h, ] %in% c(0, 1))) { - stop(paste("Argument patterns can only contain 0 and 1, pattern", h, "contains another element"), call. = FALSE) + stop( + paste( + "Argument patterns can only contain 0 and 1, pattern", + h, + "contains another element" + ), + call. = FALSE + ) } if (all(patterns[h, ] %in% 1)) { prop.one <- prop.one + freq[h] @@ -555,12 +614,24 @@ check.patterns <- function(patterns, freq, prop) { } } if (prop.one != 0) { - warning(paste("Proportion of missingness has changed from", prop, "to", (1 - prop.one) * prop, "because of pattern(s) with merely ones"), call. = FALSE) + warning( + paste( + "Proportion of missingness has changed from", + prop, + "to", + (1 - prop.one) * prop, + "because of pattern(s) with merely ones" + ), + call. = FALSE + ) prop <- (1 - prop.one) * prop freq <- freq[-row.one] freq <- recalculate.freq(freq) patterns <- patterns[-row.one, ] - warning("Frequency vector and patterns matrix have changed because of pattern(s) with merely ones", call. = FALSE) + warning( + "Frequency vector and patterns matrix have changed because of pattern(s) with merely ones", + call. = FALSE + ) } prop.zero <- 0 row.zero <- c() diff --git a/R/ampute.continuous.R b/R/ampute.continuous.R index 8ca57d672..06bb49304 100644 --- a/R/ampute.continuous.R +++ b/R/ampute.continuous.R @@ -42,7 +42,8 @@ ampute.continuous <- function(P, scores, prop, type) { } for (i in seq_along(scores)) { # The desired function is chosen - formula <- switch(type[i], + formula <- switch( + type[i], LEFT = function(x, b) logit(mean(x) - x + b), MID = function(x, b) logit(-abs(x - mean(x)) + 0.75 + b), TAIL = function(x, b) logit(abs(x - mean(x)) - 0.75 + b), @@ -63,10 +64,26 @@ ampute.continuous <- function(P, scores, prop, type) { R[[i]] <- 0 } else { if (length(scores.temp) == 1) { - warning(paste("There is only 1 candidate for pattern", i, ",it will be amputed with probability", prop), call. = FALSE) + warning( + paste( + "There is only 1 candidate for pattern", + i, + ",it will be amputed with probability", + prop + ), + call. = FALSE + ) probs <- prop } else if (length(unique(scores.temp)) == 1) { - warning(paste("The weighted sum scores of all candidates in pattern", i, "are the same, they will be amputed with probability", prop), call. = FALSE) + warning( + paste( + "The weighted sum scores of all candidates in pattern", + i, + "are the same, they will be amputed with probability", + prop + ), + call. = FALSE + ) probs <- prop } else { probs <- formula(x = scores.temp, b = shift) @@ -87,10 +104,16 @@ ampute.continuous <- function(P, scores, prop, type) { # This is a custom adaptation of function binsearch from package gtools # (version 3.5.0) that returns the adjustment of the probability curves used # in the function ampute.continuous in ampute. -bin.search <- function(fun, range = c(-8, 8), ..., target = 0, - lower = ceiling(min(range)), - upper = floor(max(range)), - maxiter = 100, showiter = FALSE) { +bin.search <- function( + fun, + range = c(-8, 8), + ..., + target = 0, + lower = ceiling(min(range)), + upper = floor(max(range)), + maxiter = 100, + showiter = FALSE +) { lo <- lower hi <- upper counter <- 0 @@ -141,12 +164,24 @@ bin.search <- function(fun, range = c(-8, 8), ..., target = 0, retval <- list(call = match.call(), numiter = counter) if (outside.range) { if (target * sign < val.lo * sign) { - warning("The desired proportion of ", target, " is too small; ", val.lo, " is used instead.") + warning( + "The desired proportion of ", + target, + " is too small; ", + val.lo, + " is used instead." + ) retval$flag <- "Lower Boundary" retval$where <- lo retval$value <- val.lo } else { - warning("The desired proportion of ", target, " is too large; ", val.hi, " is used instead.") + warning( + "The desired proportion of ", + target, + " is too large; ", + val.hi, + " is used instead." + ) retval$flag <- "Upper Boundary" retval$where <- hi retval$value <- val.hi diff --git a/R/ampute.discrete.R b/R/ampute.discrete.R index 551459bac..89b9fda00 100644 --- a/R/ampute.discrete.R +++ b/R/ampute.discrete.R @@ -39,17 +39,24 @@ ampute.discrete <- function(P, scores, prop, odds) { ng <- length(odds[i, ][!is.na(odds[i, ])]) quantiles <- quantile(scores[[i]], probs = seq.int(0, 1, by = 1 / ng)) if (anyDuplicated(quantiles) || anyNA(quantiles)) { - stop("Division of sum scores into quantiles did not succeed. Possibly + stop( + "Division of sum scores into quantiles did not succeed. Possibly the sum scores contain too few different observations (in case of categorical or dummy variables). Try using more variables to calculate the sum scores or diminish the number of quantiles in the - odds matrix", call. = FALSE) + odds matrix", + call. = FALSE + ) } # For each candidate the quantile number is specified R.temp <- rep.int(NA, length(scores[[i]])) for (k in seq_len(ng)) { - R.temp <- replace(R.temp, scores[[i]] >= quantiles[k] & - scores[[i]] <= quantiles[k + 1], k) + R.temp <- replace( + R.temp, + scores[[i]] >= quantiles[k] & + scores[[i]] <= quantiles[k + 1], + k + ) } # For each candidate, a random value between 0 and 1 is compared with the # odds probability of being missing. If random value <= prob, the candidate @@ -59,7 +66,8 @@ ampute.discrete <- function(P, scores, prop, odds) { for (l in seq_len(ng)) { prob <- (ng * prop * odds[i, l]) / sum(odds[i, ], na.rm = TRUE) if (prob >= 1.0) { - warning("Combination of odds matrix and desired proportion of + warning( + "Combination of odds matrix and desired proportion of missingness results to small quantile groups, probably decreasing the obtained proportion of missingness", call. = FALSE diff --git a/R/anova.R b/R/anova.R index 4281dce40..0abca95a5 100644 --- a/R/anova.R +++ b/R/anova.R @@ -41,7 +41,8 @@ anova.mira <- function(object, ..., method = "D1", use = "wald") { args <- alist(fit1 = modlist[[j]], fit0 = modlist[[j + 1L]], use = use) } else { args <- alist( - fit1 = modlist[[j]], fit0 = modlist[[j + 1L]], + fit1 = modlist[[j]], + fit0 = modlist[[j + 1L]], dfcom = as.numeric(unlist(dfcom[j])) ) } diff --git a/R/as.R b/R/as.R index 6e7ed02b7..2b05e9b65 100644 --- a/R/as.R +++ b/R/as.R @@ -70,13 +70,21 @@ #' @keywords mids #' @export as.mids <- function(long, where = NULL, .imp = ".imp", .id = ".id") { - if (is.numeric(.imp)) .imp <- names(long)[.imp] - if (is.numeric(.id)) .id <- names(long)[.id] - if (!.imp %in% names(long)) stop("Imputation index `.imp` not found") + if (is.numeric(.imp)) { + .imp <- names(long)[.imp] + } + if (is.numeric(.id)) { + .id <- names(long)[.id] + } + if (!.imp %in% names(long)) { + stop("Imputation index `.imp` not found") + } # no missings allowed in .imp imps <- unlist(long[, .imp], use.names = FALSE) - if (anyNA(imps)) stop("Missing values in imputation index `.imp`") + if (anyNA(imps)) { + stop("Missing values in imputation index `.imp`") + } # number of records within .imp should be the same if (any(diff(table(imps))) != 0) { @@ -88,17 +96,25 @@ as.mids <- function(long, where = NULL, .imp = ".imp", .id = ".id") { data <- long[imps == 0, keep, drop = FALSE] n <- nrow(data) if (n == 0) { - stop("Original data not found.\n Use `complete(..., action = 'long', include = TRUE)` to save original data.") + stop( + "Original data not found.\n Use `complete(..., action = 'long', include = TRUE)` to save original data." + ) } # determine m m <- length(unique(imps)) - 1 # use mice to get info on data - if (is.null(where)) where <- is.na(data) - ini <- mice(data, - m = m, where = where, maxit = 0, - remove.collinear = FALSE, allow.na = TRUE + if (is.null(where)) { + where <- is.na(data) + } + ini <- mice( + data, + m = m, + where = where, + maxit = 0, + remove.collinear = FALSE, + allow.na = TRUE ) # create default .id when .id using type from input data @@ -142,7 +158,9 @@ as.mira <- function(fitlist) { return(fitlist) } if (is.mids(fitlist)) { - stop("as.mira() cannot convert class 'mids' into 'mira'. Use with() instead.") + stop( + "as.mira() cannot convert class 'mids' into 'mira'. Use with() instead." + ) } call <- match.call() if (!is.list(fitlist)) { @@ -171,7 +189,9 @@ as.mitml.result <- function(x) { z <- NULL if (is.mira(x)) { z <- getfit(x) - } else if (is.list(x)) z <- x + } else if (is.list(x)) { + z <- x + } class(z) <- c("mitml.result", "list") z } diff --git a/R/auxiliary.R b/R/auxiliary.R index 5d617c263..50ea93a33 100644 --- a/R/auxiliary.R +++ b/R/auxiliary.R @@ -30,7 +30,13 @@ ifdo <- function(cond, action) { #' @param typ Label to signal that this is a newly added observation #' @return A long data frame with additional rows for the break ages #' @export -appendbreak <- function(data, brk, warp.model = warp.model, id = NULL, typ = "pred") { +appendbreak <- function( + data, + brk, + warp.model = warp.model, + id = NULL, + typ = "pred" +) { k <- length(brk) app <- data[data$first, ] if (!is.null(id)) { @@ -48,7 +54,8 @@ appendbreak <- function(data, brk, warp.model = warp.model, id = NULL, typ = "pr ## update age variables app$age <- rep(brk, each = nap) app$age2 <- predict(warp.model, newdata = app) - X <- splines::bs(app$age, + X <- splines::bs( + app$age, knots = brk, Boundary.knots = c(brk[1], brk[k] + 0.0001), degree = 1 @@ -97,7 +104,9 @@ single2imputes <- function(single, mis) { vars <- names(single)[nmis > 0] z <- vector("list", length(vars)) names(z) <- vars - for (j in vars) z[[j]] <- single[mis[, j], j] + for (j in vars) { + z[[j]] <- single[mis[, j], j] + } z } diff --git a/R/blocks.R b/R/blocks.R index 075eb4eac..7082cf8c3 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -35,9 +35,7 @@ #' make.blocks(nhanes) #' make.blocks(c("age", "sex", "edu")) #' @export -make.blocks <- function(x, - partition = c("scatter", "collect", "void")) { - +make.blocks <- function(x, partition = c("scatter", "collect", "void")) { # character: assign each variable to its own block if (is.character(x)) { v <- as.list(as.character(x)) @@ -62,22 +60,23 @@ make.blocks <- function(x, # data.frame: assign each variable to its own block partition <- match.arg(partition) - switch(partition, - scatter = { - v <- as.list(names(x)) - names(v) <- names(x) - }, - collect = { - v <- list(names(x)) - names(v) <- "collect" - }, - void = { - v <- list() - }, - { - v <- as.list(names(x)) - names(v) <- names(x) - } + switch( + partition, + scatter = { + v <- as.list(names(x)) + names(v) <- names(x) + }, + collect = { + v <- list(names(x)) + names(v) <- "collect" + }, + void = { + v <- list() + }, + { + v <- as.list(names(x)) + names(v) <- names(x) + } ) return(v) } @@ -106,10 +105,14 @@ name.blocks <- function(x, prefix = "B") { if (!is.list(x)) { return(make.blocks(x)) } - if (is.null(names(x))) names(x) <- rep("", length(x)) + if (is.null(names(x))) { + names(x) <- rep("", length(x)) + } inc <- 1 for (i in seq_along(x)) { - if (names(x)[i] != "") next + if (names(x)[i] != "") { + next + } if (length(x[[i]]) == 1) { names(x)[i] <- x[[i]][1] } else { diff --git a/R/blots.R b/R/blots.R index 2762d8d95..dc0c5e9b8 100644 --- a/R/blots.R +++ b/R/blots.R @@ -18,7 +18,9 @@ make.blots <- function(data, blocks = make.blocks(data)) { data <- check.dataform(data) blots <- vector("list", length(blocks)) - for (i in seq_along(blots)) blots[[i]] <- alist() + for (i in seq_along(blots)) { + blots[[i]] <- alist() + } names(blots) <- names(blocks) blots } @@ -31,7 +33,9 @@ check.blots <- function(blots, data, blocks = NULL) { } blots <- as.list(blots) - for (i in seq_along(blots)) blots[[i]] <- as.list(blots[[i]]) + for (i in seq_along(blots)) { + blots[[i]] <- as.list(blots[[i]]) + } if (length(blots) == length(blocks) && is.null(names(blots))) { names(blots) <- names(blocks) diff --git a/R/bwplot.mads.R b/R/bwplot.mads.R index bb20a630e..b8dfd12d2 100644 --- a/R/bwplot.mads.R +++ b/R/bwplot.mads.R @@ -30,12 +30,21 @@ #' @author Rianne Schouten, 2016 #' @seealso \code{\link{ampute}}, \code{\link[lattice]{bwplot}}, \code{\link{mads}} #' @export -bwplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, - descriptives = TRUE, layout = NULL, ...) { +bwplot.mads <- function( + x, + data, + which.pat = NULL, + standardized = TRUE, + descriptives = TRUE, + layout = NULL, + ... +) { if (!is.mads(x)) { stop("Object is not of class mads") } - if (missing(data)) data <- NULL + if (missing(data)) { + data <- NULL + } yvar <- data if (is.null(yvar)) { varlist <- colnames(x$amp) @@ -48,7 +57,10 @@ bwplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, } else { pat <- length(which.pat) } - formula <- as.formula(paste0(paste0(varlist, collapse = "+"), "~ factor(.amp)")) + formula <- as.formula(paste0( + paste0(varlist, collapse = "+"), + "~ factor(.amp)" + )) data <- NULL if (standardized) { dat <- data.frame(scale(x$data)) @@ -87,7 +99,8 @@ bwplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, vec3 <- paste("", varlist) var <- length(varlist) if (descriptives) { - desc <- array(NA, + desc <- array( + NA, dim = c(2 * length(which.pat), 4, var), dimnames = list( Pattern = vec1, @@ -99,29 +112,65 @@ bwplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, for (i in seq_along(which.pat)) { wp <- which.pat[i] desc[(i * 2) - 1, 2, ] <- - round(vapply(varlist, function(x) { - mean(data[data$.pat == wp & data$.amp == "Amp", x]) - }, numeric(1)), 5) + round( + vapply( + varlist, + function(x) { + mean(data[data$.pat == wp & data$.amp == "Amp", x]) + }, + numeric(1) + ), + 5 + ) desc[(i * 2), 2, ] <- - round(vapply(varlist, function(x) { - mean(data[data$.pat == wp & data$.amp == "Non-Amp", x]) - }, numeric(1)), 5) + round( + vapply( + varlist, + function(x) { + mean(data[data$.pat == wp & data$.amp == "Non-Amp", x]) + }, + numeric(1) + ), + 5 + ) desc[(i * 2) - 1, 3, ] <- - round(vapply(varlist, function(x) { - var(data[data$.pat == wp & data$.amp == "Amp", x]) - }, numeric(1)), 5) + round( + vapply( + varlist, + function(x) { + var(data[data$.pat == wp & data$.amp == "Amp", x]) + }, + numeric(1) + ), + 5 + ) desc[(i * 2), 3, ] <- - round(vapply(varlist, function(x) { - var(data[data$.pat == wp & data$.amp == "Non-Amp", x]) - }, numeric(1)), 5) + round( + vapply( + varlist, + function(x) { + var(data[data$.pat == wp & data$.amp == "Non-Amp", x]) + }, + numeric(1) + ), + 5 + ) desc[(i * 2) - 1, 4, ] <- - vapply(varlist, function(x) { - length(data[data$.pat == wp & data$.amp == "Amp", x]) - }, numeric(1)) + vapply( + varlist, + function(x) { + length(data[data$.pat == wp & data$.amp == "Amp", x]) + }, + numeric(1) + ) desc[(i * 2), 4, ] <- - vapply(varlist, function(x) { - length(data[data$.pat == wp & data$.amp == "Non-Amp", x]) - }, numeric(1)) + vapply( + varlist, + function(x) { + length(data[data$.pat == wp & data$.amp == "Non-Amp", x]) + }, + numeric(1) + ) } p[["Descriptives"]] <- desc } @@ -141,9 +190,13 @@ bwplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, for (i in seq_len(pat)) { p[[paste("Boxplot pattern", which.pat[i])]] <- lattice::bwplot( - x = formula, data = data[data$.pat == which.pat[i], ], - multiple = TRUE, outer = TRUE, layout = layout, - ylab = "", par.settings = theme, + x = formula, + data = data[data$.pat == which.pat[i], ], + multiple = TRUE, + outer = TRUE, + layout = layout, + ylab = "", + par.settings = theme, xlab = paste("Data distributions in pattern", which.pat[i]) ) } diff --git a/R/bwplot.mids.R b/R/bwplot.mids.R index 1daa463a7..c7344de96 100644 --- a/R/bwplot.mids.R +++ b/R/bwplot.mids.R @@ -137,21 +137,25 @@ #' @aliases bwplot.mids bwplot #' @method bwplot mids #' @export -bwplot.mids <- function(x, - data, - na.groups = NULL, - groups = NULL, - as.table = TRUE, - theme = mice.theme(), - mayreplicate = TRUE, - allow.multiple = TRUE, - outer = TRUE, - drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), - ..., - subscripts = TRUE, - subset = TRUE) { +bwplot.mids <- function( + x, + data, + na.groups = NULL, + groups = NULL, + as.table = TRUE, + theme = mice.theme(), + mayreplicate = TRUE, + allow.multiple = TRUE, + outer = TRUE, + drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), + ..., + subscripts = TRUE, + subset = TRUE +) { call <- match.call() - if (!is.mids(x)) stop("Argument 'x' must be a 'mids' object") + if (!is.mids(x)) { + stop("Argument 'x' must be a 'mids' object") + } ## unpack data and response indicator cd <- data.frame(complete(x, "long", include = TRUE)) @@ -160,16 +164,22 @@ bwplot.mids <- function(x, ## evaluate na.group in response indicator nagp <- eval(expr = substitute(na.groups), envir = r, enclos = parent.frame()) - if (is.expression(nagp)) nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + if (is.expression(nagp)) { + nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + } ## evaluate groups in imputed data ngp <- eval(expr = substitute(groups), envir = cd, enclos = parent.frame()) - if (is.expression(ngp)) ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + if (is.expression(ngp)) { + ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + } groups <- ngp ## evaluate subset in imputed data ss <- eval(expr = substitute(subset), envir = cd, enclos = parent.frame()) - if (is.expression(ss)) ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + if (is.expression(ss)) { + ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + } subset <- ss ## evaluate further arguments before parsing @@ -187,7 +197,11 @@ bwplot.mids <- function(x, allfactors <- unlist(lapply(cd[vnames], is.factor)) if (missing(data)) { vnames <- vnames[!allfactors] - formula <- as.formula(paste(paste(vnames, collapse = "+", sep = ""), "~.imp", sep = "")) + formula <- as.formula(paste( + paste(vnames, collapse = "+", sep = ""), + "~.imp", + sep = "" + )) } else { ## pad abbreviated formula abbrev <- length(grep("~", call$data)) == 0 @@ -204,9 +218,13 @@ bwplot.mids <- function(x, ## determine the y-variables form <- lattice::latticeParseFormula( - model = formula, data = cd, subset = subset, - groups = groups, multiple = allow.multiple, - outer = outer, subscripts = TRUE, + model = formula, + data = cd, + subset = subset, + groups = groups, + multiple = allow.multiple, + outer = outer, + subscripts = TRUE, drop = drop.unused.levels ) ynames <- unlist(lapply(strsplit(form$left.name, " \\+ "), rm.whitespace)) @@ -257,15 +275,21 @@ bwplot.mids <- function(x, if (is.null(call$scales)) { args$scales <- list() if (length(ynames) > 1) { - args$scales <- list(x = list(relation = "free"), y = list(relation = "free")) + args$scales <- list( + x = list(relation = "free"), + y = list(relation = "free") + ) } } ## ready args <- c( - x = formula, data = list(cd), + x = formula, + data = list(cd), groups = list(groups), - args, dots, subset = call$subset + args, + dots, + subset = call$subset ) ## go diff --git a/R/calltypes.R b/R/calltypes.R index 147261d1f..97526eea8 100644 --- a/R/calltypes.R +++ b/R/calltypes.R @@ -44,10 +44,17 @@ #' make.calltypes(calltypes, predictorMatrix, NULL) #' #' @export -make.calltypes <- function(calltypes, predictorMatrix, formulas, priority = "pred") { +make.calltypes <- function( + calltypes, + predictorMatrix, + formulas, + priority = "pred" +) { # Validate calltypes length if (!is.null(calltypes) && length(calltypes) != nrow(predictorMatrix)) { - stop("Length of calltypes must match the number of blocks in predictorMatrix.") + stop( + "Length of calltypes must match the number of blocks in predictorMatrix." + ) } # Default calltypes setup @@ -68,7 +75,10 @@ make.calltypes <- function(calltypes, predictorMatrix, formulas, priority = "pre # Validate entries valid_calltypes <- c("pred", "formula") if (!all(calltypes %in% valid_calltypes)) { - stop("All entries in calltypes must be one of: ", paste(valid_calltypes, collapse = ", ")) + stop( + "All entries in calltypes must be one of: ", + paste(valid_calltypes, collapse = ", ") + ) } return(calltypes) diff --git a/R/cbind.R b/R/cbind.R index 97cb0a3c0..79df4b541 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -9,7 +9,9 @@ cbind.mids <- function(x, y = NULL, ...) { return(x) } n <- nrow(x$data) - if (length(y) == 1L) y <- rep(y, n) + if (length(y) == 1L) { + y <- rep(y, n) + } if (length(y) == 0L && length(dots) > 0L) { y <- cbind.data.frame(dots) } else if (length(y) > 0L && length(dots) == 0L) { @@ -49,10 +51,11 @@ cbind.mids <- function(x, y = NULL, ...) { r <- (!is.na(y)) f <- function(j) { - mtx <- matrix(NA, - nrow = sum(!r[, j]), - ncol = x$m, - dimnames = list(row.names(y)[!r[, j]], seq_len(m)) + mtx <- matrix( + NA, + nrow = sum(!r[, j]), + ncol = x$m, + dimnames = list(row.names(y)[!r[, j]], seq_len(m)) ) as.data.frame(mtx) } @@ -68,17 +71,11 @@ cbind.mids <- function(x, y = NULL, ...) { predictorMatrix <- rbind( x$predictorMatrix, - matrix(0, - ncol = ncol(x$predictorMatrix), - nrow = ncol(y) - ) + matrix(0, ncol = ncol(x$predictorMatrix), nrow = ncol(y)) ) predictorMatrix <- cbind( predictorMatrix, - matrix(0, - ncol = ncol(y), - nrow = nrow(x$predictorMatrix) + ncol(y) - ) + matrix(0, ncol = ncol(y), nrow = nrow(x$predictorMatrix) + ncol(y)) ) rownames(predictorMatrix) <- blocknames colnames(predictorMatrix) <- varnames @@ -126,13 +123,16 @@ cbind.mids <- function(x, y = NULL, ...) { lastSeedValue = lastSeedValue, chainMean = chainMean, chainVar = chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents + ) return(midsobj) } cbind.mids.mids <- function(x, y, call) { - if (!is.mids(y)) stop("Argument `y` not a mids object") + if (!is.mids(y)) { + stop("Argument `y` not a mids object") + } if (nrow(y$data) != nrow(x$data)) { stop("The two datasets do not have the same length\n") @@ -161,8 +161,12 @@ cbind.mids.mids <- function(x, y, call) { ynew <- varnames[-(1:ncol(x$data))] xblocks <- x$blocks yblocks <- y$blocks - for (i in names(xblocks)) xblocks[[i]] <- unname(xnew[xblocks[[i]]]) - for (i in names(yblocks)) yblocks[[i]] <- unname(ynew[yblocks[[i]]]) + for (i in names(xblocks)) { + xblocks[[i]] <- unname(xnew[xblocks[[i]]]) + } + for (i in names(yblocks)) { + yblocks[[i]] <- unname(ynew[yblocks[[i]]]) + } blocks <- c(xblocks, yblocks) xynames <- c(names(xblocks), names(yblocks)) blocknames <- make.unique(xynames) @@ -199,18 +203,12 @@ cbind.mids.mids <- function(x, y, call) { # on the off diagonal blocks. predictorMatrix <- rbind( x$predictorMatrix, - matrix(0, - ncol = ncol(x$predictorMatrix), - nrow = nrow(y$predictorMatrix) - ) + matrix(0, ncol = ncol(x$predictorMatrix), nrow = nrow(y$predictorMatrix)) ) predictorMatrix <- cbind( predictorMatrix, rbind( - matrix(0, - ncol = ncol(y$predictorMatrix), - nrow = nrow(x$predictorMatrix) - ), + matrix(0, ncol = ncol(y$predictorMatrix), nrow = nrow(x$predictorMatrix)), y$predictorMatrix ) ) @@ -274,13 +272,19 @@ cbind.mids.mids <- function(x, y, call) { chainMean[seq_len(dim(x$chainMean)[1]), , ] <- x$chainMean if (iteration <= dim(y$chainMean)[2]) { - chainMean[(dim(x$chainMean)[1] + 1):dim(chainMean)[1], , ] <- y$chainMean[, seq_len(iteration), ] + chainMean[(dim(x$chainMean)[1] + 1):dim(chainMean)[1], , ] <- y$chainMean[, + seq_len(iteration), + ] } else { - chainMean[(dim(x$chainMean)[1] + 1):dim(chainMean)[1], seq_len(dim(y$chainMean)[2]), ] <- y$chainMean + chainMean[ + (dim(x$chainMean)[1] + 1):dim(chainMean)[1], + seq_len(dim(y$chainMean)[2]), + ] <- y$chainMean } chainVar <- array( - data = NA, dim = c(dim(x$chainVar)[1] + dim(y$chainVar)[1], iteration, m), + data = NA, + dim = c(dim(x$chainVar)[1] + dim(y$chainVar)[1], iteration, m), dimnames = list( c( dimnames(x$chainVar)[[1]], @@ -293,9 +297,14 @@ cbind.mids.mids <- function(x, y, call) { chainVar[seq_len(dim(x$chainVar)[1]), , ] <- x$chainVar if (iteration <= dim(y$chainVar)[2]) { - chainVar[(dim(x$chainVar)[1] + 1):dim(chainVar)[1], , ] <- y$chainVar[, seq_len(iteration), ] + chainVar[(dim(x$chainVar)[1] + 1):dim(chainVar)[1], , ] <- y$chainVar[, + seq_len(iteration), + ] } else { - chainVar[(dim(x$chainVar)[1] + 1):dim(chainVar)[1], seq_len(dim(y$chainVar)[2]), ] <- y$chainVar + chainVar[ + (dim(x$chainVar)[1] + 1):dim(chainVar)[1], + seq_len(dim(y$chainVar)[2]), + ] <- y$chainVar } loggedEvents <- x$loggedEvents @@ -324,7 +333,8 @@ cbind.mids.mids <- function(x, y, call) { lastSeedValue = lastSeedValue, chainMean = chainMean, chainVar = chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents + ) return(midsobj) } diff --git a/R/check.R b/R/check.R index 75940ec8f..153e3d07a 100644 --- a/R/check.R +++ b/R/check.R @@ -70,8 +70,11 @@ check.ignore <- function(ignore, data) { } if (length(ignore) != nrow(data)) { stop( - "length(ignore) (", length(ignore), - ") does not match nrow(data) (", nrow(data), ")." + "length(ignore) (", + length(ignore), + ") does not match nrow(data) (", + nrow(data), + ")." ) } if (sum(!ignore) < 10L) { diff --git a/R/check.deprecated.R b/R/check.deprecated.R index 0e73b1e49..f14da30f6 100644 --- a/R/check.deprecated.R +++ b/R/check.deprecated.R @@ -12,9 +12,11 @@ check.deprecated <- function(...) { if (any(wrn)) { for (i in which(wrn)) { msg <- paste0( - "The '", names(replace.args)[i], + "The '", + names(replace.args)[i], "' argument is no longer supported. Please use '", - replace.args[i], "' instead." + replace.args[i], + "' instead." ) warning(msg) } diff --git a/R/check.model.R b/R/check.model.R index 7e8c6cee8..55b0c100e 100644 --- a/R/check.model.R +++ b/R/check.model.R @@ -16,17 +16,31 @@ check.model.match <- function(model, x, method) { mmeth <- model$setup$method if (length(mmeth) && mmeth != method) { - stop(paste("Model-Method mismatch: ", deparse(formula), "\n", - " Model: ", mmeth, "\n", - " Method: ", method, "\n")) + stop(paste( + "Model-Method mismatch: ", + deparse(formula), + "\n", + " Model: ", + mmeth, + "\n", + " Method: ", + method, + "\n" + )) } xnames <- model$xnames dnames <- colnames(x) notfound <- !xnames %in% dnames if (any(notfound)) { - stop(paste("Model-Data mismatch: ", deparse(formula), "\n", - "Not found in data: ", paste(xnames[notfound], collapse = " "), "\n")) + stop(paste( + "Model-Data mismatch: ", + deparse(formula), + "\n", + "Not found in data: ", + paste(xnames[notfound], collapse = " "), + "\n" + )) } notfound <- !dnames %in% xnames diff --git a/R/check.tasks.R b/R/check.tasks.R index 43ec5be33..9e65c0907 100644 --- a/R/check.tasks.R +++ b/R/check.tasks.R @@ -1,5 +1,10 @@ -check.tasks <- function(tasks, data, models = NULL, blocks = NULL, - skip.check.tasks = FALSE) { +check.tasks <- function( + tasks, + data, + models = NULL, + blocks = NULL, + skip.check.tasks = FALSE +) { if (skip.check.tasks) { return(tasks) } @@ -24,9 +29,14 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, # 3. Check length if (length(tasks) != length(bv)) { - stop("The length of `tasks` (", length(tasks), - ") must match the number of variables in `blocks` (", length(bv),").", - call. = FALSE) + stop( + "The length of `tasks` (", + length(tasks), + ") must match the number of variables in `blocks` (", + length(bv), + ").", + call. = FALSE + ) } # 4. Check that tasks is a named vector @@ -38,28 +48,41 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, # 5. Check if all names in tasks exist in blocks notFound <- !names(tasks) %in% bv if (any(notFound)) { - stop(paste0( - "The following variables specified in `tasks` are not present in `blocks`: ", - paste(names(tasks)[notFound], collapse = ", "), ".\n", - "Ensure all specified variables match those in `blocks`." - ), call. = FALSE) + stop( + paste0( + "The following variables specified in `tasks` are not present in `blocks`: ", + paste(names(tasks)[notFound], collapse = ", "), + ".\n", + "Ensure all specified variables match those in `blocks`." + ), + call. = FALSE + ) } # 6. Check if all tasks are valid invalid_ops <- setdiff(unique(tasks), valid_tasks) if (length(invalid_ops) > 0L) { - stop(paste0( - "Invalid task(s) detected: ", paste(invalid_ops, collapse = ", "), ".\n", - "Valid tasks are: ", paste(valid_tasks, collapse = ", "), ".\n", - "Please correct the `tasks` argument." - ), call. = FALSE) + stop( + paste0( + "Invalid task(s) detected: ", + paste(invalid_ops, collapse = ", "), + ".\n", + "Valid tasks are: ", + paste(valid_tasks, collapse = ", "), + ".\n", + "Please correct the `tasks` argument." + ), + call. = FALSE + ) } # 7. Prevent "fill" if models is NULL if ("fill" %in% tasks && is.null(models)) { - stop("The task 'fill' requires a stored model, but `models` is NULL.\n", - "Please provide a valid `models` object with a trained imputation model.", - call. = FALSE) + stop( + "The task 'fill' requires a stored model, but `models` is NULL.\n", + "Please provide a valid `models` object with a trained imputation model.", + call. = FALSE + ) } # 8. Ensure that all "fill" variables have a trained model in models @@ -67,11 +90,15 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, fill_vars <- names(tasks[tasks == "fill"]) missing_models <- setdiff(fill_vars, ls(models)) if (length(missing_models) > 0L) { - stop(paste0( - "The following variables specified as 'fill' do not have stored models: ", - paste(missing_models, collapse = ", "), ".\n", - "Ensure these variables were previously fitted before using 'fill'." - ), call. = FALSE) + stop( + paste0( + "The following variables specified as 'fill' do not have stored models: ", + paste(missing_models, collapse = ", "), + ".\n", + "Ensure these variables were previously fitted before using 'fill'." + ), + call. = FALSE + ) } } @@ -81,11 +108,15 @@ check.tasks <- function(tasks, data, models = NULL, blocks = NULL, scanned <- scan.newdata(data, models) idx <- scanned$variable %in% fill_vars & !scanned$can_fill if (any(idx)) { - stop(paste0( - "The following variables in `data` cannot be filled: ", - paste(scanned$variable[idx], collapse = ", "), ".\n", - "Use scan.newdata() to diagnose mismatch between data and models." - ), call. = FALSE) + stop( + paste0( + "The following variables in `data` cannot be filled: ", + paste(scanned$variable[idx], collapse = ", "), + ".\n", + "Use scan.newdata() to diagnose mismatch between data and models." + ), + call. = FALSE + ) } } diff --git a/R/complete.R b/R/complete.R index 21d3ce17b..53daab6c2 100644 --- a/R/complete.R +++ b/R/complete.R @@ -80,13 +80,22 @@ #' dslist <- complete(imp, c(0, 3, 5), mild = TRUE) #' names(dslist) #' @export -complete.mids <- function(data, action = 1L, include = FALSE, - mild = FALSE, order = c("last", "first"), - ...) { - if (!is.mids(data)) stop("'data' not of class 'mids'") +complete.mids <- function( + data, + action = 1L, + include = FALSE, + mild = FALSE, + order = c("last", "first"), + ... +) { + if (!is.mids(data)) { + stop("'data' not of class 'mids'") + } if (data$store == "train_compact") { - stop(paste("Cannot complete compact training object.\n", - "Set 'compact = FALSE' to preserve training data and imputations.")) + stop(paste( + "Cannot complete compact training object.\n", + "Set 'compact = FALSE' to preserve training data and imputations." + )) } order <- match.arg(order) @@ -94,10 +103,16 @@ complete.mids <- function(data, action = 1L, include = FALSE, if (is.numeric(action)) { action <- as.integer(action) idx <- action[action >= 0L & action <= m] - if (include && all(idx != 0L)) idx <- c(0L, idx) + if (include && all(idx != 0L)) { + idx <- c(0L, idx) + } shape <- ifelse(mild, "mild", "stacked") } else if (is.character(action)) { - if (include) idx <- 0L:m else idx <- 1L:m + if (include) { + idx <- 0L:m + } else { + idx <- 1L:m + } shape <- match.arg(action, c("all", "long", "broad", "repeated", "stacked")) shape <- ifelse(shape == "all" || mild, "mild", shape) } else { @@ -136,9 +151,10 @@ complete.mids <- function(data, action = 1L, include = FALSE, } # must be broad or repeated cmp <- bind_cols(mylist) - names(cmp) <- paste(rep.int(names(data$data), m), - rep.int(idx, rep.int(ncol(data$data), length(idx))), - sep = "." + names(cmp) <- paste( + rep.int(names(data$data), m), + rep.int(idx, rep.int(ncol(data$data), length(idx))), + sep = "." ) if (shape == "broad") { return(cmp) @@ -163,10 +179,11 @@ single.complete <- function(data, where, imp, ell) { if (sum(where[, varname]) == nrow(imp[[varname]])) { data[where[, varname], varname] <- imp[[varname]][, ell] } else { - data[as.numeric(rownames(imp[[varname]])), varname] <- imp[[varname]][, ell] + data[as.numeric(rownames(imp[[varname]])), varname] <- imp[[varname]][, + ell + ] } } } data } - diff --git a/R/convergence.R b/R/convergence.R index cee7c755e..992f75219 100644 --- a/R/convergence.R +++ b/R/convergence.R @@ -79,12 +79,12 @@ convergence <- function(data, diagnostic = "all", parameter = "mean", ...) { # extract chain means or chain standard deviations if (parameter == "mean") { param <- lapply(seq(p), function(x) { - aperm(data$chainMean[vrbs, , , drop = FALSE], c(2, 3, 1))[, , x] + aperm(data$chainMean[vrbs, , , drop = FALSE], c(2, 3, 1))[,, x] }) } if (parameter == "sd") { param <- lapply(seq(p), function(x) { - aperm(sqrt(data$chainVar)[vrbs, , , drop = FALSE], c(2, 3, 1))[, , x] + aperm(sqrt(data$chainVar)[vrbs, , , drop = FALSE], c(2, 3, 1))[,, x] }) } names(param) <- vrbs @@ -93,26 +93,34 @@ convergence <- function(data, diagnostic = "all", parameter = "mean", ...) { if (diagnostic == "all" | diagnostic == "ac") { ac <- purrr::map(vrbs, function(.vrb) { - c(NA, dplyr::cummean(dplyr::coalesce( - purrr::map_dbl(2:t, function(.itr) { - suppressWarnings(stats::cor( - param[[.vrb]][.itr - 1, ], - param[[.vrb]][.itr, ], - use = "pairwise.complete.obs" - )) - }), 0 - ))) + 0 * param[[.vrb]][, 1] + c( + NA, + dplyr::cummean(dplyr::coalesce( + purrr::map_dbl(2:t, function(.itr) { + suppressWarnings(stats::cor( + param[[.vrb]][.itr - 1, ], + param[[.vrb]][.itr, ], + use = "pairwise.complete.obs" + )) + }), + 0 + )) + ) + + 0 * param[[.vrb]][, 1] }) out <- base::cbind(out, ac = unlist(ac)) } # compute potential scale reduction factor if (diagnostic == "all" | diagnostic == "psrf" | diagnostic == "gr") { - psrf <- purrr::map_dfr(param, ~ { - purrr::map_dfr(1:t, function(.itr) { - data.frame(psrf = rstan::Rhat(.[1:.itr, ])) - }) - }) + psrf <- purrr::map_dfr( + param, + ~ { + purrr::map_dfr(1:t, function(.itr) { + data.frame(psrf = rstan::Rhat(.[1:.itr, ])) + }) + } + ) out <- base::cbind(out, psrf) } out[is.nan(out)] <- NA diff --git a/R/correlar.R b/R/correlar.R index 44cd1890b..3cbb36410 100644 --- a/R/correlar.R +++ b/R/correlar.R @@ -62,13 +62,14 @@ #' cor2[is.na(cor2)] <- 0 #' correlar(cor2, 1) #' @export -correlar <- function(corr_matrix, - dependent_var_index, - max_steps = NULL, - crit = 0.005, - force_pd = FALSE, - check_input = TRUE) { - +correlar <- function( + corr_matrix, + dependent_var_index, + max_steps = NULL, + crit = 0.005, + force_pd = FALSE, + check_input = TRUE +) { if (check_input) { stopifnot( is.matrix(corr_matrix), @@ -93,10 +94,11 @@ correlar <- function(corr_matrix, steps <- 0L converged <- FALSE - while (length(remaining_predictors) > 0L && - (is.null(max_steps) || steps < max_steps) && - !converged) { - + while ( + length(remaining_predictors) > 0L && + (is.null(max_steps) || steps < max_steps) && + !converged + ) { # Find the predictor with the highest absolute correlation correlations <- current_matrix[dependent_var_index, remaining_predictors] best_predictor <- remaining_predictors[which.max(abs(correlations))] @@ -124,5 +126,6 @@ correlar <- function(corr_matrix, return(list( predictors = predictors[seq(steps)], - R2 = R2[seq(steps)])) + R2 = R2[seq(steps)] + )) } diff --git a/R/data.R b/R/data.R index 590b5b866..47ffcc775 100644 --- a/R/data.R +++ b/R/data.R @@ -60,12 +60,18 @@ scan.newdata <- function(data, models, print = FALSE) { in_data <- isTRUE(report$in_data[i]) in_model <- isTRUE(report$in_model[i]) - x <- if (isTRUE(in_data) && j %in% names(orig.data)) orig.data[[j]] else NULL + x <- if (isTRUE(in_data) && j %in% names(orig.data)) { + orig.data[[j]] + } else { + NULL + } mod <- if (in_model) models[[j]][[1]] else NULL report$data_class[i] <- if (!is.null(x)) { if (inherits(x, "ordered")) "ordered" else class(x)[1] - } else NA + } else { + NA + } if (!is.null(mod$class)) { report$model_class[i] <- mod$class @@ -79,7 +85,11 @@ scan.newdata <- function(data, models, print = FALSE) { if (!is.null(x) && is.factor(x) && !is.null(mod$factor$labels)) { lvls.data <- levels(x) lvls.model <- mod$factor$labels - report$levels_match[i] <- if (identical(lvls.data, lvls.model)) TRUE else FALSE + report$levels_match[i] <- if (identical(lvls.data, lvls.model)) { + TRUE + } else { + FALSE + } # report$levels_new_missing[i] <- if (any(!lvls.model %in% lvls.data)) "Y" else "N" # report$levels_extra[i] <- if (any(!lvls.data %in% lvls.model)) "Y" else "N" } else { @@ -96,18 +106,26 @@ scan.newdata <- function(data, models, print = FALSE) { } # Add can_fill column - report$can_fill <- vapply(seq_len(nrow(report)), function(i) { - if (isTRUE(report$in_model[i]) && - report$class_match[i] && - !isFALSE(report$levels_match[i]) && - report$pred_match[i]) { - TRUE - } else { - FALSE - } - }, NA) + report$can_fill <- vapply( + seq_len(nrow(report)), + function(i) { + if ( + isTRUE(report$in_model[i]) && + report$class_match[i] && + !isFALSE(report$levels_match[i]) && + report$pred_match[i] + ) { + TRUE + } else { + FALSE + } + }, + NA + ) - if (print) print(report) + if (print) { + print(report) + } report } diff --git a/R/densityplot.mids.R b/R/densityplot.mids.R index cf0883e12..821533518 100644 --- a/R/densityplot.mids.R +++ b/R/densityplot.mids.R @@ -142,25 +142,29 @@ #' @aliases densityplot.mids densityplot #' @method densityplot mids #' @export -densityplot.mids <- function(x, - data, - na.groups = NULL, - groups = NULL, - as.table = TRUE, - plot.points = FALSE, - theme = mice.theme(), - mayreplicate = TRUE, - thicker = 2.5, - allow.multiple = TRUE, - outer = TRUE, - drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), - panel = lattice::lattice.getOption("panel.densityplot"), - default.prepanel = lattice::lattice.getOption("prepanel.default.densityplot"), - ..., - subscripts = TRUE, - subset = TRUE) { +densityplot.mids <- function( + x, + data, + na.groups = NULL, + groups = NULL, + as.table = TRUE, + plot.points = FALSE, + theme = mice.theme(), + mayreplicate = TRUE, + thicker = 2.5, + allow.multiple = TRUE, + outer = TRUE, + drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), + panel = lattice::lattice.getOption("panel.densityplot"), + default.prepanel = lattice::lattice.getOption("prepanel.default.densityplot"), + ..., + subscripts = TRUE, + subset = TRUE +) { call <- match.call() - if (!is.mids(x)) stop("Argument 'x' must be a 'mids' object") + if (!is.mids(x)) { + stop("Argument 'x' must be a 'mids' object") + } ## unpack data and response indicator cd <- data.frame(complete(x, "long", include = TRUE)) @@ -168,16 +172,22 @@ densityplot.mids <- function(x, ## evaluate na.group in response indicator nagp <- eval(expr = substitute(na.groups), envir = r, enclos = parent.frame()) - if (is.expression(nagp)) nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + if (is.expression(nagp)) { + nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + } ## evaluate groups in imputed data ngp <- eval(expr = substitute(groups), envir = cd, enclos = parent.frame()) - if (is.expression(ngp)) ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + if (is.expression(ngp)) { + ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + } groups <- ngp ## evaluate subset in imputed data ss <- eval(expr = substitute(subset), envir = cd, enclos = parent.frame()) - if (is.expression(ss)) ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + if (is.expression(ss)) { + ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + } subset <- ss ## evaluate further arguments before parsing @@ -198,16 +208,24 @@ densityplot.mids <- function(x, allfactors <- unlist(lapply(cd[vnames], is.factor)) if (missing(data)) { vnames <- vnames[!allfactors & x$nmis > 2 & x$nmis < nrow(x$data) - 1] - formula <- as.formula(paste("~", paste(vnames, collapse = "+", sep = ""), sep = "")) + formula <- as.formula(paste( + "~", + paste(vnames, collapse = "+", sep = ""), + sep = "" + )) } else { formula <- data } ## determine the y-variables form <- lattice::latticeParseFormula( - model = formula, data = cd, subset = subset, - groups = groups, multiple = allow.multiple, - outer = outer, subscripts = TRUE, + model = formula, + data = cd, + subset = subset, + groups = groups, + multiple = allow.multiple, + outer = outer, + subscripts = TRUE, drop = drop.unused.levels ) xnames <- unlist(lapply(strsplit(form$right.name, " \\+ "), rm.whitespace)) ## Jul2011 @@ -237,9 +255,18 @@ densityplot.mids <- function(x, ## replicate color 2 if group=.imp is part of xnames mustreplicate <- !(!is.null(call$groups) && nona) && mayreplicate if (mustreplicate) { - theme$superpose.line$col <- rep(theme$superpose.line$col[seq_len(2)], c(1, x$m)) - theme$superpose.line$lwd <- rep(c(theme$superpose.line$lwd[1] * thicker, theme$superpose.line$lwd[1]), c(1, x$m)) - theme$superpose.symbol$col <- rep(theme$superpose.symbol$col[seq_len(2)], c(1, x$m)) + theme$superpose.line$col <- rep( + theme$superpose.line$col[seq_len(2)], + c(1, x$m) + ) + theme$superpose.line$lwd <- rep( + c(theme$superpose.line$lwd[1] * thicker, theme$superpose.line$lwd[1]), + c(1, x$m) + ) + theme$superpose.symbol$col <- rep( + theme$superpose.symbol$col[seq_len(2)], + c(1, x$m) + ) theme$superpose.symbol$pch <- c(NA, 49:(49 + x$m - 1)) } @@ -251,15 +278,21 @@ densityplot.mids <- function(x, if (is.null(call$scales)) { args$scales <- list() if (length(xnames) > 1) { - args$scales <- list(x = list(relation = "free"), y = list(relation = "free")) + args$scales <- list( + x = list(relation = "free"), + y = list(relation = "free") + ) } } ## ready args <- c( - x = formula, data = list(cd), + x = formula, + data = list(cd), groups = list(gp), - args, dots, subset = call$subset + args, + dots, + subset = call$subset ) ## go diff --git a/R/design.R b/R/design.R index 8cb330a83..3b855dee3 100644 --- a/R/design.R +++ b/R/design.R @@ -11,4 +11,4 @@ obtain.design <- function(data, formula = ~.) { } model.matrix(formula, data = mf) -} \ No newline at end of file +} diff --git a/R/edit.setup.R b/R/edit.setup.R index 542f010b6..6210fbe4c 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -1,20 +1,29 @@ -mice.edit.setup <- function(data, setup, tasks, - user.visitSequence = NULL, - allow.na = FALSE, - remove.constant = TRUE, - remove.collinear = TRUE, - remove_collinear = TRUE, - ...) { +mice.edit.setup <- function( + data, + setup, + tasks, + user.visitSequence = NULL, + allow.na = FALSE, + remove.constant = TRUE, + remove.collinear = TRUE, + remove_collinear = TRUE, + ... +) { # legacy handling - if (!remove_collinear) remove.collinear <- FALSE + if (!remove_collinear) { + remove.collinear <- FALSE + } pred <- setup$predictorMatrix meth <- setup$method vis <- setup$visitSequence post <- setup$post - if (ncol(pred) != nrow(pred) || length(meth) != nrow(pred) || - ncol(data) != nrow(pred)) { + if ( + ncol(pred) != nrow(pred) || + length(meth) != nrow(pred) || + ncol(data) != nrow(pred) + ) { return(setup) } @@ -85,7 +94,9 @@ mice.edit.setup <- function(data, setup, tasks, } if (all(pred == 0L) && didlog) { - stop("`mice` detected constant and/or collinear variables. No predictors were left after their removal.") + stop( + "`mice` detected constant and/or collinear variables. No predictors were left after their removal." + ) } # Detect passive methods diff --git a/R/filter.R b/R/filter.R index f69829e36..06d08ee7f 100644 --- a/R/filter.R +++ b/R/filter.R @@ -132,6 +132,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { lastSeedValue = lastSeedValue, chainMean = chainMean, chainVar = chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents + ) return(midsobj) } diff --git a/R/fix.coef.R b/R/fix.coef.R index 16e745001..a24aa0689 100644 --- a/R/fix.coef.R +++ b/R/fix.coef.R @@ -54,7 +54,9 @@ #' @export fix.coef <- function(model, beta = NULL) { oldcoef <- clean.coef(model) - if (is.null(beta)) beta <- oldcoef + if (is.null(beta)) { + beta <- oldcoef + } if (length(oldcoef) != length(beta)) { stop("incorrect length of 'beta'", call. = FALSE) } @@ -87,8 +89,11 @@ fix.coef <- function(model, beta = NULL) { } offset <- as.vector(mm %*% beta) uf <- . ~ 1 - if (inherits(model, "merMod")) uf <- formula(model, random.only = TRUE) - upd <- update(model, + if (inherits(model, "merMod")) { + uf <- formula(model, random.only = TRUE) + } + upd <- update( + model, formula. = uf, data = cbind(data, offset = offset), offset = offset diff --git a/R/flux.R b/R/flux.R index 37ce57d96..671433df9 100644 --- a/R/flux.R +++ b/R/flux.R @@ -66,7 +66,14 @@ flux <- function(data, local = names(data)) { fico <- fico(data) outflux <- rowSums(pat$rm) / (rowSums(pat$rm + pat$mm)) influx <- rowSums(pat$mr) / (rowSums(pat$mr + pat$rr)) - data.frame(pobs = x, influx = influx, outflux = outflux, ainb = ainb, aout = aout, fico = fico) + data.frame( + pobs = x, + influx = influx, + outflux = outflux, + ainb = ainb, + aout = aout, + fico = fico + ) } @@ -132,31 +139,53 @@ flux <- function(data, local = names(data)) { #' \emph{Statistics in Medicine}, \emph{29}, 2920-2931. #' @keywords misc #' @export -fluxplot <- function(data, local = names(data), - plot = TRUE, labels = TRUE, - xlim = c(0, 1), ylim = c(0, 1), las = 1, - xlab = "Influx", ylab = "Outflux", - main = paste("Influx-outflux pattern for", deparse(substitute(data))), - eqscplot = TRUE, pty = "s", - lwd = 1, - ...) { +fluxplot <- function( + data, + local = names(data), + plot = TRUE, + labels = TRUE, + xlim = c(0, 1), + ylim = c(0, 1), + las = 1, + xlab = "Influx", + ylab = "Outflux", + main = paste("Influx-outflux pattern for", deparse(substitute(data))), + eqscplot = TRUE, + pty = "s", + lwd = 1, + ... +) { f <- flux(data, local) if (plot) { if (eqscplot) { MASS::eqscplot( - x = f$influx, y = f$outflux, type = "n", + x = f$influx, + y = f$outflux, + type = "n", main = main, - xlab = xlab, ylab = ylab, - xlim = xlim, ylim = ylim, - pty = pty, lwd = lwd, axes = FALSE, ... + xlab = xlab, + ylab = ylab, + xlim = xlim, + ylim = ylim, + pty = pty, + lwd = lwd, + axes = FALSE, + ... ) } else { plot( - x = f$influx, y = f$outflux, type = "n", + x = f$influx, + y = f$outflux, + type = "n", main = main, - xlab = xlab, ylab = ylab, - xlim = xlim, ylim = ylim, - pty = pty, lwd = lwd, axes = FALSE, ... + xlab = xlab, + ylab = ylab, + xlim = xlim, + ylim = ylim, + pty = pty, + lwd = lwd, + axes = FALSE, + ... ) } axis(1, lwd = lwd, las = las) diff --git a/R/formula.R b/R/formula.R index 9262d84c8..85bfadc5e 100644 --- a/R/formula.R +++ b/R/formula.R @@ -24,8 +24,11 @@ #' f3 <- name.formulas(lapply(c1, as.formula)) #' f3 #' @export -make.formulas <- function(data, blocks = make.blocks(data), - predictorMatrix = NULL) { +make.formulas <- function( + data, + blocks = make.blocks(data), + predictorMatrix = NULL +) { data <- check.dataform(data) formulas <- as.list(rep("~ 0", length(blocks))) names(formulas) <- names(blocks) @@ -43,7 +46,8 @@ make.formulas <- function(data, blocks = make.blocks(data), x <- "0" } formulas[[h]] <- paste( - paste(backticks(y), collapse = "+"), "~", + paste(backticks(y), collapse = "+"), + "~", paste(backticks(x), collapse = "+") ) } @@ -106,10 +110,14 @@ name.formulas <- function(formulas, prefix = "F") { if (!all(sapply(formulas, is.formula) | sapply(formulas, is.list))) { stop("Not all elements in `formulas` are a formula or a list") } - if (is.null(names(formulas))) names(formulas) <- rep("", length(formulas)) + if (is.null(names(formulas))) { + names(formulas) <- rep("", length(formulas)) + } inc <- 1 for (i in seq_along(formulas)) { - if (names(formulas)[i] != "") next + if (names(formulas)[i] != "") { + next + } # if (hasdot(formulas[[i]]) && is.null(data)) # stop("Formula with dot requires `data` argument", call. = FALSE) y <- lhs(formulas[[i]]) @@ -147,10 +155,15 @@ check.formulas <- function(formulas, data) { #' @param include.intercept A logical that indicated whether the intercept #' should be included in the result. #' @keywords internal -extend.formulas <- function(formulas, data, blocks, predictorMatrix = NULL, - auxiliary = TRUE, - include.intercept = FALSE, - ...) { +extend.formulas <- function( + formulas, + data, + blocks, + predictorMatrix = NULL, + auxiliary = TRUE, + include.intercept = FALSE, + ... +) { # Extend formulas with predictorMatrix if (is.null(predictorMatrix)) { return(formulas) @@ -181,11 +194,16 @@ extend.formulas <- function(formulas, data, blocks, predictorMatrix = NULL, #' should be included in the result. #' @return A formula #' @keywords internal -extend.formula <- function(formula = ~0, - predictors = NULL, - auxiliary = TRUE, - include.intercept = FALSE, ...) { - if (!is.formula(formula)) formula <- ~0 +extend.formula <- function( + formula = ~0, + predictors = NULL, + auxiliary = TRUE, + include.intercept = FALSE, + ... +) { + if (!is.formula(formula)) { + formula <- ~0 + } # handle dot in RHS if (hasdot(formula)) { @@ -198,16 +216,20 @@ extend.formula <- function(formula = ~0, fr <- reformulate(c(".", backticks(predictors))) } - if (auxiliary) formula <- update(formula, fr, ...) - if (include.intercept) formula <- update(formula, ~ . + 1, ...) + if (auxiliary) { + formula <- update(formula, fr, ...) + } + if (include.intercept) { + formula <- update(formula, ~ . + 1, ...) + } formula } - handle.oldstyle.formulas <- function(formulas, data) { # converts old-style character vector to formula list - oldstyle <- length(formulas) == ncol(data) && is.vector(formulas) && + oldstyle <- length(formulas) == ncol(data) && + is.vector(formulas) && is.character(formulas) if (!oldstyle) { return(formulas) diff --git a/R/futuremice.R b/R/futuremice.R index 9217c1fbc..824ea525e 100644 --- a/R/futuremice.R +++ b/R/futuremice.R @@ -86,9 +86,18 @@ #' } #' #' @export -futuremice <- function(data, m = 5, parallelseed = NA, n.core = NULL, seed = NA, - use.logical = TRUE, future.plan = "multisession", - packages = NULL, globals = NULL, ...) { +futuremice <- function( + data, + m = 5, + parallelseed = NA, + n.core = NULL, + seed = NA, + use.logical = TRUE, + future.plan = "multisession", + packages = NULL, + globals = NULL, + ... +) { warning( "'futuremice()' is deprecated as of mice 3.18.0. ", "Please use 'mice(..., parallel = TRUE)' instead.", @@ -127,26 +136,40 @@ futuremice <- function(data, m = 5, parallelseed = NA, n.core = NULL, seed = NA, if (n.core > 1) { if (interactive()) { msg <- "Be careful; specifying seed rather than parallelseed results in duplicate imputations.\nDo you want to continue?\n" - ask <- askYesNo(msg, prompts = getOption("askYesNo", gettext(c("Yes", "No, ignore seed", "Cancel")))) + ask <- askYesNo( + msg, + prompts = getOption( + "askYesNo", + gettext(c("Yes", "No, ignore seed", "Cancel")) + ) + ) if (isTRUE(ask)) { seed <- seed - warning("Be careful; the imputations will be the same over the cores.") + warning( + "Be careful; the imputations will be the same over the cores." + ) } else if (isFALSE(ask)) { seed <- NA - message("Parallelseed is specified for you, and is accessible in the output object under $parallelseed.") + message( + "Parallelseed is specified for you, and is accessible in the output object under $parallelseed." + ) } else if (is.na(ask)) { - stop("You stopped futuremice. To obtain unique, but reproducible imputations, specify parallelseed.") + stop( + "You stopped futuremice. To obtain unique, but reproducible imputations, specify parallelseed." + ) } } else { - warning("Be careful; the imputations will be identical over the cores. Perhaps you want to specify parallelseed, for unique, but reproducible results.") + warning( + "Be careful; the imputations will be identical over the cores. Perhaps you want to specify parallelseed, for unique, but reproducible results." + ) } } } if (!is.na(parallelseed)) { set.seed(parallelseed) } else { - if(!exists(".Random.seed")) { + if (!exists(".Random.seed")) { set.seed(NULL) } parallelseed <- get( @@ -158,20 +181,14 @@ futuremice <- function(data, m = 5, parallelseed = NA, n.core = NULL, seed = NA, } # start multisession - future::plan(future.plan, - workers = n.core - ) + future::plan(future.plan, workers = n.core) # begin future imps <- furrr::future_map( n.imp.core, function(x) { - mice(data = data, - m = x, - printFlag = FALSE, - seed = seed, - ... - )}, + mice(data = data, m = x, printFlag = FALSE, seed = seed, ...) + }, .options = furrr::furrr_options( seed = TRUE, globals = globals, @@ -206,9 +223,12 @@ check.cores <- function(n.core, available, m) { n.core <- min(available - 1, m) } else { if (n.core > available | n.core > m) { - warning(paste("'n.core' exceeds the maximum number of available cores on your machine or the number of imputations, and is set to", min(available - 1, m))) + warning(paste( + "'n.core' exceeds the maximum number of available cores on your machine or the number of imputations, and is set to", + min(available - 1, m) + )) } n.core <- min(available - 1, m, n.core) } n.core -} \ No newline at end of file +} diff --git a/R/get.df.R b/R/get.df.R index 09a137b85..1c12e75e9 100644 --- a/R/get.df.R +++ b/R/get.df.R @@ -8,9 +8,10 @@ get.dfcom <- function(model, dfcom = NULL) { } # first, try the standard df.residual() function - dfcom <- tryCatch(stats::df.residual(model), - error = function(e) NULL) - if (!is.null(dfcom)) return(as.numeric(dfcom)) + dfcom <- tryCatch(stats::df.residual(model), error = function(e) NULL) + if (!is.null(dfcom)) { + return(as.numeric(dfcom)) + } # coxph model: nevent - p if (inherits(model, "coxph")) { @@ -34,10 +35,15 @@ get.dfcom <- function(model, dfcom = NULL) { get.glanced <- function(object) { - if (!is.list(object)) stop("Argument 'object' not a list", call. = FALSE) + if (!is.list(object)) { + stop("Argument 'object' not a list", call. = FALSE) + } object <- as.mira(object) - glanced <- try(data.frame(summary(getfit(object), type = "glance")), silent = TRUE) + glanced <- try( + data.frame(summary(getfit(object), type = "glance")), + silent = TRUE + ) if (inherits(glanced, "data.frame")) { # nobs is needed for pool.r.squared # broom <= 0.5.6 does not supply it; use nobs() rather than diff --git a/R/getfit.R b/R/getfit.R index 96581b066..0b2ff5ceb 100644 --- a/R/getfit.R +++ b/R/getfit.R @@ -37,7 +37,9 @@ getfit <- function(x, i = -1L, simplify = FALSE) { if (i != -1L) { return(ra[[i]]) } - if (simplify) ra <- unlist(ra) + if (simplify) { + ra <- unlist(ra) + } # hack to get pool accept both mira and general list objects class(ra) <- c("mira", "list") @@ -51,7 +53,9 @@ getfit <- function(x, i = -1L, simplify = FALSE) { #' @param x An object of class \code{mipo} #' @export getqbar <- function(x) { - if (!is.mipo(x)) stop("Not a mipo object") + if (!is.mipo(x)) { + stop("Not a mipo object") + } qbar <- x$pooled$estimate # note: not supported: component/y.values names(qbar) <- x$pooled$term diff --git a/R/ibind.R b/R/ibind.R index a33f74d48..a28eb4bfa 100644 --- a/R/ibind.R +++ b/R/ibind.R @@ -51,7 +51,9 @@ ibind <- function(x, y) { stop("Differences detected between `x$method` and `y$method`") } if (!identical(x$predictorMatrix, y$predictorMatrix)) { - stop("Differences detected between `x$predictorMatrix` and `y$predictorMatrix`") + stop( + "Differences detected between `x$predictorMatrix` and `y$predictorMatrix`" + ) } if (!identical(x$visitSequence, y$visitSequence)) { stop("Differences detected between `x$visitSequence` and `y$visitSequence`") @@ -77,12 +79,12 @@ ibind <- function(x, y) { chainMean <- chainVar <- initialize.chain(names(x$data), iteration, m) for (j in seq_len(x$m)) { - chainMean[, seq_len(x$iteration), j] <- x$chainMean[, , j] - chainVar[, seq_len(x$iteration), j] <- x$chainVar[, , j] + chainMean[, seq_len(x$iteration), j] <- x$chainMean[,, j] + chainVar[, seq_len(x$iteration), j] <- x$chainVar[,, j] } for (j in seq_len(y$m)) { - chainMean[, seq_len(y$iteration), j + x$m] <- y$chainMean[, , j] - chainVar[, seq_len(y$iteration), j + x$m] <- y$chainVar[, , j] + chainMean[, seq_len(y$iteration), j + x$m] <- y$chainMean[,, j] + chainVar[, seq_len(y$iteration), j + x$m] <- y$chainVar[,, j] } midsobj <- mids( @@ -106,6 +108,7 @@ ibind <- function(x, y) { lastSeedValue = x$lastSeedValue, chainMean = chainMean, chainVar = chainVar, - loggedEvents = x$loggedEvents) + loggedEvents = x$loggedEvents + ) return(midsobj) } diff --git a/R/initialize.imp.R b/R/initialize.imp.R index ffc76e58d..c0853fdfb 100644 --- a/R/initialize.imp.R +++ b/R/initialize.imp.R @@ -1,5 +1,14 @@ -initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, - method, nmis, data.init) { +initialize.imp <- function( + data, + m, + ignore, + where, + blocks, + visitSequence, + method, + nmis, + data.init +) { imp <- vector("list", ncol(data)) names(imp) <- names(data) r <- !is.na(data) @@ -11,11 +20,12 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, wy <- where[, j] # Determine correct NA type - na_type <- switch(class(y)[1L], - "logical" = as.logical(NA), - "factor" = as.character(NA), - "ordered" = as.character(NA), - NA_real_ + na_type <- switch( + class(y)[1L], + "logical" = as.logical(NA), + "factor" = as.character(NA), + "ordered" = as.character(NA), + NA_real_ ) # Initialize imp[[j]] with correct type @@ -44,8 +54,12 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, } # Final safety check: enforce type match with y - if (is.logical(y)) vec <- as.logical(vec) - if (is.factor(y)) vec <- factor(vec, levels = levels(y), ordered = is.ordered(y)) + if (is.logical(y)) { + vec <- as.logical(vec) + } + if (is.factor(y)) { + vec <- factor(vec, levels = levels(y), ordered = is.ordered(y)) + } imp[[j]][, i] <- vec } @@ -54,7 +68,10 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, } # Ensure imp[[j]] exists for any j used in where or blocks - vars_needed <- union(colnames(where)[colSums(where) > 0], unique(unlist(blocks))) + vars_needed <- union( + colnames(where)[colSums(where) > 0], + unique(unlist(blocks)) + ) for (j in vars_needed) { if (is.null(imp[[j]])) { if (j %in% colnames(where)) { diff --git a/R/install.on.demand.R b/R/install.on.demand.R index 42a4ce33e..ae468e54f 100644 --- a/R/install.on.demand.R +++ b/R/install.on.demand.R @@ -7,6 +7,12 @@ install.on.demand <- function(pkg, quiet = FALSE, ...) { } if (interactive()) { answer <- askYesNo(paste("Package", pkg, "needed. Install from CRAN?")) - if (answer) install.packages(pkg, repos = "https://cloud.r-project.org/", quiet = quiet) + if (answer) { + install.packages( + pkg, + repos = "https://cloud.r-project.org/", + quiet = quiet + ) + } } } diff --git a/R/internal.R b/R/internal.R index 56b860462..023556d7e 100644 --- a/R/internal.R +++ b/R/internal.R @@ -8,7 +8,12 @@ impute.with.na <- function(x, wy) !complete.cases(x) & wy check.df <- function(x, y, ry) { # If needed, writes the df warning message to the log df <- sum(ry) - ncol(x) - 1 - mess <- paste("df set to 1. # observed cases:", sum(ry), " # predictors:", ncol(x) + 1) + mess <- paste( + "df set to 1. # observed cases:", + sum(ry), + " # predictors:", + ncol(x) + 1 + ) if (df < 1 && sum(ry) > 0) { record.event(out = mess, frame = 4) } @@ -39,7 +44,13 @@ find.collinear <- function(x, threshold = 0.999, ...) { return(varnames[out]) } -updateLog <- function(out = NULL, meth = NULL, msg = NULL, fn = NULL, frame = 1) { +updateLog <- function( + out = NULL, + meth = NULL, + msg = NULL, + fn = NULL, + frame = 1 +) { pos_state <- ma_exists("state", frame)$pos pos_loggedEvents <- ma_exists("loggedEvents", frame)$pos @@ -47,13 +58,13 @@ updateLog <- function(out = NULL, meth = NULL, msg = NULL, fn = NULL, frame = 1) r <- get("loggedEvents", pos_loggedEvents) rec <- data.frame( - it = s$it, - im = s$im, - dep = s$dep, + it = s$it, + im = s$im, + dep = s$dep, meth = if (is.null(meth)) s$meth else meth, - out = if (is.null(out)) "" else out, - msg = if (is.null(msg)) NA_character_ else msg, - fn = if (is.null(fn)) as.character(sys.call(-1)[1]) else as.character(fn), + out = if (is.null(out)) "" else out, + msg = if (is.null(msg)) NA_character_ else msg, + fn = if (is.null(fn)) as.character(sys.call(-1)[1]) else as.character(fn), stringsAsFactors = FALSE ) @@ -128,16 +139,23 @@ updateLog <- function(out = NULL, meth = NULL, msg = NULL, fn = NULL, frame = 1) record.event <- function(out = NULL, meth = NULL, frame = 1, logenv = NULL) { # If no logenv passed explicitly, try .logenv from globalenv if (is.null(logenv)) { - logenv <- tryCatch(get(".logenv", envir = .GlobalEnv), error = function(e) NULL) + logenv <- tryCatch(get(".logenv", envir = .GlobalEnv), error = function(e) { + NULL + }) } - if (is.environment(logenv) && exists("state", envir = logenv, inherits = FALSE)) { + if ( + is.environment(logenv) && exists("state", envir = logenv, inherits = FALSE) + ) { s <- get("state", envir = logenv, inherits = FALSE) if (!exists("log", envir = logenv, inherits = FALSE)) { logenv$log <- data.frame( - it = integer(), im = integer(), dep = character(), - meth = character(), out = character(), + it = integer(), + im = integer(), + dep = character(), + meth = character(), + out = character(), stringsAsFactors = FALSE ) } @@ -169,20 +187,19 @@ sym <- function(x) { # This helper function was copied from # https://github.com/alexanderrobitzsch/miceadds/blob/master/R/ma_exists.R -ma_exists <- function( x, pos, n_index = 1:8) -{ +ma_exists <- function(x, pos, n_index = 1:8) { n_index <- n_index + 1 is_there <- exists(x, where = pos) obj <- NULL nn <- 0 - if (is_there){ + if (is_there) { obj <- get(x, pos) } - if (!is_there){ - for (nn in n_index){ + if (!is_there) { + for (nn in n_index) { pos <- parent.frame(n = nn) is_there <- exists(x, where = pos) - if (is_there){ + if (is_there) { obj <- get(x, pos) break } @@ -241,4 +258,3 @@ sanitize.vec <- function(vec, y) { # default (numeric, character, etc.) vec } - diff --git a/R/larspred.R b/R/larspred.R index 37040c3d3..026ae03b4 100644 --- a/R/larspred.R +++ b/R/larspred.R @@ -54,9 +54,13 @@ #' # limit to three best variables #' larspred(boys, s = 3) #' @export -larspred <- function(data, type = "lar", - s = ncol(data) - 1, max.steps = ncol(data) - 1, ...) { - +larspred <- function( + data, + type = "lar", + s = ncol(data) - 1, + max.steps = ncol(data) - 1, + ... +) { # use the lars package for variable selection install.on.demand("lars", ...) data <- check.dataform(data) @@ -64,8 +68,12 @@ larspred <- function(data, type = "lar", # initialize predictor matrix nvar <- ncol(data) ynames <- colnames(data) - predictorMatrix <- matrix(0, nrow = nvar, ncol = nvar, - dimnames = list(ynames, ynames)) + predictorMatrix <- matrix( + 0, + nrow = nvar, + ncol = nvar, + dimnames = list(ynames, ynames) + ) # fill missings with random draws from observed data init <- mice(data, maxit = 0L, m = 1L, remove.collinear = FALSE, ...) @@ -84,8 +92,7 @@ larspred <- function(data, type = "lar", lars_ry <- lars::lars(x = x, y = ry, type = type, ...) coef_y <- coef(lars_y, s = min(max.steps, s), ...) coef_ry <- coef(lars_ry, s = min(max.steps, s), ...) - preds <- union(colnames(x)[coef_y != 0], - colnames(x)[coef_ry != 0]) + preds <- union(colnames(x)[coef_y != 0], colnames(x)[coef_ry != 0]) if (length(preds)) predictorMatrix[yname, preds] <- 1 } } diff --git a/R/lm.R b/R/lm.R index f099decf1..2f03c2bfd 100644 --- a/R/lm.R +++ b/R/lm.R @@ -27,17 +27,22 @@ #' fit #' @export lm.mids <- function(formula, data, ...) { - .Deprecated("with", - msg = "Use with(imp, lm(yourmodel))." - ) + .Deprecated("with", msg = "Use with(imp, lm(yourmodel)).") # adapted 28/1/00 repeated complete data regression (lm) on a mids data set call <- match.call() if (!is.mids(data)) { stop("The data must have class mids") } - analyses <- lapply(seq_len(data$m), function(i) lm(formula, data = complete(data, i), ...)) + analyses <- lapply(seq_len(data$m), function(i) { + lm(formula, data = complete(data, i), ...) + }) # return the complete data analyses as a list of length nimp - object <- list(call = call, call1 = data$call, nmis = data$nmis, analyses = analyses) + object <- list( + call = call, + call1 = data$call, + nmis = data$nmis, + analyses = analyses + ) class(object) <- c("mira", "lm") ## FEH object } @@ -76,9 +81,7 @@ lm.mids <- function(formula, data, ...) { #' fit #' @export glm.mids <- function(formula, family = gaussian, data, ...) { - .Deprecated("with", - msg = "Use with(imp, glm(yourmodel))." - ) + .Deprecated("with", msg = "Use with(imp, glm(yourmodel)).") # adapted 04/02/00 repeated complete data regression (glm) on a mids data set call <- match.call() if (!is.mids(data)) { @@ -89,7 +92,12 @@ glm.mids <- function(formula, family = gaussian, data, ...) { function(i) glm(formula, family = family, data = complete(data, i), ...) ) # return the complete data analyses as a list of length nimp - object <- list(call = call, call1 = data$call, nmis = data$nmis, analyses = analyses) + object <- list( + call = call, + call1 = data$call, + nmis = data$nmis, + analyses = analyses + ) class(object) <- c("mira", "glm", "lm") object } diff --git a/R/mads.R b/R/mads.R index 1c2a8be86..b3268a178 100644 --- a/R/mads.R +++ b/R/mads.R @@ -75,8 +75,20 @@ #' @keywords classes #' @export mads <- function( - call, prop, patterns, freq, mech, weights, cont, type, - odds, amp, cand, scores, data) { + call, + prop, + patterns, + freq, + mech, + weights, + cont, + type, + odds, + amp, + cand, + scores, + data +) { # Validate inputs # if (!is.call(call)) stop("Argument 'call' must be a call.") # if (!is.numeric(prop)) stop("Argument 'prop' must be numeric.") @@ -157,4 +169,3 @@ summary.mads <- function(object, ...) { print(object, ...) invisible(object) } - diff --git a/R/mcar.R b/R/mcar.R index a7d73c472..81680a2d6 100644 --- a/R/mcar.R +++ b/R/mcar.R @@ -105,28 +105,34 @@ #' @export #' @importFrom stats cov pchisq spline #' @md -mcar <- function(x, - imputed = mice(x, method = "norm"), - min_n = 6, - method = "auto", - replications = 10000, - use_chisq = 30, - alpha = 0.05) { +mcar <- function( + x, + imputed = mice(x, method = "norm"), + min_n = 6, + method = "auto", + replications = 10000, + use_chisq = 30, + alpha = 0.05 +) { UseMethod("mcar", x) } #' @method mcar data.frame #' @export -mcar.data.frame <- function(x, - imputed = mice(x, method = "norm"), - min_n = 6, - method = "auto", - replications = 10000, - use_chisq = 30, - alpha = 0.05) { +mcar.data.frame <- function( + x, + imputed = mice(x, method = "norm"), + min_n = 6, + method = "auto", + replications = 10000, + use_chisq = 30, + alpha = 0.05 +) { anyfact <- sapply(x, inherits, what = "factor") if (any(anyfact)) { - stop("Be advised that this MCAR test has not been validated for categorical variables.") + stop( + "Be advised that this MCAR test has not been validated for categorical variables." + ) } if (min_n < 1) { stop("Argument 'min_n' must be greater than 1.") @@ -149,9 +155,13 @@ mcar.data.frame <- function(x, } else if (inherits(imputed, "list")) { if (!inherits(imputed[[1]], "data.frame")) { imputed <- tryCatch(lapply(imputed, data.frame), error = function(e) { - stop("Argument 'imputed' must be a list of data.frames or an object of class 'mids'. Could not coerce argument 'imputed' to class 'data.frame'.") + stop( + "Argument 'imputed' must be a list of data.frames or an object of class 'mids'. Could not coerce argument 'imputed' to class 'data.frame'." + ) }) - message("Argument 'imputed' must be an object of class 'mids', or a list of 'data.frame's. Coerced argument 'imputed' to data.frame.") + message( + "Argument 'imputed' must be an object of class 'mids', or a list of 'data.frame's. Coerced argument 'imputed' to data.frame." + ) } } if (!all(dim(x) == dim(imputed[[1]]))) { @@ -164,17 +174,23 @@ mcar.data.frame <- function(x, rowmis <- rowSums(missings) colmis <- colSums(missings) if (any(rowmis == ncol(x))) { - warning("Note that there were some rows with all missing data. Tests may be invalid for these rows. Consider removing them.") + warning( + "Note that there were some rows with all missing data. Tests may be invalid for these rows. Consider removing them." + ) # x <- x[!rowmis == ncol(x), , drop = FALSE] } if (any(colmis == nrow(x))) { - stop("Some columns contain all missing data. This will result in invalid results.") + stop( + "Some columns contain all missing data. This will result in invalid results." + ) } univals <- sapply(x, function(i) { length(unique(i)) }) if (any(univals < 2)) { - stop("Some columns are constant, not variable. This will result in invalid results.") + stop( + "Some columns are constant, not variable. This will result in invalid results." + ) } newdata <- x missings <- is.na(x) @@ -189,10 +205,22 @@ mcar.data.frame <- function(x, idmiss <- do.call(paste, as.data.frame(missings)) idpats <- do.call(paste, as.data.frame(pats == 0)) remove_these <- idmiss %in% idpats[remove_pats] - if(all(remove_these)) stop("After dropping missing data patterns with fewer than ", min_n, " cases, there were no remaining valid cases in the dataset. Consider lowering 'min_n', and be cautious about interpreting the results; these data might not be suitable for an MCAR test.") + if (all(remove_these)) { + stop( + "After dropping missing data patterns with fewer than ", + min_n, + " cases, there were no remaining valid cases in the dataset. Consider lowering 'min_n', and be cautious about interpreting the results; these data might not be suitable for an MCAR test." + ) + } out$removed_rows <- remove_these newdata <- x[!remove_these, , drop = FALSE] - imputed <- lapply(imputed, `[`, i = !remove_these, j = colnames(newdata), drop = FALSE) + imputed <- lapply( + imputed, + `[`, + i = !remove_these, + j = colnames(newdata), + drop = FALSE + ) missings <- is.na(newdata) pats <- mice::md.pattern(newdata, plot = FALSE) } @@ -236,7 +264,9 @@ mcar.data.frame <- function(x, # Perform Anderson-Darling test ------------------------------------------- - if ((method == "auto" & any(out$hawk_p < alpha)) | method == "nonparametric") { + if ( + (method == "auto" & any(out$hawk_p < alpha)) | method == "nonparametric" + ) { adout <- sapply(hawklist, function(thisimp) { anderson_darling(thisimp[["fij"]]) # First row is p }) @@ -248,23 +278,47 @@ mcar.data.frame <- function(x, } - #' @method print mcar_object #' @export print.mcar_object <- function(x, ...) { ni <- x$pat_n out <- "\nInterpretation of results:\n" cat("\nMissing data patterns:", (nrow(x$md.pattern) - 1)) - if (!is.null(x$removed_patterns)) cat(" used,", nrow(x$removed_patterns), "removed.") + if (!is.null(x$removed_patterns)) { + cat(" used,", nrow(x$removed_patterns), "removed.") + } cat("\nCases used:", sum(!x$removed_rows), "\n\n") if (!is.null(x$hawk_p)) { if (length(x$hawk_p) > 1) { hawkp <- median(x$hawk_p) - cat("Hawkins' test: median chi^2 (", x$hawk_df, ") = ", median(x$hawk_chisq), ", median p = ", hawkp, sep = "") - if (any(x$hawk_p < x$alpha) & !hawkp < x$alpha) cat(". Some p-values for Hawkins' test were significant; please inspect their values, e.g., using `plot(", deparse(substitute(x)), ")`", sep = "") + cat( + "Hawkins' test: median chi^2 (", + x$hawk_df, + ") = ", + median(x$hawk_chisq), + ", median p = ", + hawkp, + sep = "" + ) + if (any(x$hawk_p < x$alpha) & !hawkp < x$alpha) { + cat( + ". Some p-values for Hawkins' test were significant; please inspect their values, e.g., using `plot(", + deparse(substitute(x)), + ")`", + sep = "" + ) + } } else { hawkp <- x$hawk_p - cat("Hawkins' test: chi^2 (", x$hawk_df, ") = ", x$hawk_chisq, ", p = ", x$hawk_p, sep = "") + cat( + "Hawkins' test: chi^2 (", + x$hawk_df, + ") = ", + x$hawk_chisq, + ", p = ", + x$hawk_p, + sep = "" + ) } cat("\n\n") if (x$method == "auto") { @@ -280,11 +334,30 @@ print.mcar_object <- function(x, ...) { if (!is.null(x$ad_p)) { if (length(x$ad_p) > 1) { adp <- median(x$ad_p) - cat("Anderson-Darling rank test: median T = ", median(x$ad_value), ", median p = ", median(x$ad_p), sep = "") - if (any(x$ad_p < x$alpha) & !median(x$ad_p) < x$alpha) cat(". Some p-values for the Anderson-Darling test were significant; please inspect their values, e.g., using `plot(", deparse(substitute(x)), ")`", sep = "") + cat( + "Anderson-Darling rank test: median T = ", + median(x$ad_value), + ", median p = ", + median(x$ad_p), + sep = "" + ) + if (any(x$ad_p < x$alpha) & !median(x$ad_p) < x$alpha) { + cat( + ". Some p-values for the Anderson-Darling test were significant; please inspect their values, e.g., using `plot(", + deparse(substitute(x)), + ")`", + sep = "" + ) + } } else { adp <- x$ad_p - cat("Anderson-Darling rank test: T = ", x$ad_value, ", p = ", x$ad_p, sep = "") + cat( + "Anderson-Darling rank test: T = ", + x$ad_value, + ", p = ", + x$ad_p, + sep = "" + ) } cat("\n") if (x$method == "auto") { @@ -313,15 +386,31 @@ plot.mcar_object <- function(x, y, type = NULL, ...) { op <- par(mar = rep(0, 4)) on.exit(par(op)) dev.off() - if (!is.null(x$hawk_p) & !is.null(x$ad_p)) par(mfrow = c(2, 1)) + if (!is.null(x$hawk_p) & !is.null(x$ad_p)) { + par(mfrow = c(2, 1)) + } if (!is.null(x$hawk_p)) { pct <- sum(x$hawk_p < x$alpha) / length(x$hawk_p) - hist(x$hawk_p, main = NULL, xlab = paste0("Hawkins p-values, ", round(pct * 100), "% significant"), ylab = NULL) + hist( + x$hawk_p, + main = NULL, + xlab = paste0("Hawkins p-values, ", round(pct * 100), "% significant"), + ylab = NULL + ) abline(v = x$alpha, col = "red") } if (!is.null(x$ad_p)) { pct <- sum(x$ad_p < x$alpha) / length(x$ad_p) - hist(x$ad_p, main = NULL, xlab = paste0("Anderson-Darling p-values, ", round(pct * 100), "% significant"), ylab = NULL) + hist( + x$ad_p, + main = NULL, + xlab = paste0( + "Anderson-Darling p-values, ", + round(pct * 100), + "% significant" + ), + ylab = NULL + ) abline(v = x$alpha, col = "red") } } @@ -398,19 +487,36 @@ anderson_darling <- function(fij) { (1 / (n - i)) * sum(1 / seq((i + 1), (n - 1))) })) a <- (4 * g - 6) * (k - 1) + (10 - 6 * g) * j - b <- (2 * g - 4) * k^2 + 8 * h * k + (2 * g - 14 * h - 4) * - j - 8 * h + 4 * g - 6 - c <- (6 * h + 2 * g - 2) * k^2 + (4 * h - 4 * g + 6) * k + - (2 * h - 6) * j + 4 * h + b <- (2 * g - 4) * + k^2 + + 8 * h * k + + (2 * g - 14 * h - 4) * + j - + 8 * h + + 4 * g - + 6 + c <- (6 * h + 2 * g - 2) * + k^2 + + (4 * h - 4 * g + 6) * k + + (2 * h - 6) * j + + 4 * h d <- (2 * h + 6) * k^2 - 4 * h * k - var.adk <- max(((a * n^3) + (b * n^2) + (c * n) + d) / ((n - 1) * - (n - 2) * (n - 3)), 0) + var.adk <- max( + ((a * n^3) + (b * n^2) + (c * n) + d) / + ((n - 1) * + (n - 2) * + (n - 3)), + 0 + ) adk.s <- (adk - (k - 1)) / sqrt(var.adk) b0 <- c(0.675, 1.281, 1.645, 1.96, 2.326) b1 <- c(-0.245, 0.25, 0.678, 1.149, 1.822) b2 <- c(-0.105, -0.305, -0.362, -0.391, -0.396) c0 <- c( - 1.09861228866811, 2.19722457733622, 2.94443897916644, 3.66356164612965, + 1.09861228866811, + 2.19722457733622, + 2.94443897916644, + 3.66356164612965, 4.59511985013459 ) qnt <- b0 + b1 / sqrt(k - 1) + b2 / (k - 1) @@ -450,19 +556,36 @@ ad <- function(fij) { (1 / (n - i)) * sum(1 / seq((i + 1), (n - 1))) })) a <- (4 * g - 6) * (k - 1) + (10 - 6 * g) * j - b <- (2 * g - 4) * k^2 + 8 * h * k + (2 * g - 14 * h - 4) * - j - 8 * h + 4 * g - 6 - c <- (6 * h + 2 * g - 2) * k^2 + (4 * h - 4 * g + 6) * k + - (2 * h - 6) * j + 4 * h + b <- (2 * g - 4) * + k^2 + + 8 * h * k + + (2 * g - 14 * h - 4) * + j - + 8 * h + + 4 * g - + 6 + c <- (6 * h + 2 * g - 2) * + k^2 + + (4 * h - 4 * g + 6) * k + + (2 * h - 6) * j + + 4 * h d <- (2 * h + 6) * k^2 - 4 * h * k - var.adk <- max(((a * n^3) + (b * n^2) + (c * n) + d) / ((n - 1) * - (n - 2) * (n - 3)), 0) + var.adk <- max( + ((a * n^3) + (b * n^2) + (c * n) + d) / + ((n - 1) * + (n - 2) * + (n - 3)), + 0 + ) adk.s <- (adk - (k - 1)) / sqrt(var.adk) b0 <- c(0.675, 1.281, 1.645, 1.96, 2.326) b1 <- c(-0.245, 0.25, 0.678, 1.149, 1.822) b2 <- c(-0.105, -0.305, -0.362, -0.391, -0.396) c0 <- c( - 1.09861228866811, 2.19722457733622, 2.94443897916644, 3.66356164612965, + 1.09861228866811, + 2.19722457733622, + 2.94443897916644, + 3.66356164612965, 4.59511985013459 ) qnt <- b0 + b1 / sqrt(k - 1) + b2 / (k - 1) @@ -502,12 +625,7 @@ plot.md.pattern <- function(x, y, rotate.names = FALSE, ...) { shade <- ifelse(R[nrow(R):1, ], mdc(1), mdc(2)) rect(M[, 2], M[, 1], M[, 2] + 1, M[, 1] + 1, col = shade) for (i in 1:ncol(R)) { - text(i - .5, - nrow(R) + .3, - colnames(x)[i], - adj = adj, - srt = srt - ) + text(i - .5, nrow(R) + .3, colnames(x)[i], adj = adj, srt = srt) text(i - .5, -.3, nmis[order(nmis)][i]) } for (i in 1:nrow(R)) { diff --git a/R/mdc.R b/R/mdc.R index 001082669..4fb1e90bf 100644 --- a/R/mdc.R +++ b/R/mdc.R @@ -48,15 +48,17 @@ #' # lines color for observed and missing data #' mdc(c("obs", "mis"), "lin") #' @export -mdc <- function(r = "observed", - s = "symbol", - transparent = TRUE, - cso = grDevices::hcl(240, 100, 40, 0.7), - csi = grDevices::hcl(0, 100, 40, 0.7), - csc = "gray50", - clo = grDevices::hcl(240, 100, 40, 0.8), - cli = grDevices::hcl(0, 100, 40, 0.8), - clc = "gray50") { +mdc <- function( + r = "observed", + s = "symbol", + transparent = TRUE, + cso = grDevices::hcl(240, 100, 40, 0.7), + csi = grDevices::hcl(0, 100, 40, 0.7), + csc = "gray50", + clo = grDevices::hcl(240, 100, 40, 0.8), + cli = grDevices::hcl(0, 100, 40, 0.8), + clc = "gray50" +) { # cso: blue symbol color for observed data # csi: red symbol color for imputations # csc: symbol color for combined data diff --git a/R/method.R b/R/method.R index 4f5c22fb2..9c70418b3 100644 --- a/R/method.R +++ b/R/method.R @@ -26,11 +26,13 @@ #' @examples #' make.method(nhanes2) #' @export -make.method <- function(data, - where = make.where(data), - blocks = make.blocks(data), - tasks = NULL, - defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { +make.method <- function( + data, + where = make.where(data), + blocks = make.blocks(data), + tasks = NULL, + defaultMethod = c("pmm", "logreg", "polyreg", "polr") +) { method <- rep("", length(blocks)) names(method) <- names(blocks) for (j in names(blocks)) { @@ -113,8 +115,10 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { # Check whether all names(method) are non-empty and match names(blocks) if (!all(names(method) %in% names(blocks))) { - stop("All elements of 'method' must be named and match blocks.", - call. = FALSE) + stop( + "All elements of 'method' must be named and match blocks.", + call. = FALSE + ) } # check whether the requested imputation methods are on the search path @@ -134,44 +138,70 @@ check.method <- function(method, data, where, blocks, tasks, defaultMethod) { y <- data[, vname, drop = FALSE] mj <- method[j] mlist <- list( - m1 = c("logreg", "logreg.boot", "polyreg", "lda", "polr", - "lasso.logreg"), + m1 = c("logreg", "logreg.boot", "polyreg", "lda", "polr", "lasso.logreg"), m2 = c( - "norm", "norm.nob", "norm.predict", "norm.boot", - "mean", "2l.norm", "2l.pan", - "2lonly.norm", "2lonly.pan", - "quadratic", "ri", "lasso.norm" + "norm", + "norm.nob", + "norm.predict", + "norm.boot", + "mean", + "2l.norm", + "2l.pan", + "2lonly.norm", + "2lonly.pan", + "quadratic", + "ri", + "lasso.norm" ), m3 = c( - "norm", "norm.nob", "norm.predict", "norm.boot", - "mean", "2l.norm", "2l.pan", - "2lonly.norm", "2lonly.pan", - "quadratic", "logreg", "logreg.boot", - "lasso.logreg", "ri", "lasso.norm" + "norm", + "norm.nob", + "norm.predict", + "norm.boot", + "mean", + "2l.norm", + "2l.pan", + "2lonly.norm", + "2lonly.pan", + "quadratic", + "logreg", + "logreg.boot", + "lasso.logreg", + "ri", + "lasso.norm" ) ) cond1 <- sapply(y, is.numeric) cond2 <- sapply(y, is.factor) & sapply(y, nlevels) == 2 cond3 <- sapply(y, is.factor) & sapply(y, nlevels) > 2 if (any(cond1) && mj %in% mlist$m1) { - warning("Type mismatch for variable(s): ", - paste(vname[cond1], collapse = ", "), - "\nImputation method ", mj, " is for categorical data.", - call. = FALSE + warning( + "Type mismatch for variable(s): ", + paste(vname[cond1], collapse = ", "), + "\nImputation method ", + mj, + " is for categorical data.", + call. = FALSE ) } if (any(cond2) && mj %in% mlist$m2) { - warning("Type mismatch for variable(s): ", - paste(vname[cond2], collapse = ", "), - "\nImputation method ", mj, " is not for factors.", - call. = FALSE + warning( + "Type mismatch for variable(s): ", + paste(vname[cond2], collapse = ", "), + "\nImputation method ", + mj, + " is not for factors.", + call. = FALSE ) } if (any(cond3) && mj %in% mlist$m3) { - warning("Type mismatch for variable(s): ", - paste(vname[cond3], collapse = ", "), - "\nImputation method ", mj, " is not for factors with >2 levels.", - call. = FALSE + warning( + "Type mismatch for variable(s): ", + paste(vname[cond3], collapse = ", "), + "\nImputation method ", + mj, + " is not for factors with >2 levels.", + call. = FALSE ) } } @@ -188,7 +218,9 @@ overwrite.method <- function(method, blocks, tasks, models) { for (varname in blocks[[h]]) { if (tasks[varname] %in% c("fill")) { newmethod <- models[[varname]]$`1`$setup$method - if (is.null(newmethod)) next + if (is.null(newmethod)) { + next + } method[h] <- newmethod } } diff --git a/R/mice.R b/R/mice.R index a1ff28e4d..2e341fb7c 100644 --- a/R/mice.R +++ b/R/mice.R @@ -385,33 +385,37 @@ #' complete(imp.test2, 2) #' } #' @export -mice <- function(data, - m = 5, - method = NULL, - predictorMatrix, - ignore = NULL, - where = NULL, - blocks, - visitSequence = NULL, - formulas, - calltypes = NULL, - blots = NULL, - tasks = NULL, - models = NULL, - post = NULL, - defaultMethod = c("pmm", "logreg", "polyreg", "polr"), - maxit = 5, - printFlag = TRUE, - seed = NA, - data.init = NULL, - compact = FALSE, - parallel = FALSE, - n.core = NULL, - ...) { +mice <- function( + data, + m = 5, + method = NULL, + predictorMatrix, + ignore = NULL, + where = NULL, + blocks, + visitSequence = NULL, + formulas, + calltypes = NULL, + blots = NULL, + tasks = NULL, + models = NULL, + post = NULL, + defaultMethod = c("pmm", "logreg", "polyreg", "polr"), + maxit = 5, + printFlag = TRUE, + seed = NA, + data.init = NULL, + compact = FALSE, + parallel = FALSE, + n.core = NULL, + ... +) { call <- match.call() check.deprecated(...) - if (!is.na(seed)) set.seed(seed) + if (!is.na(seed)) { + set.seed(seed) + } # check form of data and m data <- check.dataform(data) @@ -420,7 +424,9 @@ mice <- function(data, # Set up parallel backend if requested if (parallel) { if (!requireNamespace("future.apply", quietly = TRUE)) { - stop("Please install the 'future.apply' package to use parallel execution.") + stop( + "Please install the 'future.apply' package to use parallel execution." + ) } available <- future::availableCores() @@ -490,9 +496,11 @@ mice <- function(data, formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- construct.blocks(formulas, predictorMatrix) - predictorMatrix <- make.predictorMatrix(data, - blocks = blocks, - predictorMatrix = predictorMatrix) + predictorMatrix <- make.predictorMatrix( + data, + blocks = blocks, + predictorMatrix = predictorMatrix + ) calltypes <- make.calltypes(calltypes, predictorMatrix, formulas, "formula") } @@ -514,8 +522,11 @@ mice <- function(data, where <- check.where(where, data, blocks) user.visitSequence <- visitSequence - visitSequence <- check.visitSequence(visitSequence, - data = data, where = where, blocks = blocks + visitSequence <- check.visitSequence( + visitSequence, + data = data, + where = where, + blocks = blocks ) predictorMatrix <- mice.edit.predictorMatrix( predictorMatrix = predictorMatrix, @@ -525,19 +536,32 @@ mice <- function(data, ) tasks <- check.tasks(tasks, data, models, blocks, skip.check.tasks = FALSE) store <- ifelse(length(unique(tasks)) == 1L, tasks[1L], "train") - if (compact && store == "train") store <- "train_compact" + if (compact && store == "train") { + store <- "train_compact" + } method <- check.method( - method = method, data = data, where = where, - blocks = blocks, tasks = tasks, + method = method, + data = data, + where = where, + blocks = blocks, + tasks = tasks, defaultMethod = defaultMethod ) post <- check.post(post, data) blots <- check.blots(blots, data, blocks) ignore <- check.ignore(ignore, data) - loggedEvents <- data.frame(it = 0L, im = 0L, dep = "", meth = "", out = "", - msg = NA_character_, fn = NA_character_, stringsAsFactors = FALSE) + loggedEvents <- data.frame( + it = 0L, + im = 0L, + dep = "", + meth = "", + out = "", + msg = NA_character_, + fn = NA_character_, + stringsAsFactors = FALSE + ) state <- list(it = 0L, im = 0L, dep = "", meth = "", log = TRUE) setup <- list( @@ -559,19 +583,41 @@ mice <- function(data, # initialize imputations nmis <- apply(is.na(data), 2L, sum) imp <- initialize.imp( - data, m, ignore, where, blocks, visitSequence, - method, nmis, data.init + data, + m, + ignore, + where, + blocks, + visitSequence, + method, + nmis, + data.init ) # and iterate... from <- 1L to <- from + maxit - 1L q <- sampler( - data, m, ignore, where, imp, blocks, method, - visitSequence, predictorMatrix, formulas, - calltypes, blots, tasks, models, - post, c(from, to), printFlag, ..., - parallel = parallel) + data, + m, + ignore, + where, + imp, + blocks, + method, + visitSequence, + predictorMatrix, + formulas, + calltypes, + blots, + tasks, + models, + post, + c(from, to), + printFlag, + ..., + parallel = parallel + ) if (!state$log || nrow(loggedEvents) == 1L) { loggedEvents <- NULL @@ -600,17 +646,23 @@ mice <- function(data, ignore = ignore, seed = seed, iteration = q$iteration, - lastSeedValue = get(".Random.seed", - envir = globalenv(), mode = "integer", - inherits = FALSE), + lastSeedValue = get( + ".Random.seed", + envir = globalenv(), + mode = "integer", + inherits = FALSE + ), chainMean = q$chainMean, chainVar = q$chainVar, loggedEvents = loggedEvents, - store = store) + store = store + ) if (!is.null(midsobj$loggedEvents)) { - warning("Number of logged events: ", nrow(midsobj$loggedEvents), - call. = FALSE + warning( + "Number of logged events: ", + nrow(midsobj$loggedEvents), + call. = FALSE ) } return(midsobj) diff --git a/R/mice.impute.2l.bin.R b/R/mice.impute.2l.bin.R index a061d8e93..2c3ee5f9c 100644 --- a/R/mice.impute.2l.bin.R +++ b/R/mice.impute.2l.bin.R @@ -35,11 +35,20 @@ #' imp <- mice(data, method = "2l.bin", pred = pred, maxit = 1, m = 1, seed = 1) #' } #' @export -mice.impute.2l.bin <- function(y, ry, x, type, - wy = NULL, intercept = TRUE, ...) { +mice.impute.2l.bin <- function( + y, + ry, + x, + type, + wy = NULL, + intercept = TRUE, + ... +) { install.on.demand("lme4", ...) - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } if (intercept) { x <- cbind(1, as.matrix(x)) type <- c(2, type) @@ -56,21 +65,30 @@ mice.impute.2l.bin <- function(y, ry, x, type, yobs <- y[ry] # create formula, use [-1] to remove intercept - fr <- ifelse(length(rande) > 1, + fr <- ifelse( + length(rande) > 1, paste("+ ( 1 +", paste(rande[-1L], collapse = "+")), "+ ( 1 " ) randmodel <- paste( - "yobs ~ ", paste(fixe[-1L], collapse = "+"), - fr, "|", clust, ")" + "yobs ~ ", + paste(fixe[-1L], collapse = "+"), + fr, + "|", + clust, + ")" ) - suppressWarnings(fit <- try( - lme4::glmer(formula(randmodel), - data = data.frame(yobs, xobs), - family = binomial), - silent = TRUE - )) + suppressWarnings( + fit <- try( + lme4::glmer( + formula(randmodel), + data = data.frame(yobs, xobs), + family = binomial + ), + silent = TRUE + ) + ) if (!is.null(attr(fit, "class"))) { if (attr(fit, "class") == "try-error") { warning("glmer does not run. Simplify imputation model") @@ -84,7 +102,8 @@ mice.impute.2l.bin <- function(y, ry, x, type, beta.star <- beta + rv %*% rnorm(ncol(rv)) # calculate psi* - psi.hat <- matrix(lme4::VarCorr(fit)[[1L]], + psi.hat <- matrix( + lme4::VarCorr(fit)[[1L]], nrow = dim(lme4::VarCorr(fit)[[1L]])[1L] ) s <- nrow(psi.hat) * psi.hat @@ -98,25 +117,30 @@ mice.impute.2l.bin <- function(y, ry, x, type, ev <- eigen(temp) if (mode(ev$values) == "complex") { ev$values <- suppressWarnings(as.numeric(ev$values)) - ev$vectors <- suppressWarnings(matrix(as.numeric(ev$vectors), + ev$vectors <- suppressWarnings(matrix( + as.numeric(ev$vectors), nrow = length(ev$values) )) warning("The cov matrix is complex") } if (sum(ev$values < 0) > 0) { ev$values[ev$values < 0] <- 0 - temp <- ev$vectors %*% diag(ev$values, nrow = length(ev$values)) %*% t(ev$vectors) + temp <- ev$vectors %*% + diag(ev$values, nrow = length(ev$values)) %*% + t(ev$vectors) } deco <- ev$vectors %*% diag(sqrt(ev$values), nrow = length(ev$values)) temp.psi.star <- stats::rWishart( 1, nrow(rancoef) + nrow(psi.hat), diag(nrow(psi.hat)) - )[, , 1L] + )[,, 1L] psi.star <- MASS::ginv(deco %*% temp.psi.star %*% t(deco)) # psi.star positive definite? - if (!isSymmetric(psi.star)) psi.star <- (psi.star + t(psi.star)) / 2 + if (!isSymmetric(psi.star)) { + psi.star <- (psi.star + t(psi.star)) / 2 + } valprop <- eigen(psi.star) if (sum(valprop$values < 0) > 0) { valprop$values[valprop$values < 0] <- 0 @@ -129,11 +153,13 @@ mice.impute.2l.bin <- function(y, ry, x, type, # the main imputation task for (i in clmis) { bi.star <- t(MASS::mvrnorm( - n = 1L, mu = rep(0, nrow(psi.star)), + n = 1L, + mu = rep(0, nrow(psi.star)), Sigma = psi.star )) idx <- wy & (x[, clust] == i) - logit <- X[idx, , drop = FALSE] %*% beta.star + + logit <- X[idx, , drop = FALSE] %*% + beta.star + Z[idx, , drop = FALSE] %*% matrix(bi.star, ncol = 1) vec <- rbinom(nrow(logit), 1, as.vector(1 / (1 + exp(-logit)))) if (is.factor(y)) { diff --git a/R/mice.impute.2l.lmer.R b/R/mice.impute.2l.lmer.R index e48d39ffc..aaca7aa60 100644 --- a/R/mice.impute.2l.lmer.R +++ b/R/mice.impute.2l.lmer.R @@ -45,9 +45,19 @@ #' @family univariate-2l #' @keywords datagen #' @export -mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ...) { +mice.impute.2l.lmer <- function( + y, + ry, + x, + type, + wy = NULL, + intercept = TRUE, + ... +) { install.on.demand("lme4", ...) - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } if (intercept) { x <- cbind(1, as.matrix(x)) @@ -69,19 +79,25 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... Zobs <- Z[ry, , drop = FALSE] # create formula - fr <- ifelse(length(rande) > 1, + fr <- ifelse( + length(rande) > 1, paste("+ ( 1 +", paste(rande[-1L], collapse = "+")), "+ ( 1 " ) randmodel <- paste( - "yobs ~ ", paste(fixe[-1L], collapse = "+"), - fr, "|", clust, ")" + "yobs ~ ", + paste(fixe[-1L], collapse = "+"), + fr, + "|", + clust, + ")" + ) + suppressWarnings( + fit <- try( + lme4::lmer(formula(randmodel), data = data.frame(yobs, xobs)), + silent = TRUE + ) ) - suppressWarnings(fit <- try( - lme4::lmer(formula(randmodel), - data = data.frame(yobs, xobs)), - silent = TRUE - )) if (inherits(fit, "try-error")) { warning("lmer does not run. Simplify imputation model") return(y[wy]) @@ -92,11 +108,13 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... dc <- object@devcomp dd <- dc$dims if (dd[["useSc"]]) { - dc$cmp[[if (dd[["REML"]]) { - "sigmaREML" - } else { - "sigmaML" - }]] + dc$cmp[[ + if (dd[["REML"]]) { + "sigmaREML" + } else { + "sigmaML" + } + ]] } else { 1 } @@ -121,7 +139,7 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... rancoef <- as.matrix(lme4::ranef(fit)[[1]]) lambda <- t(rancoef) %*% rancoef df.psi <- nrow(rancoef) - temp.psi.star <- stats::rWishart(1, df.psi, diag(nrow(lambda)))[, , 1] + temp.psi.star <- stats::rWishart(1, df.psi, diag(nrow(lambda)))[,, 1] temp <- MASS::ginv(lambda) ev <- eigen(temp) if (sum(ev$values > 0) == length(ev$values)) { @@ -145,7 +163,9 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... Zi <- as.matrix(Zobs[xobs[, clust] == jj, ]) yi <- yobs[xobs[, clust] == jj] sigma2 <- diag(sigma2star, nrow = nrow(Zi)) - Mi <- psi.star %*% t(Zi) %*% MASS::ginv(Zi %*% psi.star %*% t(Zi) + sigma2) + Mi <- psi.star %*% + t(Zi) %*% + MASS::ginv(Zi %*% psi.star %*% t(Zi) + sigma2) myi <- Mi %*% (yi - Xi %*% beta.star) vyi <- psi.star - Mi %*% Zi %*% psi.star } else { @@ -157,7 +177,8 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... # generating bi.star using eigenvalues deco1 <- eigen(vyi) if (sum(deco1$values > 0) == length(deco1$values)) { - A <- deco1$vectors %*% sqrt(diag(deco1$values, nrow = length(deco1$values))) + A <- deco1$vectors %*% + sqrt(diag(deco1$values, nrow = length(deco1$values))) bi.star <- myi + A %*% rnorm(length(myi)) } else { # generating bi.star using svd @@ -173,8 +194,10 @@ mice.impute.2l.lmer <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... # imputation y[wy & x[, clust] == jj] <- as.vector( - as.matrix(X[wy & x[, clust] == jj, , drop = FALSE]) %*% beta.star + - as.matrix(Z[wy & x[, clust] == jj, , drop = FALSE]) %*% as.matrix(bi.star) + + as.matrix(X[wy & x[, clust] == jj, , drop = FALSE]) %*% + beta.star + + as.matrix(Z[wy & x[, clust] == jj, , drop = FALSE]) %*% + as.matrix(bi.star) + rnorm(sum(wy & x[, clust] == jj)) * sqrt(sigma2star) ) } diff --git a/R/mice.impute.2l.norm.R b/R/mice.impute.2l.norm.R index 0d468eb0f..1d0ad0df6 100644 --- a/R/mice.impute.2l.norm.R +++ b/R/mice.impute.2l.norm.R @@ -46,7 +46,15 @@ #' @family univariate-2l #' @keywords datagen #' @export -mice.impute.2l.norm <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ...) { +mice.impute.2l.norm <- function( + y, + ry, + x, + type, + wy = NULL, + intercept = TRUE, + ... +) { if (intercept) { x <- cbind(1, as.matrix(x)) type <- c(2, type) @@ -54,9 +62,13 @@ mice.impute.2l.norm <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... ## Initialize n.iter <- 100 - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } n.class <- length(unique(x[, type == -2])) - if (n.class == 0) stop("No class variable") + if (n.class == 0) { + stop("No class variable") + } gf.full <- factor(x[, type == -2], labels = seq_len(n.class)) gf <- gf.full[ry] XG <- split.data.frame(as.matrix(x[ry, type == 2]), gf) @@ -79,35 +91,59 @@ mice.impute.2l.norm <- function(y, ry, x, type, wy = NULL, intercept = TRUE, ... for (class in seq_len(n.class)) { vv <- symridge(inv.sigma2[class] * X.SS[[class]] + inv.psi, ...) bees.var <- chol2inv(chol(vv)) - bees[class, ] <- drop(bees.var %*% (crossprod(inv.sigma2[class] * XG[[class]], yg[[class]]) + inv.psi %*% mu)) + + bees[class, ] <- drop( + bees.var %*% + (crossprod(inv.sigma2[class] * XG[[class]], yg[[class]]) + + inv.psi %*% mu) + ) + drop(rnorm(n = n.rc) %*% chol(symridge(bees.var, ...))) ss[class] <- crossprod(yg[[class]] - XG[[class]] %*% bees[class, ]) } ## Draw mu - mu <- colMeans(bees) + drop(rnorm(n = n.rc) %*% - chol(chol2inv(chol(symridge(inv.psi, ...))) / n.class)) + mu <- colMeans(bees) + + drop( + rnorm(n = n.rc) %*% + chol(chol2inv(chol(symridge(inv.psi, ...))) / n.class) + ) ## Draw psi inv.psi <- rwishart( df = n.class - n.rc - 1, - SqrtSigma = chol(chol2inv(chol(symridge(crossprod(t(t(bees) - mu)), ...)))) + SqrtSigma = chol(chol2inv(chol(symridge( + crossprod(t(t(bees) - mu)), + ... + )))) ) ## Draw sigma2 - inv.sigma2 <- rgamma(n.class, n.g / 2 + 1 / (2 * theta), scale = 2 * theta / (ss * theta + sigma2.0)) + inv.sigma2 <- rgamma( + n.class, + n.g / 2 + 1 / (2 * theta), + scale = 2 * theta / (ss * theta + sigma2.0) + ) ## Draw sigma2.0 H <- 1 / mean(inv.sigma2) # Harmonic mean - sigma2.0 <- rgamma(1, n.class / (2 * theta) + 1, scale = 2 * theta * H / n.class) + sigma2.0 <- rgamma( + 1, + n.class / (2 * theta) + 1, + scale = 2 * theta * H / n.class + ) ## Draw theta G <- exp(mean(log(1 / inv.sigma2))) # Geometric mean - theta <- 1 / rgamma(1, n.class / 2 - 1, scale = 2 / (n.class * (sigma2.0 / H - log(sigma2.0) + log(G) - 1))) + theta <- 1 / + rgamma( + 1, + n.class / 2 - 1, + scale = 2 / (n.class * (sigma2.0 / H - log(sigma2.0) + log(G) - 1)) + ) } ## Generate imputations - imps <- rnorm(n = sum(wy), sd = sqrt(1 / inv.sigma2[gf.full[wy]])) + rowSums(as.matrix(x[wy, type == 2, drop = FALSE]) * bees[gf.full[wy], ]) + imps <- rnorm(n = sum(wy), sd = sqrt(1 / inv.sigma2[gf.full[wy]])) + + rowSums(as.matrix(x[wy, type == 2, drop = FALSE]) * bees[gf.full[wy], ]) imps } diff --git a/R/mice.impute.2l.pan.R b/R/mice.impute.2l.pan.R index 0c5cd4c60..8c1819475 100644 --- a/R/mice.impute.2l.pan.R +++ b/R/mice.impute.2l.pan.R @@ -5,7 +5,6 @@ # 3 ... introduce aggregated effects (i.e. group means) # 4 ... fixed, random and aggregated effects - #' Imputation by a two-level normal model using \code{pan} #' #' Imputes univariate missing data using a two-level normal model with @@ -114,8 +113,16 @@ #' # random x effects and group mean of x #' # predM1["y","x"] <- 4 #' @export -mice.impute.2l.pan <- function(y, ry, x, type, intercept = TRUE, paniter = 500, - groupcenter.slope = FALSE, ...) { +mice.impute.2l.pan <- function( + y, + ry, + x, + type, + intercept = TRUE, + paniter = 500, + groupcenter.slope = FALSE, + ... +) { install.on.demand("pan", ...) ## append intercept @@ -130,8 +137,12 @@ mice.impute.2l.pan <- function(y, ry, x, type, intercept = TRUE, paniter = 500, colnames(x0) <- c(colnames(x)[type == -2], colnames(x)[type %in% c(3, 4)]) type0 <- c(-2, rep.int(1, ncol(x0) - 1)) x0.aggr <- as.matrix(.mice.impute.2l.groupmean( - y = y, ry = ry, x = x0, - type = type0, grmeanwarning = FALSE, ... + y = y, + ry = ry, + x = x0, + type = type0, + grmeanwarning = FALSE, + ... )) colnames(x0.aggr) <- paste0("M.", colnames(x0)[-1]) # groupcentering @@ -159,7 +170,8 @@ mice.impute.2l.pan <- function(y, ry, x, type, intercept = TRUE, paniter = 500, sortgroups <- any(diff(subj) < 0) if (sortgroups) { dfr <- data.frame( - "group" = group, "ry" = ry, + "group" = group, + "ry" = ry, "index" = seq(1, length(ry)) ) dfr <- dfr[order(dfr$group), ] @@ -180,17 +192,30 @@ mice.impute.2l.pan <- function(y, ry, x, type, intercept = TRUE, paniter = 500, zcol <- which(type1 == 2) # noninformative priors prior <- list( - a = ncol(y1), Binv = diag(rep(1, ncol(y1))), - c = ncol(y1) * length(zcol), Dinv = diag(rep(1, ncol(y1) * length(zcol))) + a = ncol(y1), + Binv = diag(rep(1, ncol(y1))), + c = ncol(y1) * length(zcol), + Dinv = diag(rep(1, ncol(y1) * length(zcol))) ) - if (length(subj) != nrow(y1)) stop("No class variable") + if (length(subj) != nrow(y1)) { + stop("No class variable") + } # pan imputation ii <- 0 while (ii == 0) { s1 <- round(runif(1, 1, 10^7)) - imput <- pan::pan(y1, subj, pred, xcol, zcol, prior, seed = s1, iter = paniter) + imput <- pan::pan( + y1, + subj, + pred, + xcol, + zcol, + prior, + seed = s1, + iter = paniter + ) res <- imput$y ii <- 1 - any(is.na(res)) # check for invalid imputations: pan occasionally produces NaNs @@ -208,12 +233,30 @@ mice.impute.2l.pan <- function(y, ry, x, type, intercept = TRUE, paniter = 500, # compute cluster groupmean -.mice.impute.2l.groupmean <- function(y, ry, x, type, grmeanwarning = TRUE, ...) { - if ((ncol(x) > 2) & grmeanwarning) warning("\nMore than one variable is requested to be aggregated.\n") +.mice.impute.2l.groupmean <- function( + y, + ry, + x, + type, + grmeanwarning = TRUE, + ... +) { + if ((ncol(x) > 2) & grmeanwarning) { + warning("\nMore than one variable is requested to be aggregated.\n") + } # calculate aggregated values - a1 <- aggregate(x[, type %in% c(1, 2)], list(x[, type == -2]), mean, na.rm = TRUE) + a1 <- aggregate( + x[, type %in% c(1, 2)], + list(x[, type == -2]), + mean, + na.rm = TRUE + ) i1 <- match(x[, type == -2], a1[, 1]) ximp <- as.matrix(a1[i1, -1]) - colnames(ximp) <- paste(names(type)[type %in% c(1, 2)], names(type)[type == -2], sep = ".") + colnames(ximp) <- paste( + names(type)[type %in% c(1, 2)], + names(type)[type == -2], + sep = "." + ) return(ximp) } diff --git a/R/mice.impute.2lonly.mean.R b/R/mice.impute.2lonly.mean.R index 4048be4e2..847962dd5 100644 --- a/R/mice.impute.2lonly.mean.R +++ b/R/mice.impute.2lonly.mean.R @@ -67,17 +67,24 @@ mice.impute.2lonly.mean <- function(y, ry, x, type, wy = NULL, ...) { if (is.factor(y)) { ym <- aggregate(yobs, list(classobs), median, na.rm = TRUE) ym$x <- as.integer(ym$x) - return(apply(as.matrix(classmis), 1, + return(apply( + as.matrix(classmis), + 1, function(z, y, lev) lev[y[z == y[, 1], 2]], - y = ym, lev = levels(y), ... + y = ym, + lev = levels(y), + ... )) } # otherwise: return the class means ym <- aggregate(yobs, list(classobs), mean, na.rm = TRUE) - z <- apply(as.matrix(classmis), 1, + z <- apply( + as.matrix(classmis), + 1, function(z, y) y[z == y[, 1], 2], - y = ym, ... + y = ym, + ... ) z[is.nan(z)] <- NA z diff --git a/R/mice.impute.2lonly.norm.R b/R/mice.impute.2lonly.norm.R index 0e86f00a1..0f1259a40 100644 --- a/R/mice.impute.2lonly.norm.R +++ b/R/mice.impute.2lonly.norm.R @@ -125,8 +125,13 @@ #' @export mice.impute.2lonly.norm <- function(y, ry, x, type, wy = NULL, ...) { imp <- .imputation.level2( - y = y, ry = ry, x = x, type = type, wy = wy, - method = "norm", ... + y = y, + ry = ry, + x = x, + type = type, + wy = wy, + method = "norm", + ... ) imp } diff --git a/R/mice.impute.2lonly.pmm.R b/R/mice.impute.2lonly.pmm.R index 5455b5990..a9a1d77ca 100644 --- a/R/mice.impute.2lonly.pmm.R +++ b/R/mice.impute.2lonly.pmm.R @@ -98,16 +98,25 @@ #' @export mice.impute.2lonly.pmm <- function(y, ry, x, type, wy = NULL, ...) { .imputation.level2( - y = y, ry = ry, x = x, type = type, wy = wy, - method = "pmm", ... + y = y, + ry = ry, + x = x, + type = type, + wy = wy, + method = "pmm", + ... ) } # imputation function at level 2 # can be done with norm and pmm .imputation.level2 <- function(y, ry, x, type, wy, method, ...) { - if (sum(type == -2L) != 1L) stop("No class variable") - if (is.null(wy)) wy <- !ry + if (sum(type == -2L) != 1L) { + stop("No class variable") + } + if (is.null(wy)) { + wy <- !ry + } # handle categorical data ylev <- NULL @@ -129,8 +138,12 @@ mice.impute.2lonly.pmm <- function(y, ry, x, type, wy = NULL, ...) { if (length(csom) > 0L) { stop(paste0( - "Method 2lonly.", method, " found the following clusters with partially missing\n", - " level-2 data: ", paste(csom, collapse = ", "), "\n", + "Method 2lonly.", + method, + " found the following clusters with partially missing\n", + " level-2 data: ", + paste(csom, collapse = ", "), + "\n", " Method 2lonly.mean can fix such inconsistencies." )) } @@ -151,16 +164,22 @@ mice.impute.2lonly.pmm <- function(y, ry, x, type, wy = NULL, ...) { # norm imputation at level 2 if (method == "norm") { ximp2 <- mice.impute.norm( - y = y2, ry = ry2, x = x2, - wy = wy2, ... + y = y2, + ry = ry2, + x = x2, + wy = wy2, + ... ) } # pmm imputation at level 2 if (method == "pmm") { ximp2 <- mice.impute.pmm( - y = y2, ry = ry2, x = x2, - wy = wy2, ... + y = y2, + ry = ry2, + x = x2, + wy = wy2, + ... ) } @@ -171,9 +190,7 @@ mice.impute.2lonly.pmm <- function(y, ry, x, type, wy = NULL, ...) { # turn back into factor if (!is.null(ylev)) { - ximp <- factor(as.integer(ximp), - levels = 1L:length(ylev), - labels = ylev) + ximp <- factor(as.integer(ximp), levels = 1L:length(ylev), labels = ylev) } ximp diff --git a/R/mice.impute.cart.R b/R/mice.impute.cart.R index 2e50d015b..c1df23c8a 100644 --- a/R/mice.impute.cart.R +++ b/R/mice.impute.cart.R @@ -46,8 +46,15 @@ #' plot(imp) #' @keywords datagen #' @export -mice.impute.cart <- function(y, ry, x, wy = NULL, minbucket = 5, cp = 1e-04, - ...) { +mice.impute.cart <- function( + y, + ry, + x, + wy = NULL, + minbucket = 5, + cp = 1e-04, + ... +) { install.on.demand("rpart") if (is.null(wy)) { @@ -63,15 +70,21 @@ mice.impute.cart <- function(y, ry, x, wy = NULL, minbucket = 5, cp = 1e-04, xmis <- data.frame(x[wy, , drop = FALSE]) yobs <- y[ry] if (!is.factor(yobs)) { - fit <- rpart::rpart(yobs ~ ., - data = cbind(yobs, xobs), method = "anova", + fit <- rpart::rpart( + yobs ~ ., + data = cbind(yobs, xobs), + method = "anova", control = rpart::rpart.control(minbucket = minbucket, cp = cp, ...) ) leafnr <- floor(as.numeric(row.names(fit$frame[fit$where, ]))) fit$frame$yval <- as.numeric(row.names(fit$frame)) nodes <- predict(object = fit, newdata = xmis) donor <- lapply(nodes, function(s) yobs[leafnr == s]) - impute <- vapply(seq_along(donor), function(s) sample(donor[[s]], 1), numeric(1)) + impute <- vapply( + seq_along(donor), + function(s) sample(donor[[s]], 1), + numeric(1) + ) } else { # escape with same impute if the dependent does not vary cat.has.all.obs <- table(yobs) == sum(ry) @@ -86,19 +99,16 @@ mice.impute.cart <- function(y, ry, x, wy = NULL, minbucket = 5, cp = 1e-04, # likely to present problems further down the road # potential problem case: table(yobs): 0 10 15, then # droplevels may forget about category 1 - fit <- rpart::rpart(yobs ~ ., - data = xy, method = "class", + fit <- rpart::rpart( + yobs ~ ., + data = xy, + method = "class", control = rpart::rpart.control(minbucket = minbucket, cp = cp, ...) ) nodes <- predict(object = fit, newdata = xmis) - impute <- apply(nodes, - MARGIN = 1, - FUN = function(s) { - sample(colnames(nodes), - size = 1, prob = s - ) - } - ) + impute <- apply(nodes, MARGIN = 1, FUN = function(s) { + sample(colnames(nodes), size = 1, prob = s) + }) } impute } diff --git a/R/mice.impute.jomoImpute.R b/R/mice.impute.jomoImpute.R index 80ec6ded7..23bb02f3a 100644 --- a/R/mice.impute.jomoImpute.R +++ b/R/mice.impute.jomoImpute.R @@ -61,13 +61,24 @@ #' imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, maxit = 1) #' } #' @export -mice.impute.jomoImpute <- function(data, formula, type, m = 1, silent = TRUE, - format = "imputes", ...) { +mice.impute.jomoImpute <- function( + data, + formula, + type, + m = 1, + silent = TRUE, + format = "imputes", + ... +) { install.on.demand("mitml", ...) nat <- mitml::jomoImpute( - data = data, formula = formula, type = type, - m = m, silent = silent, ... + data = data, + formula = formula, + type = type, + m = m, + silent = silent, + ... ) if (format == "native") { diff --git a/R/mice.impute.lasso.logreg.R b/R/mice.impute.lasso.logreg.R index 21eebb2b5..5c3285bd0 100644 --- a/R/mice.impute.lasso.logreg.R +++ b/R/mice.impute.lasso.logreg.R @@ -41,7 +41,9 @@ #' @export mice.impute.lasso.logreg <- function(y, ry, x, wy = NULL, nfolds = 10, ...) { install.on.demand("glmnet", ...) - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } # Bootstrap sample n1 <- sum(ry) @@ -52,7 +54,8 @@ mice.impute.lasso.logreg <- function(y, ry, x, wy = NULL, nfolds = 10, ...) { # Train imputation model cv_lasso <- glmnet::cv.glmnet( - x = dotxobs, y = dotyobs, + x = dotxobs, + y = dotyobs, family = "binomial", nfolds = nfolds, alpha = 1 diff --git a/R/mice.impute.lasso.norm.R b/R/mice.impute.lasso.norm.R index 12efcfa0b..4fcc65cd6 100644 --- a/R/mice.impute.lasso.norm.R +++ b/R/mice.impute.lasso.norm.R @@ -43,7 +43,9 @@ mice.impute.lasso.norm <- function(y, ry, x, wy = NULL, nfolds = 10, ...) { install.on.demand("glmnet", ...) # Bootstrap sample - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } n1 <- sum(ry) s <- sample(n1, n1, replace = TRUE) x_glmnet <- cbind(1, x) @@ -52,7 +54,8 @@ mice.impute.lasso.norm <- function(y, ry, x, wy = NULL, nfolds = 10, ...) { # Train imputation model cv_lasso <- glmnet::cv.glmnet( - x = dotxobs, y = dotyobs, + x = dotxobs, + y = dotyobs, family = "gaussian", nfolds = nfolds, alpha = 1 diff --git a/R/mice.impute.lasso.pmm.R b/R/mice.impute.lasso.pmm.R index 92ff74765..9be4830da 100644 --- a/R/mice.impute.lasso.pmm.R +++ b/R/mice.impute.lasso.pmm.R @@ -120,14 +120,19 @@ #' # to get old behavior: as.integer(y)) #' table(mice.impute.lasso.pmm(y, ry, x, quantify = FALSE)) #' @export -mice.impute.lasso.pmm <- function(y, ry, x, wy = NULL, - donors = 5L, - matchtype = ifelse(sum(ry) >= 1000L, 0L, 1L), - remove.values = NULL, - quantify = TRUE, - trim = 1L, - dfmax = NULL, - ...) { +mice.impute.lasso.pmm <- function( + y, + ry, + x, + wy = NULL, + donors = 5L, + matchtype = ifelse(sum(ry) >= 1000L, 0L, 1L), + remove.values = NULL, + quantify = TRUE, + trim = 1L, + dfmax = NULL, + ... +) { stopifnot(ncol(x) >= 2L) x <- as.matrix(x) @@ -183,8 +188,10 @@ mice.impute.lasso.pmm <- function(y, ry, x, wy = NULL, # Create partial LASSO path with dfmax non-zero coefficients dfmax <- ifelse(is.null(dfmax), ncol(x), dfmax) - fit <- do.call(glmnet::glmnet, - c(list(x = xobs, y = yobs, dfmax = dfmax), dots)) + fit <- do.call( + glmnet::glmnet, + c(list(x = xobs, y = yobs, dfmax = dfmax), dots) + ) # Find lambda from partial LASSO path indices <- which(fit$df <= dfmax) @@ -205,8 +212,10 @@ mice.impute.lasso.pmm <- function(y, ry, x, wy = NULL, s <- sample(n, n, replace = TRUE) xobs1 <- x[ry & complete.cases(x, y), , drop = FALSE][s, , drop = FALSE] yobs1 <- as.numeric(ynum[ry][s]) - fit1 <- do.call(glmnet::glmnet, - c(list(x = xobs1, y = yobs1, lambda = lambda), dots)) + fit1 <- do.call( + glmnet::glmnet, + c(list(x = xobs1, y = yobs1, lambda = lambda), dots) + ) yhatobs <- predict(fit, newx = xobs, s = lambda) yhatmis <- predict(fit1, newx = ximp, s = lambda) } @@ -216,8 +225,10 @@ mice.impute.lasso.pmm <- function(y, ry, x, wy = NULL, s <- sample(n, n, replace = TRUE) xobs1 <- x[ry & complete.cases(x, y), , drop = FALSE][s, , drop = FALSE] yobs1 <- as.numeric(ynum[ry][s]) - fit1 <- do.call(glmnet::glmnet, - c(list(x = xobs1, y = yobs1, lambda = lambda), dots)) + fit1 <- do.call( + glmnet::glmnet, + c(list(x = xobs1, y = yobs1, lambda = lambda), dots) + ) yhatobs <- predict(fit1, newx = xobs, s = lambda) yhatmis <- predict(fit1, newx = ximp, s = lambda) } diff --git a/R/mice.impute.lasso.select.logreg.R b/R/mice.impute.lasso.select.logreg.R index 42a864511..dc1dd9038 100644 --- a/R/mice.impute.lasso.select.logreg.R +++ b/R/mice.impute.lasso.select.logreg.R @@ -47,11 +47,20 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.lasso.select.logreg <- function(y, ry, x, wy = NULL, nfolds = 10, ...) { +mice.impute.lasso.select.logreg <- function( + y, + ry, + x, + wy = NULL, + nfolds = 10, + ... +) { install.on.demand("glmnet", ...) # Body - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x_glmnet <- cbind(1, x) xobs <- x_glmnet[ry, , drop = FALSE] xmis <- x[wy, ] @@ -60,22 +69,24 @@ mice.impute.lasso.select.logreg <- function(y, ry, x, wy = NULL, nfolds = 10, .. # Train imputation model # used later in the estiamtion require this. cv_lasso <- glmnet::cv.glmnet( - x = xobs, y = yobs, + x = xobs, + y = yobs, family = "binomial", nfolds = nfolds, alpha = 1 ) # Define Active Set - glmnet_coefs <- as.matrix(coef(cv_lasso, - s = "lambda.min" - ))[, 1] + glmnet_coefs <- as.matrix(coef(cv_lasso, s = "lambda.min"))[, 1] AS <- which((glmnet_coefs != 0)[-1]) # Non-zero reg coefficinets # Perform regular logreg draw xas <- x_glmnet[, AS, drop = FALSE] vec <- mice.impute.logreg( - y = y, ry = ry, x = xas, wy = wy, + y = y, + ry = ry, + x = xas, + wy = wy, ... ) vec diff --git a/R/mice.impute.lasso.select.norm.R b/R/mice.impute.lasso.select.norm.R index 53bbf4a4a..e5bb2b1fd 100644 --- a/R/mice.impute.lasso.select.norm.R +++ b/R/mice.impute.lasso.select.norm.R @@ -49,11 +49,20 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.lasso.select.norm <- function(y, ry, x, wy = NULL, nfolds = 10, ...) { +mice.impute.lasso.select.norm <- function( + y, + ry, + x, + wy = NULL, + nfolds = 10, + ... +) { install.on.demand("glmnet", ...) # Body - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x_glmnet <- cbind(1, x) xobs <- x_glmnet[ry, , drop = FALSE] xmis <- x[wy, ] @@ -62,22 +71,24 @@ mice.impute.lasso.select.norm <- function(y, ry, x, wy = NULL, nfolds = 10, ...) # Train imputation model # used later in the estiamtion require this. cv_lasso <- glmnet::cv.glmnet( - x = xobs, y = yobs, + x = xobs, + y = yobs, family = "gaussian", nfolds = nfolds, alpha = 1 ) # Define Active Set - glmnet_coefs <- as.matrix(coef(cv_lasso, - s = "lambda.min" - ))[, 1] + glmnet_coefs <- as.matrix(coef(cv_lasso, s = "lambda.min"))[, 1] AS <- which((glmnet_coefs != 0)[-1]) # Non-zero reg coefficinets # Perform regular norm draw from Bayesian linear model xas <- x_glmnet[, AS, drop = FALSE] vec <- mice.impute.norm( - y = y, ry = ry, x = xas, wy = wy, + y = y, + ry = ry, + x = xas, + wy = wy, ... ) vec diff --git a/R/mice.impute.lda.R b/R/mice.impute.lda.R index b86ce057f..f9cbc39a9 100644 --- a/R/mice.impute.lda.R +++ b/R/mice.impute.lda.R @@ -43,7 +43,9 @@ #' @export mice.impute.lda <- function(y, ry, x, wy = NULL, ...) { install.on.demand("MASS", ...) - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } fy <- as.factor(y) nc <- length(levels(fy)) diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index a095c4527..3a53a7845 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -43,12 +43,20 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.logreg <- function(y, ry, x, wy = NULL, - task = "impute", model = NULL, - ...) { +mice.impute.logreg <- function( + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + ... +) { check.model.exists(model, task) method <- "logreg" - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } n <- sum(ry) # augment data in order to evade perfect prediction @@ -64,16 +72,19 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, if (task == "fill") { cols <- check.model.match(model, x, method) lp <- x[wy, cols, drop = FALSE] %*% model$beta.dot - return(logreg.draw(lp, - levels = model$factor$labels, - class = model$class[1L])) + return(logreg.draw( + lp, + levels = model$factor$labels, + class = model$class[1L] + )) } expr <- expression(glm.fit( x = x[ry, , drop = FALSE], y = y[ry], family = quasibinomial(link = logit), - weights = w[ry])) + weights = w[ry] + )) fit <- eval(expr) fit.sum <- summary.glm(fit) beta <- coef(fit) @@ -81,9 +92,7 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, beta.star <- beta + rv %*% rnorm(ncol(rv)) if (task == "train") { - model$setup <- list(method = method, - n = n, - task = task) + model$setup <- list(method = method, n = n, task = task) model$beta.hat <- drop(beta) model$beta.dot <- drop(beta.star) model$factor <- list(labels = levels(y), quant = c(0, 1)) @@ -92,9 +101,11 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, } lp <- x[wy, , drop = FALSE] %*% beta.star - return(logreg.draw(lp, - levels = levels(y), - class = if (is.ordered(y)) "ordered" else class(y)[1L])) + return(logreg.draw( + lp, + levels = levels(y), + class = if (is.ordered(y)) "ordered" else class(y)[1L] + )) } logreg.draw <- function(lp, levels = NULL, class = NULL) { @@ -107,10 +118,12 @@ logreg.draw <- function(lp, levels = NULL, class = NULL) { return(as.logical(draws)) } if (class %in% c("factor", "ordered")) { - return(factor(draws, - levels = c(0, 1), - labels = levels, - ordered = (class == "ordered"))) + return(factor( + draws, + levels = c(0, 1), + labels = levels, + ordered = (class == "ordered") + )) } } @@ -144,7 +157,9 @@ logreg.draw <- function(lp, levels = NULL, class = NULL) { #' @keywords datagen #' @export mice.impute.logreg.boot <- function(y, ry, x, wy = NULL, ...) { - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } # draw a bootstrap sample for yobs and xobs xobs <- x[ry, , drop = FALSE] @@ -216,7 +231,12 @@ augment <- function(y, ry, x, wy, maxcat = 50) { maxx <- apply(x, 2, max, na.rm = TRUE) nr <- 2 * p * k a <- matrix(mean, nrow = nr, ncol = p, byrow = TRUE) - b <- matrix(rep(c(rep.int(c(0.5, -0.5), k), rep.int(0, nr)), length = nr * p), nrow = nr, ncol = p, byrow = FALSE) + b <- matrix( + rep(c(rep.int(c(0.5, -0.5), k), rep.int(0, nr)), length = nr * p), + nrow = nr, + ncol = p, + byrow = FALSE + ) c <- matrix(sd, nrow = nr, ncol = p, byrow = TRUE) d <- a + b * c d <- pmax(matrix(minx, nrow = nr, ncol = p, byrow = TRUE), d, na.rm = TRUE) @@ -233,7 +253,9 @@ augment <- function(y, ry, x, wy, maxcat = 50) { } else { as.factor(levels(y)[c(y, e)]) } - } else c(y, e) + } else { + c(y, e) + } rya <- c(ry, rep.int(TRUE, nr)) wya <- c(wy, rep.int(FALSE, nr)) wa <- c(rep.int(1, length(y)), rep.int((p + 1) / nr, nr)) diff --git a/R/mice.impute.midastouch.R b/R/mice.impute.midastouch.R index aeeda31bc..f0d24fdf3 100644 --- a/R/mice.impute.midastouch.R +++ b/R/mice.impute.midastouch.R @@ -78,10 +78,18 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.midastouch <- function(y, ry, x, wy = NULL, ridge = 1e-05, - midas.kappa = NULL, - outout = TRUE, neff = NULL, - debug = NULL, ...) { +mice.impute.midastouch <- function( + y, + ry, + x, + wy = NULL, + ridge = 1e-05, + midas.kappa = NULL, + outout = TRUE, + neff = NULL, + debug = NULL, + ... +) { if (is.null(wy)) { wy <- !ry } @@ -115,7 +123,8 @@ mice.impute.midastouch <- function(y, ry, x, wy = NULL, ridge = 1e-05, if (!is.null(debug)) { midastouch.inputlist$omega <- omega assign( - x = "midastouch.inputlist", value = midastouch.inputlist, + x = "midastouch.inputlist", + value = midastouch.inputlist, envir = get(debug) ) } @@ -157,8 +166,10 @@ mice.impute.midastouch <- function(y, ry, x, wy = NULL, ridge = 1e-05, if (outout) { # P-step if out of sample predictions for donors # estimate one model per donor by leave-one-out - XXarray_pre <- t(t(apply(X = Xobs, MARGIN = 1, FUN = tcrossprod)) * - omega) + XXarray_pre <- t( + t(apply(X = Xobs, MARGIN = 1, FUN = tcrossprod)) * + omega + ) ridgeind <- c(1:(m - 1)) * (m + 1) + 1 if (ridge > 0) { XXarray_pre[ridgeind, ] <- XXarray_pre[ridgeind, ] * (1 + ridge) @@ -172,9 +183,14 @@ mice.impute.midastouch <- function(y, ry, x, wy = NULL, ridge = 1e-05, } Xyarray <- c(Xy) - t(Xobs * yobs * omega) - BETAarray <- apply(rbind(XXarray, Xyarray), 2, function(x, m) { - solve(a = matrix(head(x, m^2), m), b = tail(x, m)) - }, m = m) + BETAarray <- apply( + rbind(XXarray, Xyarray), + 2, + function(x, m) { + solve(a = matrix(head(x, m^2), m), b = tail(x, m)) + }, + m = m + ) YHATdon <- rowSums(Xobs * t(BETAarray)) # each recipient has nobs different yhats YHATrec <- Xmis %*% BETAarray @@ -182,10 +198,13 @@ mice.impute.midastouch <- function(y, ry, x, wy = NULL, ridge = 1e-05, dist.mat <- YHATdon - t(YHATrec) } else { yhat.mis <- c(Xmis %*% beta) - dist.mat <- yhat.obs - matrix( - data = yhat.mis, nrow = nobs, ncol = nmis, - byrow = TRUE - ) + dist.mat <- yhat.obs - + matrix( + data = yhat.mis, + nrow = nobs, + ncol = nmis, + byrow = TRUE + ) } # convert distances to drawing probs // ensure real results @@ -201,7 +220,9 @@ mice.impute.midastouch <- function(y, ry, x, wy = NULL, ridge = 1e-05, assign(x = "midastouch.neff", value = list(), envir = get(neff)) } midastouch.neff <- get("midastouch.neff", envir = get(neff)) - midastouch.neff[[length(midastouch.neff) + 1]] <- mean(1 / rowSums((t(delta.mat) / csums)^2)) + midastouch.neff[[length(midastouch.neff) + 1]] <- mean( + 1 / rowSums((t(delta.mat) / csums)^2) + ) assign(x = "midastouch.neff", value = midastouch.neff, envir = get(neff)) } diff --git a/R/mice.impute.mnar.logreg.R b/R/mice.impute.mnar.logreg.R index 5e64f8732..7ec5157af 100644 --- a/R/mice.impute.mnar.logreg.R +++ b/R/mice.impute.mnar.logreg.R @@ -1,11 +1,20 @@ #' @rdname mice.impute.mnar #' @export -mice.impute.mnar.logreg <- function(y, ry, x, wy = NULL, - ums = NULL, umx = NULL, ...) { +mice.impute.mnar.logreg <- function( + y, + ry, + x, + wy = NULL, + ums = NULL, + umx = NULL, + ... +) { ## Undentifiable part: u <- parse.ums(x, ums = ums, umx = umx, ...) - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } wyold <- wy ## Identifiable part: exactly the same as mice.impute.logreg @@ -33,8 +42,13 @@ mice.impute.mnar.logreg <- function(y, ry, x, wy = NULL, beta.star <- beta + rv %*% rnorm(ncol(rv)) ## Draw imputations - p <- 1 / (1 + exp(-(x[wy, , drop = FALSE] %*% beta.star + - u$x[wyold, , drop = FALSE] %*% u$delta))) + p <- 1 / + (1 + + exp( + -(x[wy, , drop = FALSE] %*% + beta.star + + u$x[wyold, , drop = FALSE] %*% u$delta) + )) vec <- (runif(nrow(p)) <= p) vec[vec] <- 1 if (is.factor(y)) { diff --git a/R/mice.impute.mnar.norm.R b/R/mice.impute.mnar.norm.R index c1da2a3ca..3f1d8ff27 100644 --- a/R/mice.impute.mnar.norm.R +++ b/R/mice.impute.mnar.norm.R @@ -145,18 +145,30 @@ #' # from old version at https://github.com/moreno-betancur/NARFCS #' pool(with(impNARFCS, lm(Y ~ X + Z)))$pooled$estimate #' @export -mice.impute.mnar.norm <- function(y, ry, x, wy = NULL, - ums = NULL, umx = NULL, ...) { +mice.impute.mnar.norm <- function( + y, + ry, + x, + wy = NULL, + ums = NULL, + umx = NULL, + ... +) { ## Undentifiable part: u <- parse.ums(x, ums = ums, umx = umx, ...) ## Identifiable part: exactly the same as mice.impute.norm - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x <- cbind(1, as.matrix(x)) parm <- .norm.draw(y, ry, x, ...) ## Draw imputations - return(x[wy, , drop = FALSE] %*% parm$beta + - u$x[wy, , drop = FALSE] %*% u$delta + - rnorm(sum(wy)) * parm$sigma) + return( + x[wy, , drop = FALSE] %*% + parm$beta + + u$x[wy, , drop = FALSE] %*% u$delta + + rnorm(sum(wy)) * parm$sigma + ) } diff --git a/R/mice.impute.mpmm.R b/R/mice.impute.mpmm.R index 601fd2cc1..3f5e6407d 100644 --- a/R/mice.impute.mpmm.R +++ b/R/mice.impute.mpmm.R @@ -65,15 +65,21 @@ mpmm.impute <- function(data, ...) { r <- !is.na(data) mpat <- apply(r, 1, function(x) paste(as.numeric(x), collapse = "")) nmpat <- length(unique(mpat)) - if (nmpat != 2) stop("There are more than one missingness patterns") + if (nmpat != 2) { + stop("There are more than one missingness patterns") + } r <- unique(r) r <- r[rowSums(r) < ncol(r), ] y <- data[, which(r == FALSE), drop = FALSE] ry <- !is.na(y)[, 1] x <- data[, which(r == TRUE), drop = FALSE] wy <- !ry - ES <- eigen(solve(cov(y[ry, , drop = FALSE], y[ry, , drop = FALSE])) %*% cov(y[ry, , drop = FALSE], x[ry, , drop = FALSE]) - %*% solve(cov(x[ry, , drop = FALSE], x[ry, , drop = FALSE])) %*% cov(x[ry, , drop = FALSE], y[ry, , drop = FALSE])) + ES <- eigen( + solve(cov(y[ry, , drop = FALSE], y[ry, , drop = FALSE])) %*% + cov(y[ry, , drop = FALSE], x[ry, , drop = FALSE]) %*% + solve(cov(x[ry, , drop = FALSE], x[ry, , drop = FALSE])) %*% + cov(x[ry, , drop = FALSE], y[ry, , drop = FALSE]) + ) parm <- as.matrix(ES$vectors[, 1]) z <- as.matrix(y) %*% parm imp <- mice.impute.pmm(z, ry, x) diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index cf9530973..8ffa0b13e 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -34,13 +34,21 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.norm <- function(y, ry, x, wy = NULL, - task = "impute", model = NULL, - ridge = 1e-05, - ...) { +mice.impute.norm <- function( + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + ridge = 1e-05, + ... +) { check.model.exists(model, task) method <- "norm" - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x <- cbind(1, as.matrix(x)) if (task == "fill") { cols <- check.model.match(model, x, method) @@ -52,10 +60,12 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, parm <- .norm.draw(y, ry, x, ridge = ridge, ...) if (task == "train") { - model$setup <- list(method = method, - n = sum(ry), - task = task, - ridge = ridge) + model$setup <- list( + method = method, + n = sum(ry), + task = task, + ridge = ridge + ) model$beta.hat <- drop(parm$coef) model$beta.dot <- drop(parm$beta) model$sigma.hat <- parm$sigma.hat @@ -210,10 +220,12 @@ estimice <- function(x, y, ls.meth = "qr", ridge = 1e-05, ...) { } get.printFlag <- function(start = 4) { - while (inherits( - try(get("printFlag", parent.frame(start)), silent = TRUE), - "try-error" - )) { + while ( + inherits( + try(get("printFlag", parent.frame(start)), silent = TRUE), + "try-error" + ) + ) { start <- start + 1 } get("printFlag", parent.frame(start)) diff --git a/R/mice.impute.norm.boot.R b/R/mice.impute.norm.boot.R index 3d7bd034c..a71129ee7 100644 --- a/R/mice.impute.norm.boot.R +++ b/R/mice.impute.norm.boot.R @@ -18,7 +18,9 @@ #' @keywords datagen #' @export mice.impute.norm.boot <- function(y, ry, x, wy = NULL, ...) { - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x <- cbind(1, as.matrix(x)) n1 <- sum(ry) s <- sample(n1, n1, replace = TRUE) diff --git a/R/mice.impute.norm.nob.R b/R/mice.impute.norm.nob.R index a265d9a08..7a07d1840 100644 --- a/R/mice.impute.norm.nob.R +++ b/R/mice.impute.norm.nob.R @@ -36,7 +36,9 @@ #' @keywords datagen #' @export mice.impute.norm.nob <- function(y, ry, x, wy = NULL, ...) { - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x <- cbind(1, as.matrix(x)) parm <- .norm.fix(y, ry, x, ...) x[wy, ] %*% parm$beta + rnorm(sum(wy)) * parm$sigma diff --git a/R/mice.impute.norm.predict.R b/R/mice.impute.norm.predict.R index 4c273376c..a6efe963c 100644 --- a/R/mice.impute.norm.predict.R +++ b/R/mice.impute.norm.predict.R @@ -35,7 +35,9 @@ #' @keywords datagen #' @export mice.impute.norm.predict <- function(y, ry, x, wy = NULL, ...) { - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } x <- cbind(1, as.matrix(x)) p <- estimice(x[ry, , drop = FALSE], y[ry], ...) x[wy, , drop = FALSE] %*% p$c diff --git a/R/mice.impute.panImpute.R b/R/mice.impute.panImpute.R index f2ea809c0..73ba5d5d9 100644 --- a/R/mice.impute.panImpute.R +++ b/R/mice.impute.panImpute.R @@ -58,13 +58,24 @@ #' pred["B1", "hyp"] <- -2 #' imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, maxit = 1) #' @export -mice.impute.panImpute <- function(data, formula, type, m = 1, silent = TRUE, - format = "imputes", ...) { +mice.impute.panImpute <- function( + data, + formula, + type, + m = 1, + silent = TRUE, + format = "imputes", + ... +) { install.on.demand("mitml", ...) nat <- mitml::panImpute( - data = data, formula = formula, type = type, - m = m, silent = silent, ... + data = data, + formula = formula, + type = type, + m = m, + silent = silent, + ... ) if (format == "native") { diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index a1c9d4004..14bbed3ab 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -168,16 +168,29 @@ #' # to get old behavior (before mice v3.16.4): as.integer(y)) #' mice.impute.pmm(y, ry, x, quantify = FALSE) #' @export -mice.impute.pmm <- function(y, ry, x, wy = NULL, - task = "impute", model = NULL, - exclude = NULL, trim = 1L, quantify = TRUE, - ridge = 1e-05, matchtype = 1L, - donors = 5L, nbins = NULL, use.matcher = FALSE, - mlocal = 1L, ...) -{ +mice.impute.pmm <- function( + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + exclude = NULL, + trim = 1L, + quantify = TRUE, + ridge = 1e-05, + matchtype = 1L, + donors = 5L, + nbins = NULL, + use.matcher = FALSE, + mlocal = 1L, + ... +) { check.model.exists(model, task) method <- "pmm" - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } # Remove excluded values and trim small categories if (is.factor(y)) { @@ -201,10 +214,12 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, if (task == "fill") { cols <- check.model.match(model, x, method) yhatmis <- x[wy, cols, drop = FALSE] %*% model$beta.dot - impy <- draw.neighbors.pmm(yhatmis, - edges = model$edges, - lookup = model$lookup, - mlocal = mlocal) + impy <- draw.neighbors.pmm( + yhatmis, + edges = model$edges, + lookup = model$lookup, + mlocal = mlocal + ) return(impy) } @@ -240,35 +255,42 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, # >>> Train task: Store model in environment nbins <- initialize.nbins(nbins, length(yhatobs), length(unique(yhatobs))) donors <- initialize.donors(donors, length(yhatobs)) - edges <- quantile(yhatobs, probs = seq(0, 1, length.out = nbins + 1L), - type = 7L, na.rm = TRUE) + edges <- quantile( + yhatobs, + probs = seq(0, 1, length.out = nbins + 1L), + type = 7L, + na.rm = TRUE + ) lookup <- bin.yhat(yhatobs, ynum[ry], k = donors, edges = edges) # Store the imputation model in models environment - model$setup <- list(method = method, - n = length(yhatobs), - donors = donors, - matchtype = matchtype, - quantify = quantify, - exclude = exclude, - trim = trim, - task = task, - nbins = nbins, - ridge = ridge) + model$setup <- list( + method = method, + n = length(yhatobs), + donors = donors, + matchtype = matchtype, + quantify = quantify, + exclude = exclude, + trim = trim, + task = task, + nbins = nbins, + ridge = ridge + ) model$beta.hat <- beta.hat model$beta.dot <- beta.dot model$edges <- edges - model$lookup <- matrix((unquantify(lookup, f$quant, levels(y))), - nrow = nbins) + model$lookup <- matrix((unquantify(lookup, f$quant, levels(y))), nrow = nbins) model$factor <- list(labels = f$labels, quant = f$quant) model$xnames <- colnames(x) model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] # Compute imputations from model - impy <- draw.neighbors.pmm(yhatmis, - edges = model$edges, - lookup = model$lookup, - mlocal = mlocal) + impy <- draw.neighbors.pmm( + yhatmis, + edges = model$edges, + lookup = model$lookup, + mlocal = mlocal + ) return(impy) } @@ -323,7 +345,8 @@ bin.yhat <- function(yhat, y, k, edges) { rep(values, k) } else { sample(values, size = k, replace = length(values) < k) - }})) + } + })) return(lookup) } @@ -348,8 +371,10 @@ draw.neighbors.pmm <- function(yhat, edges, lookup, mlocal = 1L) { selected_bin <- ifelse(runif(n) < p_left, bin, pmin(bin + 1L, nbins)) # Vectorized sampling from lookup table - indices <- matrix(sample(1L:ncol(lookup), n * mlocal, replace = TRUE), nrow = n) + indices <- matrix( + sample(1L:ncol(lookup), n * mlocal, replace = TRUE), + nrow = n + ) impy <- matrix(lookup[cbind(selected_bin, indices)], nrow = n, ncol = mlocal) return(impy) } - diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index 7bd6dc7cc..f12efed1c 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -55,12 +55,22 @@ #' @family univariate imputation functions #' @keywords datagen #' @export -mice.impute.polr <- function(y, ry, x, wy = NULL, - task = "impute", model = NULL, - nnet.maxit = NULL, nnet.MaxNWts = NULL, - maxit = NULL, MaxNWts = NULL, reltol = NULL, - warmstart = FALSE, polr.to.loggedEvents = FALSE, - ...) { +mice.impute.polr <- function( + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + nnet.maxit = NULL, + nnet.MaxNWts = NULL, + maxit = NULL, + MaxNWts = NULL, + reltol = NULL, + warmstart = FALSE, + polr.to.loggedEvents = FALSE, + ... +) { check.model.exists(model, task) method <- "polr" if (is.null(wy)) { @@ -100,10 +110,15 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, control = list( trace = 0L, maxit = ifelse(is.null(maxit), 100L, maxit), - reltol = ifelse(is.null(reltol), 0.0001, reltol)) + reltol = ifelse(is.null(reltol), 0.0001, reltol) + ) ) - if (warmstart && task == "train" && - !is.null(model$beta.dot) && !is.null(model$zeta.mis)) { + if ( + warmstart && + task == "train" && + !is.null(model$beta.dot) && + !is.null(model$zeta.mis) + ) { dots$start <- c(model$beta.dot, model$zeta.mis) } @@ -113,9 +128,7 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, xy <- cbind.data.frame(y, x) execute <- "polr" fun <- MASS::polr - args <- c(list(formula = formula(xy), - data = xy[ry, , drop = FALSE]), - dots) + args <- c(list(formula = formula(xy), data = xy[ry, , drop = FALSE]), dots) fit <- try(suppressWarnings(do.call(fun, args)), silent = TRUE) if (inherits(fit, "try-error")) { if (polr.to.loggedEvents) { @@ -123,23 +136,33 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, } execute <- "multinom" impy <- mice.impute.polyreg( - y = y, ry = ry, x = x, wy = wy, - task = task, model = model, - nnet.maxit = nnet.maxit, nnet.MaxNWts = nnet.MaxNWts, - maxit = maxit, MaxNWts = MaxNWts, reltol = reltol, - warmstart = warmstart, ...) + y = y, + ry = ry, + x = x, + wy = wy, + task = task, + model = model, + nnet.maxit = nnet.maxit, + nnet.MaxNWts = nnet.MaxNWts, + maxit = maxit, + MaxNWts = MaxNWts, + reltol = reltol, + warmstart = warmstart, + ... + ) } # Save for future use if (task == "train" && execute == "polr") { - model$setup <- list(method = method, - n = sum(ry), - task = task, - maxit = dots$control$maxit, - reltol = dots$control$reltol, - warmstart = warmstart) - model$result <- list(value = fit$value, - convergence = fit$convergence) + model$setup <- list( + method = method, + n = sum(ry), + task = task, + maxit = dots$control$maxit, + reltol = dots$control$reltol, + warmstart = warmstart + ) + model$result <- list(value = fit$value, convergence = fit$convergence) model$beta.dot <- setNames(coef(fit), colnames(x)) model$zeta.mis <- fit$zeta model$factor <- list(labels = levels(y), quant = NULL) @@ -162,9 +185,13 @@ mice.impute.polr <- function(y, ry, x, wy = NULL, } polr.draw <- function(x, beta, zeta, levels, class = NULL) { - if (nrow(x) == 0L) return(character(0)) + if (nrow(x) == 0L) { + return(character(0)) + } eta <- x %*% beta - cumpr <- plogis(matrix(zeta, nrow(x), length(zeta), byrow = TRUE) - as.vector(eta)) + cumpr <- plogis( + matrix(zeta, nrow(x), length(zeta), byrow = TRUE) - as.vector(eta) + ) post <- t(apply(cumpr, 1L, function(x) diff(c(0, x, 1)))) un <- rep(runif(nrow(x)), each = length(levels)) draws <- un > apply(post, 1L, cumsum) @@ -177,4 +204,3 @@ polr.draw <- function(x, beta, zeta, levels, class = NULL) { return(out) } - diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index a7396cb8d..44fb49110 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -58,15 +58,25 @@ #' @keywords datagen #' @export mice.impute.polyreg <- function( - y, ry, x, wy = NULL, - task = "impute", model = NULL, - nnet.maxit = NULL, nnet.MaxNWts = NULL, - maxit = NULL, MaxNWts = NULL, reltol = NULL, - warmstart = FALSE, ...) { - + y, + ry, + x, + wy = NULL, + task = "impute", + model = NULL, + nnet.maxit = NULL, + nnet.MaxNWts = NULL, + maxit = NULL, + MaxNWts = NULL, + reltol = NULL, + warmstart = FALSE, + ... +) { check.model.exists(model, task) method <- "polyreg" - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } # Augment data aug <- augment(y, ry, x, wy) @@ -84,8 +94,8 @@ mice.impute.polyreg <- function( x = x, beta = model$beta.dot, levels = model$factor$labels, - class = model$class[1L]) - ) + class = model$class[1L] + )) } # Escape perfect prediction @@ -95,8 +105,12 @@ mice.impute.polyreg <- function( } # Set hyperparameters - if (!missing(nnet.maxit)) maxit <- nnet.maxit - if (!missing(nnet.MaxNWts)) MaxNWts <- nnet.MaxNWts + if (!missing(nnet.maxit)) { + maxit <- nnet.maxit + } + if (!missing(nnet.MaxNWts)) { + MaxNWts <- nnet.MaxNWts + } MaxNWts_needed <- 100L + as.integer(ncol(x) * (length(levels(y)) - 1)) dots <- list(...) dots$maxit <- ifelse(is.null(maxit), 100L, maxit) @@ -109,13 +123,19 @@ mice.impute.polyreg <- function( # Fit multinomial model y <- droplevels(y) xy <- cbind.data.frame(y, x) - fit <- do.call(nnet::multinom, c( - list(formula(xy), - data = xy[ry, , drop = FALSE], - weights = w[ry], - model = FALSE, trace = FALSE), - dots - )) + fit <- do.call( + nnet::multinom, + c( + list( + formula(xy), + data = xy[ry, , drop = FALSE], + weights = w[ry], + model = FALSE, + trace = FALSE + ), + dots + ) + ) # Process beta coefficients x <- x[wy, , drop = FALSE] @@ -144,7 +164,9 @@ mice.impute.polyreg <- function( convergence = fit$convergence ) model$beta.dot <- beta - if (warmstart) model$wts <- fit$wts + if (warmstart) { + model$wts <- fit$wts + } model$factor <- list(labels = levels(y), quant = NULL) model$class <- if (is.ordered(y)) "ordered" else class(y)[1L] model$xnames <- colnames(x) @@ -160,7 +182,9 @@ mice.impute.polyreg <- function( } polyreg.draw <- function(x, beta, levels, class = NULL) { - if (nrow(x) == 0L) return(character(0)) + if (nrow(x) == 0L) { + return(character(0)) + } lp <- x %*% beta p <- exp(lp) / rowSums(exp(lp) + 1) post <- cbind(1 - rowSums(p), p) diff --git a/R/mice.impute.quadratic.R b/R/mice.impute.quadratic.R index 54f02458c..234f360f8 100644 --- a/R/mice.impute.quadratic.R +++ b/R/mice.impute.quadratic.R @@ -74,9 +74,24 @@ #' cmp <- complete(imp) #' points(cmp$x[is.na(dat$x)], cmp$xx[is.na(dat$x)], col = mdc(2)) #' @export -mice.impute.quadratic <- function(y, ry, x, wy = NULL, quad.outcome = NULL, ...) { - if (is.null(quad.outcome)) stop("Argument 'quad.outcome' for mice.impute.quadratic has not been specified") - if (!quad.outcome %in% colnames(x)) stop("The name specified for the outcome in 'quad.outcome' can not be found in the data") +mice.impute.quadratic <- function( + y, + ry, + x, + wy = NULL, + quad.outcome = NULL, + ... +) { + if (is.null(quad.outcome)) { + stop( + "Argument 'quad.outcome' for mice.impute.quadratic has not been specified" + ) + } + if (!quad.outcome %in% colnames(x)) { + stop( + "The name specified for the outcome in 'quad.outcome' can not be found in the data" + ) + } if (is.null(wy)) { wy <- !ry } @@ -107,13 +122,17 @@ mice.impute.quadratic <- function(y, ry, x, wy = NULL, quad.outcome = NULL, ...) mean(x[ry, quad.outcome]) - sd(x[ry, quad.outcome]), mean(x[ry, quad.outcome]) + sd(x[ry, quad.outcome]), mean(x[ry, quad.outcome]) - sd(x[ry, quad.outcome]), - mean(x[ry, quad.outcome]), mean(x[ry, quad.outcome]), - mean(x[ry, quad.outcome]), mean(x[ry, quad.outcome]) + mean(x[ry, quad.outcome]), + mean(x[ry, quad.outcome]), + mean(x[ry, quad.outcome]), + mean(x[ry, quad.outcome]) ), zstar = c( zstar[ry], - mean(zstar[ry]), mean(zstar[ry]), - mean(zstar[ry]), mean(zstar[ry]), + mean(zstar[ry]), + mean(zstar[ry]), + mean(zstar[ry]), + mean(zstar[ry]), mean(zstar[ry]) + sd(zstar[ry]), mean(zstar[ry]) - sd(zstar[ry]), mean(zstar[ry]) + sd(zstar[ry]), @@ -122,14 +141,18 @@ mice.impute.quadratic <- function(y, ry, x, wy = NULL, quad.outcome = NULL, ...) ) w <- c(rep(1, nrow(data.augment) - 8), rep(3 / 8, 8)) # calculate regression parameters for - vobs <- glm(V ~ q + zstar + q * zstar, + vobs <- glm( + V ~ q + zstar + q * zstar, family = quasibinomial, - data = data.augment, weights = w + data = data.augment, + weights = w ) # impute Vmis newdata <- data.frame(q = x[wy, quad.outcome], zstar = zstar[wy]) - prob <- predict(vobs, - newdata = newdata, type = "response", + prob <- predict( + vobs, + newdata = newdata, + type = "response", na.action = na.exclude ) idy <- rbinom(sum(wy), 1, prob = prob) diff --git a/R/mice.impute.rf.R b/R/mice.impute.rf.R index 428526d88..d7eedee51 100644 --- a/R/mice.impute.rf.R +++ b/R/mice.impute.rf.R @@ -58,12 +58,20 @@ #' plot(imp) #' } #' @export -mice.impute.rf <- function(y, ry, x, wy = NULL, ntree = 10, - rfPackage = c("ranger", "randomForest", "literanger"), - ...) { +mice.impute.rf <- function( + y, + ry, + x, + wy = NULL, + ntree = 10, + rfPackage = c("ranger", "randomForest", "literanger"), + ... +) { rfPackage <- match.arg(rfPackage) - if (is.null(wy)) wy <- !ry + if (is.null(wy)) { + wy <- !ry + } ntree <- max(1, ntree) # safety nmis <- sum(wy) @@ -72,7 +80,8 @@ mice.impute.rf <- function(y, ry, x, wy = NULL, ntree = 10, yobs <- y[ry] # Find eligible donors - f <- switch(rfPackage, + f <- switch( + rfPackage, randomForest = .randomForest.donors, ranger = .ranger.donors, literanger = .literanger.donor @@ -81,9 +90,13 @@ mice.impute.rf <- function(y, ry, x, wy = NULL, ntree = 10, forest <- f(xobs, xmis, yobs, ntree, ...) # Short-circuit when using literanger interface - if (rfPackage == "literanger") return(forest) + if (rfPackage == "literanger") { + return(forest) + } # Sample from donors - if (nmis == 1) forest <- array(forest, dim = c(1, ntree)) + if (nmis == 1) { + forest <- array(forest, dim = c(1, ntree)) + } apply(forest, MARGIN = 1, FUN = function(s) sample(unlist(s), 1)) } @@ -96,7 +109,8 @@ mice.impute.rf <- function(y, ry, x, wy = NULL, ntree = 10, fit <- randomForest::randomForest( x = xobs, y = yobs, - ntree = 1, ... + ntree = 1, + ... ) leafnr <- predict(object = fit, newdata = xobs, nodes = TRUE) leafnr <- as.vector(attr(leafnr, "nodes")) @@ -114,11 +128,18 @@ mice.impute.rf <- function(y, ry, x, wy = NULL, ntree = 10, install.on.demand("ranger", ...) # Fit all trees at once - fit <- suppressWarnings(ranger::ranger(x = xobs, y = yobs, num.trees = ntree, ...)) + fit <- suppressWarnings(ranger::ranger( + x = xobs, + y = yobs, + num.trees = ntree, + ... + )) nodes <- predict( - object = fit, data = rbind(xobs, xmis), - type = "terminalNodes", predict.all = TRUE + object = fit, + data = rbind(xobs, xmis), + type = "terminalNodes", + predict.all = TRUE ) nodes <- ranger::predictions(nodes) nodes_obs <- nodes[1:nrow(xobs), , drop = FALSE] @@ -142,7 +163,8 @@ mice.impute.rf <- function(y, ry, x, wy = NULL, ntree = 10, dots <- dots[intersect(names(dots), setdiff(lr_formals, c('x', 'y')))] fit <- do.call( - literanger::train, c(list(x = xobs, y = yobs, n_tree = ntree), dots) + literanger::train, + c(list(x = xobs, y = yobs, n_tree = ntree), dots) ) predict(object = fit, newdata = xmis, prediction_type = "inbag")$values } diff --git a/R/mice.impute.ri.R b/R/mice.impute.ri.R index c1b2327ce..136c0082b 100755 --- a/R/mice.impute.ri.R +++ b/R/mice.impute.ri.R @@ -58,10 +58,13 @@ mice.impute.ri <- function(y, ry, x, wy = NULL, ri.maxit = 10, ...) { # Imputation of y given rdot .y.draw <- function(y, ry, rdot, xy, wy, ...) { parm <- .norm.draw(y, ry, cbind(xy, rdot), ...) - if (all(rdot[ry] == 1) || all(rdot[ry] == 0)) parm$coef[length(parm$coef)] <- 0 + if (all(rdot[ry] == 1) || all(rdot[ry] == 0)) { + parm$coef[length(parm$coef)] <- 0 + } ydot <- y rydot <- as.logical(rdot) - ydot[wy] <- xy[wy, , drop = FALSE] %*% parm$beta[-length(parm$coef), ] + + ydot[wy] <- xy[wy, , drop = FALSE] %*% + parm$beta[-length(parm$coef), ] + rnorm(sum(wy)) * parm$sigma ydot[wy & !rydot] <- ydot[wy & !rydot] - parm$coef[length(parm$coef)] ydot diff --git a/R/mice.impute.sample.R b/R/mice.impute.sample.R index 4a49e7737..7163ac2d2 100644 --- a/R/mice.impute.sample.R +++ b/R/mice.impute.sample.R @@ -23,6 +23,8 @@ mice.impute.sample <- function(y, ry, x = NULL, wy = NULL, ...) { if (length(yry) < 1) { return(rnorm(sum(wy))) } - if (length(yry) == 1) yry <- rep(yry, 2) + if (length(yry) == 1) { + yry <- rep(yry, 2) + } sample(yry, size = sum(wy), replace = TRUE) } diff --git a/R/mice.mids.R b/R/mice.mids.R index a10d57c22..da7481424 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -59,11 +59,15 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { # overwrite obj with combined obj + imp.newdata if (!is.null(newdata)) { ignore <- rep(FALSE, nrow(obj$data)) - if (!is.null(obj$ignore)) ignore <- obj$ignore + if (!is.null(obj$ignore)) { + ignore <- obj$ignore + } newdata <- check.newdata(newdata, obj$data) - imp.newdata <- mice(newdata, - m = obj$m, maxit = 0, + imp.newdata <- mice( + newdata, + m = obj$m, + maxit = 0, remove.collinear = FALSE, remove.constant = FALSE ) @@ -87,13 +91,21 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { loggedEvents <- obj$loggedEvents state <- list( - it = 0, im = 0, co = 0, dep = "", meth = "", + it = 0, + im = 0, + co = 0, + dep = "", + meth = "", log = !is.null(loggedEvents) ) if (is.null(loggedEvents)) { loggedEvents <- data.frame( - it = 0, im = 0, co = 0, dep = "", - meth = "", out = "" + it = 0, + im = 0, + co = 0, + dep = "", + meth = "", + out = "" ) } @@ -101,20 +113,37 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { call <- match.call() imp <- obj$imp where <- obj$where - if (is.null(where)) where <- is.na(obj$data) + if (is.null(where)) { + where <- is.na(obj$data) + } blocks <- obj$blocks - if (is.null(blocks)) blocks <- make.blocks(obj$data) + if (is.null(blocks)) { + blocks <- make.blocks(obj$data) + } ## OK. Iterate. sumIt <- obj$iteration + maxit from <- obj$iteration + 1 to <- from + maxit - 1 q <- sampler( - obj$data, obj$m, obj$ignore, where, imp, blocks, - obj$method, obj$visitSequence, obj$predictorMatrix, - obj$formulas, obj$calltypes, obj$blots, - obj$tasks, obj$models, - obj$post, c(from, to), printFlag, ... + obj$data, + obj$m, + obj$ignore, + where, + imp, + blocks, + obj$method, + obj$visitSequence, + obj$predictorMatrix, + obj$formulas, + obj$calltypes, + obj$blots, + obj$tasks, + obj$models, + obj$post, + c(from, to), + printFlag, + ... ) imp <- q$imp @@ -123,11 +152,13 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { vnames <- unique(unlist(obj$blocks)) nvis <- length(vnames) if (!is.null(obj$chainMean)) { - chainMean <- chainVar <- array(0, + chainMean <- chainVar <- array( + 0, dim = c(nvis, to, obj$m), dimnames = list( vnames, - seq_len(to), paste("Chain", seq_len(obj$m)) + seq_len(to), + paste("Chain", seq_len(obj$m)) ) ) for (j in seq_len(nvis)) { @@ -173,12 +204,16 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { ignore = obj$ignore, seed = obj$seed, iteration = sumIt, - lastSeedValue = get(".Random.seed", - envir = globalenv(), mode = "integer", - inherits = FALSE), + lastSeedValue = get( + ".Random.seed", + envir = globalenv(), + mode = "integer", + inherits = FALSE + ), chainMean = chainMean, chainVar = chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents + ) if (!is.null(newdata)) { include <- c( diff --git a/R/mice.theme.R b/R/mice.theme.R index 058772b9e..bd83aa9d5 100644 --- a/R/mice.theme.R +++ b/R/mice.theme.R @@ -24,8 +24,12 @@ mice.theme <- function(transparent = TRUE, alpha.fill = 0.3) { } return(c(grDevices::hcl(240, 100, 40), grDevices::hcl(0, 100, 40))) } - if (missing(transparent)) transparent <- supports.transparent() - if (missing(alpha.fill)) alpha.fill <- ifelse(transparent, 0.3, 0) + if (missing(transparent)) { + transparent <- supports.transparent() + } + if (missing(alpha.fill)) { + alpha.fill <- ifelse(transparent, 0.3, 0) + } list( superpose.symbol = list( col = mdc(1:2), diff --git a/R/mids.R b/R/mids.R index 66db9f5e1..a3d1d592f 100644 --- a/R/mids.R +++ b/R/mids.R @@ -152,36 +152,41 @@ #' print(imp) #' @export mids <- function( - data = data.frame(), - imp = list(), - m = integer(), - where = matrix, - blocks = list(), - call = match.call(), - nmis = integer(), - method = character(), - predictorMatrix = matrix(), - visitSequence = character(), - formulas = list(), - calltypes = character(), - post = character(), - blots = list(), - tasks = character(), - models = new.env(), - ignore = logical(), - seed = integer(), - iteration = integer(), - lastSeedValue = tryCatch( - get(".Random.seed", envir = globalenv(), mode = "integer", inherits = FALSE), - error = function(e) NULL + data = data.frame(), + imp = list(), + m = integer(), + where = matrix, + blocks = list(), + call = match.call(), + nmis = integer(), + method = character(), + predictorMatrix = matrix(), + visitSequence = character(), + formulas = list(), + calltypes = character(), + post = character(), + blots = list(), + tasks = character(), + models = new.env(), + ignore = logical(), + seed = integer(), + iteration = integer(), + lastSeedValue = tryCatch( + get( + ".Random.seed", + envir = globalenv(), + mode = "integer", + inherits = FALSE ), - chainMean = list(), - chainVar = list(), - loggedEvents = data.frame(), - version = packageVersion("mice"), - date = Sys.Date(), - store = "impute") { - + error = function(e) NULL + ), + chainMean = list(), + chainVar = list(), + loggedEvents = data.frame(), + version = packageVersion("mice"), + date = Sys.Date(), + store = "impute" +) { if (store == "impute") { obj <- list( data = data, @@ -208,7 +213,8 @@ mids <- function( loggedEvents = loggedEvents, version = packageVersion("mice"), date = Sys.Date(), - store = store) + store = store + ) } else if (store == "train") { obj <- list( data = data, @@ -236,7 +242,8 @@ mids <- function( loggedEvents = loggedEvents, version = packageVersion("mice"), date = Sys.Date(), - store = store) + store = store + ) } else if (store == "train_compact") { obj <- list( m = m, @@ -251,7 +258,8 @@ mids <- function( store = store, version = packageVersion("mice"), date = Sys.Date(), - call = call) + call = call + ) } else if (store == "fill") { obj <- list( data = data, @@ -261,7 +269,8 @@ mids <- function( store = store, version = packageVersion("mice"), date = Sys.Date(), - call = call) + call = call + ) } else { stop("store must be one of 'impute', 'train', 'train_compact', or 'fill'") } @@ -297,21 +306,38 @@ mids <- function( #' imp <- mice(nhanes, print = FALSE) #' plot(imp, bmi + chl ~ .it | .ms, layout = c(2, 1)) #' @export -plot.mids <- function(x, y = NULL, theme = mice.theme(), layout = c(2, 3), - type = "l", col = 1:10, lty = 1, ...) { +plot.mids <- function( + x, + y = NULL, + theme = mice.theme(), + layout = c(2, 3), + type = "l", + col = 1:10, + lty = 1, + ... +) { strip.combined <- function(which.given, which.panel, factor.levels, ...) { if (which.given == 1) { - lattice::panel.rect(0, 0, 1, 1, - col = theme$strip.background$col, border = 1 + lattice::panel.rect( + 0, + 0, + 1, + 1, + col = theme$strip.background$col, + border = 1 ) lattice::panel.text( - x = 0, y = 0.5, pos = 4, + x = 0, + y = 0.5, + pos = 4, lab = factor.levels[which.panel[which.given]] ) } if (which.given == 2) { lattice::panel.text( - x = 1, y = 0.5, pos = 2, + x = 1, + y = 0.5, + pos = 2, lab = factor.levels[which.panel[which.given]] ) } @@ -379,8 +405,13 @@ plot.mids <- function(x, y = NULL, theme = mice.theme(), layout = c(2, 3), rm(.m) tp <- lattice::xyplot( - x = formula, data = data, groups = .m, - type = type, lty = lty, col = col, layout = layout, + x = formula, + data = data, + groups = .m, + type = type, + lty = lty, + col = col, + layout = layout, scales = list( y = list(relation = "free"), x = list(alternating = FALSE) diff --git a/R/mids2mplus.R b/R/mids2mplus.R index 6d6029e06..e5389d926 100644 --- a/R/mids2mplus.R +++ b/R/mids2mplus.R @@ -26,36 +26,68 @@ #' @seealso \code{\link[=mids-class]{mids}}, \code{\link{mids2spss}} #' @keywords manip #' @export -mids2mplus <- function(imp, file.prefix = "imp", path = getwd(), sep = "\t", dec = ".", silent = FALSE) { +mids2mplus <- function( + imp, + file.prefix = "imp", + path = getwd(), + sep = "\t", + dec = ".", + silent = FALSE +) { m <- imp$m file.list <- matrix(0, m, 1) script <- matrix(0, 3, 1) for (i in seq_len(m)) { - write.table(complete(imp, i), + write.table( + complete(imp, i), file = file.path(path, paste0(file.prefix, i, ".dat")), - sep = sep, dec = dec, col.names = FALSE, row.names = FALSE + sep = sep, + dec = dec, + col.names = FALSE, + row.names = FALSE ) file.list[i, ] <- paste0(file.prefix, i, ".dat") } - write.table(file.list, + write.table( + file.list, file = file.path(path, paste0(file.prefix, "list.dat")), - sep = sep, dec = dec, col.names = FALSE, row.names = FALSE, quote = FALSE + sep = sep, + dec = dec, + col.names = FALSE, + row.names = FALSE, + quote = FALSE ) names <- paste(colnames(complete(imp, 1)), collapse = " ") script[1, ] <- paste0("DATA: FILE IS ", file.prefix, "list.dat;") script[2, ] <- "TYPE = IMPUTATION;" script[3, ] <- paste0("VARIABLE: NAMES ARE ", names, ";") - write.table(script, + write.table( + script, file = file.path(path, paste0(file.prefix, "list.inp")), - sep = sep, dec = dec, col.names = FALSE, row.names = FALSE, quote = FALSE + sep = sep, + dec = dec, + col.names = FALSE, + row.names = FALSE, + quote = FALSE ) if (!silent) { cat( - "Data values written to", file.path(path, paste0(file.prefix, 1, ".dat")), - "through", paste0(file.prefix, m, ".dat"), "\n" + "Data values written to", + file.path(path, paste0(file.prefix, 1, ".dat")), + "through", + paste0(file.prefix, m, ".dat"), + "\n" + ) + cat( + "Data names written to", + file.path(path, paste0(file.prefix, "list.dat")), + "\n" + ) + cat( + "Mplus code written to", + file.path(path, paste0(file.prefix, "list.inp")), + "\n" ) - cat("Data names written to", file.path(path, paste0(file.prefix, "list.dat")), "\n") - cat("Mplus code written to", file.path(path, paste0(file.prefix, "list.inp")), "\n") } } diff --git a/R/mids2spss.R b/R/mids2spss.R index dbbddfaa0..179c52548 100644 --- a/R/mids2spss.R +++ b/R/mids2spss.R @@ -44,8 +44,13 @@ #' @seealso \code{\link[=mids-class]{mids}} #' @keywords manip #' @export -mids2spss <- function(imp, filename = "midsdata", - path = getwd(), compress = FALSE, silent = FALSE) { +mids2spss <- function( + imp, + filename = "midsdata", + path = getwd(), + compress = FALSE, + silent = FALSE +) { .id <- NULL # avoid empty global variable binding install.on.demand("haven") # extract a completed dataset (long format - all imputations stacked) diff --git a/R/mipo.R b/R/mipo.R index 540014fe0..666b1d1e0 100644 --- a/R/mipo.R +++ b/R/mipo.R @@ -53,42 +53,63 @@ NULL #' @rdname mipo #' @export mipo <- function(mira.obj, ...) { - if (!is.mira(mira.obj)) stop("`mira.obj` not of class `mira`") + if (!is.mira(mira.obj)) { + stop("`mira.obj` not of class `mira`") + } structure(pool(mira.obj, ...), class = c("mipo")) } #' @return The \code{summary} method returns a data frame with summary statistics of the pooled analysis. #' @rdname mipo #' @export -summary.mipo <- function(object, type = c("tests", "all"), - conf.int = FALSE, conf.level = .95, - exponentiate = FALSE, ...) { +summary.mipo <- function( + object, + type = c("tests", "all"), + conf.int = FALSE, + conf.level = .95, + exponentiate = FALSE, + ... +) { type <- match.arg(type) x <- object$pooled - z <- summary_mipo.workhorse(x = x, type = type, - conf.int = conf.int, conf.level = conf.level, - exponentiate = exponentiate, ...) + z <- summary_mipo.workhorse( + x = x, + type = type, + conf.int = conf.int, + conf.level = conf.level, + exponentiate = exponentiate, + ... + ) class(z) <- c("mipo.summary", "data.frame") z } -summary_mipo.workhorse <- function(x, type, - conf.int, conf.level, - exponentiate, ...) { +summary_mipo.workhorse <- function( + x, + type, + conf.int, + conf.level, + exponentiate, + ... +) { m <- x$m[1L] std.error <- sqrt(x$t) statistic <- x$estimate / std.error p.value <- 2 * (pt(abs(statistic), pmax(x$df, 0.001), lower.tail = FALSE)) - z <- data.frame(x, - std.error = std.error, - statistic = statistic, - p.value = p.value + z <- data.frame( + x, + std.error = std.error, + statistic = statistic, + p.value = p.value + ) + z <- process_mipo( + z, + x, + conf.int = conf.int, + conf.level = conf.level, + exponentiate = exponentiate ) - z <- process_mipo(z, x, - conf.int = conf.int, - conf.level = conf.level, - exponentiate = exponentiate) if (type == "tests") { out <- c("m", "riv", "lambda", "fmi", "ubar", "b", "t", "dfcom") @@ -116,8 +137,13 @@ print.mipo.summary <- function(x, ...) { #' @rdname mipo #' @keywords internal -process_mipo <- function(z, x, conf.int = FALSE, conf.level = .95, - exponentiate = FALSE) { +process_mipo <- function( + z, + x, + conf.int = FALSE, + conf.level = .95, + exponentiate = FALSE +) { if (exponentiate) { # save transformation function for use on confidence interval trans <- exp @@ -166,10 +192,7 @@ confidence <- function(pooled, parm, level = 0.95, ...) { a <- c(a, 1 - a) fac <- qt(a, df) pct <- fmt.perc(a, 3) - ci <- array(NA, - dim = c(length(parm), 2L), - dimnames = list(parm, pct) - ) + ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(parm, pct)) ci[, 1] <- cf[parm] + qt(a[1], df[parm]) * se[parm] ci[, 2] <- cf[parm] + qt(a[2], df[parm]) * se[parm] return(ci) diff --git a/R/mira.R b/R/mira.R index 1d07aaaec..31df82cef 100644 --- a/R/mira.R +++ b/R/mira.R @@ -52,11 +52,11 @@ #' @aliases mira mira-class #' @export mira <- function( - call = match.call(), - call1 = match.call(), - nmis = integer(), - analyses = list()) { - + call = match.call(), + call1 = match.call(), + nmis = integer(), + analyses = list() +) { # Create the object as a list obj <- list( call = as.call(call), diff --git a/R/models.R b/R/models.R index 44a73c96c..51bf89410 100644 --- a/R/models.R +++ b/R/models.R @@ -16,7 +16,6 @@ #' } #' @keywords internal initialize.models.env <- function(models = NULL, tasks, method, blocks, m) { - # Import models into environment from a model list object if (is.list(models)) { models <- import.models.env(models) @@ -88,7 +87,9 @@ initialize.models.env <- function(models = NULL, tasks, method, blocks, m) { #' print(models_list) #' @keywords internal export.models.env <- function(env, m = NULL) { - if (!is.environment(env)) stop("Input must be an environment") + if (!is.environment(env)) { + stop("Input must be an environment") + } env_to_list <- function(env) { obj_list <- as.list(env, all.names = TRUE) @@ -102,7 +103,7 @@ export.models.env <- function(env, m = NULL) { # Convert first-level environment into a list models_list <- env_to_list(env) - m <- ifelse(is.null(m), max(sapply(models_list, length)), m) + m <- ifelse(is.null(m), max(sapply(models_list, length)), m) # Restructure each task's models into a vector of m lists for (varname in names(models_list)) { @@ -165,18 +166,24 @@ export.models.env <- function(env, m = NULL) { #' print(models_env$a$`1`$model) # Should be "Model A1" #' @keywords internal import.models.env <- function(models_list) { - if (!is.list(models_list)) stop("Input must be a list") + if (!is.list(models_list)) { + stop("Input must be a list") + } models_env <- new.env(parent = emptyenv()) for (varname in names(models_list)) { - models_env[[varname]] <- new.env(parent = emptyenv()) # Create first-level environment + models_env[[varname]] <- new.env(parent = emptyenv()) # Create first-level environment for (i in seq_along(models_list[[varname]])) { iteration_data <- models_list[[varname]][[i]] - if (length(iteration_data) > 0) { # Only create non-empty environments - models_env[[varname]][[as.character(i)]] <- list2env(iteration_data, parent = emptyenv()) + if (length(iteration_data) > 0) { + # Only create non-empty environments + models_env[[varname]][[as.character(i)]] <- list2env( + iteration_data, + parent = emptyenv() + ) } } } diff --git a/R/nimp.R b/R/nimp.R index 11eed7901..7a3cdd980 100644 --- a/R/nimp.R +++ b/R/nimp.R @@ -19,6 +19,8 @@ nimp <- function(where, blocks = make.blocks(where)) { nwhere <- apply(where, 2, sum) nimp <- vector("integer", length = length(blocks)) names(nimp) <- names(blocks) - for (i in seq_along(blocks)) nimp[i] <- sum(nwhere[blocks[[i]]]) + for (i in seq_along(blocks)) { + nimp[i] <- sum(nwhere[blocks[[i]]]) + } nimp } diff --git a/R/parlmice.R b/R/parlmice.R index 4572546a7..aedca14e6 100644 --- a/R/parlmice.R +++ b/R/parlmice.R @@ -71,8 +71,16 @@ #' } #' #' @export -parlmice <- function(data, m = 5, seed = NA, cluster.seed = NA, n.core = NULL, - n.imp.core = NULL, cl.type = "PSOCK", ...) { +parlmice <- function( + data, + m = 5, + seed = NA, + cluster.seed = NA, + n.core = NULL, + n.imp.core = NULL, + cl.type = "PSOCK", + ... +) { warning( "'parlmice()' is deprecated as of mice 3.18.0. ", "Please use 'mice(..., parallel = TRUE)' instead.", @@ -106,7 +114,9 @@ parlmice <- function(data, m = 5, seed = NA, cluster.seed = NA, n.core = NULL, n.imp.core <- specs$imps } if (!is.na(seed) && n.core > 1) { - warning("Using the same seed across streams. Consider using cluster.seed for distinct streams.") + warning( + "Using the same seed across streams. Consider using cluster.seed for distinct streams." + ) } args <- match.call(mice, expand.dots = TRUE) @@ -114,11 +124,20 @@ parlmice <- function(data, m = 5, seed = NA, cluster.seed = NA, n.core = NULL, args$m <- n.imp.core cl <- parallel::makeCluster(n.core, type = cl.type) - parallel::clusterExport(cl, - varlist = c("data", "m", "seed", "cluster.seed", - "n.core", "n.imp.core", "cl.type", - ls(parent.frame())), - envir = environment()) + parallel::clusterExport( + cl, + varlist = c( + "data", + "m", + "seed", + "cluster.seed", + "n.core", + "n.imp.core", + "cl.type", + ls(parent.frame()) + ), + envir = environment() + ) parallel::clusterExport(cl, varlist = "do.call") parallel::clusterEvalQ(cl, library(mice)) @@ -126,8 +145,9 @@ parlmice <- function(data, m = 5, seed = NA, cluster.seed = NA, n.core = NULL, parallel::clusterSetRNGStream(cl, cluster.seed) } - imps <- parallel::parLapply(cl = cl, X = 1:n.core, - function(x) do.call(mice, as.list(args), envir = environment())) + imps <- parallel::parLapply(cl = cl, X = 1:n.core, function(x) { + do.call(mice, as.list(args), envir = environment()) + }) parallel::stopCluster(cl) imp <- imps[[1]] diff --git a/R/parse.ums.R b/R/parse.ums.R index a8d23adb5..d85506a05 100644 --- a/R/parse.ums.R +++ b/R/parse.ums.R @@ -1,17 +1,24 @@ parse.ums <- function(x, ums = NULL, umx = NULL, ...) { - if (is.null(ums)) stop("Unidentifiable model specification (ums) not found.") - if (!is.null(umx)) x <- base::cbind(x, umx) + if (is.null(ums)) { + stop("Unidentifiable model specification (ums) not found.") + } + if (!is.null(umx)) { + x <- base::cbind(x, umx) + } ## Unidentifiable part # e.g. specified in blots as list(X = list(ums = "-3+2*bmi")) mnar0 <- gsub("-", "+-", ums) mnar0 <- unlist(strsplit(mnar0, "+", fixed = TRUE)) - if (mnar0[1L] == "") mnar0 <- mnar0[-1L] + if (mnar0[1L] == "") { + mnar0 <- mnar0[-1L] + } if (sum(!grepl("*", mnar0, fixed = TRUE)) == 0L) { stop("An intercept (constant) term must be included in the expression") } else if (sum(!grepl("*", mnar0, fixed = TRUE)) == 1L) { mnar0[!grepl("*", mnar0, fixed = TRUE)] <- paste( - mnar0[!grepl("*", mnar0, fixed = TRUE)], "*intercept", + mnar0[!grepl("*", mnar0, fixed = TRUE)], + "*intercept", sep = "" ) } else if (sum(!grepl("*", mnar0, fixed = TRUE)) > 1L) { @@ -22,9 +29,18 @@ parse.ums <- function(x, ums = NULL, umx = NULL, ...) { mnar.parm <- as.numeric(unlist(lapply(mnar, function(x) x[1L]))) # e.g. c("intercept","bmi") mnar.vars <- unlist(lapply(mnar, function(x) x[2L])) - mnar.parm <- mnar.parm[c(which(mnar.vars == "intercept"), which(mnar.vars != "intercept"))] - mnar.vars <- mnar.vars[c(which(mnar.vars == "intercept"), which(mnar.vars != "intercept"))] - xmnar <- as.matrix(cbind(1, as.matrix(x[, mnar.vars[!mnar.vars == "intercept"]]))) + mnar.parm <- mnar.parm[c( + which(mnar.vars == "intercept"), + which(mnar.vars != "intercept") + )] + mnar.vars <- mnar.vars[c( + which(mnar.vars == "intercept"), + which(mnar.vars != "intercept") + )] + xmnar <- as.matrix(cbind( + 1, + as.matrix(x[, mnar.vars[!mnar.vars == "intercept"]]) + )) list(delta = mnar.parm, x = xmnar) } diff --git a/R/pool.R b/R/pool.R index e6d4301f5..963e6326a 100644 --- a/R/pool.R +++ b/R/pool.R @@ -151,7 +151,9 @@ pool <- function(object, dfcom = NULL, rule = NULL, custom.t = NULL) { call <- match.call() - if (!is.list(object)) stop("Argument 'object' not a list", call. = FALSE) + if (!is.list(object)) { + stop("Argument 'object' not a list", call. = FALSE) + } object <- as.mira(object) m <- length(object$analyses) @@ -162,12 +164,18 @@ pool <- function(object, dfcom = NULL, rule = NULL, custom.t = NULL) { model <- getfit(object, 1L) dfcom <- get.dfcom(model, dfcom) - w <- summary(getfit(object), type = "tidy", exponentiate = FALSE, dfcom = dfcom) + w <- summary( + getfit(object), + type = "tidy", + exponentiate = FALSE, + dfcom = dfcom + ) pooled <- pool.vector(w, dfcom = dfcom, custom.t = custom.t, rule = rule) # mipo object rr <- list( - call = call, m = m, + call = call, + m = m, pooled = pooled, glanced = get.glanced(object) ) diff --git a/R/pool.compare.R b/R/pool.compare.R index 614569c81..2d60f4b6c 100644 --- a/R/pool.compare.R +++ b/R/pool.compare.R @@ -66,8 +66,12 @@ #' Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords htest #' @export -pool.compare <- function(fit1, fit0, method = c("wald", "likelihood"), - data = NULL) { +pool.compare <- function( + fit1, + fit0, + method = c("wald", "likelihood"), + data = NULL +) { .Deprecated("D1") # Check the arguments @@ -145,8 +149,10 @@ pool.compare <- function(fit1, fit0, method = c("wald", "likelihood"), pull(.data$deviance) deviances <- list( - dev1.M = dev1.M, dev0.M = dev0.M, - dev1.L = dev1.L, dev0.L = dev0.L + dev1.M = dev1.M, + dev0.M = dev0.M, + dev1.L = dev1.L, + dev0.L = dev0.L ) dev.M <- mean(dev0.M - dev1.M) @@ -165,13 +171,23 @@ pool.compare <- function(fit1, fit0, method = c("wald", "likelihood"), } statistic <- list( - call = call, call11 = fit1$call, call12 = fit1$call1, - call01 = fit0$call, call02 = fit0$call1, - method = method, nmis = fit1$nmis, m = m, - qbar1 = getqbar(est1), qbar0 = getqbar(est0), - ubar1 = est1$pooled$ubar, ubar0 = est0$pooled$ubar, + call = call, + call11 = fit1$call, + call12 = fit1$call1, + call01 = fit0$call, + call02 = fit0$call1, + method = method, + nmis = fit1$nmis, + m = m, + qbar1 = getqbar(est1), + qbar0 = getqbar(est0), + ubar1 = est1$pooled$ubar, + ubar0 = est0$pooled$ubar, deviances = deviances, - Dm = Dm, rm = rm, df1 = dimQ2, df2 = w, + Dm = Dm, + rm = rm, + df1 = dimQ2, + df2 = w, pvalue = pf(Dm, dimQ2, w, lower.tail = FALSE) ) statistic diff --git a/R/pool.r.squared.R b/R/pool.r.squared.R index 8dd98fe21..ac056280d 100644 --- a/R/pool.r.squared.R +++ b/R/pool.r.squared.R @@ -51,7 +51,9 @@ pool.r.squared <- function(object, adjusted = FALSE) { stop("At least two imputations are needed for pooling.\n") } if (class((object$analyses[[1]]))[1] != "lm") { - stop("r^2 can only be calculated for results of the 'lm' modeling function") + stop( + "r^2 can only be calculated for results of the 'lm' modeling function" + ) } glanced <- summary(object, type = "glance") } @@ -61,19 +63,29 @@ pool.r.squared <- function(object, adjusted = FALSE) { stop("At least two imputations are needed for pooling.\n") } if (!"r.squared" %in% colnames(object$glanced)) { - stop("r^2 can only be calculated for results of the 'lm' modeling function") + stop( + "r^2 can only be calculated for results of the 'lm' modeling function" + ) } glanced <- object$glanced } # Set up array r2 to store R2 values, Fisher z-transformations of R2 values and its variance. m <- nrow(glanced) - r2 <- matrix(NA, nrow = m, ncol = 3, dimnames = list(seq_len(m), c("R^2", "Fisher trans F^2", "se()"))) + r2 <- matrix( + NA, + nrow = m, + ncol = 3, + dimnames = list(seq_len(m), c("R^2", "Fisher trans F^2", "se()")) + ) # Fill arrays for (i in seq_len(m)) { - r2[i, 1] <- if (!adjusted) sqrt(glanced$r.squared[i]) else + r2[i, 1] <- if (!adjusted) { + sqrt(glanced$r.squared[i]) + } else { sign(glanced$adj.r.squared[i]) * sqrt(abs(glanced$adj.r.squared[i])) + } r2[i, 2] <- 0.5 * log((r2[i, 1] + 1) / (1 - r2[i, 1])) r2[i, 3] <- 1 / (glanced$nobs[i] - 3) } @@ -83,9 +95,7 @@ pool.r.squared <- function(object, adjusted = FALSE) { # Make table with results. qbar <- fit$qbar - table <- array(((exp(2 * qbar) - 1) / (1 + exp(2 * qbar)))^2, - dim = c(1, 4) - ) + table <- array(((exp(2 * qbar) - 1) / (1 + exp(2 * qbar)))^2, dim = c(1, 4)) dimnames(table) <- if (!adjusted) { list("R^2", c("est", "lo 95", "hi 95", "fmi")) @@ -93,8 +103,10 @@ pool.r.squared <- function(object, adjusted = FALSE) { list("adj R^2", c("est", "lo 95", "hi 95", "fmi")) } - table[, 2] <- ((exp(2 * (qbar - 1.96 * sqrt(fit$t))) - 1) / (1 + exp(2 * (qbar - 1.96 * sqrt(fit$t)))))^2 - table[, 3] <- ((exp(2 * (qbar + 1.96 * sqrt(fit$t))) - 1) / (1 + exp(2 * (qbar + 1.96 * sqrt(fit$t)))))^2 + table[, 2] <- ((exp(2 * (qbar - 1.96 * sqrt(fit$t))) - 1) / + (1 + exp(2 * (qbar - 1.96 * sqrt(fit$t)))))^2 + table[, 3] <- ((exp(2 * (qbar + 1.96 * sqrt(fit$t))) - 1) / + (1 + exp(2 * (qbar + 1.96 * sqrt(fit$t)))))^2 table[, 4] <- fit$f table } diff --git a/R/pool.scalar.R b/R/pool.scalar.R index 4dd469f05..27057698e 100644 --- a/R/pool.scalar.R +++ b/R/pool.scalar.R @@ -70,7 +70,13 @@ #' # check: automatic pooling using broom #' pool.syn(fit) #' @export -pool.scalar <- function(Q, U, n = Inf, k = 1, rule = c("rubin1987", "reiter2003")) { +pool.scalar <- function( + Q, + U, + n = Inf, + k = 1, + rule = c("rubin1987", "reiter2003") +) { rule <- match.arg(rule) m <- length(Q) @@ -92,8 +98,16 @@ pool.scalar <- function(Q, U, n = Inf, k = 1, rule = c("rubin1987", "reiter2003" } list( - m = m, qhat = Q, u = U, qbar = qbar, ubar = ubar, b = b, t = t, - df = df, r = r, fmi = fmi + m = m, + qhat = Q, + u = U, + qbar = qbar, + ubar = ubar, + b = b, + t = t, + df = df, + r = r, + fmi = fmi ) } diff --git a/R/pool.table.R b/R/pool.table.R index 268ab408b..719de434c 100644 --- a/R/pool.table.R +++ b/R/pool.table.R @@ -99,23 +99,28 @@ #' class(all1) <- "data.frame" #' identical(all1, all2) #' @export -pool.table <- function(w, - type = c("all", "minimal", "tests"), - conf.int = TRUE, - conf.level = 0.95, - exponentiate = FALSE, - dfcom = Inf, - custom.t = NULL, - rule = c("rubin1987", "reiter2003"), - ...) { +pool.table <- function( + w, + type = c("all", "minimal", "tests"), + conf.int = TRUE, + conf.level = 0.95, + exponentiate = FALSE, + dfcom = Inf, + custom.t = NULL, + rule = c("rubin1987", "reiter2003"), + ... +) { type <- match.arg(type) pooled <- pool.vector(w, dfcom = dfcom, custom.t = custom.t, rule = rule) - if (type %in% c("all", "tests")) - pooled <- summary_mipo.workhorse(x = pooled, - type = type, - conf.int = conf.int, - conf.level = conf.level, - exponentiate = exponentiate, - ...) + if (type %in% c("all", "tests")) { + pooled <- summary_mipo.workhorse( + x = pooled, + type = type, + conf.int = conf.int, + conf.level = conf.level, + exponentiate = exponentiate, + ... + ) + } return(pooled) } diff --git a/R/pool.vector.R b/R/pool.vector.R index 87a414a00..18e5142d3 100644 --- a/R/pool.vector.R +++ b/R/pool.vector.R @@ -1,16 +1,24 @@ -pool.vector <- function(w, dfcom = Inf, custom.t = NULL, - rule = c("rubin1987", "reiter2003")) { +pool.vector <- function( + w, + dfcom = Inf, + custom.t = NULL, + rule = c("rubin1987", "reiter2003") +) { # rubin1987: Rubin's rules for scalar estimates # reiter2003: Reiter's rules for partially synthetic data rule <- match.arg(rule) present <- hasName(w, c("estimate", "std.error")) if (!all(present)) { - stop("Column(s) not found: ", - paste(c("estimate", "std.error")[!present], collapse = ", ")) + stop( + "Column(s) not found: ", + paste(c("estimate", "std.error")[!present], collapse = ", ") + ) } - grp <- intersect(names(w), - c("term", "parameter", "contrast", "y.level", "component")) + grp <- intersect( + names(w), + c("term", "parameter", "contrast", "y.level", "component") + ) if (!length(grp)) { warning("No parameter names found. Add a column named `term`.") } @@ -20,20 +28,26 @@ pool.vector <- function(w, dfcom = Inf, custom.t = NULL, } # Convert to factor to preserve ordering - if (hasName(w, "term")) + if (hasName(w, "term")) { w$term <- factor(w$term, levels = unique(w$term)) - if (hasName(w, "parameter")) + } + if (hasName(w, "parameter")) { w$parameter <- factor(w$parameter, levels = unique(w$parameter)) - if (hasName(w, "contrast")) + } + if (hasName(w, "contrast")) { w$contrast <- factor(w$contrast, levels = unique(w$contrast)) - if (hasName(w, "y.level")) + } + if (hasName(w, "y.level")) { w$y.level <- factor(w$y.level, levels = unique(w$y.level)) - if (hasName(w, "component")) + } + if (hasName(w, "component")) { w$component <- factor(w$component, levels = unique(w$component)) + } # Prefer using robust.se when tidy object contains it - if (hasName(w, "robust.se")) + if (hasName(w, "robust.se")) { w$std.error <- w$robust.se + } # There we go.. if (rule == "rubin1987") { @@ -44,9 +58,11 @@ pool.vector <- function(w, dfcom = Inf, custom.t = NULL, qbar = mean(.data$estimate), ubar = mean(.data$std.error^2), b = var(.data$estimate), - t = ifelse(is.null(custom.t), - .data$ubar + (1 + 1 / .data$m) * .data$b, - eval(parse(text = custom.t))), + t = ifelse( + is.null(custom.t), + .data$ubar + (1 + 1 / .data$m) * .data$b, + eval(parse(text = custom.t)) + ), dfcom = dfcom, df = barnard.rubin(.data$m, .data$b, .data$t, .data$dfcom), riv = (1 + 1 / .data$m) * .data$b / .data$ubar, @@ -63,9 +79,11 @@ pool.vector <- function(w, dfcom = Inf, custom.t = NULL, qbar = mean(.data$estimate), ubar = mean(.data$std.error^2), b = var(.data$estimate), - t = ifelse(is.null(custom.t), - .data$ubar + (1 / .data$m) * .data$b, - eval(parse(text = custom.t))), + t = ifelse( + is.null(custom.t), + .data$ubar + (1 / .data$m) * .data$b, + eval(parse(text = custom.t)) + ), dfcom = dfcom, df = (.data$m - 1) * (1 + (.data$ubar / (.data$b / .data$m)))^2, riv = (1 + 1 / .data$m) * .data$b / .data$ubar, diff --git a/R/post.R b/R/post.R index 5e601151c..9cd666971 100644 --- a/R/post.R +++ b/R/post.R @@ -26,7 +26,9 @@ check.post <- function(post, data) { } # change - if (is.null(names(post))) names(post) <- colnames(data) + if (is.null(names(post))) { + names(post) <- colnames(data) + } post } diff --git a/R/predict_mi.R b/R/predict_mi.R index ab9d532ce..68862f717 100644 --- a/R/predict_mi.R +++ b/R/predict_mi.R @@ -2,8 +2,8 @@ #' #' @param object A prediction model, either a single lm object or a list of lm #' objects obtained from multiply imputed data (object can also be of class mira). -#' @param newdata An optional data frame in which to look for variables -#' with which to predict. Can be a data.frame, list, or mids object. +#' @param newdata An optional data frame in which to look for variables +#' with which to predict. Can be a data.frame, list, or mids object. #' If omitted, the fitted values are used. #' @param pool Logical indicating whether to pool the predictions (and potentially #' obtain pooled prediction intervals). @@ -39,12 +39,12 @@ #' # Make prediction matrix and ensure that set is not used as a predictor #' predmat <- mice::make.predictorMatrix(dat) #' predmat[,"set"] <- 0 -#' +#' #' # Impute missing values based on the train set -#' imp <- mice(dat, m = 5, maxit = 5 , seed = 1, predictorMatrix = predmat, +#' imp <- mice(dat, m = 5, maxit = 5 , seed = 1, predictorMatrix = predmat, #' ignore = ifelse(dat$set == "test", TRUE, FALSE), print = FALSE) #' impdats <- complete(imp, "all") -#' +#' #' # extract the training and test data sets #' traindats <- lapply(impdats, function(dat) subset(dat, set == "train", select = -set)) #' testdats <- lapply(impdats, function(dat) subset(dat, set == "test", select = -c(set))) @@ -53,37 +53,44 @@ #' fits <- lapply(traindats, function(dat) lm(age ~ bmi + hyp + chl, data = dat)) #' #' # pool the predictions with function -#' pool_preds <- mice::predict_mi(object = fits, newdata = testdats, +#' pool_preds <- mice::predict_mi(object = fits, newdata = testdats, #' pool = TRUE, interval = "prediction", level = 0.95) #' @export -predict_mi <- function(object, - newdata, - pool = TRUE, - se.fit = FALSE, - interval = c("none", "confidence", "prediction"), - level = 0.95, - ...) { +predict_mi <- function( + object, + newdata, + pool = TRUE, + se.fit = FALSE, + interval = c("none", "confidence", "prediction"), + level = 0.95, + ... +) { UseMethod("predict_mi") } #' @export -predict_mi.mira <- function(object, - newdata, - pool = TRUE, - se.fit = FALSE, - interval = c("none", "confidence", "prediction"), - level = 0.95, - ...) { +predict_mi.mira <- function( + object, + newdata, + pool = TRUE, + se.fit = FALSE, + interval = c("none", "confidence", "prediction"), + level = 0.95, + ... +) { cl <- match.call() args <- list(...) - interval <- match.arg(interval, choices = c("none", "confidence", "prediction")) + interval <- match.arg( + interval, + choices = c("none", "confidence", "prediction") + ) if (missing(newdata)) { newdata <- NULL } if (!inherits(object$analyses[[1]], "lm")) { stop("`predict_mi()` currently only works with the linear model.") } - + if (!inherits(newdata, c("list", "mids"))) { fit <- lapply( object$analyses, @@ -114,10 +121,12 @@ predict_mi.mira <- function(object, ) } if (pool) { - preds <- pool_predictions(fit, - interval = interval, - level = level, - args = args) + preds <- pool_predictions( + fit, + interval = interval, + level = level, + args = args + ) if (!se.fit) { preds <- remove_sefit(preds) } @@ -135,21 +144,23 @@ predict_mi.mira <- function(object, } #' @export -predict_mi.list <- function(object, - newdata, - pool = TRUE, - se.fit = FALSE, - interval = c("none", "confidence", "prediction"), - level = 0.95, - ...) { +predict_mi.list <- function( + object, + newdata, + pool = TRUE, + se.fit = FALSE, + interval = c("none", "confidence", "prediction"), + level = 0.95, + ... +) { cl <- match.call() args <- list(...) if (missing(newdata)) { newdata <- NULL } - + object <- as.mira(object) - + predict_mi.mira( object, newdata = newdata, @@ -162,21 +173,25 @@ predict_mi.list <- function(object, } #' @export -predict_mi.lm <- function(object, - newdata, - pool = TRUE, - se.fit = FALSE, - interval = c("none", "confidence", "prediction"), - level = 0.95, - ...) { +predict_mi.lm <- function( + object, + newdata, + pool = TRUE, + se.fit = FALSE, + interval = c("none", "confidence", "prediction"), + level = 0.95, + ... +) { cl <- match.call() args <- list(...) - interval <- match.arg(interval, choices = c("none", "confidence", "prediction")) + interval <- match.arg( + interval, + choices = c("none", "confidence", "prediction") + ) if (missing(newdata)) { newdata <- NULL } - - + if (!inherits(newdata, c("list", "mids"))) { warning( "The model `object` nor the `newdata` are the result of multiple\nimputation. Returning regular predictions instead." @@ -196,10 +211,12 @@ predict_mi.lm <- function(object, ... ) if (pool) { - preds <- pool_predictions(fit, - interval = interval, - level = level, - args = args) + preds <- pool_predictions( + fit, + interval = interval, + level = level, + args = args + ) if (!se.fit) { # TODO: maybe only calculate se.fit if necessary (se.fit = TRUE / interval \in confidence or prediction) preds <- remove_sefit(preds) @@ -221,18 +238,19 @@ predict_mi.lm <- function(object, pool_predictions <- function(predlist, interval, level, args) { m <- length(predlist) argsnames <- names(args) - + type <- return_type(args) - + pooled <- predlist[[1]] - + n <- nrow(as.matrix(pooled$fit)) - nterms <- if (type == "terms") + nterms <- if (type == "terms") { ncol(pooled$fit) - else + } else { 1 + } df <- pooled$df - + params <- sapply( # stack predictions in array: 1st dim observations predlist, @@ -247,10 +265,11 @@ pool_predictions <- function(predlist, interval, level, args) { }, simplify = "array" ) - - ubar_pred <- mean(sapply(predlist, function(x) - x$residual.scale^2)) - + + ubar_pred <- mean(sapply(predlist, function(x) { + x$residual.scale^2 + })) + pooled_preds <- apply( params, c(1, 2), @@ -273,23 +292,25 @@ pool_predictions <- function(predlist, interval, level, args) { ) } ) - + if (!is.matrix(pooled$fit)) { pooled$fit <- pooled_preds[1, , ] } else { pooled$fit[, seq_len(nterms)] <- pooled_preds[1, , ] } - + if (type == "terms") { - attr(pooled$fit, "constant") <- mean(sapply(predlist, function(x) - attr(x$fit, "constant"))) + attr(pooled$fit, "constant") <- mean(sapply(predlist, function(x) { + attr(x$fit, "constant") + })) } - + pooled$se.fit <- sqrt(pooled_preds[2, , ]) pooled$df <- pooled_preds[3, , ] - pooled$residual.scale <- mean(sapply(predlist, function(x) - x$residual.scale)) - + pooled$residual.scale <- mean(sapply(predlist, function(x) { + x$residual.scale + })) + if (interval == "confidence") { tscale <- pooled_preds[2, , ] df_pred <- pooled_preds[3, , ] @@ -297,11 +318,11 @@ pool_predictions <- function(predlist, interval, level, args) { tscale <- pooled_preds[4, , ] df_pred <- pooled_preds[5, , ] } - + if (interval %in% c("confidence", "prediction")) { lwr <- pooled_preds[1, , ] - sqrt(tscale) * qt(1 - (1 - level) / 2, df_pred) upr <- pooled_preds[1, , ] + sqrt(tscale) * qt(1 - (1 - level) / 2, df_pred) - + if (type == "terms") { pooled$lwr <- lwr pooled$upr <- upr @@ -327,4 +348,4 @@ remove_sefit <- function(fit) { fit <- fit$fit } fit -} \ No newline at end of file +} diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 800ee865b..b17c53b7f 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -24,18 +24,19 @@ #' make.predictorMatrix(nhanes) #' make.predictorMatrix(nhanes, blocks = make.blocks(nhanes, "collect")) #' @export -make.predictorMatrix <- function(data, - selection = c("all", "correlation", "lars"), - ..., - blocks = make.blocks(data), - predictorMatrix = NULL) { +make.predictorMatrix <- function( + data, + selection = c("all", "correlation", "lars"), + ..., + blocks = make.blocks(data), + predictorMatrix = NULL +) { selection <- match.arg(selection) if (selection == "all") { return(make.default.predictorMatrix(data, blocks, predictorMatrix)) } if (selection == "correlation") { - return(quickpred(data, ...) - ) + return(quickpred(data, ...)) } if (selection == "lars") { return(larspred(data, ...)) @@ -62,9 +63,7 @@ make.default.predictorMatrix <- function(data, blocks, predictorMatrix) { return(predictorMatrix) } -check.predictorMatrix <- function(predictorMatrix, - data, - blocks = NULL) { +check.predictorMatrix <- function(predictorMatrix, data, blocks = NULL) { data <- check.dataform(data) if (!is.matrix(predictorMatrix)) { @@ -93,7 +92,10 @@ check.predictorMatrix <- function(predictorMatrix, } } for (i in row.names(predictorMatrix)) { - predictorMatrix[i, grep(paste0("^", i, "$"), colnames(predictorMatrix))] <- 0 + predictorMatrix[ + i, + grep(paste0("^", i, "$"), colnames(predictorMatrix)) + ] <- 0 } return(predictorMatrix) } @@ -102,16 +104,21 @@ check.predictorMatrix <- function(predictorMatrix, if (nrow(predictorMatrix) > length(blocks)) { stop( paste0( - "predictorMatrix has more rows (", nrow(predictorMatrix), - ") than blocks (", length(blocks), ")" + "predictorMatrix has more rows (", + nrow(predictorMatrix), + ") than blocks (", + length(blocks), + ")" ), call. = FALSE ) } # borrow rownames from blocks if needed - if (is.null(rownames(predictorMatrix)) && - nrow(predictorMatrix) == length(blocks)) { + if ( + is.null(rownames(predictorMatrix)) && + nrow(predictorMatrix) == length(blocks) + ) { rownames(predictorMatrix) <- names(blocks) } if (is.null(rownames(predictorMatrix))) { @@ -119,8 +126,10 @@ check.predictorMatrix <- function(predictorMatrix, } # borrow blocknames from predictorMatrix if needed - if (is.null(names(blocks)) && - nrow(predictorMatrix) == length(blocks)) { + if ( + is.null(names(blocks)) && + nrow(predictorMatrix) == length(blocks) + ) { names(blocks) <- rownames(predictorMatrix) } if (is.null(names(blocks))) { @@ -130,15 +139,18 @@ check.predictorMatrix <- function(predictorMatrix, # check existence of row names in blocks found <- rownames(predictorMatrix) %in% names(blocks) if (!all(found)) { - stop("Names not found in blocks: ", - paste(rownames(predictorMatrix)[!found], collapse = ", "), - call. = FALSE + stop( + "Names not found in blocks: ", + paste(rownames(predictorMatrix)[!found], collapse = ", "), + call. = FALSE ) } # borrow colnames from data if needed - if (is.null(colnames(predictorMatrix)) && - ncol(predictorMatrix) == ncol(data)) { + if ( + is.null(colnames(predictorMatrix)) && + ncol(predictorMatrix) == ncol(data) + ) { colnames(predictorMatrix) <- names(data) } if (is.null(colnames(predictorMatrix))) { @@ -148,9 +160,10 @@ check.predictorMatrix <- function(predictorMatrix, # check existence of variable names on data found <- colnames(predictorMatrix) %in% names(data) if (!all(found)) { - stop("Names not found in data: ", - paste(colnames(predictorMatrix)[!found], collapse = ", "), - call. = FALSE + stop( + "Names not found in data: ", + paste(colnames(predictorMatrix)[!found], collapse = ", "), + call. = FALSE ) } @@ -160,17 +173,24 @@ check.predictorMatrix <- function(predictorMatrix, ) } -mice.edit.predictorMatrix <- function(predictorMatrix, - visitSequence, - user.visitSequence, - maxit) { +mice.edit.predictorMatrix <- function( + predictorMatrix, + visitSequence, + user.visitSequence, + maxit +) { # edit predictorMatrix to a monotone pattern - if (maxit == 1L && + if ( + maxit == 1L && !is.null(user.visitSequence) && length(user.visitSequence) == 1 && - user.visitSequence == "monotone") { + user.visitSequence == "monotone" + ) { for (i in 1L:length(visitSequence)) { - predictorMatrix[visitSequence[i], visitSequence[i:length(visitSequence)]] <- 0 + predictorMatrix[ + visitSequence[i], + visitSequence[i:length(visitSequence)] + ] <- 0 } } predictorMatrix diff --git a/R/print.R b/R/print.R index e0bccaa46..7a577de9f 100644 --- a/R/print.R +++ b/R/print.R @@ -1,5 +1,3 @@ - - #' Print a \code{mira} object #' #' @rdname print @@ -48,10 +46,14 @@ print.mice.anova.summary <- function(x, ...) { cat("\nComparisons:\n") print(x$comparisons, row.names = FALSE) cat( - "\nNumber of imputations: ", x$m, - " Method", x$method + "\nNumber of imputations: ", + x$m, + " Method", + x$method ) - if (x$method == "D2") cat(" (", x$use, ")", sep = "") + if (x$method == "D2") { + cat(" (", x$use, ")", sep = "") + } cat("\n") invisible(x) } diff --git a/R/quantify.R b/R/quantify.R index 106f7e808..fb0c1c57c 100644 --- a/R/quantify.R +++ b/R/quantify.R @@ -40,15 +40,11 @@ #' @export quantify <- function(y, ry, x, quantify = TRUE) { if (!is.factor(y)) { - return(list(ynum = y, - labels = NULL, - quant = NULL)) + return(list(ynum = y, labels = NULL, quant = NULL)) } if (!quantify) { ynum <- as.integer(y) - return(list(ynum = ynum, - labels = levels(y), - quant = 1L:length(levels(y)))) + return(list(ynum = ynum, labels = levels(y), quant = 1L:length(levels(y)))) } # replace (reduced set of) categories by optimal scaling @@ -59,9 +55,7 @@ quantify <- function(y, ry, x, quantify = TRUE) { quant <- as.vector(cca$ycoef[, 2L]) quant_expand <- quant[match(levels(y), levels(yf))] ynum <- quant_expand[match(as.character(y), levels(y))] - return(list(ynum = ynum, - labels = levels(y), - quant = quant_expand)) + return(list(ynum = ynum, labels = levels(y), quant = quant_expand)) } #' Revert quantified variables back to factor representation @@ -99,14 +93,22 @@ quantify <- function(y, ry, x, quantify = TRUE) { #' @seealso [quantify()] #' @export unquantify <- function(ynum = NULL, quant = NULL, labels = NULL) { - if (is.null(labels)) return(ynum) + if (is.null(labels)) { + return(ynum) + } - closest <- vapply(seq_along(ynum), function(i) { - y <- ynum[i] - if (is.na(y)) return(NA_character_) - i_match <- which.min(abs(y - quant)) - labels[i_match] - }, character(1)) + closest <- vapply( + seq_along(ynum), + function(i) { + y <- ynum[i] + if (is.na(y)) { + return(NA_character_) + } + i_match <- which.min(abs(y - quant)) + labels[i_match] + }, + character(1) + ) factor(closest, levels = labels) } diff --git a/R/quickpred.R b/R/quickpred.R index c28c82374..859b40760 100644 --- a/R/quickpred.R +++ b/R/quickpred.R @@ -86,25 +86,38 @@ #' # use it directly from mice #' imp <- mice(nhanes, pred = quickpred(nhanes, minpuc = 0.25, include = "age")) #' @export -quickpred <- function(data, mincor = 0.1, minpuc = 0, include = "", - exclude = "", method = "pearson", ...) { +quickpred <- function( + data, + mincor = 0.1, + minpuc = 0, + include = "", + exclude = "", + method = "pearson", + ... +) { data <- check.dataform(data) # initialize nvar <- ncol(data) - predictorMatrix <- matrix(0, nrow = nvar, ncol = nvar, - dimnames = list(names(data), names(data))) + predictorMatrix <- matrix( + 0, + nrow = nvar, + ncol = nvar, + dimnames = list(names(data), names(data)) + ) x <- data.matrix(data) r <- !is.na(x) # include predictors with # 1) pairwise correlation among data # 2) pairwise correlation of data with response indicator higher than mincor - suppressWarnings(v <- abs(cor(x, use = "pairwise.complete.obs", - method = method))) + suppressWarnings( + v <- abs(cor(x, use = "pairwise.complete.obs", method = method)) + ) v[is.na(v)] <- 0 - suppressWarnings(u <- abs(cor(y = x, x = r, use = "pairwise.complete.obs", - method = method))) + suppressWarnings( + u <- abs(cor(y = x, x = r, use = "pairwise.complete.obs", method = method)) + ) u[is.na(u)] <- 0 maxc <- pmax(v, u) predictorMatrix[maxc > mincor] <- 1 diff --git a/R/rbind.R b/R/rbind.R index eb10e6a44..d53e9a2c9 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -87,12 +87,15 @@ rbind.mids <- function(x, y = NULL, ...) { lastSeedValue = lastSeedValue, chainMean = chainMean, chainVar = chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents + ) return(midsobj) } rbind.mids.mids <- function(x, y, call) { - if (!is.mids(y)) stop("argument `y` not a mids object") + if (!is.mids(y)) { + stop("argument `y` not a mids object") + } if (ncol(y$data) != ncol(x$data)) { stop("datasets have different number of columns") @@ -157,7 +160,8 @@ rbind.mids.mids <- function(x, y, call) { iteration <- x$iteration if (x$iteration != y$iteration) { - warning("iterations differ, so no convergence diagnostics calculated", + warning( + "iterations differ, so no convergence diagnostics calculated", call. = FALSE ) chainMean <- NULL @@ -193,6 +197,7 @@ rbind.mids.mids <- function(x, y, call) { lastSeedValue = lastSeedValue, chainMean = chainMean, chainVar = chainVar, - loggedEvents = loggedEvents) + loggedEvents = loggedEvents + ) return(midsobj) } diff --git a/R/rm.whitespace.R b/R/rm.whitespace.R index 20fa4e4cc..e26dd1de1 100644 --- a/R/rm.whitespace.R +++ b/R/rm.whitespace.R @@ -2,10 +2,6 @@ rm.whitespace <- function(string, side = "both") { side <- match.arg(side, c("left", "right", "both")) - pattern <- switch(side, - left = "^\\s+", - right = "\\s+$", - both = "^\\s+|\\s+$" - ) + pattern <- switch(side, left = "^\\s+", right = "\\s+$", both = "^\\s+|\\s+$") sub(pattern, "", string) } diff --git a/R/sampler.R b/R/sampler.R index 763622aad..26836fca1 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -1,8 +1,26 @@ -sampler <- function(data, m, ignore, where, imp, blocks, method, - visitSequence, predictorMatrix, formulas, - calltypes, blots, tasks, models, - post, fromto, printFlag, ..., - parallel = FALSE, future.packages = NULL, future.seed = TRUE) { +sampler <- function( + data, + m, + ignore, + where, + imp, + blocks, + method, + visitSequence, + predictorMatrix, + formulas, + calltypes, + blots, + tasks, + models, + post, + fromto, + printFlag, + ..., + parallel = FALSE, + future.packages = NULL, + future.seed = TRUE +) { from <- fromto[1] to <- fromto[2] maxit <- to - from + 1 @@ -12,7 +30,9 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, chainMean <- chainVar <- initialize.chain(names(data), maxit, m) ## THE MAIN LOOP: GIBBS SAMPLER ## - if (maxit < 1) iteration <- 0 + if (maxit < 1) { + iteration <- 0 + } if (maxit >= 1) { if (!parallel && printFlag) { cat("\n iter imp variable") @@ -44,79 +64,135 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } } - result <- one.cycle(data, imp, r, where, i, k, visitSequence, - blocks, method, calltypes, formulas, - predictorMatrix, blots, - tasks, models, - post, ignore, printFlag, ...) + result <- one.cycle( + data, + imp, + r, + where, + i, + k, + visitSequence, + blocks, + method, + calltypes, + formulas, + predictorMatrix, + blots, + tasks, + models, + post, + ignore, + printFlag, + ... + ) data <- result$data imp <- result$imp } k2 <- k - from + 1L - stat <- get.chain.stats(data, imp, chainMean, chainVar, k2, m, blocks, visitSequence) + stat <- get.chain.stats( + data, + imp, + chainMean, + chainVar, + k2, + m, + blocks, + visitSequence + ) chainMean <- stat$chainMean chainVar <- stat$chainVar - } else { # parallel processing with future.apply - results_i <- future.apply::future_lapply(seq_len(m), function(i) { - emit_worker_log <- function(log_entry, file) { - saveRDS(log_entry, file = file) - } - - data_i <- data - imp_i <- imp - - # loop over blocks - for (h in visitSequence) { - for (j in blocks[[h]]) { - y <- data_i[, j] - ry <- r[, j] - wy <- where[, j] - data_i[(!ry) & wy, j] <- imp_i[[j]][(!ry)[wy], i] + results_i <- future.apply::future_lapply( + seq_len(m), + function(i) { + emit_worker_log <- function(log_entry, file) { + saveRDS(log_entry, file = file) } - } - result <- one.cycle(data_i, imp_i, r, where, i, k, visitSequence, - blocks, method, calltypes, formulas, - predictorMatrix, blots, - tasks, models, - post, ignore, printFlag = FALSE, ...) + data_i <- data + imp_i <- imp - data_i <- result$data - imp_i <- result$imp - mean_i <- initialize.chain(names(data), 1, 1)[[1]] - var_i <- initialize.chain(names(data), 1, 1)[[1]] - for (h in visitSequence) { - for (j in blocks[[h]]) { - if (!is.factor(data[, j])) { - var_i[j] <- var(imp_i[[j]][, i], na.rm = TRUE) - mean_i[j] <- mean(imp_i[[j]][, i], na.rm = TRUE) - } else { - nc <- as.integer(factor(imp_i[[j]][, i], levels = levels(data[, j]))) - var_i[j] <- var(nc, na.rm = TRUE) - mean_i[j] <- mean(nc, na.rm = TRUE) + # loop over blocks + for (h in visitSequence) { + for (j in blocks[[h]]) { + y <- data_i[, j] + ry <- r[, j] + wy <- where[, j] + data_i[(!ry) & wy, j] <- imp_i[[j]][(!ry)[wy], i] } } - } - # Create a log record - log.entry <- data.frame( - it = k, im = i, dep = "cycle", meth = NA_character_, - out = "success", msg = "I1001", fn = "one.cycle", - stringsAsFactors = FALSE - ) - - logfile <- file.path(tempdir(), sprintf("log_it%02d_im%02d.rds", k, i)) - emit_worker_log(log.entry, logfile) + result <- one.cycle( + data_i, + imp_i, + r, + where, + i, + k, + visitSequence, + blocks, + method, + calltypes, + formulas, + predictorMatrix, + blots, + tasks, + models, + post, + ignore, + printFlag = FALSE, + ... + ) + + data_i <- result$data + imp_i <- result$imp + mean_i <- initialize.chain(names(data), 1, 1)[[1]] + var_i <- initialize.chain(names(data), 1, 1)[[1]] + for (h in visitSequence) { + for (j in blocks[[h]]) { + if (!is.factor(data[, j])) { + var_i[j] <- var(imp_i[[j]][, i], na.rm = TRUE) + mean_i[j] <- mean(imp_i[[j]][, i], na.rm = TRUE) + } else { + nc <- as.integer(factor( + imp_i[[j]][, i], + levels = levels(data[, j]) + )) + var_i[j] <- var(nc, na.rm = TRUE) + mean_i[j] <- mean(nc, na.rm = TRUE) + } + } + } - list(imp = imp_i, mean = mean_i, var = var_i) - }, - future.packages = future.packages, - future.globals = list(initialize.chain = initialize.chain, - one.cycle = one.cycle, - get.chain.stats = get.chain.stats), - future.seed = future.seed) + # Create a log record + log.entry <- data.frame( + it = k, + im = i, + dep = "cycle", + meth = NA_character_, + out = "success", + msg = "I1001", + fn = "one.cycle", + stringsAsFactors = FALSE + ) + + logfile <- file.path( + tempdir(), + sprintf("log_it%02d_im%02d.rds", k, i) + ) + emit_worker_log(log.entry, logfile) + + list(imp = imp_i, mean = mean_i, var = var_i) + }, + future.packages = future.packages, + future.globals = list( + initialize.chain = initialize.chain, + one.cycle = one.cycle, + get.chain.stats = get.chain.stats + ), + future.seed = future.seed + ) # Combine with existing log if needed new.loggedEvents <- collect_logs() @@ -128,7 +204,9 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, } t1 <- Sys.time() - if (printFlag) cat(sprintf("\n iter %d (%s)", k, format(round(difftime(t1, t0), 2)))) + if (printFlag) { + cat(sprintf("\n iter %d (%s)", k, format(round(difftime(t1, t0), 2)))) + } k2 <- k - from + 1L for (i in seq_len(m)) { @@ -137,7 +215,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, imp[[j]][, i] <- imp_i[[j]][, i] } mean_i <- results_i[[i]]$mean - var_i <- results_i[[i]]$var + var_i <- results_i[[i]]$var for (j in names(mean_i)) { if (j %in% dimnames(chainMean)[[1]]) { chainMean[j, k2, i] <- mean_i[[j]] @@ -160,9 +238,27 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, list(iteration = maxit, imp = imp, chainMean = chainMean, chainVar = chainVar) } -one.cycle <- function(data, imp, r, where, i, k, visitSequence, - blocks, method, calltypes, formulas, predictorMatrix, - blots, tasks, models, post, ignore, printFlag, ...) { +one.cycle <- function( + data, + imp, + r, + where, + i, + k, + visitSequence, + blocks, + method, + calltypes, + formulas, + predictorMatrix, + blots, + tasks, + models, + post, + ignore, + printFlag, + ... +) { # this function makes one pass through the data # impute block-by-block @@ -176,12 +272,16 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, # univariate/multivariate logic theMethod <- method[h] empt <- theMethod == "" - univ <- !empt && !is.passive(theMethod) && + univ <- !empt && + !is.passive(theMethod) && !handles.format(paste0("mice.impute.", theMethod)) - mult <- !empt && !is.passive(theMethod) && + mult <- !empt && + !is.passive(theMethod) && handles.format(paste0("mice.impute.", theMethod)) pass <- !empt && is.passive(theMethod) && length(blocks[[h]]) == 1 - if (printFlag & !empt) cat(" ", b) + if (printFlag & !empt) { + cat(" ", b) + } # (repeated) univariate imputation - pred method if (univ) { @@ -193,14 +293,19 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, mod <- (i - 1L) %% m.train + 1L imp[[j]][, i] <- sampler.univ( - data = data, r = r, where = where, - pred = pred, formula = ff, + data = data, + r = r, + where = where, + pred = pred, + formula = ff, method = theMethod, task = tasks[j], model = models[[j]][[as.character(mod)]], - yname = j, k = k, + yname = j, + k = k, calltype = calltype, - user = user, ignore = ignore, + user = user, + ignore = ignore, ... ) @@ -227,10 +332,12 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, data[mis] <- NA fm <- paste("mice.impute", theMethod, sep = ".") - imputes <- switch(calltype, - formula = do.call(fm, list(data = data, formula = ff, ...)), - pred = do.call(fm, list(data = data, type = pred, ...)), - stop("Cannot call function of type ", calltype)) + imputes <- switch( + calltype, + formula = do.call(fm, list(data = data, formula = ff, ...)), + pred = do.call(fm, list(data = data, type = pred, ...)), + stop("Cannot call function of type ", calltype) + ) # Abort if imputes is NULL if (is.null(imputes)) { @@ -251,8 +358,11 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, newstate <- list(it = k, im = i, dep = b, meth = theMethod) wy <- where[, j] ry <- r[, j] - imp[[j]][, i] <- model.frame(as.formula(theMethod), data[wy, ], - na.action = na.pass) + imp[[j]][, i] <- model.frame( + as.formula(theMethod), + data[wy, ], + na.action = na.pass + ) data[(!ry) & wy, j] <- imp[[j]][(!ry)[wy], i] } } @@ -261,7 +371,16 @@ one.cycle <- function(data, imp, r, where, i, k, visitSequence, list(data = data, imp = imp) } -get.chain.stats <- function(data, imp, chainMean, chainVar, k2, m, blocks, visitSequence) { +get.chain.stats <- function( + data, + imp, + chainMean, + chainVar, + k2, + m, + blocks, + visitSequence +) { # Updates mean and variance of imputed values for (h in visitSequence) { for (j in blocks[[h]]) { @@ -280,12 +399,18 @@ get.chain.stats <- function(data, imp, chainMean, chainVar, k2, m, blocks, visit list(chainMean = chainMean, chainVar = chainVar) } -collect_logs <- function(path = tempdir(), pattern = "^log_it\\d+_im\\d+\\.rds$", - remove = TRUE, verbose = FALSE) { +collect_logs <- function( + path = tempdir(), + pattern = "^log_it\\d+_im\\d+\\.rds$", + remove = TRUE, + verbose = FALSE +) { log_files <- list.files(path, pattern = pattern, full.names = TRUE) if (length(log_files) == 0L) { - if (verbose) message("No log files found.") + if (verbose) { + message("No log files found.") + } return(NULL) } @@ -293,7 +418,9 @@ collect_logs <- function(path = tempdir(), pattern = "^log_it\\d+_im\\d+\\.rds$" tryCatch( readRDS(file), error = function(e) { - if (verbose) warning("Failed to read log file: ", file) + if (verbose) { + warning("Failed to read log file: ", file) + } NULL } ) diff --git a/R/sampler.univ.R b/R/sampler.univ.R index bc0856696..9f29b01b9 100644 --- a/R/sampler.univ.R +++ b/R/sampler.univ.R @@ -1,6 +1,20 @@ -sampler.univ <- function(data, r, where, pred, formula, method, task, model, - yname, k, calltype = "pred", user, ignore, - trimmer = "lindep", ...) { +sampler.univ <- function( + data, + r, + where, + pred, + formula, + method, + task, + model, + yname, + k, + calltype = "pred", + user, + ignore, + trimmer = "lindep", + ... +) { j <- yname[1L] # nothing to impute @@ -29,7 +43,8 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, y = data[, j], ry = r[, j] & !ignore, x = x, - trimmer = trimmer, ... + trimmer = trimmer, + ... ) # store the names of the features @@ -52,7 +67,8 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, y = data[, j], ry = r[, j] & !ignore, x = x, - trimmer = trimmer, ... + trimmer = trimmer, + ... ) } @@ -80,8 +96,11 @@ sampler.univ <- function(data, r, where, pred, formula, method, task, model, wy = wy, type = type[keep$cols], task = task, - model = model), - user, list(...)) + model = model + ), + user, + list(...) + ) imputes[iy] <- do.call(f, args = args) return(imputes) } @@ -116,7 +135,10 @@ prepare.formula <- function(formula, data, model, j, ct, pred, task) { ymove <- setdiff(lhs(formula), j) formula <- update(formula, paste(j, " ~ . ")) if (length(ymove) > 0L) { - formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) + formula <- update( + formula, + paste("~ . + ", paste(backticks(ymove), collapse = "+")) + ) } } diff --git a/R/squeeze.R b/R/squeeze.R index 6cb4d7d8c..4d7ec5eff 100644 --- a/R/squeeze.R +++ b/R/squeeze.R @@ -13,8 +13,11 @@ #' @return A vector of length \code{length(x)}. #' @author Stef van Buuren, 2011. #' @export -squeeze <- function(x, bounds = c(min(x[r]), max(x[r])), - r = rep.int(TRUE, length(x))) { +squeeze <- function( + x, + bounds = c(min(x[r]), max(x[r])), + r = rep.int(TRUE, length(x)) +) { if (length(r) != length(x)) { stop("Different length of vectors x and r") } diff --git a/R/stripplot.mids.R b/R/stripplot.mids.R index 0ac17bb72..bfe057c80 100644 --- a/R/stripplot.mids.R +++ b/R/stripplot.mids.R @@ -173,24 +173,28 @@ #' @aliases stripplot.mids stripplot #' @method stripplot mids #' @export -stripplot.mids <- function(x, - data, - na.groups = NULL, - groups = NULL, - as.table = TRUE, - theme = mice.theme(), - allow.multiple = TRUE, - outer = TRUE, - drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), - panel = lattice::lattice.getOption("panel.stripplot"), - default.prepanel = lattice::lattice.getOption("prepanel.default.stripplot"), - jitter.data = TRUE, - horizontal = FALSE, - ..., - subscripts = TRUE, - subset = TRUE) { +stripplot.mids <- function( + x, + data, + na.groups = NULL, + groups = NULL, + as.table = TRUE, + theme = mice.theme(), + allow.multiple = TRUE, + outer = TRUE, + drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), + panel = lattice::lattice.getOption("panel.stripplot"), + default.prepanel = lattice::lattice.getOption("prepanel.default.stripplot"), + jitter.data = TRUE, + horizontal = FALSE, + ..., + subscripts = TRUE, + subset = TRUE +) { call <- match.call() - if (!is.mids(x)) stop("Argument 'x' must be a 'mids' object") + if (!is.mids(x)) { + stop("Argument 'x' must be a 'mids' object") + } ## unpack data and response indicator cd <- data.frame(complete(x, "long", include = TRUE)) @@ -198,16 +202,22 @@ stripplot.mids <- function(x, ## evaluate na.group in response indicator nagp <- eval(expr = substitute(na.groups), envir = r, enclos = parent.frame()) - if (is.expression(nagp)) nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + if (is.expression(nagp)) { + nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + } ## evaluate groups in imputed data ngp <- eval(expr = substitute(groups), envir = cd, enclos = parent.frame()) - if (is.expression(ngp)) ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + if (is.expression(ngp)) { + ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + } groups <- ngp ## evaluate subset in imputed data ss <- eval(expr = substitute(subset), envir = cd, enclos = parent.frame()) - if (is.expression(ss)) ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + if (is.expression(ss)) { + ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + } subset <- ss ## evaluate further arguments before parsing @@ -229,7 +239,10 @@ stripplot.mids <- function(x, allfactors <- unlist(lapply(cd[vnames], is.factor)) if (missing(data)) { vnames <- vnames[!allfactors] - formula <- as.formula(paste0(paste0(vnames, collapse = "+"), "~ as.factor(.imp)")) + formula <- as.formula(paste0( + paste0(vnames, collapse = "+"), + "~ as.factor(.imp)" + )) } else { ## pad abbreviated formula abbrev <- !any(grepl("~", call$data)) @@ -246,9 +259,13 @@ stripplot.mids <- function(x, ## determine the y-variables form <- lattice::latticeParseFormula( - model = formula, data = cd, subset = subset, - groups = groups, multiple = allow.multiple, - outer = outer, subscripts = TRUE, + model = formula, + data = cd, + subset = subset, + groups = groups, + multiple = allow.multiple, + outer = outer, + subscripts = TRUE, drop = drop.unused.levels ) ynames <- unlist(lapply(strsplit(form$left.name, " \\+ "), rm.whitespace)) @@ -279,15 +296,21 @@ stripplot.mids <- function(x, if (is.null(call$scales)) { args$scales <- list() if (length(ynames) > 1) { - args$scales <- list(x = list(relation = "free"), y = list(relation = "free")) + args$scales <- list( + x = list(relation = "free"), + y = list(relation = "free") + ) } } ## ready args <- c( - x = formula, data = list(cd), + x = formula, + data = list(cd), groups = list(gp), - args, dots, subset = call$subset + args, + dots, + subset = call$subset ) ## go diff --git a/R/summary.R b/R/summary.R index 7f214b79c..45044a5b3 100644 --- a/R/summary.R +++ b/R/summary.R @@ -16,14 +16,17 @@ #' @seealso \code{\link{mira}} #' @method summary mira #' @export -summary.mira <- function(object, - type = c("tidy", "glance", "summary"), - dfcom = NULL, - ...) { +summary.mira <- function( + object, + type = c("tidy", "glance", "summary"), + dfcom = NULL, + ... +) { type <- match.arg(type) fitlist <- getfit(object) if (type == "tidy") { - v <- lapply(fitlist, tidy, effects = "fixed", parametric = TRUE, ...) %>% bind_rows() + v <- lapply(fitlist, tidy, effects = "fixed", parametric = TRUE, ...) %>% + bind_rows() } if (type == "glance") { v <- lapply(fitlist, glance, ...) %>% bind_rows() @@ -32,8 +35,9 @@ summary.mira <- function(object, # not supplied by broom <= 0.5.6 model <- getfit(object, 1L) if (!"nobs" %in% colnames(v)) { - v$nobs <- tryCatch(length(stats::residuals(model)), - error = function(e) {NULL}) + v$nobs <- tryCatch(length(stats::residuals(model)), error = function(e) { + NULL + }) } # get df.residuals @@ -65,10 +69,12 @@ summary.mice.anova <- function(object, ...) { # handle objects from D1, D2 and D3 if (is.null(out)) { - out <- list(`1 ~~ 2` = list( - result = object$result, - dfcom = object$dfcom - )) + out <- list( + `1 ~~ 2` = list( + result = object$result, + dfcom = object$dfcom + ) + ) } test <- names(out) @@ -93,8 +99,11 @@ summary.mice.anova <- function(object, ...) { structure( list( - models = ff, comparisons = rf, - m = object$m, method = object$method, use = object$use + models = ff, + comparisons = rf, + m = object$m, + method = object$method, + use = object$use ), class = c("mice.anova.summary", class(object)) ) diff --git a/R/tasks.R b/R/tasks.R index cecf02626..ae3c41d88 100644 --- a/R/tasks.R +++ b/R/tasks.R @@ -9,15 +9,16 @@ #' @examples #' make.tasks(nhanes2) #' @export -make.tasks <- function(data, - tasks = "impute", - blocks = make.blocks(data)) { +make.tasks <- function(data, tasks = "impute", blocks = make.blocks(data)) { bv <- unique(unlist(blocks)) if (length(tasks) == 1L) { tasks <- setNames(rep(tasks, length(bv)), bv) } else { if (length(tasks) != length(bv)) { - stop("length(tasks) does not match variables to be imputed", call. = FALSE) + stop( + "length(tasks) does not match variables to be imputed", + call. = FALSE + ) } names(tasks) <- bv } diff --git a/R/tidiers.R b/R/tidiers.R index cfded3f68..fa7534376 100644 --- a/R/tidiers.R +++ b/R/tidiers.R @@ -29,15 +29,20 @@ broom::glance #' \item conf.high (if called with conf.int = TRUE) #' } tidy.mipo <- function(x, conf.int = FALSE, conf.level = .95, ...) { - out <- summary(x, + out <- summary( + x, type = "all", conf.int = conf.int, conf.level = conf.level, ... ) - if ("term" %in% names(out)) out$term <- as.character(out$term) - if ("contrast" %in% names(out)) out$contrast <- as.character(out$contrast) + if ("term" %in% names(out)) { + out$term <- as.character(out$term) + } + if ("contrast" %in% names(out)) { + out$contrast <- as.character(out$contrast) + } # needed for broom <= 0.5.6 # rename variables if present @@ -48,8 +53,13 @@ tidy.mipo <- function(x, conf.int = FALSE, conf.level = .95, ...) { # order columns cols_a <- c( - "term", "estimate", "std.error", "statistic", "p.value", - "conf.low", "conf.high" + "term", + "estimate", + "std.error", + "statistic", + "p.value", + "conf.low", + "conf.high" ) cols_a <- base::intersect(cols_a, colnames(out)) cols_b <- sort(base::setdiff(colnames(out), cols_a)) @@ -71,15 +81,15 @@ tidy.mipo <- function(x, conf.int = FALSE, conf.level = .95, ...) { #' @family tidiers glance.mipo <- function(x, ...) { out <- data.frame(nimp = nrow(x$glanced)) - out$nobs <- tryCatch(x$glanced$nobs[1], - error = function(e) NULL - ) + out$nobs <- tryCatch(x$glanced$nobs[1], error = function(e) NULL) # R2 in lm models - out$r.squared <- tryCatch(pool.r.squared(x, adjusted = FALSE)[1], + out$r.squared <- tryCatch( + pool.r.squared(x, adjusted = FALSE)[1], error = function(e) NULL ) - out$adj.r.squared <- tryCatch(pool.r.squared(x, adjusted = TRUE)[1], + out$adj.r.squared <- tryCatch( + pool.r.squared(x, adjusted = TRUE)[1], error = function(e) NULL ) diff --git a/R/trim.data.R b/R/trim.data.R index bbd3c4256..071c9348a 100644 --- a/R/trim.data.R +++ b/R/trim.data.R @@ -63,17 +63,21 @@ #' imp <- mice(nhanes, m = 1, maxit = 1, print = FALSE, trimmer = "") #' @export trim.data <- function( - y, ry, x, trimmer = "lindep", allow.na = TRUE, ...) { + y, + ry, + x, + trimmer = "lindep", + allow.na = TRUE, + ... +) { stopifnot(is.matrix(x), is.logical(ry)) stopifnot(length(y) == length(ry), nrow(x) == length(y)) # handle exceptions to bypass trimming - if (allow.na && sum(ry) == 0L || - sum(ry) <= 1L || - ncol(x) < 1L || - trimmer == "") { - keep <- list(rows = ry & complete.cases(x, y), - cols = !logical(ncol(x))) + if ( + allow.na && sum(ry) == 0L || sum(ry) <= 1L || ncol(x) < 1L || trimmer == "" + ) { + keep <- list(rows = ry & complete.cases(x, y), cols = !logical(ncol(x))) } else if (trimmer == "lindep") { keep <- mice.trim.lindep(y, ry, x, ...) } else if (trimmer == "lars") { @@ -106,8 +110,14 @@ mice.trim.lindep <- function(y, ry, x, frame = 5, ...) { keep <- list( rows = complete.cases(x, y) & ry, - cols = remove.lindep(x, y, ry, frame = frame, - expects.complete.x = FALSE, ...) + cols = remove.lindep( + x, + y, + ry, + frame = frame, + expects.complete.x = FALSE, + ... + ) ) return(keep) } @@ -145,10 +155,16 @@ mice.trim.lindep <- function(y, ry, x, frame = 5, ...) { #' @rdname trim.data #' @export mice.trim.lars <- function( - y, ry, x, - lars.type = c("lar", "lasso", "forward.stagewise", "stepwise"), - lars.eps = 1e-12, - max.predictors = NULL, lars.relax = 5, minimal.cp = 1, ...) { + y, + ry, + x, + lars.type = c("lar", "lasso", "forward.stagewise", "stepwise"), + lars.eps = 1e-12, + max.predictors = NULL, + lars.relax = 5, + minimal.cp = 1, + ... +) { lars.type <- match.arg(lars.type) keep <- trim.preprocess(y, ry, x) @@ -159,8 +175,13 @@ mice.trim.lars <- function( xobs <- x[keep$rows, keep$cols, drop = FALSE] max.steps <- ifelse(is.null(max.predictors), ncol(xobs), max.predictors) - model <- lars(x = xobs, y = yobs, type = lars.type, eps = lars.eps, - max.steps = max.steps) + model <- lars( + x = xobs, + y = yobs, + type = lars.type, + eps = lars.eps, + max.steps = max.steps + ) if (any(model$R2 == 1)) { # work-around because Cp gives NaN for perfect fits coef_step <- coef(model, s = which(model$R2 == 1)) @@ -184,7 +205,6 @@ mice.trim.lars <- function( #' @rdname trim.data #' @export mice.trim.glmnet <- function(y, ry, x, dfmax = NULL, ...) { - keep <- trim.preprocess(y, ry, x) if (!any(keep$cols) || !any(keep$rows)) { return(keep) @@ -211,7 +231,6 @@ mice.trim.glmnet <- function(y, ry, x, dfmax = NULL, ...) { #' @rdname trim.data #' @export mice.trim.cv.glmnet <- function(y, ry, x, dfmax = NULL, ...) { - keep <- trim.preprocess(y, ry, x) if (!any(keep$cols) || !any(keep$rows)) { return(keep) @@ -243,8 +262,7 @@ trim.preprocess <- function(y, ry, x) { cols <- logical(ncol(x)) } - return(list(rows = ry & complete.cases(x, y), - cols = cols)) + return(list(rows = ry & complete.cases(x, y), cols = cols)) } #' Filter out constant and multi-collinear predictors before imputation @@ -272,9 +290,16 @@ trim.preprocess <- function(y, ry, x) { #' conservative filter than \code{trim.lars()}. #' @rdname trim.data #' @export -remove.lindep <- function(x, y, ry, eps = 1e-04, maxcor = 0.99, - frame = 4, expects.complete.x = TRUE, ...) { - +remove.lindep <- function( + x, + y, + ry, + eps = 1e-04, + maxcor = 0.99, + frame = 4, + expects.complete.x = TRUE, + ... +) { # handle.incomplete is a flag to indicate what to do with incomplete x if (expects.complete.x) { # classic remove.lindep diff --git a/R/validate.arguments.R b/R/validate.arguments.R index c105f40fb..648a2f0ed 100644 --- a/R/validate.arguments.R +++ b/R/validate.arguments.R @@ -1,5 +1,11 @@ -validate.arguments <- function(y, ry, x, wy, allow.x.NULL = FALSE, - allow.x.NA = FALSE) { +validate.arguments <- function( + y, + ry, + x, + wy, + allow.x.NULL = FALSE, + allow.x.NA = FALSE +) { # validate standard arguments of mice.impute functions if (!allow.x.NULL && is.null(x)) { stop("Cannot handle NULL value for `x`") diff --git a/R/visitSequence.R b/R/visitSequence.R index dd0a1443a..b203e6913 100644 --- a/R/visitSequence.R +++ b/R/visitSequence.R @@ -20,8 +20,12 @@ make.visitSequence <- function(data = NULL, blocks = NULL) { names(blocks) } -check.visitSequence <- function(visitSequence = NULL, - data, where = NULL, blocks) { +check.visitSequence <- function( + visitSequence = NULL, + data, + where = NULL, + blocks +) { if (is.null(names(blocks)) || any(is.na(names(blocks)))) { stop("Missing names in `blocks`.") } @@ -30,15 +34,21 @@ check.visitSequence <- function(visitSequence = NULL, return(make.visitSequence(data, blocks)) } - if (is.null(where)) where <- is.na(data) + if (is.null(where)) { + where <- is.na(data) + } nimp <- nimp(where, blocks) - if (length(nimp) == 0) visitSequence <- nimp + if (length(nimp) == 0) { + visitSequence <- nimp + } if (length(visitSequence) == 1 && is.character(visitSequence)) { - code <- match.arg(visitSequence, + code <- match.arg( + visitSequence, choices = c("roman", "arabic", "monotone", "revmonotone") ) - visitSequence <- switch(code, + visitSequence <- switch( + code, roman = names(blocks)[nimp > 0], arabic = rev(names(blocks)[nimp > 0]), monotone = names(blocks)[order(nimp)], diff --git a/R/where.R b/R/where.R index 856b90e0f..a2cf7ac51 100644 --- a/R/where.R +++ b/R/where.R @@ -23,12 +23,15 @@ #' fit <- with(imp, lm(chl ~ bmi + age + hyp)) #' summary(pool.syn(fit)) #' @export -make.where <- function(data, - keyword = c("missing", "all", "none", "observed")) { +make.where <- function( + data, + keyword = c("missing", "all", "none", "observed") +) { keyword <- match.arg(keyword) data <- check.dataform(data) - where <- switch(keyword, + where <- switch( + keyword, missing = is.na(data), all = matrix(TRUE, nrow = nrow(data), ncol = ncol(data)), none = matrix(FALSE, nrow = nrow(data), ncol = ncol(data)), diff --git a/R/with.R b/R/with.R index 19f2b92cd..6fbf8bdca 100644 --- a/R/with.R +++ b/R/with.R @@ -43,13 +43,26 @@ with.mids <- function(data, expr, ...) { # do the repeated analysis, store the result. for (i in seq_along(analyses)) { data.i <- complete(data, i) - analyses[[i]] <- eval(expr = substitute(expr), envir = data.i, enclos = parent.frame()) + analyses[[i]] <- eval( + expr = substitute(expr), + envir = data.i, + enclos = parent.frame() + ) if (is.expression(analyses[[i]])) { - analyses[[i]] <- eval(expr = analyses[[i]], envir = data.i, enclos = parent.frame()) + analyses[[i]] <- eval( + expr = analyses[[i]], + envir = data.i, + enclos = parent.frame() + ) } } # return the complete data analyses as a list of length nimp - object <- mira(call = call, call1 = data$call, nmis = data$nmis, analyses = analyses) + object <- mira( + call = call, + call1 = data$call, + nmis = data$nmis, + analyses = analyses + ) return(object) } diff --git a/R/xyplot.mads.R b/R/xyplot.mads.R index 085c42c0d..1395d1df8 100644 --- a/R/xyplot.mads.R +++ b/R/xyplot.mads.R @@ -31,13 +31,21 @@ #' @author Rianne Schouten, 2016 #' @seealso \code{\link{ampute}}, \code{\link{mads}} #' @export -xyplot.mads <- function(x, data, which.pat = NULL, - standardized = TRUE, layout = NULL, - colors = mdc(1:2), ...) { +xyplot.mads <- function( + x, + data, + which.pat = NULL, + standardized = TRUE, + layout = NULL, + colors = mdc(1:2), + ... +) { if (!is.mads(x)) { stop("Object is not of class mads") } - if (missing(data)) data <- NULL + if (missing(data)) { + data <- NULL + } yvar <- data if (is.null(yvar)) { varlist <- colnames(x$amp) @@ -94,7 +102,8 @@ xyplot.mads <- function(x, data, which.pat = NULL, strip.background = list(col = "grey95") ) key <- list( - columns = 2, points = list(col = colors, pch = 1), + columns = 2, + points = list(col = colors, pch = 1), text = list(c("Non-Amputed Data", "Amputed Data")) ) @@ -105,9 +114,14 @@ xyplot.mads <- function(x, data, which.pat = NULL, for (i in seq_len(pat)) { p[[paste("Scatterplot Pattern", which.pat[i])]] <- lattice::xyplot( - x = formula, data = data[data$.pat == which.pat[i], ], - groups = data$.amp, par.settings = theme, - multiple = TRUE, outer = TRUE, layout = layout, key = key, + x = formula, + data = data[data$.pat == which.pat[i], ], + groups = data$.amp, + par.settings = theme, + multiple = TRUE, + outer = TRUE, + layout = layout, + key = key, ylab = "Weighted sum scores", xlab = paste(xlab, which.pat[i]) ) diff --git a/R/xyplot.mids.R b/R/xyplot.mids.R index fd393e143..bf891050c 100644 --- a/R/xyplot.mids.R +++ b/R/xyplot.mids.R @@ -122,21 +122,27 @@ #' @aliases xyplot.mids xyplot #' @method xyplot mids #' @export -xyplot.mids <- function(x, - data, - na.groups = NULL, - groups = NULL, - as.table = TRUE, - theme = mice.theme(), - allow.multiple = TRUE, - outer = TRUE, - drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), - ..., - subscripts = TRUE, - subset = TRUE) { +xyplot.mids <- function( + x, + data, + na.groups = NULL, + groups = NULL, + as.table = TRUE, + theme = mice.theme(), + allow.multiple = TRUE, + outer = TRUE, + drop.unused.levels = lattice::lattice.getOption("drop.unused.levels"), + ..., + subscripts = TRUE, + subset = TRUE +) { call <- match.call() - if (!is.mids(x)) stop("Argument 'x' must be a 'mids' object") - if (missing(data)) stop("Missing formula") + if (!is.mids(x)) { + stop("Argument 'x' must be a 'mids' object") + } + if (missing(data)) { + stop("Missing formula") + } formula <- data ## unpack data and response indicator @@ -145,16 +151,22 @@ xyplot.mids <- function(x, ## evaluate na.group in response indicator nagp <- eval(expr = substitute(na.groups), envir = r, enclos = parent.frame()) - if (is.expression(nagp)) nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + if (is.expression(nagp)) { + nagp <- eval(expr = nagp, envir = r, enclos = parent.frame()) + } ## evaluate groups in imputed data ngp <- eval(expr = substitute(groups), envir = cd, enclos = parent.frame()) - if (is.expression(ngp)) ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + if (is.expression(ngp)) { + ngp <- eval(expr = ngp, envir = cd, enclos = parent.frame()) + } groups <- ngp ## evaluate subset in imputed data ss <- eval(expr = substitute(subset), envir = cd, enclos = parent.frame()) - if (is.expression(ss)) ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + if (is.expression(ss)) { + ss <- eval(expr = ss, envir = cd, enclos = parent.frame()) + } subset <- ss ## evaluate further arguments before parsing @@ -169,9 +181,13 @@ xyplot.mids <- function(x, ## determine the y-variables form <- lattice::latticeParseFormula( - model = formula, data = cd, subset = subset, - groups = groups, multiple = allow.multiple, - outer = outer, subscripts = TRUE, + model = formula, + data = cd, + subset = subset, + groups = groups, + multiple = allow.multiple, + outer = outer, + subscripts = TRUE, drop = drop.unused.levels ) ynames <- unlist(lapply(strsplit(form$left.name, " \\+ "), rm.whitespace)) @@ -206,9 +222,12 @@ xyplot.mids <- function(x, ## ready args <- c( - x = formula, data = list(cd), + x = formula, + data = list(cd), groups = list(gp), - args, dots, subset = call$subset + args, + dots, + subset = call$subset ) ## go diff --git a/data-raw/R/brandsma.R b/data-raw/R/brandsma.R index 3e8c4bedf..9141b230f 100644 --- a/data-raw/R/brandsma.R +++ b/data-raw/R/brandsma.R @@ -54,7 +54,8 @@ mlbook2$denomina <- makemis(mlbook2$denomina) # leave as missing # How many missings are there now in mlbook2? apply(mlbook2, 2, count.na) -schools <- transmute(mlbook2, +schools <- transmute( + mlbook2, sch = as.integer(schoolnr), pup = as.integer(pupilNR_new), iqv = as.vector(scale(IQ_verb, scale = FALSE)), diff --git a/data-raw/R/employee.R b/data-raw/R/employee.R index 02909da4c..1efde4c49 100644 --- a/data-raw/R/employee.R +++ b/data-raw/R/employee.R @@ -2,12 +2,48 @@ employee <- data.frame( IQ = c( - 78, 84, 84, 85, 87, 91, 92, 94, 94, 96, 99, 105, 105, 106, 108, 112, - 113, 115, 118, 134 + 78, + 84, + 84, + 85, + 87, + 91, + 92, + 94, + 94, + 96, + 99, + 105, + 105, + 106, + 108, + 112, + 113, + 115, + 118, + 134 ), wbeing = c( - 13, 9, 10, 10, NA, 3, 12, 3, 13, NA, 6, 12, 14, 10, NA, 10, - 14, 14, 12, 11 + 13, + 9, + 10, + 10, + NA, + 3, + 12, + 3, + 13, + NA, + 6, + 12, + 14, + 10, + NA, + 10, + 14, + 14, + 12, + 11 ), jobperf = c(rep(NA, 10), 7, 10, 11, 15, 10, 10, 12, 14, 16, 12) ) diff --git a/data-raw/R/nmar_demo_data.R b/data-raw/R/nmar_demo_data.R index 7d9088436..72187729b 100644 --- a/data-raw/R/nmar_demo_data.R +++ b/data-raw/R/nmar_demo_data.R @@ -1,3 +1,2 @@ - mnar_demo_data <- read.csv("data-raw/data/mnar_demo_data.csv") usethis::use_data(mnar_demo_data) diff --git a/tests/testthat/test-D1.R b/tests/testthat/test-D1.R index 77dba8ae8..31361ce04 100644 --- a/tests/testthat/test-D1.R +++ b/tests/testthat/test-D1.R @@ -1,7 +1,10 @@ context("D1") imp <- mice(nhanes2, print = FALSE, m = 10, seed = 219) -fit1 <- with(data = imp, expr = glm(hyp == "yes" ~ age + chl, family = binomial)) +fit1 <- with( + data = imp, + expr = glm(hyp == "yes" ~ age + chl, family = binomial) +) fit0 <- with(data = imp, expr = glm(hyp == "yes" ~ 1, family = binomial)) empty <- with(data = imp, expr = glm(hyp == "yes" ~ 0, family = binomial)) @@ -15,7 +18,11 @@ empty <- with(data = imp, expr = glm(hyp == "yes" ~ 0, family = binomial)) # three new ways to compare fit1 to the intercept-only model z1 <- D1(fit1, fit0) -z2 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(fit0), df.com = 21) +z2 <- mitml::testModels( + as.mitml.result(fit1), + as.mitml.result(fit0), + df.com = 21 +) z3 <- D1(fit1) test_that("compares fit1 to the intercept-only model", { @@ -25,7 +32,11 @@ test_that("compares fit1 to the intercept-only model", { # two ways to compare fit1 to the empty model z4 <- D1(fit1, empty) -z5 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(empty), df.com = 21) +z5 <- mitml::testModels( + as.mitml.result(fit1), + as.mitml.result(empty), + df.com = 21 +) test_that("compares fit1 to empty model", { expect_identical(z4$result, z5$test) @@ -35,7 +46,11 @@ test_that("compares fit1 to empty model", { context("D2") z1 <- D2(fit1, fit0) -z2 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(fit0), method = "D2") +z2 <- mitml::testModels( + as.mitml.result(fit1), + as.mitml.result(fit0), + method = "D2" +) z3 <- D2(fit1) test_that("compares fit1 to the intercept-only model", { @@ -45,7 +60,11 @@ test_that("compares fit1 to the intercept-only model", { # two ways to compare fit1 to the empty model z4 <- D2(fit1, empty) -z5 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(empty), method = "D2") +z5 <- mitml::testModels( + as.mitml.result(fit1), + as.mitml.result(empty), + method = "D2" +) test_that("compares fit1 to empty model", { expect_identical(z4$result, z5$test) diff --git a/tests/testthat/test-D3.R b/tests/testthat/test-D3.R index 32627560b..c9591e305 100644 --- a/tests/testthat/test-D3.R +++ b/tests/testthat/test-D3.R @@ -13,13 +13,18 @@ fit0 <- lapply(1:5, function(m) { }) # outcomment to evade dependency of lmtest # x1 <- lmtest::lrtest(fit1[[1]], fit0[[1]]) -x1 <- structure(list(`#Df` = c(3, 2), - LogLik = c(-137.087912980007, -137.516434459951), - Df = c(NA, -1), Chisq = c(NA, 0.857042959888474), - `Pr(>Chisq)` = c(NA, 0.354567523408569)), - class = c("anova", "data.frame"), - row.names = c("1", "2"), - heading = c("Likelihood ratio test\n", "Model 1: B ~ A\nModel 2: B ~ 1")) +x1 <- structure( + list( + `#Df` = c(3, 2), + LogLik = c(-137.087912980007, -137.516434459951), + Df = c(NA, -1), + Chisq = c(NA, 0.857042959888474), + `Pr(>Chisq)` = c(NA, 0.354567523408569) + ), + class = c("anova", "data.frame"), + row.names = c("1", "2"), + heading = c("Likelihood ratio test\n", "Model 1: B ~ A\nModel 2: B ~ 1") +) x2 <- D3(fit1 = fit1, fit0 = fit0) x3 <- mitml::testModels(fit1, fit0, method = "D3") @@ -46,7 +51,11 @@ empty <- with(data = imp, expr = lm(hyp ~ 0)) # stat1 <- pool.compare(fit1, fit0, method = "likelihood") z1 <- D3(fit1, fit0) -z2 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(fit0), method = "D3") +z2 <- mitml::testModels( + as.mitml.result(fit1), + as.mitml.result(fit0), + method = "D3" +) # This test fails # FIXME @@ -54,7 +63,6 @@ z2 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(fit0), method = " # expect_equal(z1$result[1], z2$test[1]) # }) - # using lmer suppressPackageStartupMessages(library(mitml, quietly = TRUE)) library(lme4, quietly = TRUE) @@ -62,8 +70,12 @@ library(lme4, quietly = TRUE) data(studentratings) fml <- ReadDis + SES ~ ReadAchiev + (1 | ID) set.seed(26262) -imp <- mitml::panImpute(studentratings, - formula = fml, n.burn = 1000, n.iter = 100, m = 5, +imp <- mitml::panImpute( + studentratings, + formula = fml, + n.burn = 1000, + n.iter = 100, + m = 5, silent = TRUE ) implist <- mitml::mitmlComplete(imp, print = 1:5) @@ -81,7 +93,6 @@ fit1 <- with(implist, lmer(ReadAchiev ~ ReadDis + SES + (1 | ID), REML = FALSE)) # expect_equal(z3$result[1], z4$test[1]) # }) - # glm # imp <- mice(nhanes2, print = FALSE, m = 10, seed = 219) # @@ -106,7 +117,6 @@ fit1 <- with(implist, lmer(ReadAchiev ~ ReadDis + SES + (1 | ID), REML = FALSE)) # expect_equal(z5$result[1], z6$test[1]) # }) - # data with factors imp <- mice(nhanes2, print = FALSE, m = 10, seed = 219) fit1 <- with(data = imp, expr = lm(bmi ~ age + chl + hyp)) @@ -114,11 +124,14 @@ fit0 <- with(data = imp, expr = lm(bmi ~ age)) empty <- with(data = imp, expr = lm(bmi ~ 0)) z7 <- D3(fit1, fit0) -z8 <- mitml::testModels(as.mitml.result(fit1), as.mitml.result(fit0), method = "D3") +z8 <- mitml::testModels( + as.mitml.result(fit1), + as.mitml.result(fit0), + method = "D3" +) # This test fails. # FIXME # test_that("factors: mice and mitml calculate same F", { # expect_equal(z7$result[1], z8$test[1]) # }) - diff --git a/tests/testthat/test-ampute.R b/tests/testthat/test-ampute.R index 21c61d6ef..24d50c1d0 100644 --- a/tests/testthat/test-ampute.R +++ b/tests/testthat/test-ampute.R @@ -18,11 +18,16 @@ test_that("all examples work", { my_weights[2, 1] <- 2 my_weights[3, 1] <- 0.5 - expect_error(ampute( - data = compl_boys, patterns = my_patterns, - freq = c(0.3, 0.3, 0.4), weights = my_weights, - type = c("RIGHT", "TAIL", "LEFT") - ), NA) + expect_error( + ampute( + data = compl_boys, + patterns = my_patterns, + freq = c(0.3, 0.3, 0.4), + weights = my_weights, + type = c("RIGHT", "TAIL", "LEFT") + ), + NA + ) }) test_that("all arguments work", { @@ -32,59 +37,123 @@ test_that("all arguments work", { # missingness by cells expect_error(ampute(data = complete.data, prop = 0.1, bycases = FALSE), NA) # prop with 3 dec, weigths with negative values, unequal odds matrix - expect_error(ampute( - data = complete.data, prop = 0.314, - freq = c(0.25, 0.4, 0.35), - patterns = matrix( - data = c( - 1, 0, 1, - 0, 1, 0, - 0, 1, 1 + expect_error( + ampute( + data = complete.data, + prop = 0.314, + freq = c(0.25, 0.4, 0.35), + patterns = matrix( + data = c( + 1, + 0, + 1, + 0, + 1, + 0, + 0, + 1, + 1 + ), + nrow = 3, + byrow = TRUE ), - nrow = 3, byrow = TRUE - ), - weights = matrix( - data = c( - -1, 1, 0, - -4, -4, 1, - 0, 0, -1 + weights = matrix( + data = c( + -1, + 1, + 0, + -4, + -4, + 1, + 0, + 0, + -1 + ), + nrow = 3, + byrow = TRUE ), - nrow = 3, byrow = TRUE - ), - odds = matrix( - data = c( - 1, 4, NA, NA, - 0, 3, 3, NA, - 4, 1, 1, 4 + odds = matrix( + data = c( + 1, + 4, + NA, + NA, + 0, + 3, + 3, + NA, + 4, + 1, + 1, + 4 + ), + nrow = 3, + byrow = TRUE ), - nrow = 3, byrow = TRUE + cont = FALSE ), - cont = FALSE - ), NA) + NA + ) # 1 pattern with vector for patterns and weights - expect_error(ampute( - data = complete.data, freq = 1, patterns = c(1, 0, 1), - weights = c(3, 3, 0) - ), NA) + expect_error( + ampute( + data = complete.data, + freq = 1, + patterns = c(1, 0, 1), + weights = c(3, 3, 0) + ), + NA + ) # multiple patterns given in vectors - expect_error(ampute( - data = complete.data, patterns = c(1, 0, 1, 1, 0, 0), - cont = TRUE, weights = c(1, 4, -2, 0, 1, 2), - type = c("LEFT", "TAIL") - ), NA) + expect_error( + ampute( + data = complete.data, + patterns = c(1, 0, 1, 1, 0, 0), + cont = TRUE, + weights = c(1, 4, -2, 0, 1, 2), + type = c("LEFT", "TAIL") + ), + NA + ) # one pattern with odds vector - expect_error(ampute( - data = complete.data, patterns = c(1, 0, 1), - weights = c(4, 1, 0), odds = c(2, 1), cont = FALSE - ), NA) + expect_error( + ampute( + data = complete.data, + patterns = c(1, 0, 1), + weights = c(4, 1, 0), + odds = c(2, 1), + cont = FALSE + ), + NA + ) # argument standardized expect_error(ampute(data = complete.data, std = FALSE), NA) # sum scores cannot be NaN - dich.data <- matrix(c( - 0, 0, 0, 1, 0, 0, 0, 0, 0, - 1, 0, 0, 0, 0, 0, 0, 0, 0 - ), ncol = 2, byrow = FALSE) + dich.data <- matrix( + c( + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ), + ncol = 2, + byrow = FALSE + ) wss <- expect_warning(ampute(data = dich.data, mech = "MNAR")$scores) check_na <- function(x) { return(any(is.na(x))) @@ -105,8 +174,14 @@ test_that("function works around unusual arguments", { expect_warning(ampute(data = nasty.data, mech = "MCAR"), NA) # patterns - expect_error(ampute(data = complete.data, patterns = c(0, 0, 0), mech = "MCAR"), NA) - expect_error(ampute(data = complete.data, patterns = c(0, 0, 1, 0, 0, 0), mech = "MNAR"), NA) + expect_error( + ampute(data = complete.data, patterns = c(0, 0, 0), mech = "MCAR"), + NA + ) + expect_error( + ampute(data = complete.data, patterns = c(0, 0, 1, 0, 0, 0), mech = "MNAR"), + NA + ) expect_warning(ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))) # freq @@ -127,33 +202,59 @@ test_that("function works around unusual arguments", { ) expect_warning( ampute( - data = complete.data, mech = "MCAR", + data = complete.data, + mech = "MCAR", odds = matrix( data = c( - 1, 4, NA, NA, - 0, 3, 3, NA, - 4, 1, 1, 4 + 1, + 4, + NA, + NA, + 0, + 3, + 3, + NA, + 4, + 1, + 1, + 4 ), - nrow = 3, byrow = TRUE - ), cont = FALSE + nrow = 3, + byrow = TRUE + ), + cont = FALSE ), "Odds matrix is not used when mechanism is MCAR" ) expect_warning( ampute( - data = complete.data, mech = "MCAR", + data = complete.data, + mech = "MCAR", weights = c(1, 3, 4) ), "Weights matrix is not used when mechanism is MCAR" ) - expect_warning(ampute(data = complete.data, odds = matrix( - data = c( - 1, 4, NA, NA, - 0, 3, 3, NA, - 4, 1, 1, 4 - ), - nrow = 3, byrow = TRUE - ))) + expect_warning(ampute( + data = complete.data, + odds = matrix( + data = c( + 1, + 4, + NA, + NA, + 0, + 3, + 3, + NA, + 4, + 1, + 1, + 4 + ), + nrow = 3, + byrow = TRUE + ) + )) expect_warning(ampute(data = complete.data, cont = FALSE, type = "LEFT")) }) @@ -215,30 +316,49 @@ test_that("error messages work properly", { ampute(data = complete.data, weights = c(1, 2, 1, 4)), "Length of weight vector does not match #variables" ) - expect_error(ampute( - data = complete.data, - odds = matrix(c(1, 4, -3, 2, 1, 1), nrow = 3), - cont = FALSE - ), "Odds matrix can only have positive values") + expect_error( + ampute( + data = complete.data, + odds = matrix(c(1, 4, -3, 2, 1, 1), nrow = 3), + cont = FALSE + ), + "Odds matrix can only have positive values" + ) expect_error( ampute( data = complete.data, patterns = matrix( data = c( - 1, 0, 1, - 0, 1, 0, - 0, 1, 1 + 1, + 0, + 1, + 0, + 1, + 0, + 0, + 1, + 1 ), - nrow = 3, byrow = TRUE + nrow = 3, + byrow = TRUE ), weights = matrix( data = c( - -1, 1, 0, - -4, -4, 1, - 0, 0, -1, - 1, 1, 0 + -1, + 1, + 0, + -4, + -4, + 1, + 0, + 0, + -1, + 1, + 1, + 0 ), - nrow = 4, byrow = TRUE + nrow = 4, + byrow = TRUE ) ), "The objects patterns and weights are not matching" @@ -248,19 +368,34 @@ test_that("error messages work properly", { data = complete.data, patterns = matrix( data = c( - 1, 0, 1, - 0, 1, 0, - 0, 1, 1 + 1, + 0, + 1, + 0, + 1, + 0, + 0, + 1, + 1 ), - nrow = 3, byrow = TRUE + nrow = 3, + byrow = TRUE ), odds = matrix( data = c( - 1, 4, NA, NA, - 0, 3, 3, 0 + 1, + 4, + NA, + NA, + 0, + 3, + 3, + 0 ), - nrow = 2, byrow = TRUE - ), cont = FALSE + nrow = 2, + byrow = TRUE + ), + cont = FALSE ), "The objects patterns and odds are not matching" ) @@ -271,98 +406,136 @@ test_that("error messages work properly", { test_that("patterns and weights matrices have right dimensions", { suppressWarnings( expect_true(all( - ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))$patterns == c(0, 1, 0) + ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))$patterns == + c(0, 1, 0) )) ) suppressWarnings( expect_true(all( - ampute(data = complete.data, patterns = c(0, 1, 0, 1, 1, 1))$patterns == c(0, 1, 0) + ampute(data = complete.data, patterns = c(0, 1, 0, 1, 1, 1))$patterns == + c(0, 1, 0) )) ) suppressWarnings( expect_true(all( - ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1))$patterns == c(0, 1, 0) + ampute( + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1) + )$patterns == + c(0, 1, 0) )) ) suppressWarnings( expect_true(all( ampute( - data = complete.data, patterns = c(1, 1, 1, 0, 1, 0), + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0), weights = c(1, 0, 0, 0, 1, 0) - )$weights == c(0, 1, 0) + )$weights == + c(0, 1, 0) )) ) suppressWarnings( expect_true(all( ampute( - data = complete.data, patterns = c(0, 1, 0, 1, 1, 1), + data = complete.data, + patterns = c(0, 1, 0, 1, 1, 1), weights = c(1, 0, 0, 0, 1, 0) - )$weights == c(1, 0, 0) + )$weights == + c(1, 0, 0) )) ) suppressWarnings( expect_true(all( ampute( - data = complete.data, patterns = c(0, 1, 0, 1, 1, 1), + data = complete.data, + patterns = c(0, 1, 0, 1, 1, 1), weights = c(1, 0, 0) - )$weights == c(1, 0, 0) + )$weights == + c(1, 0, 0) )) ) suppressWarnings( expect_true(all( ampute( - data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1), + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1), weights = c(1, 0, 0) - )$weights == c(1, 0, 0) + )$weights == + c(1, 0, 0) )) ) suppressWarnings( expect_true(all( ampute( - data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1), + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1), weights = c(1, 0, 0, 0, 1, 0, 0, 0, 1) - )$weights == c(0, 1, 0) + )$weights == + c(0, 1, 0) )) ) }) test_that("prop and freq are properly adjusted when patterns contain only 1's", { suppressWarnings( - expect_equal(ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))$prop, 0.25) + expect_equal( + ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))$prop, + 0.25 + ) ) suppressWarnings( - expect_equal(ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))$freq, 1) + expect_equal( + ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0))$freq, + 1 + ) ) suppressWarnings( expect_equal( - ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 0, 1, 0))$prop, 1 / 3 + ampute( + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 0, 1, 0) + )$prop, + 1 / 3 ) ) suppressWarnings( expect_true(all( - ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 0, 1, 0))$freq == c(0.5, 0.5) + ampute( + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 0, 1, 0) + )$freq == + c(0.5, 0.5) )) ) suppressWarnings( expect_equal( - ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1))$prop, 1 / 3 * 0.5 + ampute( + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1) + )$prop, + 1 / 3 * 0.5 ) ) suppressWarnings( expect_equal( - ampute(data = complete.data, patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1))$freq, 1 + ampute( + data = complete.data, + patterns = c(1, 1, 1, 0, 1, 0, 1, 1, 1) + )$freq, + 1 ) ) }) @@ -413,12 +586,14 @@ data <- replicate( # Ampute pattern covNum <- NUM_VAR_DF - 1 misPatCov1 <- t(combn( - x = covNum, m = 1, + x = covNum, + m = 1, FUN = function(x) replace(rep(1, covNum), x, 0) )) misPat1 <- cbind(rep(1, choose(covNum, 1)), misPatCov1) misPatCov2 <- t(combn( - x = covNum, m = 2, + x = covNum, + m = 2, FUN = function(x) replace(rep(1, covNum), x, 0) )) misPat2 <- cbind(rep(1, choose(covNum, 2)), misPatCov2) diff --git a/tests/testthat/test-anova.R b/tests/testthat/test-anova.R index e0b5513f4..560c2c119 100644 --- a/tests/testthat/test-anova.R +++ b/tests/testthat/test-anova.R @@ -36,4 +36,3 @@ test_that("runs tests for the Cox model", { expect_error(D3(m2, m1)) expect_silent(anova(m3, m2, m1)) }) - diff --git a/tests/testthat/test-as.mids.R b/tests/testthat/test-as.mids.R index 548920c92..447ead4e3 100644 --- a/tests/testthat/test-as.mids.R +++ b/tests/testthat/test-as.mids.R @@ -36,10 +36,10 @@ test_that("as.mids() produces a `mids` object", { expect_is(test3, "mids") expect_is(test4, "mids") expect_is(test5, "mids") -# expect_is(test7, "mids") -# expect_is(test8, "mids") -# expect_is(test9, "mids") -# expect_is(test10, "mids") + # expect_is(test7, "mids") + # expect_is(test8, "mids") + # expect_is(test9, "mids") + # expect_is(test10, "mids") expect_error( as.mids(X[-(1:10), ], "mids"), "Unequal group sizes in imputation index `.imp`" @@ -55,8 +55,14 @@ test_that("complete() reproduces the original data", { expect_true(identical(complete(test2, action = "long", include = TRUE), X)) expect_true(identical(complete(test3, action = "long", include = TRUE), X)) expect_true(identical(complete(test4, action = "long", include = TRUE), X)) - expect_true(identical(complete(test5, action = "long", include = TRUE)[, -6], X[, -6])) - expect_true(identical(complete(test6, action = "long", include = TRUE)[, -(5:6)], X[, rev][, -(1:2)])) + expect_true(identical( + complete(test5, action = "long", include = TRUE)[, -6], + X[, -6] + )) + expect_true(identical( + complete(test6, action = "long", include = TRUE)[, -(5:6)], + X[, rev][, -(1:2)] + )) }) # works with dplyr @@ -75,4 +81,3 @@ test_that("collinearity is logged during as.mids()", { mids_obj <- suppressWarnings(as.mids(X3)) expect_true(any(grepl("collinear", mids_obj$loggedEvents$meth))) }) - diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 8544975ea..cd113e468 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -1,6 +1,11 @@ context("blocks") -imp <- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 1, print = FALSE) +imp <- mice( + nhanes, + blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), + m = 1, + print = FALSE +) # plot(imp) test_that("removes variables from 'where'", { @@ -10,13 +15,35 @@ test_that("removes variables from 'where'", { # reprex https://github.com/amices/mice/issues/326 imp1 <- mice(nhanes, seed = 1, m = 1, maxit = 2, print = FALSE) -imp2 <- mice(nhanes, blocks = list(c("bmi", "hyp"), "chl"), calltype = c("pred", "pred"), m = 1, maxit = 2, seed = 1, print = FALSE) +imp2 <- mice( + nhanes, + blocks = list(c("bmi", "hyp"), "chl"), + calltype = c("pred", "pred"), + m = 1, + maxit = 2, + seed = 1, + print = FALSE +) test_that("expands a univariate method to all variables in the block", { expect_identical(complete(imp1, 1), complete(imp2, 1)) }) -imp3 <- mice(nhanes, blocks = list(c("hyp", "bmi"), "chl"), m = 1, maxit = 2, seed = 1, print = FALSE) -imp4 <- mice(nhanes, visitSequence = c("hyp", "bmi", "chl"), m = 1, maxit = 2, seed = 1, print = FALSE) +imp3 <- mice( + nhanes, + blocks = list(c("hyp", "bmi"), "chl"), + m = 1, + maxit = 2, + seed = 1, + print = FALSE +) +imp4 <- mice( + nhanes, + visitSequence = c("hyp", "bmi", "chl"), + m = 1, + maxit = 2, + seed = 1, + print = FALSE +) test_that("blocks alter the visit sequence", { expect_identical(complete(imp3, 1), complete(imp3, 1)) }) diff --git a/tests/testthat/test-blots.R b/tests/testthat/test-blots.R index d836cc541..1f7fd619f 100644 --- a/tests/testthat/test-blots.R +++ b/tests/testthat/test-blots.R @@ -2,11 +2,25 @@ context("blots") # global change of donors argument blocks1 <- name.blocks(list(c("bmi", "chl"), "hyp")) -imp0 <- mice(nhanes, blocks = blocks1, donors = 10, m = 1, maxit = 1, print = FALSE) +imp0 <- mice( + nhanes, + blocks = blocks1, + donors = 10, + m = 1, + maxit = 1, + print = FALSE +) # vary donors, depending on block blots1 <- list(B1 = list(donors = 10), hyp = list(donors = 1)) -imp1 <- mice(nhanes, blocks = blocks1, blots = blots1, m = 1, maxit = 1, print = FALSE) +imp1 <- mice( + nhanes, + blocks = blocks1, + blots = blots1, + m = 1, + maxit = 1, + print = FALSE +) test_that("errors when mixing same global and local argument", { expect_error( diff --git a/tests/testthat/test-cbind.R b/tests/testthat/test-cbind.R index 7da60d4c5..c26eb4fd9 100644 --- a/tests/testthat/test-cbind.R +++ b/tests/testthat/test-cbind.R @@ -19,7 +19,13 @@ data1 <- data[, c("age", "bmi")] data2 <- data[, c("hyp", "chl")] imp1 <- mice(data1, m = 1, maxit = 1, print = FALSE) -imp2 <- mice(data2, blocks = list(c("hyp", "chl")), m = 1, maxit = 1, print = FALSE) +imp2 <- mice( + data2, + blocks = list(c("hyp", "chl")), + m = 1, + maxit = 1, + print = FALSE +) imp <- cbind(imp1, imp2) test_that("combines imputations with blocks", { @@ -42,8 +48,20 @@ test_that("duplicate variable adds a column", { }) # handling of duplicate blocks -imp1 <- mice(data1, blocks = list(c("age", "bmi"), "hyp"), m = 1, maxit = 1, print = FALSE) -imp2 <- mice(data2, blocks = list(c("hyp", "chl")), m = 1, maxit = 1, print = FALSE) +imp1 <- mice( + data1, + blocks = list(c("age", "bmi"), "hyp"), + m = 1, + maxit = 1, + print = FALSE +) +imp2 <- mice( + data2, + blocks = list(c("hyp", "chl")), + m = 1, + maxit = 1, + print = FALSE +) imp <- cbind(imp1, imp2) impc <- mice.mids(imp, max = 2, print = FALSE) @@ -53,7 +71,13 @@ test_that("duplicate blocks names renames block", { # cbind - no second argument -imp1 <- mice(nhanes, blocks = list(c("bmi", "chl"), "hyp"), print = FALSE, maxit = 1, m = 1) +imp1 <- mice( + nhanes, + blocks = list(c("bmi", "chl"), "hyp"), + print = FALSE, + maxit = 1, + m = 1 +) imp2 <- cbind(imp1) imp3 <- cbind(imp1, NULL) imp4 <- cbind(imp1, character(0)) @@ -85,7 +109,8 @@ test_that("appends names vectors and constants", { # matrix, factor, data.frame # NOTE: cbind() dispatches to wrong function if there is a data.frame # so use cbind.mids() -imp8 <- mice:::cbind.mids(imp1, +imp8 <- mice:::cbind.mids( + imp1, ma = matrix(1:50, nrow = 25, ncol = 2), age = nhanes2$age, df = nhanes2[, c("hyp", "chl")] @@ -95,9 +120,9 @@ test_that("appends matrix, factor and data.frame", { }) # impc <- mice.mids(imp8, max = 2, print = FALSE) - # NOTE: now using own version of cbind() -imp9 <- cbind(imp1, +imp9 <- cbind( + imp1, ma = matrix(1:50, nrow = 25, ncol = 2), age = nhanes2$age, df = nhanes2[, c("hyp", "chl")] diff --git a/tests/testthat/test-check.visitSequence.R b/tests/testthat/test-check.visitSequence.R index 6f573d178..a27e0e8af 100644 --- a/tests/testthat/test-check.visitSequence.R +++ b/tests/testthat/test-check.visitSequence.R @@ -4,7 +4,9 @@ data <- mice::nhanes test_that("mice() takes numerical and character visitSequence", { expect_silent(imp <- mice(data, visitSequence = 4:1, m = 1, print = FALSE)) - expect_silent(imp <- mice(data, visitSequence = rev(names(data)), m = 1, print = FALSE)) + expect_silent( + imp <- mice(data, visitSequence = rev(names(data)), m = 1, print = FALSE) + ) }) test_that("Passive variable is moved to end of visitSequence when not user-defined", { @@ -24,7 +26,7 @@ test_that("Passive variable is moved to end of visitSequence when not user-defin expect_setequal(imp1$visitSequence, c("x", "y", "p")) }) -df <- mice::boys[,c(2, 3, 6)] +df <- mice::boys[, c(2, 3, 6)] meth <- mice::make.method(df) mods <- list( hgt ~ wgt + gen, @@ -32,6 +34,14 @@ mods <- list( wgt ~ hgt + gen ) test_that("method and formulas can have different orders", { - expect_silent(imp <- mice::mice(df, method = meth, formulas = mods, m = 1, - maxit = 1, print = FALSE)) + expect_silent( + imp <- mice::mice( + df, + method = meth, + formulas = mods, + m = 1, + maxit = 1, + print = FALSE + ) + ) }) diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R index 1046a82ec..ca3610fca 100644 --- a/tests/testthat/test-data.R +++ b/tests/testthat/test-data.R @@ -4,7 +4,10 @@ set.seed(123) df <- data.frame( factor2 = factor(sample(c("Yes", "No"), 20, replace = TRUE)), factor3 = factor(sample(c("Low", "Medium", "High"), 20, replace = TRUE)), - factor4 = factor(sample(c("A", "B", "C", "D"), 20, replace = TRUE), ordered = TRUE), + factor4 = factor( + sample(c("A", "B", "C", "D"), 20, replace = TRUE), + ordered = TRUE + ), logical1 = sample(c(TRUE, FALSE), 20, replace = TRUE), logical2 = sample(c(TRUE, FALSE), 20, replace = TRUE), numeric1 = rnorm(20) @@ -16,7 +19,16 @@ for (i in seq_len(nrow(missing_idx))) { df[missing_idx[i, 1], missing_idx[i, 2]] <- NA } -expect_warning(trained <- mice(df, m = 2, maxit = 2, seed = 1, tasks = "train", print = FALSE)) +expect_warning( + trained <- mice( + df, + m = 2, + maxit = 2, + seed = 1, + tasks = "train", + print = FALSE + ) +) # make single-row new data with correct type newdata <- make.newdata(models = trained$models, vars = names(df)) @@ -44,7 +56,8 @@ newdata <- data.frame( factor4 = NA, logical1 = NA, logical2 = NA, - numeric1 = NA) + numeric1 = NA +) result2 <- scan.newdata(data = newdata, models = trained$models) @@ -58,4 +71,3 @@ test_that("coerce.newdata() can coerces wrong to correct types", { expect_false(identical(attr(result2, "data"), newdata)) expect_true(identical(coerced1, coerced2)) }) - diff --git a/tests/testthat/test-filter.R b/tests/testthat/test-filter.R index 7df579f22..706a551f1 100644 --- a/tests/testthat/test-filter.R +++ b/tests/testthat/test-filter.R @@ -1,4 +1,3 @@ - context("filter.mids") imp <- mice(nhanes, m = 2, maxit = 1, print = FALSE, seed = 1) diff --git a/tests/testthat/test-formulas.R b/tests/testthat/test-formulas.R index ccb3c6ad8..f1ea068f1 100644 --- a/tests/testthat/test-formulas.R +++ b/tests/testthat/test-formulas.R @@ -18,4 +18,3 @@ test_that("model.matrix() deletes incomplete cases", { form <- list(bmi ~ poly(chl, 2) + age + hyp) pred <- make.predictorMatrix(nhanes) imp1 <- mice(data, form = form, pred = pred, m = 1, maxit = 2, print = FALSE) - diff --git a/tests/testthat/test-internals.R b/tests/testthat/test-internals.R index 90389dfd5..341290843 100644 --- a/tests/testthat/test-internals.R +++ b/tests/testthat/test-internals.R @@ -19,8 +19,16 @@ ry <- rep(TRUE, 5) # data frame for storing the event log state <- list(it = 0, im = 0, dep = "y", meth = "test", log = TRUE) -loggedEvents <- data.frame(it = 0L, im = 0L, dep = "", meth = "", out = "", - msg = NA_character_, fn = NA_character_, stringsAsFactors = FALSE) +loggedEvents <- data.frame( + it = 0L, + im = 0L, + dep = "", + meth = "", + out = "", + msg = NA_character_, + fn = NA_character_, + stringsAsFactors = FALSE +) fr <- 2 diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index 9ee4009bb..87105756c 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -21,19 +21,24 @@ test_that("Case A finds formulas", { pred1 <- matrix(1, nrow = 4, ncol = 4) pred2 <- matrix(1, nrow = 2, ncol = 2) -pred3 <- matrix(1, - nrow = 2, ncol = 2, +pred3 <- matrix( + 1, + nrow = 2, + ncol = 2, dimnames = list(c("bmi", "hyp"), c("bmi", "hyp")) ) -pred4 <- matrix(1, - nrow = 2, ncol = 3, +pred4 <- matrix( + 1, + nrow = 2, + ncol = 3, dimnames = list(c("bmi", "hyp"), c("bmi", "hyp", "chl")) ) imp1 <- mice(data, predictorMatrix = pred1, print = FALSE, m = 1, maxit = 1) imp3 <- mice(data, predictorMatrix = pred3, print = FALSE, m = 1, maxit = 1) test_that("Case B tests the predictorMatrix", { expect_equal(nrow(imp1$predictorMatrix), 4L) - expect_error(mice(data, + expect_error(mice( + data, predictorMatrix = pred2, "Missing row/column names in `predictorMatrix`." )) @@ -58,18 +63,57 @@ test_that("Case B finds formulas", { # Case C: Only blocks argument -imp1.0 <- mice(data, blocks = list("bmi", "chl", "hyp"), m = 1, maxit = 0, seed = 11) -imp2.0 <- mice(data, blocks = list(c("bmi", "chl"), "hyp"), m = 1, maxit = 0, seed = 11) -imp3.0 <- mice(data, blocks = list(all = c("bmi", "chl", "hyp")), m = 1, maxit = 0, seed = 11) +imp1.0 <- mice( + data, + blocks = list("bmi", "chl", "hyp"), + m = 1, + maxit = 0, + seed = 11 +) +imp2.0 <- mice( + data, + blocks = list(c("bmi", "chl"), "hyp"), + m = 1, + maxit = 0, + seed = 11 +) +imp3.0 <- mice( + data, + blocks = list(all = c("bmi", "chl", "hyp")), + m = 1, + maxit = 0, + seed = 11 +) test_that("Case C imputations are identical after initialization", { expect_identical(complete(imp1.0), complete(imp2.0)) expect_identical(complete(imp1.0), complete(imp3.0)) }) -imp1 <- mice(data, blocks = list("bmi", "chl", "hyp"), print = FALSE, m = 1, maxit = 1, seed = 11) -imp2 <- mice(data, blocks = list(c("bmi", "chl"), "hyp"), print = FALSE, m = 1, maxit = 1, seed = 11) -imp3 <- mice(data, blocks = list(all = c("bmi", "chl", "hyp")), print = FALSE, m = 1, maxit = 1, seed = 11) +imp1 <- mice( + data, + blocks = list("bmi", "chl", "hyp"), + print = FALSE, + m = 1, + maxit = 1, + seed = 11 +) +imp2 <- mice( + data, + blocks = list(c("bmi", "chl"), "hyp"), + print = FALSE, + m = 1, + maxit = 1, + seed = 11 +) +imp3 <- mice( + data, + blocks = list(all = c("bmi", "chl", "hyp")), + print = FALSE, + m = 1, + maxit = 1, + seed = 11 +) test_that("Case C finds blocks", { expect_identical(names(imp2$blocks), c("B1", "hyp")) @@ -91,7 +135,6 @@ test_that("Case C yields same imputations for FCS and multivariate", { }) - # Case D: Only formulas argument # univariate models @@ -100,16 +143,26 @@ form1 <- list( hyp ~ age + bmi + chl, chl ~ age + bmi + hyp ) -imp1 <- mice(data, - formulas = form1, method = "norm.nob", - print = FALSE, m = 1, maxit = 1, seed = 12199 +imp1 <- mice( + data, + formulas = form1, + method = "norm.nob", + print = FALSE, + m = 1, + maxit = 1, + seed = 12199 ) # same model using dot notation form2 <- list(bmi ~ ., hyp ~ ., chl ~ .) -imp2 <- mice(data, - formulas = form2, method = "norm.nob", - print = FALSE, m = 1, maxit = 1, seed = 12199 +imp2 <- mice( + data, + formulas = form2, + method = "norm.nob", + print = FALSE, + m = 1, + maxit = 1, + seed = 12199 ) # multivariate models (= repeated univariate) @@ -117,16 +170,26 @@ form3 <- list( bmi + hyp ~ age + chl, chl ~ age + bmi + hyp ) -imp3 <- mice(data, - formulas = form3, method = "norm.nob", - print = FALSE, m = 1, maxit = 1, seed = 12199 +imp3 <- mice( + data, + formulas = form3, + method = "norm.nob", + print = FALSE, + m = 1, + maxit = 1, + seed = 12199 ) # same model using dot notation form4 <- list(bmi + hyp ~ ., chl ~ .) -imp4 <- mice(data, - formulas = form4, method = "norm.nob", - print = FALSE, m = 1, maxit = 1, seed = 12199 +imp4 <- mice( + data, + formulas = form4, + method = "norm.nob", + print = FALSE, + m = 1, + maxit = 1, + seed = 12199 ) test_that("Case D yields same imputations for dot notation", { @@ -149,12 +212,54 @@ pred1 <- make.predictorMatrix(data, blocks = blocks1) pred2 <- make.predictorMatrix(data, blocks = blocks2) pred3 <- make.predictorMatrix(data, blocks = blocks3) -imp1 <- mice(data, blocks = blocks1, pred = pred1, m = 1, maxit = 1, print = FALSE) -imp1a <- mice(data, blocks = blocks1, pred = matrix(1, nr = 4, nc = 4), m = 1, maxit = 1, print = FALSE) -imp2 <- mice(data, blocks = blocks2, pred = pred2, m = 1, maxit = 1, print = FALSE) -imp2a <- mice(data, blocks = blocks2, pred = matrix(1, nr = 2, nc = 4), m = 1, maxit = 1, print = FALSE) -imp3 <- mice(data, blocks = blocks3, pred = pred3, m = 1, maxit = 1, print = FALSE) -imp3a <- mice(data, blocks = blocks3, pred = matrix(1, nr = 1, nc = 4), m = 1, maxit = 1, print = FALSE) +imp1 <- mice( + data, + blocks = blocks1, + pred = pred1, + m = 1, + maxit = 1, + print = FALSE +) +imp1a <- mice( + data, + blocks = blocks1, + pred = matrix(1, nr = 4, nc = 4), + m = 1, + maxit = 1, + print = FALSE +) +imp2 <- mice( + data, + blocks = blocks2, + pred = pred2, + m = 1, + maxit = 1, + print = FALSE +) +imp2a <- mice( + data, + blocks = blocks2, + pred = matrix(1, nr = 2, nc = 4), + m = 1, + maxit = 1, + print = FALSE +) +imp3 <- mice( + data, + blocks = blocks3, + pred = pred3, + m = 1, + maxit = 1, + print = FALSE +) +imp3a <- mice( + data, + blocks = blocks3, + pred = matrix(1, nr = 1, nc = 4), + m = 1, + maxit = 1, + print = FALSE +) test_that("Case E borrows rownames from blocks", { expect_identical(rownames(imp1a$predictorMatrix), names(blocks1)) @@ -224,13 +329,29 @@ form3 <- list( form4 <- list(bmi + hyp ~ ., chl ~ .) # blocks1 and form1 are compatible -imp1 <- mice(data, formulas = form1, pred = matrix(1, nr = 4, nc = 4), m = 1, maxit = 1, print = FALSE, seed = 3) +imp1 <- mice( + data, + formulas = form1, + pred = matrix(1, nr = 4, nc = 4), + m = 1, + maxit = 1, + print = FALSE, + seed = 3 +) test_that("Case F combines forms and pred in blocks", { expect_identical(unname(imp1$calltype), c(rep("formula", 3), "pred")) }) # dots and unnamed predictorMatrix -imp2 <- mice(data, formulas = form2, pred = matrix(1, nr = 4, nc = 4), m = 1, maxit = 1, print = FALSE, seed = 3) +imp2 <- mice( + data, + formulas = form2, + pred = matrix(1, nr = 4, nc = 4), + m = 1, + maxit = 1, + print = FALSE, + seed = 3 +) test_that("Case F dots and specified form produce same imputes", { expect_identical(complete(imp1), complete(imp2)) }) @@ -244,8 +365,24 @@ test_that("Case F generates error if it cannot handle non-square predictor", { }) ## Error in formulas[[h]] : subscript out of bounds -imp3 <- mice(data, formulas = form3, pred = pred1, m = 1, maxit = 0, print = FALSE, seed = 3) -imp3a <- mice(data, formulas = form3, pred = pred1, m = 1, maxit = 1, print = FALSE, seed = 3) +imp3 <- mice( + data, + formulas = form3, + pred = pred1, + m = 1, + maxit = 0, + print = FALSE, + seed = 3 +) +imp3a <- mice( + data, + formulas = form3, + pred = pred1, + m = 1, + maxit = 1, + print = FALSE, + seed = 3 +) # err on matrix columns nh <- nhanes @@ -256,4 +393,3 @@ test_that("MICE does not accept data.frames with embedded matrix ", { "Cannot handle columns with class matrix: hyp" ) }) - diff --git a/tests/testthat/test-mice.R b/tests/testthat/test-mice.R index 62730de08..b6cedcc68 100644 --- a/tests/testthat/test-mice.R +++ b/tests/testthat/test-mice.R @@ -15,25 +15,49 @@ test_that("Data set in returned mids object is identical to nhanes data set", { context("mice: blocks") test_that("blocks run as expected", { - expect_silent(imp1b <<- mice(nhanes, - blocks = list(c("age", "hyp"), chl = "chl", "bmi"), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) - expect_silent(imp2b <<- mice(nhanes2, - blocks = list(c("age", "hyp", "bmi"), "chl", "bmi"), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) + expect_silent( + imp1b <<- mice( + nhanes, + blocks = list(c("age", "hyp"), chl = "chl", "bmi"), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) + expect_silent( + imp2b <<- mice( + nhanes2, + blocks = list(c("age", "hyp", "bmi"), "chl", "bmi"), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) # expect_silent(imp3b <<- mice(nhanes2, # blocks = list(c("hyp", "hyp", "hyp"), "chl", "bmi"), # print = FALSE, m = 1, maxit = 1, seed = 1)) - expect_silent(imp4b <<- mice(boys, - blocks = list(c("gen", "phb"), "tv"), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) - expect_silent(imp5b <<- mice(nhanes, - blocks = list(c("age", "hyp")), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) + expect_silent( + imp4b <<- mice( + boys, + blocks = list(c("gen", "phb"), "tv"), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) + expect_silent( + imp5b <<- mice( + nhanes, + blocks = list(c("age", "hyp")), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) }) test_that("Block names are generated automatically", { @@ -55,21 +79,37 @@ test_that("Method `polr` works with one block", { # check for equality of `scatter` and `collect` for univariate models # the following models yield the same imputations -imp1 <- mice(nhanes, +imp1 <- mice( + nhanes, blocks = make.blocks(nhanes, "scatter"), - print = FALSE, m = 1, maxit = 1, seed = 123 + print = FALSE, + m = 1, + maxit = 1, + seed = 123 ) -imp2 <- mice(nhanes, +imp2 <- mice( + nhanes, blocks = make.blocks(nhanes, "collect"), - print = FALSE, m = 1, maxit = 1, seed = 123 + print = FALSE, + m = 1, + maxit = 1, + seed = 123 ) -imp3 <- mice(nhanes, +imp3 <- mice( + nhanes, blocks = list("age", c("bmi", "hyp", "chl")), - print = FALSE, m = 1, maxit = 1, seed = 123 + print = FALSE, + m = 1, + maxit = 1, + seed = 123 ) -imp4 <- mice(nhanes, +imp4 <- mice( + nhanes, blocks = list(c("bmi", "hyp", "chl"), "age"), - print = FALSE, m = 1, maxit = 1, seed = 123 + print = FALSE, + m = 1, + maxit = 1, + seed = 123 ) test_that("Univariate yield same imputes for `scatter` and `collect`", { @@ -90,38 +130,62 @@ test_that("Univariate yield same imputes for `scatter` and `collect`", { context("mice: formulas") test_that("formulas run as expected", { - expect_silent(imp1f <<- mice(nhanes, - formulas = list( - age + hyp ~ chl + bmi, - chl ~ age + hyp + bmi, - bmi ~ age + hyp + chl - ), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) - expect_warning(imp2f <<- mice(nhanes2, - formulas = list( - age + hyp + bmi ~ chl + bmi, - chl ~ age + hyp + bmi + bmi, - bmi ~ age + hyp + bmi + chl - ), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) + expect_silent( + imp1f <<- mice( + nhanes, + formulas = list( + age + hyp ~ chl + bmi, + chl ~ age + hyp + bmi, + bmi ~ age + hyp + chl + ), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) + expect_warning( + imp2f <<- mice( + nhanes2, + formulas = list( + age + hyp + bmi ~ chl + bmi, + chl ~ age + hyp + bmi + bmi, + bmi ~ age + hyp + bmi + chl + ), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) # expect_silent(imp3f <<- mice(nhanes2, # formulas = list( hyp + hyp + hyp ~ chl + bmi, # chl ~ hyp + hyp + hyp + bmi, # bmi ~ hyp + hyp + hyp + chl), # print = FALSE, m = 1, maxit = 1, seed = 1)) - expect_silent(imp4f <<- mice(boys, - formulas = list( - gen + phb ~ tv, - tv ~ gen + phb - ), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) - expect_silent(imp5f <<- mice(nhanes, - formulas = list(age + hyp ~ 1), - print = FALSE, m = 1, maxit = 1, seed = 1 - )) + expect_silent( + imp4f <<- mice( + boys, + formulas = list( + gen + phb ~ tv, + tv ~ gen + phb + ), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) + expect_silent( + imp5f <<- mice( + nhanes, + formulas = list(age + hyp ~ 1), + print = FALSE, + m = 1, + maxit = 1, + seed = 1 + ) + ) }) test_that("Formula names are generated automatically", { @@ -144,35 +208,50 @@ test_that("Method `polr` works with one block", { context("mice: where") # # all TRUE -imp1 <- mice(nhanes, - where = matrix(TRUE, nrow = 25, ncol = 4), maxit = 1, - m = 1, print = FALSE +imp1 <- mice( + nhanes, + where = matrix(TRUE, nrow = 25, ncol = 4), + maxit = 1, + m = 1, + print = FALSE ) # # all FALSE -imp2 <- mice(nhanes, - where = matrix(FALSE, nrow = 25, ncol = 4), maxit = 1, - m = 1, print = FALSE +imp2 <- mice( + nhanes, + where = matrix(FALSE, nrow = 25, ncol = 4), + maxit = 1, + m = 1, + print = FALSE ) # # alternate -imp3 <- mice(nhanes, +imp3 <- mice( + nhanes, where = matrix(c(FALSE, TRUE), nrow = 25, ncol = 4), - maxit = 1, m = 1, print = FALSE + maxit = 1, + m = 1, + print = FALSE ) # # whacky situation where we expect no imputes for the incomplete cases -imp4 <- mice(nhanes2, +imp4 <- mice( + nhanes2, where = matrix(TRUE, nrow = 25, ncol = 4), maxit = 1, - meth = c("pmm", "", "", ""), m = 1, print = FALSE + meth = c("pmm", "", "", ""), + m = 1, + print = FALSE ) test_that("`where` produces correct number of imputes", { expect_identical(nrow(imp1$imp$age), 25L) expect_identical(nrow(imp2$imp$age), 0L) expect_identical(nrow(imp3$imp$age), 12L) - expect_identical(sum(is.na(imp4$imp$age)), nrow(nhanes2) - sum(complete.cases(nhanes2))) + expect_identical( + sum(is.na(imp4$imp$age)), + nrow(nhanes2) - sum(complete.cases(nhanes2)) + ) }) @@ -189,8 +268,12 @@ test_that("`ignore` throws appropriate errors and warnings", { "not a logical" ) expect_warning( - mice(nhanes, - maxit = 1, m = 1, print = FALSE, seed = 1, + mice( + nhanes, + maxit = 1, + m = 1, + print = FALSE, + seed = 1, ignore = c(rep(FALSE, 9), rep(TRUE, nrow(nhanes) - 9)) ), "Fewer than 10 rows" @@ -201,8 +284,12 @@ test_that("`ignore` throws appropriate errors and warnings", { # Check that the ignore argument is taken into account when # calculating the results # # all FALSE -imp1 <- mice(nhanes, - maxit = 1, m = 1, print = FALSE, seed = 1, +imp1 <- mice( + nhanes, + maxit = 1, + m = 1, + print = FALSE, + seed = 1, ignore = rep(FALSE, nrow(nhanes)) ) @@ -211,8 +298,12 @@ imp2 <- mice(nhanes, maxit = 1, m = 1, print = FALSE, seed = 1) # # alternate alternate <- rep(c(TRUE, FALSE), nrow(nhanes))[1:nrow(nhanes)] -imp3 <- mice(nhanes, - maxit = 0, m = 1, print = FALSE, seed = 1, +imp3 <- mice( + nhanes, + maxit = 0, + m = 1, + print = FALSE, + seed = 1, ignore = alternate ) @@ -234,12 +325,22 @@ artificial <- data.frame( imp1 <- mice( rbind(nhanes, artificial), - maxit = 1, m = 1, print = FALSE, seed = 1, donors = 1L, matchtype = 0 + maxit = 1, + m = 1, + print = FALSE, + seed = 1, + donors = 1L, + matchtype = 0 ) imp2 <- mice( rbind(nhanes, artificial), - maxit = 1, m = 1, print = FALSE, seed = 1, donors = 1L, matchtype = 0, + maxit = 1, + m = 1, + print = FALSE, + seed = 1, + donors = 1L, + matchtype = 0, ignore = c(rep(FALSE, nrow(nhanes)), rep(TRUE, nrow(artificial))) ) diff --git a/tests/testthat/test-mice.impute.2l.bin.R b/tests/testthat/test-mice.impute.2l.bin.R index 1e4d59d63..91f4d4fa6 100644 --- a/tests/testthat/test-mice.impute.2l.bin.R +++ b/tests/testthat/test-mice.impute.2l.bin.R @@ -20,7 +20,14 @@ pred["outcome", "patientID"] <- -2 ## `... <- NULL` produced warnings. ## expect_silent() -imp <- mice(data, method = "2l.bin", print = FALSE, pred = pred, m = 1, maxit = 1) +imp <- mice( + data, + method = "2l.bin", + print = FALSE, + pred = pred, + m = 1, + maxit = 1 +) test_that("mice::mice.impute.2l.bin() accepts factor outcome", { expect_false(anyNA(complete(imp))) }) @@ -35,6 +42,15 @@ pred <- make.predictorMatrix(data) pred["outcome", "ID"] <- -2 test_that("mice::mice.impute.2l.bin() accepts 0/1 outcome", { - expect_silent(imp <- mice(data, method = "2l.bin", print = FALSE, pred = pred, m = 1, maxit = 1)) + expect_silent( + imp <- mice( + data, + method = "2l.bin", + print = FALSE, + pred = pred, + m = 1, + maxit = 1 + ) + ) expect_false(anyNA(complete(imp))) }) diff --git a/tests/testthat/test-mice.impute.2l.lmer.R b/tests/testthat/test-mice.impute.2l.lmer.R index c5cae0ea4..24ae95f89 100644 --- a/tests/testthat/test-mice.impute.2l.lmer.R +++ b/tests/testthat/test-mice.impute.2l.lmer.R @@ -5,7 +5,16 @@ pred <- make.predictorMatrix(d) pred["lpo", "sch"] <- -2 test_that("mice::mice.impute.2l.lmer() runs empty model", { - expect_silent(imp <- mice(d, method = "2l.lmer", print = FALSE, pred = pred, m = 1, maxit = 1)) + expect_silent( + imp <- mice( + d, + method = "2l.lmer", + print = FALSE, + pred = pred, + m = 1, + maxit = 1 + ) + ) expect_false(anyNA(complete(imp))) }) @@ -15,6 +24,15 @@ pred <- make.predictorMatrix(d) pred[c("lpo", "iqv"), "sch"] <- -2 test_that("2l.lmer() runs random intercept, one predictor", { - expect_silent(imp <- mice(d, method = "2l.lmer", print = FALSE, pred = pred, m = 1, maxit = 1)) + expect_silent( + imp <- mice( + d, + method = "2l.lmer", + print = FALSE, + pred = pred, + m = 1, + maxit = 1 + ) + ) expect_false(anyNA(complete(imp))) }) diff --git a/tests/testthat/test-mice.impute.2l.norm.R b/tests/testthat/test-mice.impute.2l.norm.R index f1f91228a..797587c8b 100644 --- a/tests/testthat/test-mice.impute.2l.norm.R +++ b/tests/testthat/test-mice.impute.2l.norm.R @@ -5,6 +5,15 @@ pred <- make.predictorMatrix(d1) pred["lpo", "sch"] <- -2 test_that("mice::mice.impute.2l.norm() runs empty model", { - expect_silent(imp <- mice(d1, method = "2l.norm", print = FALSE, pred = pred, m = 1, maxit = 1)) + expect_silent( + imp <- mice( + d1, + method = "2l.norm", + print = FALSE, + pred = pred, + m = 1, + maxit = 1 + ) + ) expect_false(anyNA(complete(imp))) }) diff --git a/tests/testthat/test-mice.impute.2lonly.norm.R b/tests/testthat/test-mice.impute.2lonly.norm.R index 9824727ee..94be79371 100644 --- a/tests/testthat/test-mice.impute.2lonly.norm.R +++ b/tests/testthat/test-mice.impute.2lonly.norm.R @@ -12,10 +12,26 @@ data <- data.frame( patid = rep(1:4, each = 5), sex = rep(c(1, 2, 1, 2), each = 5), crp = c( - 68, 78, 93, NA, 143, - 5, 7, 9, 13, NA, - 97, NA, 56, 52, 34, - 22, 30, NA, NA, 45 + 68, + 78, + 93, + NA, + 143, + 5, + 7, + 9, + 13, + NA, + 97, + NA, + 56, + 52, + 34, + 22, + 30, + NA, + NA, + 45 ) ) pred <- make.predictorMatrix(data) @@ -26,9 +42,13 @@ data[3, "sex"] <- NA test_that("2lonly.norm stops with partially missing level-2 data", { expect_error( - mice(data, + mice( + data, method = c("", "2lonly.norm", "2l.pan"), - predictorMatrix = pred, maxit = 1, m = 2, print = FALSE + predictorMatrix = pred, + maxit = 1, + m = 2, + print = FALSE ), "Method 2lonly.norm found the following clusters with partially missing\n level-2 data: 1\n Method 2lonly.mean can fix such inconsistencies." ) diff --git a/tests/testthat/test-mice.impute.lasso.logreg.R b/tests/testthat/test-mice.impute.lasso.logreg.R index fc1d955c7..ab5f9668f 100644 --- a/tests/testthat/test-mice.impute.lasso.logreg.R +++ b/tests/testthat/test-mice.impute.lasso.logreg.R @@ -47,20 +47,17 @@ for (j in 1:2) { # Imputations meth <- make.method(X) meth[1:2] <- "lasso.logreg" -durr_default <- mice(X, - m = 2, maxit = 2, method = meth, - print = FALSE -) -durr_custom <- mice(X, - m = 2, maxit = 2, method = meth, +durr_default <- mice(X, m = 2, maxit = 2, method = meth, print = FALSE) +durr_custom <- mice( + X, + m = 2, + maxit = 2, + method = meth, nfolds = 5, print = FALSE ) meth[1:2] <- "logreg" -logreg_default <- mice(X, - m = 2, maxit = 2, method = meth, - print = FALSE -) +logreg_default <- mice(X, m = 2, maxit = 2, method = meth, print = FALSE) # Tests test_that("mice call works", { @@ -232,20 +229,17 @@ for (j in 1:2) { # Imputations meth <- make.method(X) meth[1:2] <- "lasso.select.logreg" -iurr_default <- mice(X, - m = 2, maxit = 2, method = meth, - print = FALSE -) -iurr_custom <- mice(X, - m = 2, maxit = 2, method = meth, - nfolds = 5, - print = FALSE +iurr_default <- mice(X, m = 2, maxit = 2, method = meth, print = FALSE) +iurr_custom <- mice( + X, + m = 2, + maxit = 2, + method = meth, + nfolds = 5, + print = FALSE ) meth[1:2] <- "logreg" -logreg_default <- mice(X, - m = 2, maxit = 2, method = meth, - print = FALSE -) +logreg_default <- mice(X, m = 2, maxit = 2, method = meth, print = FALSE) # Tests test_that("mice call works", { diff --git a/tests/testthat/test-mice.impute.lasso.norm.R b/tests/testthat/test-mice.impute.lasso.norm.R index 241f545d2..c64b37337 100644 --- a/tests/testthat/test-mice.impute.lasso.norm.R +++ b/tests/testthat/test-mice.impute.lasso.norm.R @@ -30,14 +30,20 @@ test_that("Returns requested length", { ######################### boys_cont <- boys[, 1:4] -durr_default <- mice(boys_cont, - m = 2, maxit = 2, method = "lasso.norm", - print = FALSE +durr_default <- mice( + boys_cont, + m = 2, + maxit = 2, + method = "lasso.norm", + print = FALSE ) -durr_custom <- mice(boys_cont, - m = 2, maxit = 2, method = "lasso.norm", - nfolds = 5, - print = FALSE +durr_custom <- mice( + boys_cont, + m = 2, + maxit = 2, + method = "lasso.norm", + nfolds = 5, + print = FALSE ) test_that("mice call works", { @@ -156,14 +162,20 @@ test_that("Returns requested length when all predictors are important", { ######################### boys_cont <- boys[, 1:4] -iurr_default <- mice(boys_cont, - m = 2, maxit = 2, method = "lasso.select.norm", - print = FALSE +iurr_default <- mice( + boys_cont, + m = 2, + maxit = 2, + method = "lasso.select.norm", + print = FALSE ) -iurr_custom <- mice(boys_cont, - m = 2, maxit = 2, method = "lasso.select.norm", - nfolds = 5, - print = FALSE +iurr_custom <- mice( + boys_cont, + m = 2, + maxit = 2, + method = "lasso.select.norm", + nfolds = 5, + print = FALSE ) test_that("mice call works", { diff --git a/tests/testthat/test-mice.impute.logreg.R b/tests/testthat/test-mice.impute.logreg.R index 8573297de..c68b43d88 100644 --- a/tests/testthat/test-mice.impute.logreg.R +++ b/tests/testthat/test-mice.impute.logreg.R @@ -50,5 +50,3 @@ perfectPred <- tryCatch( test_that("Complete separation results in same class as well behaved case", { expect_true(all.equal(class(wellBehaved), class(perfectPred))) }) - - diff --git a/tests/testthat/test-mice.impute.mpmm.R b/tests/testthat/test-mice.impute.mpmm.R index 45171d4f2..3f81cb88e 100644 --- a/tests/testthat/test-mice.impute.mpmm.R +++ b/tests/testthat/test-mice.impute.mpmm.R @@ -6,7 +6,7 @@ x <- rnorm(1000) e <- rnorm(1000, 0, 1) y <- beta1 * x + beta2 * x^2 + e # dat <- data.frame(x = x, x2 = x^2, y = y) # worked -dat <- data.frame(y = y, x = x, x2 = x^2) # did not work +dat <- data.frame(y = y, x = x, x2 = x^2) # did not work m <- as.logical(rbinom(1000, 1, 0.25)) dat[m, c("x", "x2")] <- NA blk <- list("y", c("x", "x2")) @@ -16,5 +16,3 @@ imp <- mice(dat, blocks = blk, method = meth, print = FALSE, m = 1, maxit = 1) test_that("mpmm() works for any column order in data", { expect_identical(complete(imp)$x^2, complete(imp)$x2) }) - - diff --git a/tests/testthat/test-mice.impute.norm.R b/tests/testthat/test-mice.impute.norm.R index 8227db8ad..096821cf3 100644 --- a/tests/testthat/test-mice.impute.norm.R +++ b/tests/testthat/test-mice.impute.norm.R @@ -38,7 +38,11 @@ test_that("Correct estimation method used", { ##################################### # TEST 2: extremely high correlation # ##################################### -x <- matrix(c(1:1000, seq(from = 2, to = 2000, by = 2)) + rnorm(1000), nrow = 1000, ncol = 2) +x <- matrix( + c(1:1000, seq(from = 2, to = 2000, by = 2)) + rnorm(1000), + nrow = 1000, + ncol = 2 +) y <- t(c(5, 3) %*% t(x)) y[5:6] <- NA ry <- !is.na(y) @@ -64,9 +68,36 @@ test_that("Correct estimation method used", { # TEST 3: correct imputation model # ##################################### -expect_warning(imp.qr <- mice(mammalsleep[, -1], ls.meth = "qr", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) -expect_warning(imp.svd <- mice(mammalsleep[, -1], ls.meth = "svd", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) -expect_warning(imp.ridge <- mice(mammalsleep[, -1], ls.meth = "ridge", seed = 123, print = FALSE, use.matcher = TRUE, trimmer = "lindep")) +expect_warning( + imp.qr <- mice( + mammalsleep[, -1], + ls.meth = "qr", + seed = 123, + print = FALSE, + use.matcher = TRUE, + trimmer = "lindep" + ) +) +expect_warning( + imp.svd <- mice( + mammalsleep[, -1], + ls.meth = "svd", + seed = 123, + print = FALSE, + use.matcher = TRUE, + trimmer = "lindep" + ) +) +expect_warning( + imp.ridge <- mice( + mammalsleep[, -1], + ls.meth = "ridge", + seed = 123, + print = FALSE, + use.matcher = TRUE, + trimmer = "lindep" + ) +) test_that("Imputations are equal", { expect_equal(imp.qr$imp, imp.svd$imp) @@ -78,9 +109,15 @@ test_that("Imputations are equal", { ##################################### # test on faulty imputation model (exactly singular system) -expect_warning(imp.qr <- mice(mammalsleep, ls.meth = "qr", seed = 123, print = FALSE)) -expect_warning(imp.svd <- mice(mammalsleep, ls.meth = "svd", seed = 123, print = FALSE)) -expect_warning(imp.ridge <- mice(mammalsleep, ls.meth = "ridge", seed = 123, print = FALSE)) +expect_warning( + imp.qr <- mice(mammalsleep, ls.meth = "qr", seed = 123, print = FALSE) +) +expect_warning( + imp.svd <- mice(mammalsleep, ls.meth = "svd", seed = 123, print = FALSE) +) +expect_warning( + imp.ridge <- mice(mammalsleep, ls.meth = "ridge", seed = 123, print = FALSE) +) test_that("Imputations are not equal", { expect_false(identical(imp.qr$imp, imp.svd$imp)) diff --git a/tests/testthat/test-mice.impute.panImpute.R b/tests/testthat/test-mice.impute.panImpute.R index 453990448..0ceaa3855 100644 --- a/tests/testthat/test-mice.impute.panImpute.R +++ b/tests/testthat/test-mice.impute.panImpute.R @@ -14,9 +14,14 @@ blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("panImpute", "pmm") pred <- make.predictorMatrix(nhanes, blocks = blocks) pred["B1", "hyp"] <- -2 -imp <- mice(nhanes, - blocks = blocks, method = method, pred = pred, - maxit = 1, seed = 1, print = FALSE +imp <- mice( + nhanes, + blocks = blocks, + method = method, + pred = pred, + maxit = 1, + seed = 1, + print = FALSE ) z <- complete(imp) diff --git a/tests/testthat/test-mice.impute.pmm.R b/tests/testthat/test-mice.impute.pmm.R index ec7355db6..e9c96bbc4 100644 --- a/tests/testthat/test-mice.impute.pmm.R +++ b/tests/testthat/test-mice.impute.pmm.R @@ -38,8 +38,18 @@ y <- factor(br[r, "tv"]) # impute factor by optimizing canonical correlation y, x data1 <- data.frame(y, x) test_that("cancor proceeds normally", { - expect_silent(imp1 <- mice(data1, method = "pmm", remove.collinear = FALSE, eps = 0, - maxit = 1, m = 1, print = FALSE, seed = 1)) + expect_silent( + imp1 <- mice( + data1, + method = "pmm", + remove.collinear = FALSE, + eps = 0, + maxit = 1, + m = 1, + print = FALSE, + seed = 1 + ) + ) }) # > cca$xcoef[, 2L] @@ -75,8 +85,18 @@ data2$age25 <- data2$age # impute factor by optimizing canonical correlation y, x test_that("cancor proceeds normally with many duplicates", { - expect_warning(imp2 <- mice(data2, method = "pmm", remove.collinear = FALSE, eps = 0, - maxit = 1, m = 1, seed = 1, print = FALSE)) + expect_warning( + imp2 <- mice( + data2, + method = "pmm", + remove.collinear = FALSE, + eps = 0, + maxit = 1, + m = 1, + seed = 1, + print = FALSE + ) + ) }) # add junk variables @@ -109,7 +129,16 @@ data3$j25 <- rnorm(nrow(data3)) test_that("cancor with many junk variables does not crash", { - expect_warning(imp3 <- mice(data3, method = "pmm", remove.collinear = FALSE, eps = 0, - maxit = 1, m = 1, seed = 1, print = FALSE)) + expect_warning( + imp3 <- mice( + data3, + method = "pmm", + remove.collinear = FALSE, + eps = 0, + maxit = 1, + m = 1, + seed = 1, + print = FALSE + ) + ) }) - diff --git a/tests/testthat/test-models.R b/tests/testthat/test-models.R index 1317f975e..15635ddda 100644 --- a/tests/testthat/test-models.R +++ b/tests/testthat/test-models.R @@ -1,6 +1,13 @@ context("models") -trained <- mice(nhanes2, m = 2, maxit = 1, print = FALSE, tasks = "train", seed = 1) +trained <- mice( + nhanes2, + m = 2, + maxit = 1, + print = FALSE, + tasks = "train", + seed = 1 +) models_env <- import.models.env(trained$models) models_list <- export.models.env(models_env) diff --git a/tests/testthat/test-newdata.R b/tests/testthat/test-newdata.R index 7088192cd..46178c836 100644 --- a/tests/testthat/test-newdata.R +++ b/tests/testthat/test-newdata.R @@ -36,9 +36,14 @@ artificial <- data.frame( row.names = paste0("a", 1:2) ) -imp1 <- mice(nhanes, - maxit = 1, m = 1, print = FALSE, seed = 1, - donors = 1L, matchtype = 0 +imp1 <- mice( + nhanes, + maxit = 1, + m = 1, + print = FALSE, + seed = 1, + donors = 1L, + matchtype = 0 ) imp2 <- mice.mids(imp1, newdata = artificial, maxit = 1, print = FALSE) diff --git a/tests/testthat/test-parallel-miceadds.R b/tests/testthat/test-parallel-miceadds.R index e14a5f0e7..cf76a458a 100644 --- a/tests/testthat/test-parallel-miceadds.R +++ b/tests/testthat/test-parallel-miceadds.R @@ -5,19 +5,30 @@ library(miceadds) set.seed(1) N <- 100 x <- stats::rnorm(N) -z <- 0.5*x + stats::rnorm(N, sd=.7) -y <- stats::rnorm(N, mean=.3*x - .2*z, sd=1 ) -dat <- data.frame(x,z,y) -dat[ seq(1,N,3), c("x","y") ] <- NA -dat[ seq(1,N,4), "z" ] <- NA +z <- 0.5 * x + stats::rnorm(N, sd = .7) +y <- stats::rnorm(N, mean = .3 * x - .2 * z, sd = 1) +dat <- data.frame(x, z, y) +dat[seq(1, N, 3), c("x", "y")] <- NA +dat[seq(1, N, 4), "z"] <- NA #-- use imputation methods from miceadds method <- c("x" = "rlm", "z" = "lm", "y" = "lqs") #-- impute data - single threaded set.seed(1) -expect_silent(imps <- mice::mice(dat, method = method, maxit = 2, print = FALSE)) +expect_silent( + imps <- mice::mice(dat, method = method, maxit = 2, print = FALSE) +) #-- impute data - parallel set.seed(1) -expect_no_error(impp <- mice::mice(dat, method = method, maxit = 2, parallel = TRUE, future.packages = "miceadds", print = FALSE)) +expect_no_error( + impp <- mice::mice( + dat, + method = method, + maxit = 2, + parallel = TRUE, + future.packages = "miceadds", + print = FALSE + ) +) diff --git a/tests/testthat/test-parallel-sampler.R b/tests/testthat/test-parallel-sampler.R index fc32b8546..52598089a 100644 --- a/tests/testthat/test-parallel-sampler.R +++ b/tests/testthat/test-parallel-sampler.R @@ -17,29 +17,87 @@ test_that("sampler() works identically in sequential and parallel modes", { calltypes <- make.calltypes(NULL, predictorMatrix, formulas, "pred") blots <- vector("list", length(blocks)) names(blots) <- names(blocks) - tasks <- check.tasks(tasks = NULL, data, models = NULL, blocks, skip.check.tasks = FALSE) + tasks <- check.tasks( + tasks = NULL, + data, + models = NULL, + blocks, + skip.check.tasks = FALSE + ) models <- NULL post <- rep("", ncol(data)) names(post) <- names(data) visitSequence <- seq_along(blocks) - imp_init <- mice:::initialize.imp(data, m, ignore, where, blocks, visitSequence, method, nmis = colSums(where), data.init = NULL) + imp_init <- mice:::initialize.imp( + data, + m, + ignore, + where, + blocks, + visitSequence, + method, + nmis = colSums(where), + data.init = NULL + ) fromto <- c(1, maxit) # Run sampler in sequential mode - out_seq <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, - visitSequence, predictorMatrix, formulas, calltypes, - blots, tasks, models, - post, fromto, printFlag = FALSE, parallel = FALSE) + out_seq <- mice:::sampler( + data, + m, + ignore, + where, + imp_init, + blocks, + method, + visitSequence, + predictorMatrix, + formulas, + calltypes, + blots, + tasks, + models, + post, + fromto, + printFlag = FALSE, + parallel = FALSE + ) # Reset imputations - imp_init <- mice:::initialize.imp(data, m, ignore, where, blocks, visitSequence, method, nmis = colSums(where), data.init = NULL) + imp_init <- mice:::initialize.imp( + data, + m, + ignore, + where, + blocks, + visitSequence, + method, + nmis = colSums(where), + data.init = NULL + ) # Run sampler in parallel mode future::plan("multisession") - out_par <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, - visitSequence, predictorMatrix, formulas, calltypes, - blots, tasks, models, - post, fromto, printFlag = FALSE, parallel = TRUE) + out_par <- mice:::sampler( + data, + m, + ignore, + where, + imp_init, + blocks, + method, + visitSequence, + predictorMatrix, + formulas, + calltypes, + blots, + tasks, + models, + post, + fromto, + printFlag = FALSE, + parallel = TRUE + ) future::plan("sequential") # Compare output structure and content @@ -63,21 +121,52 @@ test_that("sampler() collects loggedEvents in parallel mode", { calltypes <- make.calltypes(NULL, predictorMatrix, formulas, "pred") blots <- vector("list", length(blocks)) names(blots) <- names(blocks) - tasks <- check.tasks(tasks = NULL, data, models = NULL, blocks, skip.check.tasks = FALSE) + tasks <- check.tasks( + tasks = NULL, + data, + models = NULL, + blocks, + skip.check.tasks = FALSE + ) models <- NULL post <- rep("", ncol(data)) names(post) <- names(data) visitSequence <- seq_along(blocks) - imp_init <- mice:::initialize.imp(data, m, ignore, where, blocks, visitSequence, method, nmis = colSums(where), data.init = NULL) + imp_init <- mice:::initialize.imp( + data, + m, + ignore, + where, + blocks, + visitSequence, + method, + nmis = colSums(where), + data.init = NULL + ) fromto <- c(1, 1) future::plan("multisession") - out <- mice:::sampler(data, m, ignore, where, imp_init, blocks, method, - visitSequence, predictorMatrix, formulas, calltypes, - blots, tasks, models, - post, fromto, printFlag = FALSE, parallel = TRUE) + out <- mice:::sampler( + data, + m, + ignore, + where, + imp_init, + blocks, + method, + visitSequence, + predictorMatrix, + formulas, + calltypes, + blots, + tasks, + models, + post, + fromto, + printFlag = FALSE, + parallel = TRUE + ) future::plan("sequential") expect_true(is.data.frame(out$loggedEvents) || is.null(out$loggedEvents)) }) - diff --git a/tests/testthat/test-parlmice.R b/tests/testthat/test-parlmice.R index 852bac2cf..72f350267 100644 --- a/tests/testthat/test-parlmice.R +++ b/tests/testthat/test-parlmice.R @@ -44,7 +44,10 @@ test_that("n.imp.core not specified", { # Should return error test_that("n.core larger than logical CPU cores", { - expect_error(suppresWarnings(parlmice(nhanes, n.core = parallel::detectCores() + 1))) + expect_error(suppresWarnings(parlmice( + nhanes, + n.core = parallel::detectCores() + 1 + ))) }) # # NOT RUN ON R CMD CHECK AND CRAN CHECK - TOO MANY PARALLEL PROCESSES SPAWNED diff --git a/tests/testthat/test-pool.R b/tests/testthat/test-pool.R index 8fa8891ef..6acc31167 100644 --- a/tests/testthat/test-pool.R +++ b/tests/testthat/test-pool.R @@ -8,7 +8,14 @@ context("pool") # https://stefvanbuuren.name/fimd/ suppressWarnings(RNGversion("3.5.0")) -imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE, trimmer = "lindep") +imp <- mice( + nhanes2, + print = FALSE, + maxit = 2, + seed = 121, + use.matcher = TRUE, + trimmer = "lindep" +) fit <- with(imp, lm(bmi ~ chl + age + hyp)) est <- pool(fit) # fitlist <- fit$analyses @@ -29,7 +36,10 @@ test_that("retains same numerical result", { imp <- mice(nhanes2, print = FALSE, m = 10, seed = 219) fit0 <- with(data = imp, expr = glm(hyp == "yes" ~ 1, family = binomial)) -fit1 <- with(data = imp, expr = glm(hyp == "yes" ~ chl + bmi, family = binomial)) +fit1 <- with( + data = imp, + expr = glm(hyp == "yes" ~ chl + bmi, family = binomial) +) D1(fit1, fit0) D3(fit1, fit0) @@ -66,7 +76,9 @@ LLlogistic <- function(formula, data, coefs) { logistic <- function(mu) exp(mu) / (1 + exp(mu)) Xb <- model.matrix(formula, data) %*% coefs y <- model.frame(formula, data)[1][, 1] - if (is.factor(y)) y <- (0:1)[y] + if (is.factor(y)) { + y <- (0:1)[y] + } p <- logistic(Xb) ## in case values of categorical var are other than 0 and 1. y <- (y - min(y)) / (max(y) - min(y)) @@ -81,7 +93,11 @@ model.null <- update(model1, formula = . ~ 1) ll1 <- LLlogistic(formula = formula(model1), data = bwt, coefs = coef(model1)) ll0 <- LLlogistic(formula = formula(model0), data = bwt, coefs = coef(model0)) -llnull <- LLlogistic(formula = formula(model.null), data = bwt, coefs = coef(model.null)) +llnull <- LLlogistic( + formula = formula(model.null), + data = bwt, + coefs = coef(model.null) +) identical(deviance(model1), ll1, num.eq = FALSE) identical(deviance(model0), ll0, num.eq = FALSE) @@ -107,8 +123,17 @@ bwt.mis$smoke[runif(nrow(bwt)) < 0.001] <- NA bwt.mis$lwt[runif(nrow(bwt)) < 0.01] <- NA imp <- mice(bwt.mis, print = FALSE, m = 10) -fit1 <- with(data = imp, expr = glm(low ~ age + lwt + race + smoke + ptd + ht + ui + ftv, family = binomial)) -fit0 <- with(data = imp, glm(low ~ lwt + race + smoke + ptd + ht + ui, family = binomial)) +fit1 <- with( + data = imp, + expr = glm( + low ~ age + lwt + race + smoke + ptd + ht + ui + ftv, + family = binomial + ) +) +fit0 <- with( + data = imp, + glm(low ~ lwt + race + smoke + ptd + ht + ui, family = binomial) +) D1(fit1, fit0) D3(fit1, fit0) @@ -118,15 +143,18 @@ D3(fit1, fit0) fit <- lm(bmi ~ age + hyp + chl, data = nhanes) coef(fit) formula(fit) -newformula <- bmi ~ 0 + I(18.26966503 - 5.78652468 * age + 2.10467529 * hyp + 0.08044924 * chl) -newformula <- . ~ 0 + I(18.26966503 * 1L - 5.78652468 * age + 2.10467529 * hyp + 0.08044924 * chl) +newformula <- bmi ~ 0 + + I(18.26966503 - 5.78652468 * age + 2.10467529 * hyp + 0.08044924 * chl) +newformula <- . ~ 0 + + I(18.26966503 * 1L - 5.78652468 * age + 2.10467529 * hyp + 0.08044924 * chl) fit2 <- update(fit, formula = newformula) coef(fit2) summary(fit) summary(fit2) cor(predict(fit), predict(fit) + residuals(fit))^2 cor(predict(fit2), predict(fit2) + residuals(fit2))^2 -newformula <- bmi ~ 0 + offset(18.26966503 - 5.78652468 * age + 2.10467529 * hyp + 0.08044924 * chl) +newformula <- bmi ~ 0 + + offset(18.26966503 - 5.78652468 * age + 2.10467529 * hyp + 0.08044924 * chl) fit3 <- update(fit, formula = newformula) coef(fit3) summary(fit3) @@ -137,8 +165,12 @@ suppressPackageStartupMessages(library(mitml, quietly = TRUE)) library(lme4, quietly = TRUE) data(studentratings) fml <- ReadDis + SES ~ ReadAchiev + (1 | ID) -imp <- mitml::panImpute(studentratings, - formula = fml, n.burn = 1000, n.iter = 100, m = 5, +imp <- mitml::panImpute( + studentratings, + formula = fml, + n.burn = 1000, + n.iter = 100, + m = 5, silent = TRUE ) implist <- mitml::mitmlComplete(imp, print = 1:5) diff --git a/tests/testthat/test-predict_mi.R b/tests/testthat/test-predict_mi.R index 8499fe711..ad70b7250 100644 --- a/tests/testthat/test-predict_mi.R +++ b/tests/testthat/test-predict_mi.R @@ -15,27 +15,38 @@ dat$set <- c(rep("train", 20), rep("test", 5)) # Make prediction matrix and ensure that set is not used as a predictor predmat <- mice::make.predictorMatrix(dat) -predmat[,"set"] <- 0 +predmat[, "set"] <- 0 # train imputation model -imp <- mice(dat, m = 5, maxit = 5 ,seed = 1, - predictorMatrix = predmat, - ignore = ifelse(dat$set == "test", TRUE, FALSE)) +imp <- mice( + dat, + m = 5, + maxit = 5, + seed = 1, + predictorMatrix = predmat, + ignore = ifelse(dat$set == "test", TRUE, FALSE) +) # extract the training and test data sets impdats <- mice::complete(imp, "all") -traindats <- lapply(impdats, function(dat) subset(dat, set == "train", select = -set)) -testdats <- lapply(impdats, function(dat) subset(dat, set == "test", select = -c(set))) +traindats <- lapply(impdats, function(dat) { + subset(dat, set == "train", select = -set) +}) +testdats <- lapply(impdats, function(dat) { + subset(dat, set == "test", select = -c(set)) +}) # predict age with other variables with training datasets fits <- lapply(traindats, function(dat) lm(age ~ bmi + hyp + chl, data = dat)) # pool the predictions with function -pool_preds <- predict_mi(object = fits, - newdata = testdats, - pool = TRUE, - interval = "prediction", - level = 0.95) +pool_preds <- predict_mi( + object = fits, + newdata = testdats, + pool = TRUE, + interval = "prediction", + level = 0.95 +) # obtain the imputed value and the width of the prediction interval predfunc <- function(model, data, level) { @@ -44,10 +55,10 @@ predfunc <- function(model, data, level) { xmat <- model.matrix(summfit$terms, data = as.data.frame(data)) se <- sqrt(1 + rowSums((xmat %*% summfit$cov.unscaled) * xmat)) xfit <- xmat %*% model$coefficients - + # variance var_pred = se^2 * sigma^2 - + return(cbind(model = xfit, var_pred = var_pred)) } @@ -55,7 +66,7 @@ predfunc <- function(model, data, level) { preds_all <- Map(predfunc, model = fits, data = testdats, level = 0.95) # change the list to a df -preds <- unlist(preds_all) %>% +preds <- unlist(preds_all) %>% array(dim = c(nrow(testdats[[1]]), 2, 5)) # our estimand is the predicted value @@ -77,7 +88,7 @@ t_vector <- numeric(length(Q_bar)) for (i in 1:length(Q_bar)) { df_vector[i] <- mice:::barnard.rubin(5, B[i], T_var[i]) - t_vector[i] <- qt(1 - (1-0.95)/2, df_vector[i]) + t_vector[i] <- qt(1 - (1 - 0.95) / 2, df_vector[i]) } # Calculate bounds using individual t-values @@ -86,8 +97,8 @@ upr <- Q_bar + t_vector * sqrt(T_var) # check if the output structure is as expected test_that("Output class is correct", { - expect_type(pool_preds, "double") # checks storage type - expect_true(is.matrix(pool_preds)) # checks it's a matrix + expect_type(pool_preds, "double") # checks storage type + expect_true(is.matrix(pool_preds)) # checks it's a matrix }) # check if result is the same, for by hand or in function @@ -97,4 +108,3 @@ test_that("retains same numerical result", { expect_equal(unname(pool_preds[, 2]), lwr, tolerance = 0.00001) expect_equal(unname(pool_preds[, 3]), upr, tolerance = 0.00001) }) - diff --git a/tests/testthat/test-quantify.R b/tests/testthat/test-quantify.R index 45e5a8e8b..27881158b 100644 --- a/tests/testthat/test-quantify.R +++ b/tests/testthat/test-quantify.R @@ -1,19 +1,30 @@ set.seed(123) test_that("quantify() and unquantify() work correctly for factors", { - y <- factor(sample(c("A", "B", "C"), 10, replace = TRUE), levels = c("A", "B", "C")) + y <- factor( + sample(c("A", "B", "C"), 10, replace = TRUE), + levels = c("A", "B", "C") + ) x <- matrix(rnorm(10 * 3), ncol = 3) ry <- sample(c(TRUE), 10, replace = TRUE) # Quantify the factor (optimal scaling) f <- mice:::quantify(y, ry, x) ynum_quantified <- f$ynum - y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + y_reconstructed_quantified <- mice:::unquantify( + ynum_quantified, + quant = f$quant, + labels = f$labels + ) expect_equal(y, y_reconstructed_quantified) # Integer coding f <- mice:::quantify(y, ry, x, quantify = FALSE) ynum_quantified <- f$ynum - y_reconstructed_integer <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + y_reconstructed_integer <- mice:::unquantify( + ynum_quantified, + quant = f$quant, + labels = f$labels + ) expect_equal(y, y_reconstructed_integer) # Test 1: Levels should remain in the original order @@ -27,11 +38,15 @@ test_that("quantify() and unquantify() work correctly for factors", { # Handle missing values, with extra level y_with_na <- y y_with_na[c(2, 5)] <- NA - ry[c(2,5)] <- FALSE + ry[c(2, 5)] <- FALSE f <- mice:::quantify(y_with_na, ry, x) ynum_quantified_na <- f$ynum - y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, quant = f$quant, labels = f$labels) + y_reconstructed_na <- mice:::unquantify( + ynum_quantified_na, + quant = f$quant, + labels = f$labels + ) expect_equal(y_with_na, y_reconstructed_na) expect_true(is.na(y_reconstructed_na[2])) @@ -47,23 +62,35 @@ test_that("quantify() and unquantify() work correctly for numeric variables", { # Pass through a numeric variable f <- mice:::quantify(y, ry, x) ynum_quantified <- f$ynum - y_reconstructed_quantified <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + y_reconstructed_quantified <- mice:::unquantify( + ynum_quantified, + quant = f$quant, + labels = f$labels + ) expect_equal(y, y_reconstructed_quantified) # Pass through, integer coding f <- mice:::quantify(y, ry, x, quantify = FALSE) ynum_quantified <- f$ynum - y_reconstructed_integer <- mice:::unquantify(ynum_quantified, quant = f$quant, labels = f$labels) + y_reconstructed_integer <- mice:::unquantify( + ynum_quantified, + quant = f$quant, + labels = f$labels + ) expect_equal(y, y_reconstructed_integer) # Handle missing values, with extra level y_with_na <- y y_with_na[c(2, 5)] <- NA - ry[c(2,5)] <- FALSE + ry[c(2, 5)] <- FALSE f <- mice:::quantify(y_with_na, ry, x) ynum_quantified_na <- f$ynum - y_reconstructed_na <- mice:::unquantify(ynum_quantified_na, quant = f$quant, labels = f$labels) + y_reconstructed_na <- mice:::unquantify( + ynum_quantified_na, + quant = f$quant, + labels = f$labels + ) expect_equal(y_with_na, y_reconstructed_na) }) @@ -100,4 +127,3 @@ test_that("quantify and unquantify handle small sample edge cases", { # will be identical to the original factor expect_true(identical(y2_back, y2)) }) - diff --git a/tests/testthat/test-quickpred.R b/tests/testthat/test-quickpred.R index ca96549a0..ccd9cb591 100644 --- a/tests/testthat/test-quickpred.R +++ b/tests/testthat/test-quickpred.R @@ -1,64 +1,54 @@ context("quickpred") test_that("returns square binary matrix", { + predictorMatrix <- quickpred(nhanes) - predictorMatrix <- quickpred(nhanes) - - expect_is(predictorMatrix, 'matrix') - expect_equal(nrow(predictorMatrix), ncol(predictorMatrix)) - expect_in(predictorMatrix, c(0, 1)) - + expect_is(predictorMatrix, 'matrix') + expect_equal(nrow(predictorMatrix), ncol(predictorMatrix)) + expect_in(predictorMatrix, c(0, 1)) }) test_that("mincor supports scalar, vector, matrix", { - - n_col <- ncol(nhanes) - expect_in(quickpred(nhanes, mincor=0), c(0, 1)) - expect_in(quickpred(nhanes, mincor=1), 0) - expect_in(quickpred(nhanes, mincor=rep(0.1, n_col)), c(0, 1)) - expect_in( - quickpred(nhanes, mincor=matrix(rep(0.1, n_col*n_col), ncol=n_col)), - c(0, 1) - ) - + n_col <- ncol(nhanes) + expect_in(quickpred(nhanes, mincor = 0), c(0, 1)) + expect_in(quickpred(nhanes, mincor = 1), 0) + expect_in(quickpred(nhanes, mincor = rep(0.1, n_col)), c(0, 1)) + expect_in( + quickpred(nhanes, mincor = matrix(rep(0.1, n_col * n_col), ncol = n_col)), + c(0, 1) + ) }) test_that("minpuc supports scalar, vector, matrix", { - - n_col <- ncol(nhanes) - expect_in(quickpred(nhanes, minpuc=0), c(0, 1)) - expect_in(quickpred(nhanes, minpuc=rep(0.1, n_col)), c(0, 1)) - expect_in( - quickpred(nhanes, minpuc=matrix(rep(0.1, n_col*n_col), ncol=n_col)), - c(0, 1) - ) - + n_col <- ncol(nhanes) + expect_in(quickpred(nhanes, minpuc = 0), c(0, 1)) + expect_in(quickpred(nhanes, minpuc = rep(0.1, n_col)), c(0, 1)) + expect_in( + quickpred(nhanes, minpuc = matrix(rep(0.1, n_col * n_col), ncol = n_col)), + c(0, 1) + ) }) test_that("include one or more variables", { - - result_include_bmi <- quickpred(nhanes, include="bmi") - has_missing <- apply(is.na(nhanes), 2, any) - not_bmi <- setdiff(names(nhanes)[has_missing], "bmi") - expect_in(result_include_bmi[not_bmi, "bmi"], 1) - - expect_in(quickpred(nhanes, include=names(nhanes)), c(0, 1)) - - n_col <- ncol(nhanes) - result_include_all <- quickpred(nhanes, include=names(nhanes)) - expect_in( - result_include_all[has_missing, ] - (1 - diag(n_col)[has_missing,]), - 0 - ) - + result_include_bmi <- quickpred(nhanes, include = "bmi") + has_missing <- apply(is.na(nhanes), 2, any) + not_bmi <- setdiff(names(nhanes)[has_missing], "bmi") + expect_in(result_include_bmi[not_bmi, "bmi"], 1) + + expect_in(quickpred(nhanes, include = names(nhanes)), c(0, 1)) + + n_col <- ncol(nhanes) + result_include_all <- quickpred(nhanes, include = names(nhanes)) + expect_in( + result_include_all[has_missing, ] - (1 - diag(n_col)[has_missing, ]), + 0 + ) }) test_that("exclude one or more variables", { + result_exclude_age <- quickpred(nhanes, exclude = "age") + expect_in(result_exclude_age[, "age"], 0) - result_exclude_age <- quickpred(nhanes, exclude="age") - expect_in(result_exclude_age[, "age"], 0) - - result_exclude_all <- quickpred(nhanes, exclude=names(nhanes)) - expect_in(result_exclude_all, 0) - + result_exclude_all <- quickpred(nhanes, exclude = names(nhanes)) + expect_in(result_exclude_all, 0) }) diff --git a/tests/testthat/test-rbind.R b/tests/testthat/test-rbind.R index 6dfff3909..517e4b3ba 100644 --- a/tests/testthat/test-rbind.R +++ b/tests/testthat/test-rbind.R @@ -5,10 +5,26 @@ test_that("Constant variables are not imputed by default", { expect_equal(sum(is.na(complete(imp1))), 6L) }) -expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, remove.constant = FALSE)) +expect_warning( + imp1b <<- mice( + nhanes[1:13, ], + m = 2, + maxit = 1, + print = FALSE, + remove.constant = FALSE + ) +) test_that("Constant variables are imputed for remove.constant = FALSE", { - expect_warning(imp1b <<- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE, - remove.constant = FALSE, trimmer = "lindep")) + expect_warning( + imp1b <<- mice( + nhanes[1:13, ], + m = 2, + maxit = 1, + print = FALSE, + remove.constant = FALSE, + trimmer = "lindep" + ) + ) expect_equal(sum(is.na(complete(imp1b))), 0L) }) @@ -16,14 +32,19 @@ imp2 <- mice(nhanes[14:25, ], m = 2, maxit = 1, print = FALSE) imp3 <- mice(nhanes2, m = 2, maxit = 1, print = FALSE) imp4 <- mice(nhanes2, m = 1, maxit = 1, print = FALSE) expect_warning(imp5 <<- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE)) -expect_error(imp6 <<- mice(nhanes[1:13, 2:3], m = 2, maxit = 2, print = FALSE), - "`mice` detected constant and/or collinear variables. No predictors were left after their removal.") +expect_error( + imp6 <<- mice(nhanes[1:13, 2:3], m = 2, maxit = 2, print = FALSE), + "`mice` detected constant and/or collinear variables. No predictors were left after their removal." +) nh3 <- nhanes colnames(nh3) <- c("AGE", "bmi", "hyp", "chl") imp7 <- mice(nh3[14:25, ], m = 2, maxit = 2, print = FALSE) expect_warning(imp8 <<- mice(nhanes[1:13, ], m = 2, maxit = 2, print = FALSE)) -imp9 <- mice(nhanes, - m = 2, maxit = 1, print = FALSE, +imp9 <- mice( + nhanes, + m = 2, + maxit = 1, + print = FALSE, ignore = c(rep(FALSE, 20), rep(TRUE, 5)) ) @@ -85,9 +106,13 @@ set.seed <- 818 x <- rnorm(10) D <- data.frame(x = x, y = 2 * x + rnorm(10)) D[c(2:4, 7), 1] <- NA -expect_error(D_mids <<- mice(D[1:5, ], print = FALSE), - "`mice` detected constant and/or collinear variables. No predictors were left after their removal.") -expect_warning(D_mids <<- mice(D[1:5, ], print = FALSE, remove.collinear = FALSE)) +expect_error( + D_mids <<- mice(D[1:5, ], print = FALSE), + "`mice` detected constant and/or collinear variables. No predictors were left after their removal." +) +expect_warning( + D_mids <<- mice(D[1:5, ], print = FALSE, remove.collinear = FALSE) +) D_rbind <- mice:::rbind.mids(D_mids, D[6:10, ]) cmp <- complete(D_rbind, 1) @@ -121,7 +146,14 @@ odd <- as.logical((1:nrow(data)) %% 2) # method 1: ignore + where where <- make.where(data) where[odd, ] <- FALSE -imp1 <- mice(nhanes, ignore = odd, where = where, seed = 1, m = 2, print = FALSE) +imp1 <- mice( + nhanes, + ignore = odd, + where = where, + seed = 1, + m = 2, + print = FALSE +) c1 <- complete(imp1, 2) # method 2: filter + rbind diff --git a/tests/testthat/test-tasks.R b/tests/testthat/test-tasks.R index 7737fc70b..b5cd591fa 100644 --- a/tests/testthat/test-tasks.R +++ b/tests/testthat/test-tasks.R @@ -1,29 +1,116 @@ context("tasks") test_that("m filling recycles training models", { - expect_silent(imp1 <- mice(nhanes2, m = 2, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) + expect_silent( + imp1 <- mice( + nhanes2, + m = 2, + maxit = 1, + tasks = "train", + method = "pmm", + print = FALSE + ) + ) expect_false(is.null(imp1$models$bmi[[1]]$lookup)) - expect_silent(imp2 <- mice(nhanes2, m = 4, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) - expect_silent(imp2 <- mice(nhanes2[1, ], m = 2, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent( + imp2 <- mice( + nhanes2, + m = 4, + maxit = 1, + tasks = "fill", + method = "pmm", + models = imp1$models, + print = FALSE + ) + ) + expect_silent( + imp2 <- mice( + nhanes2[1, ], + m = 2, + maxit = 1, + tasks = "fill", + method = "pmm", + models = imp1$models, + print = FALSE + ) + ) }) test_that("fully synthetic datasets can be created from completely observed variables", { - dataset <- complete(mice(nhanes2, m = 1, maxit = 1, method = "pmm", print = FALSE)) - expect_silent(imp1 <- mice(dataset, m = 2, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) + dataset <- complete(mice( + nhanes2, + m = 1, + maxit = 1, + method = "pmm", + print = FALSE + )) + expect_silent( + imp1 <- mice( + dataset, + m = 2, + maxit = 1, + tasks = "train", + method = "pmm", + print = FALSE + ) + ) expect_false(is.null(imp1$models$age[[1]]$lookup)) - expect_silent(imp2 <- mice(dataset, where = make.where(dataset, "all"), m = 2, maxit = 3, task = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_silent( + imp2 <- mice( + dataset, + where = make.where(dataset, "all"), + m = 2, + maxit = 3, + task = "fill", + method = "pmm", + models = imp1$models, + print = FALSE + ) + ) synt1 <- complete(imp2, 1) synt2 <- complete(imp2, 2) }) test_that("the procedure informs the user about a mismatch between model and data", { - expect_silent(imp1 <- mice(nhanes2, m = 3, maxit = 1, tasks = "train", method = "pmm", print = FALSE)) + expect_silent( + imp1 <- mice( + nhanes2, + m = 3, + maxit = 1, + tasks = "train", + method = "pmm", + print = FALSE + ) + ) newdata <- nhanes2 - newdata$age <- factor(newdata$age, levels = c(levels(newdata$age), "not_a_level")) + newdata$age <- factor( + newdata$age, + levels = c(levels(newdata$age), "not_a_level") + ) newdata$age[1] <- "not_a_level" newdata$age[2] <- NA - expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_error( + imp2 <- mice( + newdata, + m = 1, + maxit = 1, + tasks = "fill", + method = "pmm", + models = imp1$models, + print = FALSE + ) + ) newdata <- nhanes2 levels(newdata$age)[levels(newdata$age) == "60-99"] <- "60+" - expect_error(imp2 <- mice(newdata, m = 1, maxit = 1, tasks = "fill", method = "pmm", models = imp1$models, print = FALSE)) + expect_error( + imp2 <- mice( + newdata, + m = 1, + maxit = 1, + tasks = "fill", + method = "pmm", + models = imp1$models, + print = FALSE + ) + ) }) diff --git a/tests/testthat/test-tidiers.R b/tests/testthat/test-tidiers.R index 611a75c68..f89017807 100644 --- a/tests/testthat/test-tidiers.R +++ b/tests/testthat/test-tidiers.R @@ -1,7 +1,14 @@ context("tidiers") data(nhanes) -imp <- mice::mice(nhanes, maxit = 2, m = 2, seed = 1, print = FALSE, use.matcher = TRUE) +imp <- mice::mice( + nhanes, + maxit = 2, + m = 2, + seed = 1, + print = FALSE, + use.matcher = TRUE +) fit_mira <- with(imp, lm(chl ~ age + bmi)) fit_mipo <- mice::pool(fit_mira) From 55891436d54ff4afbf432493662bd074a729f462 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 11:27:04 +0200 Subject: [PATCH 134/147] Warn on POSIX date-time columns in check.dataform() (#746) Root cause: POSIXct/POSIXlt variables are stored internally as seconds since 1970, yielding values around 1e9-1e10. When used as predictors in norm-based imputation, the resulting X'X matrix has diagonal entries around 1e18-1e20, making it computationally singular and causing solve() to fail with a near-zero reciprocal condition number. Consequence: mice() crashes with an opaque "system is computationally singular" error in estimice(), giving the user no indication that the cause is a date-time variable with extreme scale. Fix: add a check in check.dataform() that detects POSIXct/POSIXlt columns and emits an early warning naming the offending variables and explaining the remedy (convert to Date, numeric, or a standardised numeric before imputing). The warning fires before any imputation runs, giving the user actionable guidance at the earliest opportunity. --- R/check.R | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/R/check.R b/R/check.R index 153e3d07a..797e00a51 100644 --- a/R/check.R +++ b/R/check.R @@ -33,6 +33,19 @@ check.dataform <- function(data) { paste(colnames(data)[dup], collapse = ", ") ) } + + posix <- sapply(data, function(x) inherits(x, c("POSIXct", "POSIXlt"))) + if (any(posix)) { + warning( + "Data contain POSIX date-time columns: ", + paste(colnames(data)[posix], collapse = ", "), + ".\nPOSIX variables are stored as seconds since 1970 (large numbers ~1e9-1e10) ", + "and can cause near-singular matrices in norm-based imputation methods. ", + "Consider converting to numeric, Date, or a standardised numeric variable before imputing.", + call. = FALSE + ) + } + data } From 71c53af6526e2e46563c0cb8e096d09e5936d79e Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 11:37:23 +0200 Subject: [PATCH 135/147] Improve error when ridge-penalised solve() also fails in estimice() (#746) When solve() fails on the predictor matrix, estimice() applies a ridge penalty and retries. This fixes multicollinearity (correlated columns) but not extreme scale differences: the ridge penalty is proportional to diag(X'X), so it leaves the condition number unchanged when one predictor has values orders of magnitude larger than others (e.g. a POSIXct column stored as ~1e10 seconds). Previously the second solve() call was unprotected, so it crashed with the same opaque "system is computationally singular" message, and the informative multicollinearity message was never reached. Fix: wrap the ridge-penalised solve() in tryCatch() in both the qr and svd branches of estimice(). On failure it now stops with a message that distinguishes the scale problem from the multicollinearity problem and points the user toward the remedy. --- NEWS.md | 3 +++ R/mice.impute.norm.R | 26 ++++++++++++++++++++++++-- 2 files changed, 27 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index ec9bbeff8..09435333b 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,8 @@ # mice 3.18.1.9000 +- Adds an early warning in `mice()` when the data contain `POSIXct` or `POSIXlt` date-time columns. Such variables are stored as large numbers (~1e9–1e10) that can make the predictor matrix near-singular in norm-based methods, causing an opaque `solve()` crash. The warning names the offending columns and suggests converting them to `Date` or a standardised numeric before imputing (#746) +- Improves the error message in `estimice()` when the ridge-penalised `solve()` also fails. Previously this crashed silently; it now stops with a message explaining that extreme predictor scales (e.g. POSIX date-time columns) are the likely cause and suggests standardising or removing such variables (#746) + > **Experimental**: Native support for parallel imputation. - The `mice()` function now supports parallel execution of imputations via the new `parallel = TRUE` argument. When enabled, instead of sequentially calculating `m` imputations at a given iteration, the `m` chains are distributed across available CPU cores using the `future` and `future.apply` frameworks. diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 8ffa0b13e..b8ba5beac 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -169,7 +169,18 @@ estimice <- function(x, y, ls.meth = "qr", ridge = 1e-05, ...) { # calculate ridge penalty pen <- diag(xtx) * ridge # add ridge penalty to allow inverse of v - v <- solve(xtx + diag(pen)) + v <- tryCatch( + solve(xtx + diag(pen)), + error = function(e) { + stop( + "mice could not invert the predictor matrix, even after applying a ridge penalty.\n", + "This is likely caused by predictors on very different scales ", + "(e.g. POSIX date-time variables stored as large numbers ~1e9-1e10).\n", + "Consider standardising your predictors or removing date-time variables before imputing.", + call. = FALSE + ) + } + ) mess <- paste0( "mice detected that your data are (nearly) multi-collinear.\n", "It applied a ridge penalty to continue calculations, but the results can be unstable.\n", @@ -204,7 +215,18 @@ estimice <- function(x, y, ls.meth = "qr", ridge = 1e-05, ...) { # calculate ridge penalty pen <- diag(xtx) * ridge # add ridge penalty to allow inverse of v - v <- solve(xtx + diag(pen)) + v <- tryCatch( + solve(xtx + diag(pen)), + error = function(e) { + stop( + "mice could not invert the predictor matrix, even after applying a ridge penalty.\n", + "This is likely caused by predictors on very different scales ", + "(e.g. POSIX date-time variables stored as large numbers ~1e9-1e10).\n", + "Consider standardising your predictors or removing date-time variables before imputing.", + call. = FALSE + ) + } + ) mess <- paste0( "mice detected that your data are (nearly) multi-collinear.\n", "It applied a ridge penalty to continue calculations, but the results can be unstable.\n", From 9e5fd1c8833bd775e2c575ab45259f079ffe151d Mon Sep 17 00:00:00 2001 From: Frederik Fabricius-Bjerre Date: Wed, 8 Oct 2025 18:46:29 +0200 Subject: [PATCH 136/147] =?UTF-8?q?Update=20BR=20df=20calculation=20to=20h?= =?UTF-8?q?andle=20low=20=CE=BB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- R/barnard.rubin.R | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/R/barnard.rubin.R b/R/barnard.rubin.R index 59d065de3..d1b6536be 100644 --- a/R/barnard.rubin.R +++ b/R/barnard.rubin.R @@ -1,7 +1,10 @@ barnard.rubin <- function(m, b, t, dfcom = Inf) { lambda <- (1 + 1 / m) * b / t - lambda[lambda < 1e-04] <- 1e-04 - dfold <- (m - 1) / lambda^2 - dfobs <- (dfcom + 1) / (dfcom + 3) * dfcom * (1 - lambda) - ifelse(is.infinite(dfcom), dfold, dfold * dfobs / (dfold + dfobs)) + dfold <- (m - 1) / (lambda^2) + if (is.infinite(dfcom)) { + return(dfold) + } + tmp <- (1 - lambda) * (1 + dfcom) * dfcom + df_br <- (m - 1) * tmp / ((dfcom + 3) * (m - 1) + (lambda^2) * tmp) + df_br } From 7bd610945c509c6053845289a57bb04ca973ad37 Mon Sep 17 00:00:00 2001 From: Frederik Fabricius-Bjerre Date: Wed, 8 Oct 2025 18:46:58 +0200 Subject: [PATCH 137/147] =?UTF-8?q?Provide=20tests=20that=20show=20equalit?= =?UTF-8?q?y=20with=20old=20implementation=20when=20=CE=BB>=3D1e-04?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tests/testthat/test-banard.rubin.R | 84 ++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 tests/testthat/test-banard.rubin.R diff --git a/tests/testthat/test-banard.rubin.R b/tests/testthat/test-banard.rubin.R new file mode 100644 index 000000000..de2a193c6 --- /dev/null +++ b/tests/testthat/test-banard.rubin.R @@ -0,0 +1,84 @@ +# old implementation +barnard.rubin_old <- function(m, b, t, dfcom = Inf) { + lambda <- (1 + 1 / m) * b / t + lambda[lambda < 1e-04] <- 1e-04 + dfold <- (m - 1) / lambda^2 + dfobs <- (dfcom + 1) / (dfcom + 3) * dfcom * (1 - lambda) + ifelse(is.infinite(dfcom), dfold, dfold * dfobs / (dfold + dfobs)) +} + +test_that("new BR matches old for lambda >= 1e-4 and dfcom finite", { + set.seed(1) + M <- 10 + df_com <- 100 + t <- 1 + lambdas <- seq(1, 1e-04, length.out = 100) + for (lambda in lambdas) { + b <- lambda * t / (1 + 1 / M) + + old <- barnard.rubin_old(M, b, t, df_com) + new <- barnard.rubin(M, b, t, df_com) + + expect_equal(new, old) + } + df_com <- Inf + for (lambda in lambdas) { + b <- lambda * t / (1 + 1 / M) + + old <- barnard.rubin_old(M, b, t, df_com) + new <- barnard.rubin(M, b, t, df_com) + + expect_equal(new, old) + } +}) + +test_that("new BR differs from old for lambda < 1e-4 and dfcom finite", { + M <- 10 + df_com <- 100 + t <- 1 + lambdas_small <- seq(1e-04, 0, length.out = 100) + for (lambda in lambdas_small[-1]) { + b <- lambda * t / (1 + 1 / M) + + old <- barnard.rubin_old(M, b, t, df_com) + new <- barnard.rubin(M, b, t, df_com) + + expect_true(new != old) + } +}) + +test_that("new BR handles tiny lambda without flooring and approaches correct limit", { + M <- 10 + df_com <- 100 + t <- 1 + lambda <- c(0, 1e-8, 1e-6, 5e-5) # includes values below the old 1e-4 floor + b <- lambda * t / (1 + 1 / M) + + old <- barnard.rubin_old(M, b, t, df_com) + new <- barnard.rubin(M, b, t, df_com) + + # limit at lambda = 0 + limit0 <- df_com * (df_com + 1) / (df_com + 3) + + # new is finite and near the limit; old is biased by flooring + expect_true(all(is.finite(new))) + expect_equal(new[1], limit0) # exactly λ = 0 + expect_true(all(abs(new - limit0) <= abs(old - limit0))) +}) + +test_that("dfcom = Inf reduces to nu_old for both when lambda >= 1e-4", { + M <- 8 + t <- 2 + + for (lambda in c(1e-4, 1e-3, 0.2)) { + b <- lambda * t / (1 + 1 / M) + + old <- barnard.rubin_old(M, b, t, dfcom = Inf) + new <- barnard.rubin(M, b, t, dfcom = Inf) + nu_old <- (M - 1) / (lambda^2) + + # old floors only affects λ < 1e-4, so here they should agree with nu_old + expect_equal(old, nu_old) + expect_equal(new, nu_old) + } +}) From b1ae5dcc579f93d0cfd94e7f86e9762ef56ce096 Mon Sep 17 00:00:00 2001 From: Anya DeCarlo Date: Thu, 6 Nov 2025 18:35:26 -0800 Subject: [PATCH 138/147] Add fallback for lmer objects in pool() without requiring broom.mixed Problem: pool() fails when used with lmer/lmerMod objects unless the broom.mixed package is installed. This forces users to install an entire additional package just to extract coefficients that are already accessible through lme4's built-in fixef() and vcov() methods. Solution: Added a tryCatch wrapper in summary.mira() that detects when tidy() fails for lmerMod objects and falls back to manual coefficient extraction using: - lme4::fixef() for fixed effects estimates - stats::vcov() for variance-covariance matrix - Standard error calculation via sqrt(diag(vcov())) This produces identical results to broom.mixed (verified in test_verify_accuracy.R with zero numerical difference) while eliminating the dependency. Benefits: - Reduces package dependencies and installation overhead - Improves security by avoiding unnecessary package installations - Maintains backward compatibility (still works with broom.mixed if installed) - Uses only built-in methods already present in lme4 and base R Testing: Run test_lmer_fix.R to verify pool() works without broom.mixed Run test_verify_accuracy.R to verify results match broom.mixed exactly Fixes common error: 'No tidy method for objects of class lmerMod' Related to issues about broom.mixed dependency requirements --- R/summary.R | 26 ++++++++++++++++-- test_lmer_fix.R | 35 ++++++++++++++++++++++++ test_verify_accuracy.R | 62 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 121 insertions(+), 2 deletions(-) create mode 100644 test_lmer_fix.R create mode 100644 test_verify_accuracy.R diff --git a/R/summary.R b/R/summary.R index 45044a5b3..08c11bf52 100644 --- a/R/summary.R +++ b/R/summary.R @@ -25,8 +25,30 @@ summary.mira <- function( type <- match.arg(type) fitlist <- getfit(object) if (type == "tidy") { - v <- lapply(fitlist, tidy, effects = "fixed", parametric = TRUE, ...) %>% - bind_rows() + # Try standard tidy() first, with fallback for lmer objects + v <- tryCatch( + { + lapply(fitlist, tidy, effects = "fixed", parametric = TRUE, ...) %>% + bind_rows() + }, + error = function(e) { + if (inherits(fitlist[[1]], "lmerMod") && + grepl("No.*tidy.*method", e$message, ignore.case = TRUE)) { + lapply(fitlist, function(fit) { + coefs <- lme4::fixef(fit) + se <- sqrt(diag(as.matrix(stats::vcov(fit)))) + data.frame( + term = names(coefs), + estimate = as.numeric(coefs), + std.error = as.numeric(se), + stringsAsFactors = FALSE + ) + }) %>% bind_rows() + } else { + stop(e) + } + } + ) } if (type == "glance") { v <- lapply(fitlist, glance, ...) %>% bind_rows() diff --git a/test_lmer_fix.R b/test_lmer_fix.R new file mode 100644 index 000000000..557cf4277 --- /dev/null +++ b/test_lmer_fix.R @@ -0,0 +1,35 @@ +# Test script for lmer pooling fix +# This tests whether pool() works with lmer objects without broom.mixed + +library(mice) +library(lme4) + +# Create simple test data with missing values +set.seed(123) +n <- 100 +test_data <- data.frame( + id = rep(1:20, each = 5), + x = rnorm(n), + y = rnorm(n) +) + +# Introduce some missingness +test_data$y[sample(1:n, 20)] <- NA + +# Impute +cat("Running imputation...\n") +imp <- mice(test_data, m = 5, print = FALSE, seed = 456) + +# Fit mixed model +cat("Fitting mixed models to imputed data...\n") +fit <- with(imp, lmer(y ~ x + (1 | id))) + +# Try to pool - this should work now without broom.mixed! +cat("\nAttempting to pool results (without broom.mixed)...\n") +pooled <- pool(fit) + +cat("\n=== SUCCESS! ===\n") +cat("Pooled results:\n") +print(summary(pooled)) + +cat("\n✅ The fix works! pool() can now handle lmer objects without broom.mixed\n") diff --git a/test_verify_accuracy.R b/test_verify_accuracy.R new file mode 100644 index 000000000..74ade9875 --- /dev/null +++ b/test_verify_accuracy.R @@ -0,0 +1,62 @@ +# Verification test: Compare our manual extraction vs broom.mixed +# This proves we're getting the EXACT same numbers + +library(mice) +library(lme4) + +# Create test data +set.seed(123) +n <- 100 +test_data <- data.frame( + id = rep(1:20, each = 5), + x = rnorm(n), + y = rnorm(n) +) +test_data$y[sample(1:n, 20)] <- NA + +# Impute +cat("Running imputation...\n") +imp <- mice(test_data, m = 5, print = FALSE, seed = 456) + +# Fit mixed model +cat("Fitting mixed models...\n") +fit <- with(imp, lmer(y ~ x + (1 | id))) + +cat("\n=== METHOD 1: Our Fix (without broom.mixed) ===\n") +pooled_our_fix <- pool(fit) +print(summary(pooled_our_fix)) + +cat("\n=== METHOD 2: With broom.mixed (the old way) ===\n") +library(broom.mixed) +pooled_broom <- pool(fit) +print(summary(pooled_broom)) + +cat("\n=== COMPARISON ===\n") +our_results <- summary(pooled_our_fix) +broom_results <- summary(pooled_broom) + +cat("\nIntercept estimate difference:", + abs(our_results$estimate[1] - broom_results$estimate[1]), "\n") +cat("Intercept SE difference:", + abs(our_results$std.error[1] - broom_results$std.error[1]), "\n") +cat("x coefficient estimate difference:", + abs(our_results$estimate[2] - broom_results$estimate[2]), "\n") +cat("x coefficient SE difference:", + abs(our_results$std.error[2] - broom_results$std.error[2]), "\n") + +# Check if they're identical (within floating point precision) +if (all.equal(our_results$estimate, broom_results$estimate, tolerance = 1e-10) == TRUE && + all.equal(our_results$std.error, broom_results$std.error, tolerance = 1e-10) == TRUE) { + cat("\n✅ PERFECT MATCH! Our fix produces IDENTICAL results to broom.mixed\n") + cat("We're not making up numbers - we're using the exact same math!\n") +} else { + cat("\n❌ WARNING: Results differ!\n") +} + +cat("\n=== MANUAL VERIFICATION ===\n") +cat("Let's also manually check one imputation to prove the math:\n\n") +single_fit <- getfit(fit, 1) +cat("Manual fixef() extraction:\n") +print(lme4::fixef(single_fit)) +cat("\nManual vcov() extraction (standard errors):\n") +print(sqrt(diag(vcov(single_fit)))) From 0232a85348166a8b0c16c8670db6a6dfbf5b3c50 Mon Sep 17 00:00:00 2001 From: Volker Date: Mon, 5 Jan 2026 12:01:37 +0100 Subject: [PATCH 139/147] Fix run=FALSE bug ampute --- R/ampute.R | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/R/ampute.R b/R/ampute.R index ad673f230..d91ba533b 100644 --- a/R/ampute.R +++ b/R/ampute.R @@ -218,7 +218,7 @@ ampute <- function( if (is.null(data)) { stop("Argument data is missing, with no default", call. = FALSE) } - data.in <- data # preserve an original set to inject the NA's in later + # data.in <- data # preserve an original set to inject the NA's in later data <- check.dataform(data) if (anyNA(data)) { stop("Data cannot contain NAs", call. = FALSE) @@ -226,9 +226,9 @@ ampute <- function( if (ncol(data) < 2) { stop("Data should contain at least two columns", call. = FALSE) } - data <- data.frame(data) + numdata <- data if (any(vapply(data, Negate(is.numeric), logical(1))) && mech != "MCAR") { - data <- as.data.frame(sapply(data, as.numeric)) + numdata <- as.data.frame(sapply(data, as.numeric)) warning( "Data is made numeric internally, because the calculation of weights requires numeric data", call. = FALSE @@ -430,9 +430,10 @@ ampute <- function( # Create empty objects P <- NULL scores <- NULL - missing.data <- NULL + data.in <- NULL # Apply function (run = TRUE) or merely return objects (run = FALSE) if (run) { + data.in <- data # Assign cases to the patterns according probs # Because 0 and 1 will be used for missingness, # the numbering of the patterns will start from 2 @@ -470,7 +471,7 @@ ampute <- function( } else { scores <- sumscores( P = P, - data = data, + data = numdata, std = std, weights = weights, patterns = patterns @@ -491,19 +492,19 @@ ampute <- function( ) } } - missing.data <- data for (i in seq_len(nrow(patterns.new))) { if (any(P == (i + 1))) { - missing.data[R[[i]] == 0, patterns.new[i, ] == 0] <- NA + data.in[R[[i]] == 0, patterns.new[i, ] == 0] <- NA } } } + # Create return object names(patterns.new) <- names(data) names(weights) <- names(data) call <- match.call() - data.in[is.na(data.frame(missing.data))] <- NA + result <- mads( call = call, prop = prop, @@ -517,7 +518,7 @@ ampute <- function( amp = data.in, cand = P - 1, scores = scores, - data = as.data.frame(data) + data = data ) return(result) } From 66680f447a521dcea14d76de6a1a81dc0e656ba9 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 24 Feb 2026 15:00:30 +0100 Subject: [PATCH 140/147] Add support for `options(mice.printFlag = FALSE)` --- R/mice.R | 2 +- R/mice.mids.R | 2 +- man/mice.Rd | 2 +- man/mice.mids.Rd | 8 +++++++- 4 files changed, 10 insertions(+), 4 deletions(-) diff --git a/R/mice.R b/R/mice.R index 2e341fb7c..2d436c775 100644 --- a/R/mice.R +++ b/R/mice.R @@ -402,7 +402,7 @@ mice <- function( post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, - printFlag = TRUE, + printFlag = getOption("mice.printFlag", TRUE), seed = NA, data.init = NULL, compact = FALSE, diff --git a/R/mice.mids.R b/R/mice.mids.R index da7481424..3f510a352 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -47,7 +47,7 @@ #' identical(imp$imp, imp2$imp) #' # #' @export -mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { +mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = getOption("mice.printFlag", TRUE), ...) { if (!is.mids(obj)) { stop("Object should be of type mids.") } diff --git a/man/mice.Rd b/man/mice.Rd index a7e38b7e8..f4ba1687a 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -23,7 +23,7 @@ mice( post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, - printFlag = TRUE, + printFlag = getOption("mice.printFlag", TRUE), seed = NA, data.init = NULL, compact = FALSE, diff --git a/man/mice.mids.Rd b/man/mice.mids.Rd index 3892534cf..c77eb761e 100644 --- a/man/mice.mids.Rd +++ b/man/mice.mids.Rd @@ -4,7 +4,13 @@ \alias{mice.mids} \title{Multivariate Imputation by Chained Equations (Iteration Step)} \usage{ -mice.mids(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) +mice.mids( + obj, + newdata = NULL, + maxit = 1, + printFlag = getOption("mice.printFlag", TRUE), + ... +) } \arguments{ \item{obj}{An object of class \code{mids}, typically produces by a previous From 7187e2fca6950677e189612073c6a41cde896e4c Mon Sep 17 00:00:00 2001 From: edbonneville <24227122+edbonneville@users.noreply.github.com> Date: Mon, 23 Mar 2026 21:06:32 +0100 Subject: [PATCH 141/147] Edit nelsonaalen() to pass args to coxph() + return full cumhazard when near-ties are corrected by aeqSurv() --- R/nelsonaalen.R | 12 +++++++++--- man/nelsonaalen.Rd | 4 +++- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/R/nelsonaalen.R b/R/nelsonaalen.R index 5f8974b80..a5cb1e521 100644 --- a/R/nelsonaalen.R +++ b/R/nelsonaalen.R @@ -11,6 +11,7 @@ #' @param data A data frame containing the data. #' @param timevar The name of the time variable in \code{data}. #' @param statusvar The name of the event variable, e.g. death in \code{data}. +#' @param \dots Other named arguments passed down to \code{survival::coxph()} #' @return A vector with \code{nrow(data)} elements containing the Nelson-Aalen #' estimates of the cumulative hazard function. #' @author Stef van Buuren, 2012 @@ -36,7 +37,7 @@ #' ch <- nelsonaalen(eng, time, status) #' plot(x = time, y = ch, ylab = "Cumulative hazard", xlab = "Time") #' @export -nelsonaalen <- function(data, timevar, statusvar) { +nelsonaalen <- function(data, timevar, statusvar, ...) { if (!is.data.frame(data)) { stop("Data must be a data frame") } @@ -45,7 +46,12 @@ nelsonaalen <- function(data, timevar, statusvar) { time <- data[, timevar, drop = TRUE] status <- data[, statusvar, drop = TRUE] - hazard <- survival::basehaz(survival::coxph(survival::Surv(time, status) ~ 1)) - idx <- match(time, hazard[, "time"]) + coxph_obj <- survival::coxph(survival::Surv(time, status) ~ 1, ...) + hazard <- survival::basehaz(coxph_obj) + + # Adjust depending on near-tie correction + idx <- if (coxph_obj$timefix) { + match(coxph_obj$y[, "time"], hazard[, "time"]) + } else match(time, hazard[, "time"]) hazard[idx, "hazard"] } diff --git a/man/nelsonaalen.Rd b/man/nelsonaalen.Rd index a3b08ab83..f487e8348 100644 --- a/man/nelsonaalen.Rd +++ b/man/nelsonaalen.Rd @@ -5,7 +5,7 @@ \alias{hazard} \title{Cumulative hazard rate or Nelson-Aalen estimator} \usage{ -nelsonaalen(data, timevar, statusvar) +nelsonaalen(data, timevar, statusvar, ...) } \arguments{ \item{data}{A data frame containing the data.} @@ -13,6 +13,8 @@ nelsonaalen(data, timevar, statusvar) \item{timevar}{The name of the time variable in \code{data}.} \item{statusvar}{The name of the event variable, e.g. death in \code{data}.} + +\item{\dots}{Other named arguments passed down to \code{survival::coxph()}} } \value{ A vector with \code{nrow(data)} elements containing the Nelson-Aalen From 0d143e41a21e703930e47d0a6fb3ba14fdd11b8e Mon Sep 17 00:00:00 2001 From: kss2k Date: Tue, 21 Apr 2026 14:25:33 +0200 Subject: [PATCH 142/147] Replace partial matches with full matches --- R/mice.impute.norm.R | 2 +- R/pool.r.squared.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index b8ba5beac..b89a0c49e 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -160,7 +160,7 @@ estimice <- function(x, y, ls.meth = "qr", ridge = 1e-05, ...) { df <- max(length(y) - ncol(x), 1) if (ls.meth == "qr") { qr <- lm.fit(x = x, y = y) - c <- t(qr$coef) + c <- t(qr$coefficients) f <- qr$fitted.values r <- t(qr$residuals) v <- try(solve(as.matrix(crossprod(qr.R(qr$qr)))), silent = TRUE) diff --git a/R/pool.r.squared.R b/R/pool.r.squared.R index ac056280d..db209b2cc 100644 --- a/R/pool.r.squared.R +++ b/R/pool.r.squared.R @@ -107,6 +107,6 @@ pool.r.squared <- function(object, adjusted = FALSE) { (1 + exp(2 * (qbar - 1.96 * sqrt(fit$t)))))^2 table[, 3] <- ((exp(2 * (qbar + 1.96 * sqrt(fit$t))) - 1) / (1 + exp(2 * (qbar + 1.96 * sqrt(fit$t)))))^2 - table[, 4] <- fit$f + table[, 4] <- fit$fmi table } From e48ab36655da2c22342e410e9e622720bedbb1d7 Mon Sep 17 00:00:00 2001 From: Volker Date: Mon, 5 Jan 2026 12:03:58 +0100 Subject: [PATCH 143/147] Remove outcommented line --- R/ampute.R | 1 - 1 file changed, 1 deletion(-) diff --git a/R/ampute.R b/R/ampute.R index d91ba533b..e8fa0d421 100644 --- a/R/ampute.R +++ b/R/ampute.R @@ -218,7 +218,6 @@ ampute <- function( if (is.null(data)) { stop("Argument data is missing, with no default", call. = FALSE) } - # data.in <- data # preserve an original set to inject the NA's in later data <- check.dataform(data) if (anyNA(data)) { stop("Data cannot contain NAs", call. = FALSE) From 67930d4e28c4a7443f0a72f6309c5eedfaca2fc0 Mon Sep 17 00:00:00 2001 From: Ben Bolker Date: Mon, 1 Dec 2025 20:44:47 -0500 Subject: [PATCH 144/147] explicitly load 'toenail' data from the 'mice' package (avoid 'lme4' conflict) --- tests/testthat/test-mice.impute.2l.bin.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-mice.impute.2l.bin.R b/tests/testthat/test-mice.impute.2l.bin.R index 91f4d4fa6..fc202d36a 100644 --- a/tests/testthat/test-mice.impute.2l.bin.R +++ b/tests/testthat/test-mice.impute.2l.bin.R @@ -33,7 +33,7 @@ test_that("mice::mice.impute.2l.bin() accepts factor outcome", { }) # toenail: outcome is 0/1 -data("toenail") +data("toenail", package = "mice") data <- tidyr::complete(toenail, ID, visit) %>% tidyr::fill(treatment) %>% dplyr::select(-month) From 18c104237ceeed8142bc9f68774cc3954112439b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 12:33:07 +0200 Subject: [PATCH 145/147] Update documentation Co-Authored-By: Claude Sonnet 4.6 --- man/filter.mids.Rd | 5 ++--- man/make.method.Rd | 20 +++++++++++++++++++- man/predict_mi.Rd | 4 ++-- 3 files changed, 23 insertions(+), 6 deletions(-) diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index bd5c9256a..e213c8646 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -14,9 +14,8 @@ logical value, and are defined in terms of the variables in \code{.data$data}. If multiple expressions are specified, they are combined with the \code{&} operator. Only rows for which all conditions evaluate to \code{TRUE} are kept.} -\item{.preserve}{Relevant when the \code{.data} input is grouped. -If \code{.preserve = FALSE} (the default), the grouping structure -is recalculated based on the resulting data, otherwise the grouping is kept as is.} +\item{.preserve}{Relevant when the \code{.data} input is grouped. If \code{.preserve = FALSE} (the default), the grouping structure is recalculated based on the +resulting data, otherwise the grouping is kept as is.} } \value{ An S3 object of class \code{mids} diff --git a/man/make.method.Rd b/man/make.method.Rd index d54caa628..2efdf56fe 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -70,7 +70,25 @@ Vector of \code{length(blocks)} element with method names \description{ This helper function creates a valid \code{method} vector. The \code{method} vector is an argument to the \code{mice} function that -specifies the method for each block. +specifies the imputation method for each block. +} +\details{ +A method is assigned to every variable whose type can be imputed, +regardless of whether the current data contain missing values. This is +intentional: the same \code{mice()} setup can be re-used across tasks +(see the \code{tasks} argument). For example, a model trained on complete +data (\code{task = "train"}) retains its method so that it can later be +applied to new data that do have missing values (\code{task = "fill"}). + +Under the default \code{task = "impute"}, variables with nothing to impute +according to the \code{where} matrix receive an empty string \code{""}, +preserving the behaviour of earlier versions. Under \code{"train"} and +\code{"fill"} tasks the method is kept regardless of missingness in the +current data. + +To find out which variables have missing values in the current data, use +\code{mids$nmis} after running \code{mice()}, or +\code{colSums(is.na(data))} beforehand. } \examples{ make.method(nhanes2) diff --git a/man/predict_mi.Rd b/man/predict_mi.Rd index bcc0ceb60..3580b03a5 100644 --- a/man/predict_mi.Rd +++ b/man/predict_mi.Rd @@ -107,7 +107,7 @@ predmat <- mice::make.predictorMatrix(dat) predmat[,"set"] <- 0 # Impute missing values based on the train set -imp <- mice(dat, m = 5, maxit = 5 , seed = 1, predictorMatrix = predmat, +imp <- mice(dat, m = 5, maxit = 5 , seed = 1, predictorMatrix = predmat, ignore = ifelse(dat$set == "test", TRUE, FALSE), print = FALSE) impdats <- complete(imp, "all") @@ -119,7 +119,7 @@ testdats <- lapply(impdats, function(dat) subset(dat, set == "test", select = -c fits <- lapply(traindats, function(dat) lm(age ~ bmi + hyp + chl, data = dat)) # pool the predictions with function -pool_preds <- mice::predict_mi(object = fits, newdata = testdats, +pool_preds <- mice::predict_mi(object = fits, newdata = testdats, pool = TRUE, interval = "prediction", level = 0.95) } \seealso{ From a75ebf8c14252784de57d235d712bbfa0390c64f Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 13:06:36 +0200 Subject: [PATCH 146/147] Fix invalid factor level warning in mice.impute.midastouch() (#738) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Root cause: mice.impute.midastouch() converted y to numeric via as.numeric(y) for internal calculations, but then returned y[obsind][index] from the already-converted numeric vector. When y was a factor, this returned integer codes (1, 2, ...) rather than factor values, causing "invalid factor level" warnings when mice() tried to assign the numeric codes back into a factor column — especially when factor levels were not labelled 1, 2, ... (e.g. 0/1 factors or labelled factors like "X"/"Y"). Fix: save the original y as y.original before the numeric conversion and index into y.original for the return value, matching the pattern used by mice.impute.pmm() which returns y[ry][idx] to preserve the original type. --- R/mice.impute.midastouch.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/R/mice.impute.midastouch.R b/R/mice.impute.midastouch.R index f0d24fdf3..b0471de52 100644 --- a/R/mice.impute.midastouch.R +++ b/R/mice.impute.midastouch.R @@ -104,6 +104,7 @@ mice.impute.midastouch <- function( x <- data.matrix(x) storage.mode(x) <- "numeric" X <- cbind(1, x) + y.original <- y y <- as.numeric(y) # get data dimensions @@ -228,6 +229,6 @@ mice.impute.midastouch <- function( # return result index <- apply(probs, 2, sample, x = nobs, size = 1, replace = FALSE) - yimp <- y[obsind][index] + yimp <- y.original[obsind][index] yimp } From df7b02a064ccc3ded3e53ae92c70d91bc938ef77 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Sun, 3 May 2026 13:07:26 +0200 Subject: [PATCH 147/147] Add NEWS entry for midastouch factor fix (#738) --- NEWS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/NEWS.md b/NEWS.md index 09435333b..9bd87804a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,6 @@ # mice 3.18.1.9000 +- Fixes `mice.impute.midastouch()` producing "invalid factor level" warnings when imputing factors with non-default levels (e.g. `0/1` or labelled factors). The function converted `y` to numeric for internal calculations but returned integer codes instead of the original factor values, causing assignment failures in the completed data. Fix: preserve the original `y` and return from it (#738) - Adds an early warning in `mice()` when the data contain `POSIXct` or `POSIXlt` date-time columns. Such variables are stored as large numbers (~1e9–1e10) that can make the predictor matrix near-singular in norm-based methods, causing an opaque `solve()` crash. The warning names the offending columns and suggests converting them to `Date` or a standardised numeric before imputing (#746) - Improves the error message in `estimice()` when the ridge-penalised `solve()` also fails. Previously this crashed silently; it now stops with a message explaining that extreme predictor scales (e.g. POSIX date-time columns) are the likely cause and suggests standardising or removing such variables (#746)