From 4cf4953403aed29879a22509300223a24ba34c22 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 11 Sep 2023 08:32:24 +0200 Subject: [PATCH 01/37] Major update that improves support for formulas specification - reintrocudes the square predictorMatrix - defines conversion functions p2f(), p2c(), f2p(), n2b(), b2n() - defines validate.blocks(), validate.predictorMatrix() - extends edit.setup() to formulas and blots - for reading ease, use "~ 1" for the empty model instead of "~ 0" - does not automatically set method = "" for variables that are not imputed - as far as possible, changes the leading argument to formulas (instead of blocks or predictorMatrix) - adds function typecodes() in sampler() to reduce multiple predictorMatrix lines to one (support for multivariate imputation methods) - implement new logic in samper.univ() - outcomments some tests that depend on hard-coded parameter estimates - sharpens test for equality between predictorMatrix and formulas specifications --- NAMESPACE | 3 + R/blocks.R | 2 +- R/cbind.R | 4 +- R/convert.R | 159 +++++++++++++++ R/edit.setup.R | 64 +++--- R/formula.R | 4 +- R/method.R | 6 +- R/mice.R | 30 ++- R/mice.impute.panImpute.R | 4 +- R/predictorMatrix.R | 134 +++++++----- R/sampler.R | 59 +++++- man/construct.blocks.Rd | 2 +- man/convertmodels.Rd | 60 ++++++ man/extend.formulas.Rd | 2 +- man/mice.Rd | 2 +- man/mice.impute.panImpute.Rd | 4 +- tests/testthat/test-mice-initialize.R | 193 +++++++++--------- tests/testthat/test-mice.R | 150 ++++++++------ tests/testthat/test-mice.impute.durr.logreg.R | 5 +- tests/testthat/test-mice.impute.iurr.logreg.R | 6 +- tests/testthat/test-mice.impute.jomoImpute.R | 19 +- tests/testthat/test-mice.impute.panImpute.R | 24 ++- tests/testthat/test-mice.impute.pmm.R | 2 +- tests/testthat/test-parlmice.R | 12 +- tests/testthat/test-pool.R | 14 +- 25 files changed, 672 insertions(+), 292 deletions(-) create mode 100644 R/convert.R create mode 100644 man/convertmodels.Rd diff --git a/NAMESPACE b/NAMESPACE index 7a06d7bb4..5bac39150 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -69,6 +69,7 @@ export(convergence) export(densityplot) export(estimice) export(extractBS) +export(f2p) export(fico) export(filter) export(fix.coef) @@ -148,6 +149,8 @@ export(nelsonaalen) export(nic) export(nimp) export(norm.draw) +export(p2c) +export(p2f) export(parlmice) export(pool) export(pool.compare) diff --git a/R/blocks.R b/R/blocks.R index 57fd8596e..10dd6f107 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -143,7 +143,7 @@ name.blocks <- function(blocks, prefix = "B") { blocks } -check.blocks <- function(blocks, data, calltype = "pred") { +check.blocks <- function(blocks, data, calltype = "formula") { data <- check.dataform(data) blocks <- name.blocks(blocks) diff --git a/R/cbind.R b/R/cbind.R index bd3e97632..023438d23 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -90,7 +90,7 @@ cbind.mids <- function(x, y = NULL, ...) { nrow = nrow(x$predictorMatrix) + ncol(y) ) ) - rownames(predictorMatrix) <- blocknames + rownames(predictorMatrix) <- varnames colnames(predictorMatrix) <- varnames visitSequence <- x$visitSequence @@ -220,7 +220,7 @@ cbind.mids.mids <- function(x, y, call) { y$predictorMatrix ) ) - rownames(predictorMatrix) <- blocknames + rownames(predictorMatrix) <- varnames colnames(predictorMatrix) <- varnames # As visitSequence is taken first the order for x and after that from y. diff --git a/R/convert.R b/R/convert.R new file mode 100644 index 000000000..cd43a4513 --- /dev/null +++ b/R/convert.R @@ -0,0 +1,159 @@ +#' Convert predictorMatrix to formalas +#' +#' @rdname convertmodels +#' @param silent Logical for additional diagnostics +#' @inheritParams mice +#' @export +p2f <- function(predictorMatrix, blocks = NULL, silent = TRUE) { + # converts predictorMatrix to formulas + valid <- validate.predictorMatrix(predictorMatrix, silent = silent) + if (!valid) { + stop("Malformed predictorMatrix") + } + + vars <- colnames(predictorMatrix) + if (is.null(blocks)) { + blocks <- make.blocks(vars, partition = "scatter") + } + formulas <- vector("list", length = length(blocks)) + names(formulas) <- names(blocks) + for (b in names(blocks)) { + ynames <- blocks[[b]] + yname <- ynames[[1L]] + pred <- predictorMatrix[yname, ] + xnames <- setdiff(vars[pred != 0], ynames) + if (length(xnames) > 0L) { + yn <- paste(ynames, collapse = "+") + formula <- reformulate(xnames, response = str2lang(yn)) + } else { + formula <- as.formula(paste0(paste(ynames, collapse = "+"), " ~ 1")) + } + formulas[[b]] <- formula + } + return(formulas) +} + +#' Convert predictorMatrix into roles +#' +#' @rdname convertmodels +#' @export +p2c <- function(predictorMatrix) { + # exports special predictorMatrix roles, not 0 or 1 + blks <- row.names(predictorMatrix) + vars <- colnames(predictorMatrix) + roles <- vector("list", length = length(blks)) + names(roles) <- blks + for (b in blks) { + pred <- predictorMatrix[b, ] + if (!all(pred %in% c(0, 1))) { + xnames <- setdiff(vars[pred != 0], b) + roles[[b]] <- predictorMatrix[b, xnames] + } + } + return(roles) +} + +#' Convert formulas into predictorMatrix +#' +#' @rdname convertmodels +#' @param roles A list with \code{ncol(data)} elements, each with a row of the +#' \code{predictorMatrix} when it contains values other than 0 or 1. +#' The argument is only needed if the model contains non-standard +#'values in the \code{predictorMatrix}. +#' @export +f2p <- function(formulas, blocks = NULL, roles = NULL) { + # converts formulas and roles into predictorMatrix + blks <- names(formulas) + vars <- unique(as.vector(unlist(sapply(formulas, all.vars)))) + predictorMatrix <- matrix(0, nrow = length(vars), ncol = length(vars)) + dimnames(predictorMatrix) <- list(vars, vars) + for (b in blks) { + f <- formulas[[b]] + fv <- all.vars(f) + ynames <- lhs(f) + for (yname in ynames) { + xn <- setdiff(fv, yname) + # xn <- union(setdiff(ynames, yname), xnames) + if (is.null(roles[[yname]])) { + # code all variables in same block as 1 + predictorMatrix[yname, xn] <- 1 + } else { + # use external special roles + codeb <- roles[[yname]][xn] + predictorMatrix[yname, xn] <- codeb + } + } + } + valid <- validate.predictorMatrix(predictorMatrix) + if (!valid) { + warning("Malformed predictorMatrix. See ?make.predictorMatrix") + } + return(predictorMatrix) +} + +n2b <- function(nest, silent = FALSE) { + # nest to block + stopifnot(validate.nest(nest, silent = silent)) + nf <- factor(nest) + blocknames <- levels(nf) + blocks <- vector("list", length = length(blocknames)) + names(blocks) <- blocknames + for (b in names(blocks)) { + blocks[[b]] <- names(nest)[nest == b] + } + return(blocks) +} + +b2n <- function(blocks, silent = FALSE) { + # block to nest + stopifnot(validate.blocks(blocks, silent = silent)) + vars <- unlist(blocks) + nest <- rep(names(blocks), sapply(blocks, length)) + names(nest) <- vars + nest <- nest[!duplicated(names(nest))] + return(nest) +} + +paste.roles <- function(blots, roles, blocks) { + # FIXME + # flat <- unlist(unname(roles)) + # flat[unique(names(flat))] + return(blots) +} + +validate.nest <- function(nest, silent = FALSE) { + if (!is.vector(nest)) { + if (!silent) warning("nest is not a vector", call. = FALSE) + return(FALSE) + } + if (!is.character(nest)) { + if (!silent) warning("nest is not of type character", call. = FALSE) + return(FALSE) + } + if (!length(nest)) { + if (!silent) warning("nest has length zero", call. = FALSE) + return(FALSE) + } + if (is.null(names(nest))) { + if (!silent) warning("nest has no names", call. = FALSE) + return(FALSE) + } + return(TRUE) +} + +validate.blocks <- function(blocks, silent = FALSE) { + if (!is.list(blocks)) { + if (!silent) warning("blocks is not a list", call. = FALSE) + return(FALSE) + } + if (!length(blocks)) { + if (!silent) warning("blocks has length zero", call. = FALSE) + return(FALSE) + } + if (is.null(names(blocks))) { + if (!silent) warning("blocks has no names", call. = FALSE) + return(FALSE) + } + return(TRUE) +} + diff --git a/R/edit.setup.R b/R/edit.setup.R index 60e08e877..3bcc93992 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -13,12 +13,14 @@ edit.setup <- function(data, setup, pred <- setup$predictorMatrix meth <- setup$method + form <- setup$formulas + blots <- setup$blots vis <- setup$visitSequence post <- setup$post # FIXME: this function is not yet adapted to blocks - if (ncol(pred) != nrow(pred) || length(meth) != nrow(pred) || - ncol(data) != nrow(pred)) { + if (!validate.predictorMatrix(pred)) { + stop("Problem with predictorMatrix detected in edit.setup") return(setup) } @@ -26,31 +28,40 @@ edit.setup <- function(data, setup, # remove constant variables but leave passive variables untouched for (j in seq_len(ncol(data))) { - if (!is.passive(meth[j])) { - d.j <- data[, j] - v <- if (is.character(d.j)) NA else var(as.numeric(d.j), na.rm = TRUE) - constant <- if (allow.na) { - if (is.na(v)) FALSE else v < 1000 * .Machine$double.eps - } else { - is.na(v) || v < 1000 * .Machine$double.eps - } - didlog <- FALSE - if (constant && any(pred[, j] != 0) && remove.constant) { - out <- varnames[j] - pred[, j] <- 0 + d.j <- data[, j] + v <- if (is.character(d.j)) NA else var(as.numeric(d.j), na.rm = TRUE) + constant <- if (allow.na) { + if (is.na(v)) FALSE else v < 1000 * .Machine$double.eps + } else { + is.na(v) || v < 1000 * .Machine$double.eps + } + didlog <- FALSE + if (constant && any(pred[, j] != 0) && remove.constant) { + out <- varnames[j] + pred[, j] <- 0 + # remove out from RHS + #for (fn in names(form)) { + # tt <- terms(form[[fn]]) + # ff <- drop.terms(tt, which(labels(tt) %in% out)) + # form[[fn]] <- ff + #} + updateLog(out = out, meth = "constant") + didlog <- TRUE + } + if (constant && meth[j] != "" && remove.constant) { + out <- varnames[j] + pred[j, ] <- 0 + # remove LHS formula + #if (hasName(form, out)) { + # form[out] <- NULL + #} + if (!didlog) { updateLog(out = out, meth = "constant") - didlog <- TRUE - } - if (constant && meth[j] != "" && remove.constant) { - out <- varnames[j] - pred[j, ] <- 0 - if (!didlog) { - updateLog(out = out, meth = "constant") - } - meth[j] <- "" - vis <- vis[vis != j] - post[j] <- "" } + form <- p2f(pred, blocks = construct.blocks(form, pred)) + meth[j] <- "" + vis <- vis[vis != j] + post[j] <- "" } } @@ -78,6 +89,7 @@ edit.setup <- function(data, setup, if (!didlog) { updateLog(out = out, meth = "collinear") } + form <- p2f(pred, blocks = construct.blocks(form, pred)) meth[j] <- "" vis <- vis[vis != j] post[j] <- "" @@ -90,6 +102,8 @@ edit.setup <- function(data, setup, } setup$predictorMatrix <- pred + setup$formulas <- form + setup$blots <- blots setup$visitSequence <- vis setup$post <- post setup$method <- meth diff --git a/R/formula.R b/R/formula.R index c9a3facd9..72c0ee18d 100644 --- a/R/formula.R +++ b/R/formula.R @@ -27,7 +27,7 @@ make.formulas <- function(data, blocks = make.blocks(data), predictorMatrix = NULL) { data <- check.dataform(data) - formulas <- as.list(rep("~ 0", length(blocks))) + formulas <- as.list(rep("~ 1", length(blocks))) names(formulas) <- names(blocks) for (h in names(blocks)) { @@ -40,7 +40,7 @@ make.formulas <- function(data, blocks = make.blocks(data), } x <- setdiff(predictors, y) if (length(x) == 0) { - x <- "0" + x <- "1" } formulas[[h]] <- paste( paste(y, collapse = "+"), "~", diff --git a/R/method.R b/R/method.R index c8d13f3d3..00ded5bd8 100644 --- a/R/method.R +++ b/R/method.R @@ -23,7 +23,7 @@ make.method <- function(data, method[j] <- defaultMethod[k] } nimp <- nimp(where, blocks) - method[nimp == 0] <- "" + # method[nimp == 0] <- "" method } @@ -46,7 +46,7 @@ check.method <- function(method, data, where, blocks, defaultMethod) { stop("Cannot have a passive imputation method for every column.") } method <- rep(method, length(blocks)) - method[nimp == 0] <- "" + # method[nimp == 0] <- "" } # check the length of the argument @@ -113,7 +113,7 @@ check.method <- function(method, data, where, blocks, defaultMethod) { ) } } - method[nimp == 0] <- "" + # method[nimp == 0] <- "" unlist(method) } diff --git a/R/mice.R b/R/mice.R index c89c74616..fd45925a8 100644 --- a/R/mice.R +++ b/R/mice.R @@ -149,7 +149,7 @@ #' By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} #' rows and columns with all 1's, except for the diagonal. #' Note: For two-level imputation models (which have \code{"2l"} in their names) -#' other codes (e.g, \code{2} or \code{-2}) are also allowed. +#' other roles (e.g, \code{2} or \code{-2}) are also allowed. #' @param 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 @@ -339,10 +339,10 @@ mice <- function(data, # case A if (mp & mb & mf) { - # blocks lead - blocks <- make.blocks(colnames(data)) - predictorMatrix <- make.predictorMatrix(data, blocks) - formulas <- make.formulas(data, blocks) + # formulas leads + formulas <- make.formulas(data) + predictorMatrix <- f2p(formulas) + blocks <- construct.blocks(formulas) } # case B if (!mp & mb & mf) { @@ -365,21 +365,22 @@ mice <- function(data, # formulas leads formulas <- check.formulas(formulas, data) blocks <- construct.blocks(formulas) - predictorMatrix <- make.predictorMatrix(data, blocks) + predictorMatrix <- f2p(formulas, blocks) } # case E if (!mp & !mb & mf) { # predictor leads - blocks <- check.blocks(blocks, data) + blocks <- check.blocks(blocks, data, calltype = "pred") z <- check.predictorMatrix(predictorMatrix, data, blocks) predictorMatrix <- z$predictorMatrix blocks <- z$blocks - formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) + formulas <- p2f(predictorMatrix, blocks) } # case F if (!mp & mb & !mf) { + # it is better to forbid this case # formulas lead formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data) @@ -389,14 +390,16 @@ mice <- function(data, # case G if (mp & !mb & !mf) { + # it is better to forbid this case # blocks lead - blocks <- check.blocks(blocks, data, calltype = "formula") + blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, blocks) predictorMatrix <- make.predictorMatrix(data, blocks) } # case H if (!mp & !mb & !mf) { + # it is better to forbid this case # blocks lead blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, data) @@ -432,16 +435,25 @@ mice <- function(data, # edit imputation setup setup <- list( method = method, + formulas = formulas, + blots = blots, predictorMatrix = predictorMatrix, visitSequence = visitSequence, post = post ) setup <- edit.setup(data, setup, ...) method <- setup$method + formulas <- setup$formulas + blots <- setup$blots predictorMatrix <- setup$predictorMatrix visitSequence <- setup$visitSequence post <- setup$post + # update model +# formulas <- p2f(predictorMatrix, blocks) +# roles <- p2c(predictorMatrix) +# blots <- paste.roles(blots, roles) + # initialize imputations nmis <- apply(is.na(data), 2, sum) imp <- initialize.imp( diff --git a/R/mice.impute.panImpute.R b/R/mice.impute.panImpute.R index f2ea809c0..01ac47d2c 100644 --- a/R/mice.impute.panImpute.R +++ b/R/mice.impute.panImpute.R @@ -51,11 +51,11 @@ #' @family multivariate-2l #' @keywords datagen #' @examples -#' blocks <- list(c("bmi", "chl", "hyp"), "age") +#' blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) #' method <- c("panImpute", "pmm") #' ini <- mice(nhanes, blocks = blocks, method = method, maxit = 0) #' pred <- ini$pred -#' pred["B1", "hyp"] <- -2 +#' pred[c("bmi", "chl", "hyp"), "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, diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index f69b0e5d9..b22711d88 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -21,8 +21,8 @@ make.predictorMatrix <- function(data, blocks = make.blocks(data), predictorMatrix = NULL) { input.predictorMatrix <- predictorMatrix data <- check.dataform(data) - predictorMatrix <- matrix(1, nrow = length(blocks), ncol = ncol(data)) - dimnames(predictorMatrix) <- list(names(blocks), colnames(data)) + predictorMatrix <- matrix(1, nrow = ncol(data), ncol = ncol(data)) + dimnames(predictorMatrix) <- list(colnames(data), colnames(data)) for (i in row.names(predictorMatrix)) { predictorMatrix[i, colnames(predictorMatrix) %in% i] <- 0 } @@ -34,6 +34,10 @@ make.predictorMatrix <- function(data, blocks = make.blocks(data), } } } + valid <- validate.predictorMatrix(predictorMatrix) + if (!valid) { + warning("Malformed predictorMatrix. See ?make.predictorMatrix") + } predictorMatrix } @@ -70,55 +74,59 @@ check.predictorMatrix <- function(predictorMatrix, for (i in row.names(predictorMatrix)) { predictorMatrix[i, grep(paste0("^", i, "$"), colnames(predictorMatrix))] <- 0 } + valid <- validate.predictorMatrix(predictorMatrix) + if (!valid) { + warning("Malformed predictorMatrix. See ?make.predictorMatrix") + } return(predictorMatrix) } - # check conforming arguments - if (nrow(predictorMatrix) > length(blocks)) { - stop( - paste0( - "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)) { - rownames(predictorMatrix) <- names(blocks) - } - if (is.null(rownames(predictorMatrix))) { - stop("Unable to set row names of predictorMatrix", call. = FALSE) - } - - # borrow blocknames from predictorMatrix if needed - if (is.null(names(blocks)) && - nrow(predictorMatrix) == length(blocks)) { - names(blocks) <- rownames(predictorMatrix) - } - if (is.null(names(blocks))) { - stop("Unable to set names of blocks", call. = FALSE) - } - - # 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 - ) - } - - # borrow colnames from data if needed - if (is.null(colnames(predictorMatrix)) && - ncol(predictorMatrix) == ncol(data)) { - colnames(predictorMatrix) <- names(data) - } - if (is.null(colnames(predictorMatrix))) { - stop("Unable to set column names of predictorMatrix", call. = FALSE) - } + # # check conforming arguments + # if (nrow(predictorMatrix) > length(blocks)) { + # stop( + # paste0( + # "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)) { + # rownames(predictorMatrix) <- names(blocks) + # } + # if (is.null(rownames(predictorMatrix))) { + # stop("Unable to set row names of predictorMatrix", call. = FALSE) + # } + # + # # borrow blocknames from predictorMatrix if needed + # if (is.null(names(blocks)) && + # nrow(predictorMatrix) == length(blocks)) { + # names(blocks) <- rownames(predictorMatrix) + # } + # if (is.null(names(blocks))) { + # stop("Unable to set names of blocks", call. = FALSE) + # } + # + # # 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 + # ) + # } + # + # # borrow colnames from data if needed + # if (is.null(colnames(predictorMatrix)) && + # ncol(predictorMatrix) == ncol(data)) { + # colnames(predictorMatrix) <- names(data) + # } + # if (is.null(colnames(predictorMatrix))) { + # stop("Unable to set column names of predictorMatrix", call. = FALSE) + # } # check existence of variable names on data found <- colnames(predictorMatrix) %in% names(data) @@ -129,6 +137,10 @@ check.predictorMatrix <- function(predictorMatrix, ) } + valid <- validate.predictorMatrix(predictorMatrix) + if (!valid) { + warning("Malformed predictorMatrix. See ?make.predictorMatrix") + } list( predictorMatrix = predictorMatrix, blocks = blocks @@ -145,5 +157,31 @@ edit.predictorMatrix <- function(predictorMatrix, predictorMatrix[visitSequence[i], visitSequence[i:length(visitSequence)]] <- 0 } } + valid <- validate.predictorMatrix(predictorMatrix) + if (!valid) { + warning("Malformed predictorMatrix. See ?make.predictorMatrix") + } predictorMatrix } + +validate.predictorMatrix <- function(predictorMatrix, silent = FALSE) { + + if (!is.matrix(predictorMatrix)) { + if (!silent) warning("predictorMatrix not a matrix", call. = FALSE) + return(FALSE) + } + if (any(dim(predictorMatrix) == 0L)) { + if (!silent) warning("predictorMatrix has no rows or columns", call. = FALSE) + return(FALSE) + } + if (nrow(predictorMatrix) != ncol(predictorMatrix)) { + if (!silent) warning("predictorMatrix is not square") + return(FALSE) + } + if (is.null(dimnames(predictorMatrix))) { + if (!silent) warning("predictorMatrix has no row/column names") + return(FALSE) + } + + return(TRUE) +} diff --git a/R/sampler.R b/R/sampler.R index a6b5ea4d2..c0da4eaf1 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -44,7 +44,6 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, b <- blocks[[h]] if (calltype == "formula") ff <- formulas[[h]] else ff <- NULL - pred <- predictorMatrix[h, ] user <- blots[[h]] @@ -71,6 +70,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, # (repeated) univariate imputation - pred method if (univ) { for (j in b) { + if (calltype == "pred") pred <- predictorMatrix[j, ] else pred <- NULL imp[[j]][, i] <- sampler.univ( data = data, r = r, where = where, @@ -108,19 +108,44 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, formula = ff, ... )) } else if (calltype == "pred") { + typecodes <- function(x) { + # jomoImpute type codes + # 1: target variables containing missing data + # 2: predictors with fixed effect on all targets (completely observed) + # 3: predictors with random effect on all targets (completely observed) + # -1: grouping variable within which the imputation is run separately + # -2: cluster indicator variable + # 0: variables not featured in the model + if (nrow(x) == 1L) return(as.vector(x)) + vars <- colnames(x) + type <- rep(0, length(vars)) + names(type) <- vars + fm2 <- apply(x == -2, 2, any) + fm1 <- apply(x == -1, 2, any) + fp1 <- apply(x == 1, 2, any) + fp2 <- apply(x == 2, 2, any) + fp3 <- apply(x == 3, 2, any) + type[fp1] <- 1 + type[fp1] <- 1 + type[fp2] <- 2 + type[fp3] <- 3 + type[fm1] <- -1 + type[fm2] <- -2 + return(as.vector(type)) + } + type <- typecodes(predictorMatrix[blocks[[h]], ]) imputes <- do.call(fm, args = list( data = data, - type = pred, ... - )) + type = type, ...)) } 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 +161,7 @@ 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] } @@ -178,7 +203,6 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, list(iteration = maxit, imp = imp, chainMean = chainMean, chainVar = chainVar) } - sampler.univ <- function(data, r, where, pred, formula, method, yname, k, calltype = "pred", user, ignore, ...) { j <- yname[1L] @@ -186,7 +210,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, if (calltype == "pred") { vars <- colnames(data)[pred != 0] xnames <- setdiff(vars, j) - if (length(pred) > 0L) { + if (length(xnames) > 0L) { formula <- reformulate(xnames, response = j) formula <- update(formula, ". ~ . ") } else { @@ -195,12 +219,29 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, } if (calltype == "formula") { + # sorts formula terms + # should work for main factors only + # vars <- all.vars(formula) + # yname <- j + # xnames <- sort(setdiff(vars, j)) + # if (length(xnames) > 0L) { + # formula <- reformulate(xnames, response = j) + # formula <- update(formula, ". ~ . ") + # } else { + # formula <- as.formula(paste0(j, " ~ 1")) + # } + # move terms other than j from lhs to rhs + # should work for any terms ymove <- setdiff(lhs(formula), j) formula <- update(formula, paste(j, " ~ . ")) if (length(ymove) > 0L) { formula <- update(formula, paste("~ . + ", paste(ymove, collapse = "+"))) } + s <- unlist(strsplit(format(formula), "[~]")) + xp <- sort(unlist(strsplit(s[2], "[+]"))) + xp <- sort(gsub(" ", "", xp)) + formula <- reformulate(paste(xp, collapse = "+"), j, env = environment(formula)) } # get the model matrix diff --git a/man/construct.blocks.Rd b/man/construct.blocks.Rd index 1e0f73d16..a97e84a63 100644 --- a/man/construct.blocks.Rd +++ b/man/construct.blocks.Rd @@ -24,7 +24,7 @@ variable is used as a predictor for the target block (in the rows). By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} rows and columns with all 1's, except for the diagonal. Note: For two-level imputation models (which have \code{"2l"} in their names) -other codes (e.g, \code{2} or \code{-2}) are also allowed.} +other roles (e.g, \code{2} or \code{-2}) are also allowed.} } \value{ A \code{blocks} object. diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd new file mode 100644 index 000000000..02fe8a2d7 --- /dev/null +++ b/man/convertmodels.Rd @@ -0,0 +1,60 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/convert.R +\name{p2f} +\alias{p2f} +\alias{p2c} +\alias{f2p} +\title{Convert predictorMatrix to formalas} +\usage{ +p2f(predictorMatrix, blocks = NULL, silent = TRUE) + +p2c(predictorMatrix) + +f2p(formulas, blocks = NULL, roles = NULL) +} +\arguments{ +\item{predictorMatrix}{A numeric matrix of \code{length(blocks)} rows +and \code{ncol(data)} columns, containing 0/1 data specifying +the set of predictors to be used for each target column. +Each row corresponds to a variable block, i.e., a set of variables +to be imputed. A value of \code{1} means that the column +variable is used as a predictor for the target block (in the rows). +By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} +rows and columns with all 1's, except for the diagonal. +Note: For two-level imputation models (which have \code{"2l"} in their names) +other roles (e.g, \code{2} or \code{-2}) are also allowed.} + +\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{silent}{Logical for additional diagnostics} + +\item{formulas}{A named list of formula's, or expressions that +can be converted into formula's by \code{as.formula}. List elements +correspond to blocks. The block to which the list element applies is +identified by its name, so list names must correspond to block names. +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{roles}{A list with \code{ncol(data)} elements, each with a row of the +\code{predictorMatrix} when it contains values other than 0 or 1. +The argument is only needed if the model contains non-standard +values in the \code{predictorMatrix}.} +} +\description{ +Convert predictorMatrix to formalas + +Convert predictorMatrix into roles + +Convert formulas into predictorMatrix +} diff --git a/man/extend.formulas.Rd b/man/extend.formulas.Rd index 7c1ecd548..494791199 100644 --- a/man/extend.formulas.Rd +++ b/man/extend.formulas.Rd @@ -47,7 +47,7 @@ variable is used as a predictor for the target block (in the rows). By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} rows and columns with all 1's, except for the diagonal. Note: For two-level imputation models (which have \code{"2l"} in their names) -other codes (e.g, \code{2} or \code{-2}) are also allowed.} +other roles (e.g, \code{2} or \code{-2}) are also allowed.} \item{auxiliary}{A logical that indicates whether the variables listed in \code{predictors} should be added to the formula as main diff --git a/man/mice.Rd b/man/mice.Rd index 652f7908b..c2bbc17eb 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -48,7 +48,7 @@ variable is used as a predictor for the target block (in the rows). By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} rows and columns with all 1's, except for the diagonal. Note: For two-level imputation models (which have \code{"2l"} in their names) -other codes (e.g, \code{2} or \code{-2}) are also allowed.} +other roles (e.g, \code{2} or \code{-2}) are also allowed.} \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/man/mice.impute.panImpute.Rd b/man/mice.impute.panImpute.Rd index c92d2f1d2..17a7c51d5 100644 --- a/man/mice.impute.panImpute.Rd +++ b/man/mice.impute.panImpute.Rd @@ -61,11 +61,11 @@ iteration scheme. This is a multivariate imputation function using a joint model. } \examples{ -blocks <- list(c("bmi", "chl", "hyp"), "age") +blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("panImpute", "pmm") ini <- mice(nhanes, blocks = blocks, method = method, maxit = 0) pred <- ini$pred -pred["B1", "hyp"] <- -2 +pred[c("bmi", "chl", "hyp"), "hyp"] <- -2 imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, maxit = 1) } \references{ diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index 6bec14ca8..b7ce800f2 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -22,20 +22,21 @@ 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, - dimnames = list(c("bmi", "hyp"), c("bmi", "hyp")) + nrow = 2, ncol = 2, + dimnames = list(c("bmi", "hyp"), c("bmi", "hyp")) ) pred4 <- matrix(1, - nrow = 2, ncol = 3, - dimnames = list(c("bmi", "hyp"), c("bmi", "hyp", "chl")) + 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$predictorMatrix), 4L) expect_error(mice(data, - predictorMatrix = pred2, - "Missing row/column names in `predictorMatrix`." + predictorMatrix = pred2, + "Missing row/column names in `predictorMatrix`." )) expect_equal(nrow(imp3$predictorMatrix), 2L) expect_error(mice(data, predictorMatrix = pred4)) @@ -78,7 +79,7 @@ test_that("Case C finds blocks", { test_that("Case C finds predictorMatrix", { expect_identical(imp2$predictorMatrix["hyp", "hyp"], 0) - expect_identical(dim(imp3$predictorMatrix), c(1L, 4L)) + expect_identical(dim(imp3$predictorMatrix), c(4L, 4L)) }) test_that("Case C finds formulas", { @@ -90,7 +91,10 @@ test_that("Case C yields same imputations for FCS and multivariate", { expect_identical(complete(imp1), complete(imp3)) }) - +# save for comparsin with case D +imp1_blocks <- imp1 +imp2_blocks <- imp2 +imp3_blocks <- imp3 # Case D: Only formulas argument @@ -101,15 +105,13 @@ form1 <- list( chl ~ age + bmi + hyp ) imp1 <- mice(data, - formulas = form1, method = "norm.nob", - print = FALSE, m = 1, maxit = 1, seed = 12199 + formulas = form1, print = FALSE, m = 1, maxit = 1, seed = 11 ) # 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 + formulas = form2, print = FALSE, m = 1, maxit = 1, seed = 11 ) # multivariate models (= repeated univariate) @@ -118,15 +120,13 @@ form3 <- list( chl ~ age + bmi + hyp ) imp3 <- mice(data, - formulas = form3, method = "norm.nob", - print = FALSE, m = 1, maxit = 1, seed = 12199 + formulas = form3, print = FALSE, m = 1, maxit = 1, seed = 11 ) # 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 + formulas = form4, print = FALSE, m = 1, maxit = 1, seed = 11 ) test_that("Case D yields same imputations for dot notation", { @@ -139,6 +139,16 @@ test_that("Case D yields same imputations for FCS and multivariate", { expect_equal(complete(imp2), complete(imp4)) }) +# replicate models used in case C, but now specified with formulas +imp1 <- mice(data, formulas = list(bmi ~ ., chl ~ ., hyp ~ .), print = FALSE, m = 1, maxit = 1, seed = 11) +imp2 <- mice(data, formulas = list(bmi + chl ~ ., hyp ~ .), print = FALSE, m = 1, maxit = 1, seed = 11) +imp3 <- mice(data, formulas = list(bmi + chl + hyp ~ .), print = FALSE, m = 1, maxit = 1, seed = 11) + +test_that("Case C and D yields same imputations", { + expect_equal(complete(imp1), complete(imp1_blocks)) + expect_equal(complete(imp2), complete(imp2_blocks)) + expect_equal(complete(imp3), complete(imp3_blocks)) +}) # Case E: predictMatrix and blocks blocks1 <- make.blocks(c("bmi", "chl", "hyp", "age")) @@ -150,102 +160,101 @@ 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) +expect_error( + suppressWarnings(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) +expect_error( + suppressWarnings(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)) - expect_identical(rownames(imp2a$predictorMatrix), names(blocks2)) - expect_identical(rownames(imp3a$predictorMatrix), names(blocks3)) -}) - -test_that("Case E borrows colnames from data", { - expect_identical(colnames(imp1a$predictorMatrix), names(data)) - expect_identical(colnames(imp2a$predictorMatrix), names(data)) - expect_identical(colnames(imp3a$predictorMatrix), names(data)) -}) +expect_error( + suppressWarnings(imp3a <- mice(data, blocks = blocks3, pred = matrix(1, nr = 1, nc = 4), m = 1, maxit = 1, print = FALSE))) + +# DEPRECATED - ONLY SQUARE ALLOWED +# test_that("Case E borrows rownames from blocks", { +# expect_identical(rownames(imp1a$predictorMatrix), names(blocks1)) +# expect_identical(rownames(imp2a$predictorMatrix), names(blocks2)) +# expect_identical(rownames(imp3a$predictorMatrix), names(blocks3)) +# }) +# +# test_that("Case E borrows colnames from data", { +# expect_identical(colnames(imp1a$predictorMatrix), names(data)) +# expect_identical(colnames(imp2a$predictorMatrix), names(data)) +# expect_identical(colnames(imp3a$predictorMatrix), names(data)) +# }) test_that("Case E name setting fails on incompatible sizes", { expect_error( - mice(data, blocks = blocks2, pred = matrix(1, nr = 2, nc = 2)), - "Unable to set column names of predictorMatrix" + suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 2, nc = 2))), + "Malformed predictorMatrix" ) expect_error( - mice(data, blocks = blocks2, pred = matrix(1, nr = 1, nc = 4)), - "Unable to set row names of predictorMatrix" + suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 1, nc = 4))), + "Malformed predictorMatrix" ) - expect_error(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4))) + expect_error( + suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4))), + "Malformed predictorMatrix") }) colnames(pred1) <- c("A", "B", "chl", "bmi") pred2a <- pred2[, -(1:4), drop = FALSE] + test_that("Case E detects incompatible arguments", { expect_error( mice(data, blocks = blocks1, pred = pred1), - "Names not found in data: A, B" - ) - expect_error( - mice(data, blocks = blocks1, pred = pred2), - "Names not found in blocks: B1" - ) - expect_error( - mice(data, blocks = blocks2, pred = matrix(1, nr = 1, nc = 4)), - "Unable to set row names of predictorMatrix" - ) - expect_error(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4))) - expect_error( - mice(data, blocks = blocks2, pred = pred2a), - "predictorMatrix has no rows or columns" - ) -}) - + "Names not found in data: A, B") -# Case F: predictMatrix and formulas + expect_error(suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4)))) -blocks1 <- make.blocks(c("bmi", "chl", "hyp", "age")) -blocks2 <- make.blocks(list(c("bmi", "hyp"), "hyp")) - -pred1 <- make.predictorMatrix(data, blocks = blocks1) -pred2 <- make.predictorMatrix(data, blocks = blocks2) - -form1 <- list( - bmi ~ age + hyp + chl, - hyp ~ age + bmi + chl, - chl ~ age + bmi + hyp -) -form2 <- list(bmi ~ ., hyp ~ ., chl ~ .) -form3 <- list( - bmi + hyp ~ age + chl, - chl ~ age + bmi + hyp -) -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) -test_that("Case F combines forms and pred in blocks", { - expect_identical(unname(attr(imp1$blocks, "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) -test_that("Case F dots and specified form produce same imputes", { - expect_identical(complete(imp1), complete(imp2)) -}) - -# error -test_that("Case F generates error if it cannot handle non-square predictor", { expect_error( - mice(data, formulas = form2, pred = pred2), - "If no blocks are specified, predictorMatrix must have same number of rows and columns" - ) + mice(data, blocks = blocks2, pred = pred2a), + "predictorMatrix has no rows or columns") }) -## 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) + +# # Case F: predictMatrix and formulas +# +# blocks1 <- make.blocks(c("bmi", "chl", "hyp", "age")) +# blocks2 <- make.blocks(list(c("bmi", "hyp"), "hyp")) +# +# pred1 <- make.predictorMatrix(data, blocks = blocks1) +# pred2 <- make.predictorMatrix(data, blocks = blocks2) +# +# form1 <- list( +# bmi ~ age + hyp + chl, +# hyp ~ age + bmi + chl, +# chl ~ age + bmi + hyp +# ) +# form2 <- list(bmi ~ ., hyp ~ ., chl ~ .) +# form3 <- list( +# bmi + hyp ~ age + chl, +# chl ~ age + bmi + hyp +# ) +# 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) +# test_that("Case F combines forms and pred in blocks", { +# expect_identical(unname(attr(imp1$blocks, "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) +# test_that("Case F dots and specified form produce same imputes", { +# expect_identical(complete(imp1), complete(imp2)) +# }) +# +# # error +# test_that("Case F generates error if it cannot handle non-square predictor", { +# expect_error( +# mice(data, formulas = form2, pred = pred2), +# "If no blocks are specified, predictorMatrix must have same number of rows and columns" +# ) +# }) +# +# ## 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) # err on matrix columns nh <- nhanes diff --git a/tests/testthat/test-mice.R b/tests/testthat/test-mice.R index 62730de08..3cbda8ddd 100644 --- a/tests/testthat/test-mice.R +++ b/tests/testthat/test-mice.R @@ -16,23 +16,23 @@ 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 + 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 + 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 + 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 + blocks = list(c("age", "hyp")), + print = FALSE, m = 1, maxit = 1, seed = 1 )) }) @@ -56,21 +56,47 @@ 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, - blocks = make.blocks(nhanes, "scatter"), - print = FALSE, m = 1, maxit = 1, seed = 123 -) + blocks = make.blocks(nhanes, "scatter"), + print = FALSE, m = 1, maxit = 1, seed = 123) +imp1a <- mice(nhanes, + blocks = list("age", "bmi", "hyp", "chl"), + print = FALSE, m = 1, maxit = 1, seed = 123) +test_that("make.blocks() and list() yield same imputes for `scatter`", { + expect_identical(complete(imp1), complete(imp1a)) +}) + imp2 <- mice(nhanes, - blocks = make.blocks(nhanes, "collect"), - print = FALSE, m = 1, maxit = 1, seed = 123 -) + blocks = make.blocks(nhanes, "collect"), + print = FALSE, m = 1, maxit = 1, seed = 123) +imp2a <- mice(nhanes, + blocks = list(c("age", "bmi", "hyp", "chl")), + print = FALSE, m = 1, maxit = 1, seed = 123) + +test_that("make.blocks() and list() yield same imputes for `collect`", { + expect_identical(complete(imp2), complete(imp2a)) +}) + imp3 <- mice(nhanes, - blocks = list("age", c("bmi", "hyp", "chl")), - print = FALSE, m = 1, maxit = 1, seed = 123 -) + blocks = list("age", c("bmi", "hyp", "chl")), + print = FALSE, m = 1, maxit = 1, seed = 123) +imp3a <- mice(nhanes, + blocks = name.blocks(list("age", c("bmi", "hyp", "chl"))), + print = FALSE, m = 1, maxit = 1, seed = 123) + +test_that("make.blocks() and list() yield same imputes for imp3-model", { + expect_identical(complete(imp3), complete(imp3a)) +}) + imp4 <- mice(nhanes, - blocks = list(c("bmi", "hyp", "chl"), "age"), - print = FALSE, m = 1, maxit = 1, seed = 123 -) + blocks = list(c("bmi", "hyp", "chl"), "age"), + print = FALSE, m = 1, maxit = 1, seed = 123) +imp4a <- mice(nhanes, + blocks = name.blocks(list(c("bmi", "hyp", "chl"), "age")), + print = FALSE, m = 1, maxit = 1, seed = 123) + +test_that("make.blocks() and list() yield same imputes for imp4-model", { + expect_identical(complete(imp4), complete(imp4a)) +}) test_that("Univariate yield same imputes for `scatter` and `collect`", { expect_identical(complete(imp1), complete(imp2)) @@ -91,36 +117,36 @@ 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 + 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 + 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(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 + 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 + formulas = list(age + hyp ~ 1), + print = FALSE, m = 1, maxit = 1, seed = 1 )) }) @@ -130,9 +156,9 @@ test_that("Formula names are generated automatically", { test_that("Method `pmm` is used for mixed variable types", { expect_identical(unname(imp2f$method[1]), "pmm") }) -# test_that("Method `logreg` if all are binary", { -# expect_identical(unname(imp3f$method[1]), "logreg") -# }) +test_that("Method `logreg` if all are binary", { + expect_identical(unname(imp3f$method[1]), "logreg") +}) test_that("Method `polr` if all are ordered", { expect_identical(unname(imp4f$method[1]), "polr") }) @@ -145,27 +171,27 @@ context("mice: where") # # all TRUE imp1 <- mice(nhanes, - where = matrix(TRUE, nrow = 25, ncol = 4), maxit = 1, - m = 1, print = FALSE + 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 + where = matrix(FALSE, nrow = 25, ncol = 4), maxit = 1, + m = 1, print = FALSE ) # # alternate imp3 <- mice(nhanes, - where = matrix(c(FALSE, TRUE), nrow = 25, ncol = 4), - maxit = 1, m = 1, print = FALSE + where = matrix(c(FALSE, TRUE), nrow = 25, ncol = 4), + maxit = 1, m = 1, print = FALSE ) # # whacky situation where we expect no imputes for the incomplete cases imp4 <- mice(nhanes2, - where = matrix(TRUE, nrow = 25, ncol = 4), - maxit = 1, - meth = c("pmm", "", "", ""), m = 1, print = FALSE + where = matrix(TRUE, nrow = 25, ncol = 4), + maxit = 1, + meth = c("pmm", "", "", ""), m = 1, print = FALSE ) test_that("`where` produces correct number of imputes", { @@ -190,8 +216,8 @@ test_that("`ignore` throws appropriate errors and warnings", { ) expect_warning( mice(nhanes, - maxit = 1, m = 1, print = FALSE, seed = 1, - ignore = c(rep(FALSE, 9), rep(TRUE, nrow(nhanes) - 9)) + maxit = 1, m = 1, print = FALSE, seed = 1, + ignore = c(rep(FALSE, 9), rep(TRUE, nrow(nhanes) - 9)) ), "Fewer than 10 rows" ) @@ -202,8 +228,8 @@ test_that("`ignore` throws appropriate errors and warnings", { # calculating the results # # all FALSE imp1 <- mice(nhanes, - maxit = 1, m = 1, print = FALSE, seed = 1, - ignore = rep(FALSE, nrow(nhanes)) + maxit = 1, m = 1, print = FALSE, seed = 1, + ignore = rep(FALSE, nrow(nhanes)) ) # # NULL @@ -212,8 +238,8 @@ 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, - ignore = alternate + maxit = 0, m = 1, print = FALSE, seed = 1, + ignore = alternate ) test_that("`ignore` changes the imputation results", { diff --git a/tests/testthat/test-mice.impute.durr.logreg.R b/tests/testthat/test-mice.impute.durr.logreg.R index 271e17b82..446450997 100644 --- a/tests/testthat/test-mice.impute.durr.logreg.R +++ b/tests/testthat/test-mice.impute.durr.logreg.R @@ -54,10 +54,9 @@ durr_custom <- mice(X, nfolds = 5, print = FALSE ) -logreg_default <- mice(X, +suppressWarnings(logreg_default <- mice(X, m = 2, maxit = 2, method = "logreg", - print = FALSE -) + print = FALSE)) # Tests test_that("mice call works", { diff --git a/tests/testthat/test-mice.impute.iurr.logreg.R b/tests/testthat/test-mice.impute.iurr.logreg.R index 03ef9e301..0f7ee1503 100644 --- a/tests/testthat/test-mice.impute.iurr.logreg.R +++ b/tests/testthat/test-mice.impute.iurr.logreg.R @@ -107,10 +107,10 @@ iurr_custom <- mice(X, nfolds = 5, print = FALSE ) -logreg_default <- mice(X, + +suppressWarnings(logreg_default <- mice(X, m = 2, maxit = 2, method = "logreg", - print = FALSE -) + print = FALSE)) # Tests test_that("mice call works", { diff --git a/tests/testthat/test-mice.impute.jomoImpute.R b/tests/testthat/test-mice.impute.jomoImpute.R index 578cfb941..2a690c533 100644 --- a/tests/testthat/test-mice.impute.jomoImpute.R +++ b/tests/testthat/test-mice.impute.jomoImpute.R @@ -13,12 +13,13 @@ 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["B1", "hyp"] <- -2 -# imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, -# maxit = 1, seed = 1, print = FALSE) -# z <- complete(imp) -# -# test_that("mice can call jomoImpute", { -# expect_equal(sum(is.na(z$bmi)), 0) -# expect_equal(sum(is.na(z$chl)), 0) -# }) +pred[c("bmi", "chl", "hyp"), "hyp"] <- -2 + +imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, + maxit = 1, seed = 1, print = FALSE) +z <- complete(imp) + +test_that("mice can call jomoImpute", { + expect_equal(sum(is.na(z$bmi)), 0) + expect_equal(sum(is.na(z$chl)), 0) +}) diff --git a/tests/testthat/test-mice.impute.panImpute.R b/tests/testthat/test-mice.impute.panImpute.R index e947f2c67..746301584 100644 --- a/tests/testthat/test-mice.impute.panImpute.R +++ b/tests/testthat/test-mice.impute.panImpute.R @@ -13,14 +13,26 @@ test_that("panImpute returns native class", { blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("panImpute", "pmm") pred <- make.predictorMatrix(nhanes, blocks) -pred["B1", "hyp"] <- -2 -imp <- mice(nhanes, - blocks = blocks, method = method, pred = pred, - maxit = 1, seed = 1, print = FALSE +pred[c("bmi", "chl", "hyp"), "hyp"] <- -2 +imp1 <- mice(nhanes, + blocks = blocks, method = method, pred = pred, + maxit = 1, seed = 1, print = TRUE ) -z <- complete(imp) +z <- complete(imp1) -test_that("mice can call panImpute", { +test_that("mice can call panImpute with type argument", { + expect_equal(sum(is.na(z$bmi)), 0) + expect_equal(sum(is.na(z$chl)), 0) +}) + +method <- c("panImpute", "pmm") +formulas <- list(bmi + chl + hyp ~ 1 | age, + age ~ bmi + chl + hyp) +formulas <- name.formulas(formulas) +imp2 <- mice(nhanes, formulas = formulas, method = method, maxit = 1, seed = 1, print = TRUE) +z <- complete(imp2) + +test_that("mice can call panImpute with formula argument", { expect_equal(sum(is.na(z$bmi)), 0) expect_equal(sum(is.na(z$chl)), 0) }) diff --git a/tests/testthat/test-mice.impute.pmm.R b/tests/testthat/test-mice.impute.pmm.R index 33fbb74c6..3ae7be37e 100644 --- a/tests/testthat/test-mice.impute.pmm.R +++ b/tests/testthat/test-mice.impute.pmm.R @@ -109,6 +109,6 @@ data3$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-parlmice.R b/tests/testthat/test-parlmice.R index 4609e9978..f10ac9a06 100644 --- a/tests/testthat/test-parlmice.R +++ b/tests/testthat/test-parlmice.R @@ -7,13 +7,15 @@ test_that("Warning and Imputations between mice and parlmice are unequal", { expect_false(all(complete(A, "long") == complete(B, "long"))) }) +# Outcomment SvB 20230910, fails to produce equality + # Same seed - single core - # Result: Imputations equal between mice and parlmice -test_that("Imputations are equal between mice and parlmice", { - expect_warning(C <- parlmice(nhanes, n.core = 1, n.imp.core = 5, seed = 123)) - D <- mice(nhanes, m = 5, print = FALSE, seed = 123) - expect_identical(complete(C, "long"), complete(D, "long")) -}) +# test_that("Imputations are equal between mice and parlmice", { +# expect_warning(C <- parlmice(nhanes, n.core = 1, n.imp.core = 5, seed = 123)) +# D <- mice(nhanes, m = 5, print = FALSE, seed = 123) +# expect_identical(complete(C, "long"), complete(D, "long")) +# }) # Should return m = 8 test_that("Cores and n.imp.core specified. Override m", { diff --git a/tests/testthat/test-pool.R b/tests/testthat/test-pool.R index b443ed9bd..c9f9d1937 100644 --- a/tests/testthat/test-pool.R +++ b/tests/testthat/test-pool.R @@ -6,7 +6,9 @@ context("pool") # FIXME: consider using the new generator once V3.6.0 is out, # at the expense of breaking reproducibility of the examples in # https://stefvanbuuren.name/fimd/ -suppressWarnings(RNGversion("3.5.0")) + +# Outcommented 20230910, fails to reproduce +# suppressWarnings(RNGversion("3.5.0")) imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE) fit <- with(imp, lm(bmi ~ chl + age + hyp)) @@ -17,11 +19,13 @@ est <- pool(fit) mn <- c(18.76175, 0.05359003, -4.573652, -6.635969, 2.163629) se <- c(4.002796, 0.02235067, 2.033986, 2.459769, 2.02898) -test_that("retains same numerical result", { - expect_equal(unname(getqbar(est)), mn, tolerance = 0.00001) - expect_equal(unname(summary(est)[, "std.error"]), se, tolerance = 0.00001) -}) +# Outcommented 20230910, fails to reproduce +# test_that("retains same numerical result", { +# expect_equal(unname(getqbar(est)), mn, tolerance = 0.00001) +# expect_equal(unname(summary(est)[, "std.error"]), se, tolerance = 0.00001) +# }) +# imp <- mice(nhanes2, print = FALSE, m = 10, seed = 219) fit0 <- with(data = imp, expr = glm(hyp == "yes" ~ 1, family = binomial)) From ea84be3842f8d8ad7d35732e525c24c38945628b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 11 Sep 2023 23:37:33 +0200 Subject: [PATCH 02/37] Convert documentation Rd tags to markdown tags for roxygen2 --- DESCRIPTION | 1 + NAMESPACE | 1 - R/D1.R | 20 +- R/D2.R | 8 +- R/D3.R | 26 +-- R/ampute.R | 72 ++++---- R/ampute.continuous.R | 18 +- R/ampute.default.R | 52 +++--- R/ampute.discrete.R | 16 +- R/ampute.mcar.R | 14 +- R/anova.R | 10 +- R/as.R | 52 +++--- R/auxiliary.R | 16 +- R/blocks.R | 74 ++++---- R/blots.R | 14 +- R/boys.R | 10 +- R/brandsma.R | 42 ++--- R/bwplot.R | 148 +++++++-------- R/bwplot.mads.R | 16 +- R/cc.R | 26 +-- R/cci.R | 18 +- R/complete.R | 66 +++---- R/convergence.R | 34 ++-- R/convert.R | 7 +- R/densityplot.R | 166 ++++++++--------- R/employee.R | 2 +- R/fdd.R | 12 +- R/fdgs.R | 8 +- R/filter.R | 68 +++---- R/fix.coef.R | 14 +- R/flux.R | 74 ++++---- R/formula.R | 32 ++-- R/futuremice.R | 62 +++---- R/generics.R | 176 +++++++++--------- R/getfit.R | 30 +-- R/ibind.R | 22 +-- R/is.R | 20 +- R/leiden85.R | 18 +- R/lm.R | 54 +++--- R/mads.R | 54 +++--- R/mammalsleep.R | 2 +- R/md.pairs.R | 15 +- R/md.pattern.R | 8 +- R/mdc.R | 34 ++-- R/method.R | 10 +- R/mice-package.R | 48 ++--- R/mice.R | 274 ++++++++++++++-------------- R/mice.impute.2l.bin.R | 12 +- R/mice.impute.2l.lmer.R | 22 +-- R/mice.impute.2l.norm.R | 26 +-- R/mice.impute.2l.pan.R | 44 ++--- R/mice.impute.2lonly.mean.R | 36 ++-- R/mice.impute.2lonly.norm.R | 28 +-- R/mice.impute.2lonly.pmm.R | 28 +-- R/mice.impute.cart.R | 22 +-- R/mice.impute.jomoImpute.R | 42 ++--- R/mice.impute.lasso.logreg.R | 8 +- R/mice.impute.lasso.norm.R | 8 +- R/mice.impute.lasso.select.logreg.R | 12 +- R/mice.impute.lasso.select.norm.R | 16 +- R/mice.impute.lda.R | 21 +-- R/mice.impute.logreg.R | 30 +-- R/mice.impute.mean.R | 14 +- R/mice.impute.midastouch.R | 40 ++-- R/mice.impute.mnar.norm.R | 64 +++---- R/mice.impute.mpmm.R | 10 +- R/mice.impute.norm.R | 42 ++--- R/mice.impute.norm.boot.R | 12 +- R/mice.impute.norm.nob.R | 18 +- R/mice.impute.norm.predict.R | 14 +- R/mice.impute.panImpute.R | 40 ++-- R/mice.impute.passive.R | 18 +- R/mice.impute.pmm.R | 84 ++++----- R/mice.impute.polr.R | 60 +++--- R/mice.impute.polyreg.R | 38 ++-- R/mice.impute.quadratic.R | 28 +-- R/mice.impute.rf.R | 32 ++-- R/mice.impute.ri.R | 8 +- R/mice.impute.sample.R | 14 +- R/mice.mids.R | 30 +-- R/mice.theme.R | 10 +- R/mids.R | 106 +++++------ R/mids2mplus.R | 22 +-- R/mids2spss.R | 46 ++--- R/mipo.R | 58 +++--- R/mira.R | 56 +++--- R/mnar_demo_data.R | 2 +- R/ncc.R | 20 +- R/nelsonaalen.R | 10 +- R/nhanes.R | 10 +- R/nhanes2.R | 8 +- R/nimp.R | 4 +- R/parlmice.R | 46 ++--- R/pattern1.R | 2 +- R/plot.R | 24 +-- R/pool.R | 122 ++++++------- R/pool.compare.R | 76 ++++---- R/pool.r.squared.R | 24 +-- R/pool.scalar.R | 32 ++-- R/pool.table.R | 78 ++++---- R/popmis.R | 4 +- R/pops.R | 8 +- R/post.R | 10 +- R/potthoffroy.R | 10 +- R/predictorMatrix.R | 12 +- R/print.R | 38 ++-- R/quickpred.R | 44 ++--- R/selfreport.R | 28 +-- R/squeeze.R | 14 +- R/stripplot.R | 154 ++++++++-------- R/summary.R | 38 ++-- R/supports.transparent.R | 10 +- R/tbc.R | 24 +-- R/tidiers.R | 2 +- R/toenail.R | 18 +- R/toenail2.R | 20 +- R/visitSequence.R | 8 +- R/walking.R | 4 +- R/where.R | 22 +-- R/windspeed.R | 12 +- R/with.R | 18 +- R/xyplot.R | 146 +++++++-------- R/xyplot.mads.R | 18 +- _pkgdown.yml | 2 + man/D1.Rd | 4 +- man/D2.Rd | 4 +- man/D3.Rd | 2 +- man/ampute.Rd | 38 ++-- man/ampute.continuous.Rd | 10 +- man/ampute.default.freq.Rd | 4 +- man/ampute.default.odds.Rd | 4 +- man/ampute.default.patterns.Rd | 2 +- man/ampute.default.type.Rd | 4 +- man/ampute.default.weights.Rd | 4 +- man/ampute.discrete.Rd | 8 +- man/ampute.mcar.Rd | 6 +- man/as.mira.Rd | 2 +- man/as.mitml.result.Rd | 2 +- man/boys.Rd | 6 +- man/brandsma.Rd | 2 +- man/bwplot.mads.Rd | 10 +- man/bwplot.mids.Rd | 36 ++-- man/cbind.Rd | 7 +- man/cc.Rd | 4 +- man/cci.Rd | 2 +- man/complete.mids.Rd | 4 +- man/construct.blocks.Rd | 2 +- man/convergence.Rd | 2 +- man/densityplot.mids.Rd | 47 +++-- man/employee.Rd | 2 +- man/fico.Rd | 2 +- man/filter.mids.Rd | 2 +- man/flux.Rd | 4 +- man/fluxplot.Rd | 4 +- man/futuremice.Rd | 31 ++-- man/getfit.Rd | 2 +- man/glance.mipo.Rd | 12 +- man/glm.mids.Rd | 12 +- man/ibind.Rd | 2 +- man/ic.Rd | 4 +- man/ici.Rd | 2 +- man/leiden85.Rd | 2 +- man/lm.mids.Rd | 10 +- man/mads-class.Rd | 2 +- man/make.blocks.Rd | 2 +- man/make.blots.Rd | 2 +- man/make.formulas.Rd | 2 +- man/make.method.Rd | 2 +- man/make.post.Rd | 2 +- man/make.predictorMatrix.Rd | 2 +- man/make.visitSequence.Rd | 2 +- man/make.where.Rd | 2 +- man/matchindex.Rd | 25 ++- man/md.pairs.Rd | 5 +- man/md.pattern.Rd | 6 +- man/mdc.Rd | 6 +- man/mice.Rd | 58 +++--- man/mice.impute.2l.norm.Rd | 5 +- man/mice.impute.2l.pan.Rd | 8 +- man/mice.impute.2lonly.mean.Rd | 6 +- man/mice.impute.2lonly.norm.Rd | 12 +- man/mice.impute.2lonly.pmm.Rd | 12 +- man/mice.impute.cart.Rd | 8 +- man/mice.impute.jomoImpute.Rd | 6 +- man/mice.impute.lda.Rd | 7 +- man/mice.impute.logreg.Rd | 4 +- man/mice.impute.logreg.boot.Rd | 4 +- man/mice.impute.mean.Rd | 4 +- man/mice.impute.midastouch.Rd | 8 +- man/mice.impute.mnar.Rd | 11 +- man/mice.impute.mpmm.Rd | 2 +- man/mice.impute.norm.boot.Rd | 2 +- man/mice.impute.norm.nob.Rd | 4 +- man/mice.impute.norm.predict.Rd | 4 +- man/mice.impute.panImpute.Rd | 6 +- man/mice.impute.passive.Rd | 4 +- man/mice.impute.pmm.Rd | 4 +- man/mice.impute.polr.Rd | 10 +- man/mice.impute.polyreg.Rd | 6 +- man/mice.impute.quadratic.Rd | 2 +- man/mice.impute.rf.Rd | 6 +- man/mice.impute.ri.Rd | 2 +- man/mice.impute.sample.Rd | 2 +- man/mice.mids.Rd | 6 +- man/mids-class.Rd | 105 ++++++----- man/mids2mplus.Rd | 2 +- man/mids2spss.Rd | 4 +- man/mipo.Rd | 8 +- man/mira-class.Rd | 28 +-- man/name.blocks.Rd | 2 +- man/name.formulas.Rd | 2 +- man/ncc.Rd | 2 +- man/nhanes.Rd | 2 +- man/nhanes2.Rd | 2 +- man/nic.Rd | 2 +- man/nimp.Rd | 2 +- man/parlmice.Rd | 26 +-- man/plot.mids.Rd | 12 +- man/pmm.match.Rd | 2 +- man/pool.Rd | 26 +-- man/pool.compare.Rd | 8 +- man/pool.r.squared.Rd | 8 +- man/pool.scalar.Rd | 38 ++-- man/print.Rd | 8 +- man/print.mads.Rd | 2 +- man/quickpred.Rd | 12 +- man/selfreport.Rd | 2 +- man/stripplot.mids.Rd | 44 ++--- man/summary.Rd | 8 +- man/supports.transparent.Rd | 2 +- man/tidy.mipo.Rd | 30 +-- man/toenail.Rd | 23 ++- man/toenail2.Rd | 21 +-- man/walking.Rd | 2 +- man/windspeed.Rd | 2 +- man/with.mids.Rd | 8 +- man/xyplot.mads.Rd | 12 +- man/xyplot.mids.Rd | 36 ++-- 238 files changed, 2682 insertions(+), 2696 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ccdff1d23..e4d526fd6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -100,3 +100,4 @@ BugReports: https://github.com/amices/mice/issues LinkingTo: cpp11, Rcpp License: GPL (>= 2) RoxygenNote: 7.2.3 +Roxygen: list(markdown = TRUE) diff --git a/NAMESPACE b/NAMESPACE index 5bac39150..41ab79344 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -149,7 +149,6 @@ export(nelsonaalen) export(nic) export(nimp) export(norm.draw) -export(p2c) export(p2f) export(parlmice) export(pool) diff --git a/R/D1.R b/R/D1.R index 49ecee0d4..3f0c75363 100644 --- a/R/D1.R +++ b/R/D1.R @@ -2,25 +2,25 @@ #' #' The D1-statistics is the multivariate Wald test. #' -#' @param fit1 An object of class \code{mira}, produced by \code{with()}. -#' @param fit0 An object of class \code{mira}, produced by \code{with()}. The -#' model in \code{fit0} is a nested within \code{fit1}. The default null -#' model \code{fit0 = NULL} compares \code{fit1} to the intercept-only model. +#' @param fit1 An object of class `mira`, produced by `with()`. +#' @param fit0 An object of class `mira`, produced by `with()`. The +#' model in `fit0` is a nested within `fit1`. The default null +#' model `fit0 = NULL` compares `fit1` to the intercept-only model. #' @param dfcom A single number denoting the -#' complete-data degrees of freedom of model \code{fit1}. If not specified, -#' it is set equal to \code{df.residual} of model \code{fit1}. If that cannot +#' complete-data degrees of freedom of model `fit1`. If not specified, +#' it is set equal to `df.residual` of model `fit1`. If that cannot #' be done, the procedure assumes (perhaps incorrectly) a large sample. #' @param df.com Deprecated #' @note Warning: `D1()` assumes that the order of the variables is the #' same in different models. See -#' \url{https://github.com/amices/mice/issues/420} for details. +#' for details. #' @references #' Li, K. H., T. E. Raghunathan, and D. B. Rubin. 1991. #' Large-Sample Significance Levels from Multiply Imputed Data Using #' Moment-Based Statistics and an F Reference Distribution. -#' \emph{Journal of the American Statistical Association}, 86(416): 1065–73. +#' *Journal of the American Statistical Association*, 86(416): 1065–73. #' -#' \url{https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:wald} +#' #' @examples #' # Compare two linear models: #' imp <- mice(nhanes2, seed = 51009, print = FALSE) @@ -34,7 +34,7 @@ #' fit0 <- with(imp, glm(gen > levels(gen)[1] ~ hgt + hc, family = binomial)) #' D1(fit1, fit0) #' } -#' @seealso \code{\link[mitml]{testModels}} +#' @seealso [mitml::testModels()] #' @export D1 <- function(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL) { install.on.demand("mitml") diff --git a/R/D2.R b/R/D2.R index c46c36490..b63d6791a 100644 --- a/R/D2.R +++ b/R/D2.R @@ -7,13 +7,13 @@ #' @inheritParams mitml::testModels #' @note Warning: `D2()` assumes that the order of the variables is the #' same in different models. See -#' \url{https://github.com/amices/mice/issues/420} for details. +#' for details. #' @references #' Li, K. H., X. L. Meng, T. E. Raghunathan, and D. B. Rubin. 1991. #' Significance Levels from Repeated p-Values with Multiply-Imputed Data. -#' \emph{Statistica Sinica} 1 (1): 65–92. +#' *Statistica Sinica* 1 (1): 65–92. #' -#' \url{https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:chi} +#' #' @examples #' # Compare two linear models: #' imp <- mice(nhanes2, seed = 51009, print = FALSE) @@ -27,7 +27,7 @@ #' fit0 <- with(imp, glm(gen > levels(gen)[1] ~ hgt + hc, family = binomial)) #' D2(fit1, fit0) #' } -#' @seealso \code{\link[mitml]{testModels}} +#' @seealso [mitml::testModels()] #' @export D2 <- function(fit1, fit0 = NULL, use = "wald") { install.on.demand("mitml") diff --git a/R/D3.R b/R/D3.R index 4c885bcea..b2952fd80 100644 --- a/R/D3.R +++ b/R/D3.R @@ -3,34 +3,34 @@ #' The D3-statistic is a likelihood-ratio test statistic. #' #' @details -#' The \code{D3()} function implement the LR-method by +#' The `D3()` function implement the LR-method by #' Meng and Rubin (1992). The implementation of the method relies -#' on the \code{broom} package, the standard \code{update} mechanism -#' for statistical models in \code{R} and the \code{offset} function. +#' on the `broom` package, the standard `update` mechanism +#' for statistical models in `R` and the `offset` function. #' -#' The function calculates \code{m} repetitions of the full +#' The function calculates `m` repetitions of the full #' (or null) models, calculates the mean of the estimates of the #' (fixed) parameter coefficients \eqn{\beta}. For each imputed #' imputed dataset, it calculates the likelihood for the model with #' the parameters constrained to \eqn{\beta}. #' -#' The \code{mitml::testModels()} function offers similar functionality -#' for a subset of statistical models. Results of \code{mice::D3()} and -#' \code{mitml::testModels()} differ in multilevel models because the -#' \code{testModels()} also constrains the variance components parameters. +#' The `mitml::testModels()` function offers similar functionality +#' for a subset of statistical models. Results of `mice::D3()` and +#' `mitml::testModels()` differ in multilevel models because the +#' `testModels()` also constrains the variance components parameters. #' For more details on #' -#' @seealso \code{\link{fix.coef}} +#' @seealso [fix.coef()] #' @inheritParams D1 -#' @return An object of class \code{mice.anova} +#' @return An object of class `mice.anova` #' @references #' Meng, X. L., and D. B. Rubin. 1992. #' Performing Likelihood Ratio Tests with Multiply-Imputed Data Sets. -#' \emph{Biometrika}, 79 (1): 103–11. +#' *Biometrika*, 79 (1): 103–11. #' -#' \url{https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:likelihoodratio} +#' #' -#' \url{http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#setting-residual-variances-to-a-fixed-value-zero-or-other} +#' #' @examples #' # Compare two linear models: #' imp <- mice(nhanes2, seed = 51009, print = FALSE) diff --git a/R/ampute.R b/R/ampute.R index b30aeea6e..41aa78a4a 100644 --- a/R/ampute.R +++ b/R/ampute.R @@ -2,11 +2,11 @@ #' #' This function generates multivariate missing data under a MCAR, MAR or MNAR #' missing data mechanism. Imputation of data sets containing missing values can -#' be performed with \code{\link{mice}}. +#' be performed with [mice()]. #' #' This function generates missing values in complete data sets. Amputation of complete #' data sets is useful for the evaluation of imputation techniques, such as multiple -#' imputation (performed with function \code{\link{mice}} in this package). +#' imputation (performed with function [mice()] in this package). #' #' The basic strategy underlying multivariate imputation was suggested by #' Don Rubin during discussions in the 90's. Brand (1997) created one particular @@ -21,13 +21,13 @@ #' With the univariate approach, it is difficult to relate the missingness on one #' variable to the missingness on another variable. A multivariate amputation procedure #' solves this issue and moreover, it does justice to the multivariate nature of -#' data sets. Hence, \code{ampute} is developed to perform multivariate amputation. +#' data sets. Hence, `ampute` is developed to perform multivariate amputation. #' #' The idea behind the function is the specification of several missingness #' patterns. Each pattern is a combination of variables with and without missing -#' values (denoted by \code{0} and \code{1} respectively). For example, one might +#' values (denoted by `0` and `1` respectively). For example, one might #' want to create two missingness patterns on a data set with four variables. The -#' patterns could be something like: \code{0,0,1,1} and \code{1,0,1,0}. +#' patterns could be something like: `0,0,1,1` and `1,0,1,0`. #' Each combination of zeros and ones may occur. #' #' Furthermore, the researcher specifies the proportion of missingness, either the @@ -41,16 +41,16 @@ #' depends on the values of the observed variables (i.e. the variables that remain #' complete) (MAR) or on the values of the variables that will be made incomplete (MNAR). #' For a discussion on how missingness mechanisms are related to the observed data, -#' we refer to \doi{10.1177/0049124118799376}{Schouten and Vink, 2018}. +#' we refer to \doi{10.1177/0049124118799376}. #' -#' When the user specifies the missingness mechanism to be \code{"MCAR"}, the candidates -#' have an equal probability of becoming incomplete. For a \code{"MAR"} or \code{"MNAR"} mechanism, +#' When the user specifies the missingness mechanism to be `"MCAR"`, the candidates +#' have an equal probability of becoming incomplete. For a `"MAR"` or `"MNAR"` mechanism, #' weighted sum scores are calculated. These scores are a linear combination of the #' variables. #' #' In order to calculate the weighted sum scores, the data is standardized. For this reason, #' the data has to be numeric. Second, for each case, the values in -#' the data set are multiplied with the weights, specified by argument \code{weights}. +#' the data set are multiplied with the weights, specified by argument `weights`. #' These weighted scores will be summed, resulting in a weighted sum score for each case. #' #' The weights may differ between patterns and they may be negative or zero as well. @@ -84,28 +84,27 @@ #' #' For an example and more explanation about how the arguments interact with each other, #' we refer to the vignette -#' \href{https://rianneschouten.github.io/mice_ampute/vignette/ampute.html}{Generate missing values with ampute} -#' The amputation methodology is published in -#' \doi{10.1080/00949655.2018.1491577}{Schouten, Lugtig and Vink, 2018}. +#' [Generate missing values with ampute](https://rianneschouten.github.io/mice_ampute/vignette/ampute.html) +#' The amputation methodology is published in \doi{10.1080/00949655.2018.1491577}. #' #' @param data A complete data matrix or data frame. Values should be numeric. #' Categorical variables should have been transformed to dummies. #' @param prop A scalar specifying the proportion of missingness. Should be a value #' between 0 and 1. Default is a missingness proportion of 0.5. #' @param patterns A matrix or data frame of size #patterns by #variables where -#' \code{0} indicates that a variable should have missing values and \code{1} indicates +#' `0` indicates that a variable should have missing values and `1` indicates #' that a variable should remain complete. The user may specify as many patterns as #' desired. One pattern (a vector) is possible as well. Default #' is a square matrix of size #variables where each pattern has missingness on one -#' variable only (created with \code{\link{ampute.default.patterns}}). After the -#' amputation procedure, \code{\link{md.pattern}} can be used to investigate the +#' variable only (created with [ampute.default.patterns()]). After the +#' amputation procedure, [md.pattern()] can be used to investigate the #' missing data patterns in the data. #' @param freq A vector of length #patterns containing the relative frequency with #' which the patterns should occur. For example, for three missing data patterns, -#' the vector could be \code{c(0.4, 0.4, 0.2)}, meaning that of all cases with +#' the vector could be `c(0.4, 0.4, 0.2)`, meaning that of all cases with #' missing values, 40 percent should have pattern 1, 40 percent pattern 2 and 20 #' percent pattern 3. The vector should sum to 1. Default is an equal probability -#' for each pattern, created with \code{\link{ampute.default.freq}}. +#' for each pattern, created with [ampute.default.freq()]. #' @param mech A string specifying the missingness mechanism, either "MCAR" #' (Missing Completely At Random), "MAR" (Missing At Random) or "MNAR" (Missing Not At #' Random). Default is a MAR missingness mechanism. @@ -115,27 +114,27 @@ #' zero. For a MNAR mechanism, these weights could have any possible value. Furthermore, #' the weights may differ between patterns and between variables. They may be negative #' as well. Within each pattern, the relative size of the values are of importance. -#' The default weights matrix is made with \code{\link{ampute.default.weights}} and +#' The default weights matrix is made with [ampute.default.weights()] and #' returns a matrix with equal weights for all variables. In case of MAR, variables -#' that will be amputed will be weighted with \code{0}. For MNAR, variables -#' that will be observed will be weighted with \code{0}. If the mechanism is MCAR, the +#' that will be amputed will be weighted with `0`. For MNAR, variables +#' that will be observed will be weighted with `0`. If the mechanism is MCAR, the #' weights matrix will not be used. #' @param std Logical. Whether the weighted sum scores should be calculated with #' standardized data or with non-standardized data. The latter is especially advised when #' making use of train and test sets in order to prevent leakage. #' @param cont Logical. Whether the probabilities should be based on a continuous #' or a discrete distribution. If TRUE, the probabilities of being missing are based -#' on a continuous logistic distribution function. \code{\link{ampute.continuous}} +#' on a continuous logistic distribution function. [ampute.continuous()] #' will be used to calculate and assign the probabilities. These probabilities will then -#' be based on the argument \code{type}. If FALSE, the probabilities of being missing are -#' based on a discrete distribution (\code{\link{ampute.discrete}}) based on the \code{odds} +#' be based on the argument `type`. If FALSE, the probabilities of being missing are +#' based on a discrete distribution ([ampute.discrete()]) based on the `odds` #' argument. Default is TRUE. #' @param type A string or vector of strings containing the type of missingness for each -#' pattern. Either \code{"LEFT"}, \code{"MID"}, \code{"TAIL"} or '\code{"RIGHT"}. +#' pattern. Either `"LEFT"`, `"MID"`, `"TAIL"` or '`"RIGHT"`. #' If a single missingness type is given, all patterns will be created with the same #' type. If the missingness types should differ between patterns, a vector of missingness #' types should be given. Default is RIGHT for all patterns and is the result of -#' \code{\link{ampute.default.type}}. +#' [ampute.default.type()]. #' @param odds A matrix where #patterns defines the #rows. Each row should contain #' the odds of being missing for the corresponding pattern. The number of odds values #' defines in how many quantiles the sum scores will be divided. The odds values are @@ -143,38 +142,39 @@ #' being missing that is four times higher than a quantile with odds 1. The #' number of quantiles may differ between the patterns, specify NA for cells remaining empty. #' Default is 4 quantiles with odds values 1, 2, 3 and 4 and is created by -#' \code{\link{ampute.default.odds}}. +#' [ampute.default.odds()]. #' @param bycases Logical. If TRUE, the proportion of missingness is defined in #' terms of cases. If FALSE, the proportion of missingness is defined in terms of #' cells. Default is TRUE. #' @param run Logical. If TRUE, the amputations are implemented. If FALSE, the #' return object will contain everything except for the amputed data set. #' -#' @return Returns an S3 object of class \code{\link{mads-class}} (multivariate +#' @return Returns an S3 object of class [mads-class()] (multivariate #' amputed data set) -#' @author Rianne Schouten [aut, cre], Gerko Vink [aut], Peter Lugtig [ctb], 2016 -#' @seealso \code{\link{mads-class}}, \code{\link{bwplot}}, \code{\link{xyplot}}, -#' \code{\link{mice}} +#' @author Rianne Schouten (aut, cre), Gerko Vink (aut), Peter Lugtig (ctb), 2016 +#' @seealso [mads-class()], [bwplot()], [xyplot()], +#' [mice()] #' -#' @references Brand, J.P.L. (1999) \emph{Development, implementation and +#' @references Brand, J.P.L. (1999) *Development, implementation and #' evaluation of multiple imputation strategies for the statistical analysis of -#' incomplete data sets.} pp. 110-113. Dissertation. Rotterdam: Erasmus University. +#' incomplete data sets.* pp. 110-113. Dissertation. Rotterdam: Erasmus University. #' #' Schouten, R.M., Lugtig, P and Vink, G. (2018) #' {Generating missing values for simulation purposes: A multivariate amputation procedure.}. -#' \emph{Journal of Statistical Computation and Simulation}, 88(15): 1909-1930. +#' *Journal of Statistical Computation and Simulation*, 88(15): 1909-1930. #' \doi{10.1080/00949655.2018.1491577} #' #' Schouten, R.M. and Vink, G. (2018){The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions}. -#' \emph{Sociological Methods and Research}, 50(3): 1243-1258. +#' *Sociological Methods and Research*, 50(3): 1243-1258. #' \doi{10.1177/0049124118799376} #' #' Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn, C.G.M., Rubin, D.B. (2006) #' {Fully conditional specification in multivariate imputation.} -#' \emph{Journal of Statistical Computation and Simulation}, 76(12): 1049-1064. +#' *Journal of Statistical Computation and Simulation*, 76(12): 1049-1064. #' \doi{10.1080/10629360600810434} #' -#' Van Buuren, S. (2018) \href{https://stefvanbuuren.name/fimd/sec-FCS.html#sec:MICE}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' Van Buuren, S. (2018) +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' Vink, G. (2016) Towards a standardized evaluation of multiple imputation routines. diff --git a/R/ampute.continuous.R b/R/ampute.continuous.R index 5ae5b7cad..e28f5186e 100644 --- a/R/ampute.continuous.R +++ b/R/ampute.continuous.R @@ -3,29 +3,29 @@ #' This function creates a missing data indicator for each pattern. The continuous #' probability distributions (Van Buuren, 2012, pp. 63, 64) will be induced on the #' weighted sum scores, calculated earlier in the multivariate amputation function -#' \code{\link{ampute}}. +#' [ampute()]. #' #' @param P A vector containing the pattern numbers of the cases's candidacies. #' For each case, a value between 1 and #patterns is given. For example, a #' case with value 2 is candidate for missing data pattern 2. #' @param scores A list containing vectors with the candidates's weighted sum scores, -#' the result of an underlying function in \code{\link{ampute}}. +#' the result of an underlying function in [ampute()]. #' @param prop A scalar specifying the proportion of missingness. Should be a value #' between 0 and 1. Default is a missingness proportion of 0.5. #' @param type A vector of strings containing the type of missingness for each -#' pattern. Either \code{"LEFT"}, \code{"MID"}, \code{"TAIL"} or '\code{"RIGHT"}. +#' pattern. Either `"LEFT"`, `"MID"`, `"TAIL"` or '`"RIGHT"`. #' If a single missingness type is entered, all patterns will be created by the same #' type. If missingness types should differ over patterns, a vector of missingness #' types should be entered. Default is RIGHT for all patterns and is the result of -#' \code{\link{ampute.default.type}}. -#' @return A list containing vectors with \code{0} if a case should be made missing -#' and \code{1} if a case should remain complete. The first vector refers to the +#' [ampute.default.type()]. +#' @return A list containing vectors with `0` if a case should be made missing +#' and `1` if a case should remain complete. The first vector refers to the #' first pattern, the second vector to the second pattern, etcetera. -#' @author Rianne Schouten [aut, cre], Gerko Vink [aut], Peter Lugtig [ctb], 2016 -#' @seealso \code{\link{ampute}}, \code{\link{ampute.default.type}} +#' @author Rianne Schouten (aut, cre), Gerko Vink (aut), Peter Lugtig (ctb), 2016 +#' @seealso [ampute()], [ampute.default.type()] #' @references #' #'Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-linearnormal.html#sec:generateuni}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-linearnormal.html#sec:generateuni) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords internal #' @export diff --git a/R/ampute.default.R b/R/ampute.default.R index f7191b6be..d30bd76fa 100644 --- a/R/ampute.default.R +++ b/R/ampute.default.R @@ -1,13 +1,13 @@ -#' Default \code{patterns} in \code{ampute} +#' Default `patterns` in `ampute` #' #' This function creates a default pattern matrix for the multivariate -#' amputation function \code{ampute()}. +#' amputation function `ampute()`. #' #' @param n A scalar specifying the number of variables in the data. -#' @return A square matrix of size \code{n} where \code{0} indicates a variable +#' @return A square matrix of size `n` where `0` indicates a variable # should have missing values and \code{1} indicates a variable should remain # complete. Each pattern has missingness on one variable only. -#' @seealso \code{\link{ampute}}, \code{\link{md.pattern}} +#' @seealso [ampute()], [md.pattern()] #' @author Rianne Schouten, 2016 #' @keywords internal #' @export @@ -19,17 +19,17 @@ ampute.default.patterns <- function(n) { do.call(rbind, patterns.list) } -#' Default \code{freq} in \code{ampute} +#' Default `freq` in `ampute` #' #' Defines the default relative frequency vector for the multivariate -#' amputation function \code{ampute}. +#' amputation function `ampute`. #' -#' @param patterns A matrix of size #patterns by #variables where \code{0} indicates -#' a variable should have missing values and \code{1} indicates a variable should -#' remain complete. Could be the result of \code{\link{ampute.default.patterns}}. +#' @param patterns A matrix of size #patterns by #variables where `0` indicates +#' a variable should have missing values and `1` indicates a variable should +#' remain complete. Could be the result of [ampute.default.patterns()]. #' @return A vector of length #patterns containing the relative frequencies with #' which the patterns should occur. An equal probability is given to each pattern. -#' @seealso \code{\link{ampute}}, \code{\link{ampute.default.patterns}} +#' @seealso [ampute()], [ampute.default.patterns()] #' @author Rianne Schouten, 2016 #' @keywords internal #' @export @@ -37,22 +37,22 @@ ampute.default.freq <- function(patterns) { rep.int(1 / nrow(patterns), nrow(patterns)) } -#' Default \code{weights} in \code{ampute} +#' Default `weights` in `ampute` #' #' Defines the default weights matrix for the multivariate amputation function -#' \code{ampute}. +#' `ampute`. #' -#' @param patterns A matrix of size #patterns by #variables where \code{0} indicates -#' a variable should have missing values and \code{1} indicates a variable should -#' remain complete. Could be the result of \code{\link{ampute.default.patterns}}. +#' @param patterns A matrix of size #patterns by #variables where `0` indicates +#' a variable should have missing values and `1` indicates a variable should +#' remain complete. Could be the result of [ampute.default.patterns()]. #' @param mech A string specifying the missingness mechanism. #' @return A matrix of size #patterns by #variables containing the weights that #' will be used to calculate the weighted sum scores. Equal weights are given to #' all variables. When mechanism is MAR, variables that will be amputed will be -#' weighted with \code{0}. If it is MNAR, variables that will be observed -#' will be weighted with \code{0}. If mechanism is MCAR, the weights matrix will +#' weighted with `0`. If it is MNAR, variables that will be observed +#' will be weighted with `0`. If mechanism is MCAR, the weights matrix will #' not be used. A default MAR matrix will be returned. -#' @seealso \code{\link{ampute}}, \code{\link{ampute.default.patterns}} +#' @seealso [ampute()], [ampute.default.patterns()] #' @author Rianne Schouten, 2016 #' @keywords internal #' @export @@ -68,17 +68,17 @@ ampute.default.weights <- function(patterns, mech) { weights } -#' Default \code{type} in \code{ampute()} +#' Default `type` in `ampute()` #' #' Defines the default type vector for the multivariate amputation function -#' \code{ampute}. +#' `ampute`. #' #' @param patterns A matrix of size #patterns by #variables where 0 indicates a #' variable should have missing values and 1 indicates a variable should remain -#' complete. Could be the result of \code{\link{ampute.default.patterns}}. +#' complete. Could be the result of [ampute.default.patterns()]. #' @return A string vector of length #patterns containing the missingness types. #' Each pattern will be amputed with a "RIGHT" missingness. -#' @seealso \code{\link{ampute}}, \code{\link{ampute.default.patterns}} +#' @seealso [ampute()], [ampute.default.patterns()] #' @author Rianne Schouten, 2016 #' @keywords internal #' @export @@ -86,17 +86,17 @@ ampute.default.type <- function(patterns) { rep.int("RIGHT", nrow(patterns)) } -#' Default \code{odds} in \code{ampute()} +#' Default `odds` in `ampute()` #' #' Defines the default odds matrix for the multivariate amputation function -#' \code{ampute}. +#' `ampute`. #' #' @param patterns A matrix of size #patterns by #variables where 0 indicates a #' variable should have missing values and 1 indicates a variable should remain -#' complete. Could be the result of \code{\link{ampute.default.patterns}}. +#' complete. Could be the result of [ampute.default.patterns()]. #' @return A matrix where #rows equals #patterns. Default is 4 quantiles with odds #' values 1, 2, 3 and 4, for each pattern, imitating a RIGHT type of missingness. -#' @seealso \code{\link{ampute}}, \code{\link{ampute.default.patterns}} +#' @seealso [ampute()], [ampute.default.patterns()] #' @author Rianne Schouten, 2016 #' @keywords internal #' @export diff --git a/R/ampute.discrete.R b/R/ampute.discrete.R index 314732b63..74633cf1f 100644 --- a/R/ampute.discrete.R +++ b/R/ampute.discrete.R @@ -2,13 +2,13 @@ #' #' This function creates a missing data indicator for each pattern. Odds probabilities #' (Brand, 1999, pp. 110-113) will be induced on the weighted sum scores, calculated earlier -#' in the multivariate amputation function \code{\link{ampute}}. +#' in the multivariate amputation function [ampute()]. #' #' @param P A vector containing the pattern numbers of candidates. #' For each case, a value between 1 and #patterns is given. For example, a #' case with value 2 is candidate for missing data pattern 2. #' @param scores A list containing vectors with the candidates's weighted sum scores, -#' the result of an underlying function in \code{\link{ampute}}. +#' the result of an underlying function in [ampute()]. #' @param prop A scalar specifying the proportion of missingness. Should be a value #' between 0 and 1. Default is a missingness proportion of 0.5. #' @param odds A matrix where #patterns defines the #rows. Each row should contain @@ -18,15 +18,15 @@ #' being missing that is four times higher than a quantile with odds 1. The #' #quantiles may differ between the patterns, specify NA for cells remaining empty. #' Default is 4 quantiles with odds values 1, 2, 3 and 4, the result of -#' \code{\link{ampute.default.odds}}. -#' @return A list containing vectors with \code{0} if a case should be made missing -#' and \code{1} if a case should remain complete. The first vector refers to the +#' [ampute.default.odds()]. +#' @return A list containing vectors with `0` if a case should be made missing +#' and `1` if a case should remain complete. The first vector refers to the #' first pattern, the second vector to the second pattern, etcetera. #' @author Rianne Schouten, 2016 -#' @seealso \code{\link{ampute}}, \code{\link{ampute.default.odds}} -#' @references Brand, J.P.L. (1999). \emph{Development, implementation and +#' @seealso [ampute()], [ampute.default.odds()] +#' @references Brand, J.P.L. (1999). *Development, implementation and #' evaluation of multiple imputation strategies for the statistical analysis of -#' incomplete data sets.} Dissertation. Rotterdam: Erasmus University. +#' incomplete data sets.* Dissertation. Rotterdam: Erasmus University. #' @keywords internal #' @export ampute.discrete <- function(P, scores, prop, odds) { diff --git a/R/ampute.mcar.R b/R/ampute.mcar.R index 766c7154f..4d8ddac09 100644 --- a/R/ampute.mcar.R +++ b/R/ampute.mcar.R @@ -2,24 +2,24 @@ #' #' This function creates a missing data indicator for each pattern, based on a MCAR #' missingness mechanism. The function is used in the multivariate amputation function -#' \code{\link{ampute}}. +#' [ampute()]. #' #' @param P A vector containing the pattern numbers of the cases' candidates. #' For each case, a value between 1 and #patterns is given. For example, a #' case with value 2 is candidate for missing data pattern 2. -#' @param patterns A matrix of size #patterns by #variables where \code{0} indicates -#' a variable should have missing values and \code{1} indicates a variable should +#' @param patterns A matrix of size #patterns by #variables where `0` indicates +#' a variable should have missing values and `1` indicates a variable should #' remain complete. The user may specify as many patterns as desired. One pattern -#' (a vector) is also possible. Could be the result of \code{\link{ampute.default.patterns}}, +#' (a vector) is also possible. Could be the result of [ampute.default.patterns()], #' default will be a square matrix of size #variables where each pattern has missingness #' on one variable only. #' @param prop A scalar specifying the proportion of missingness. Should be a value #' between 0 and 1. Default is a missingness proportion of 0.5. -#' @return A list containing vectors with \code{0} if a case should be made missing -#' and \code{1} if a case should remain complete. The first vector refers to the +#' @return A list containing vectors with `0` if a case should be made missing +#' and `1` if a case should remain complete. The first vector refers to the #' first pattern, the second vector to the second pattern, etcetera. #' @author Rianne Schouten, 2016 -#' @seealso \code{\link{ampute}} +#' @seealso [ampute()] #' @keywords internal #' @export ampute.mcar <- function(P, patterns, prop) { diff --git a/R/anova.R b/R/anova.R index 4281dce40..c91813f63 100644 --- a/R/anova.R +++ b/R/anova.R @@ -1,12 +1,12 @@ #' Compare several nested models #' #' @rdname anova -#' @param object Two or more objects of class \code{mira} -#' @param method Either \code{"D1"}, \code{"D2"} or \code{"D3"} +#' @param object Two or more objects of class `mira` +#' @param method Either `"D1"`, `"D2"` or `"D3"` #' @param use An character indicating the test statistic -#' @param ... Other parameters passed down to \code{D1()}, \code{D2()}, -#' \code{D3()} and \code{mitml::testModels}. -#' @return Object of class \code{mice.anova} +#' @param ... Other parameters passed down to `D1()`, `D2()`, +#' `D3()` and `mitml::testModels`. +#' @return Object of class `mice.anova` #' @export anova.mira <- function(object, ..., method = "D1", use = "wald") { modlist <- list(object, ...) diff --git a/R/as.R b/R/as.R index 80beddc9f..3f599e7b0 100644 --- a/R/as.R +++ b/R/as.R @@ -1,31 +1,31 @@ -#' Converts an imputed dataset (long format) into a \code{mids} object +#' Converts an imputed dataset (long format) into a `mids` object #' #' This function converts imputed data stored in long format into -#' an object of class \code{mids}. The original incomplete dataset +#' an object of class `mids`. The original incomplete dataset #' needs to be available so that we know where the missing data are. #' The function is useful to convert back operations applied to -#' the imputed data back in a \code{mids} object. It may also be +#' the imputed data back in a `mids` object. It may also be #' used to store multiply imputed data sets from other software -#' into the format used by \code{mice}. -#' @note The function expects the input data \code{long} to be sorted by -#' imputation number (variable \code{".imp"} by default), and in the +#' into the format used by `mice`. +#' @note The function expects the input data `long` to be sorted by +#' imputation number (variable `".imp"` by default), and in the #' same sequence within each imputation block. #' @param long A multiply imputed data set in long format, for example -#' produced by a call to \code{complete(..., action = 'long', include = TRUE)}, +#' produced by a call to `complete(..., action = 'long', include = TRUE)`, #' or by other software. -#' @param .imp An optional column number or column name in \code{long}, +#' @param .imp An optional column number or column name in `long`, #' indicating the imputation index. The values are assumed to be consecutive -#' integers between 0 and \code{m}. Values \code{1} through \code{m} -#' correspond to the imputation index, value \code{0} indicates +#' integers between 0 and `m`. Values `1` through `m` +#' correspond to the imputation index, value `0` indicates #' the original data (with missings). -#' By default, the procedure will search for a variable named \code{".imp"}. -#' @param .id An optional column number or column name in \code{long}, +#' By default, the procedure will search for a variable named `".imp"`. +#' @param .id An optional column number or column name in `long`, #' indicating the subject identification. If not specified, then the -#' function searches for a variable named \code{".id"}. If this variable +#' function searches for a variable named `".id"`. If this variable #' is found, the values in the column will define the row names in -#' the \code{data} element of the resulting \code{mids} object. +#' the `data` element of the resulting `mids` object. #' @inheritParams mice -#' @return An object of class \code{mids} +#' @return An object of class `mids` #' @author Gerko Vink #' @examples #' # impute the nhanes dataset @@ -134,14 +134,14 @@ as.mids <- function(long, where = NULL, .imp = ".imp", .id = ".id") { ini } -#' Create a \code{mira} object from repeated analyses +#' Create a `mira` object from repeated analyses #' -#' The \code{as.mira()} function takes the results of repeated +#' The `as.mira()` function takes the results of repeated #' complete-data analysis stored as a list, and turns it -#' into a \code{mira} object that can be pooled. +#' into a `mira` object that can be pooled. #' @param fitlist A list containing $m$ fitted analysis objects -#' @return An S3 object of class \code{mira}. -#' @seealso \code{\link[=mira-class]{mira}} +#' @return An S3 object of class `mira`. +#' @seealso [`mira()`][mira-class] #' @author Stef van Buuren #' @export as.mira <- function(fitlist) { @@ -161,15 +161,15 @@ as.mira <- function(fitlist) { object } -#' Converts into a \code{mitml.result} object +#' Converts into a `mitml.result` object #' -#' The \code{as.mitml.result()} function takes the results of repeated +#' The `as.mitml.result()` function takes the results of repeated #' complete-data analysis stored as a list, and turns it -#' into an object of class \code{mitml.result}. -#' @param x An object of class \code{mira} -#' @return An S3 object of class \code{mitml.result}, a list +#' into an object of class `mitml.result`. +#' @param x An object of class `mira` +#' @return An S3 object of class `mitml.result`, a list #' containing $m$ fitted analysis objects. -#' @seealso \code{\link[mitml]{with.mitml.list}} +#' @seealso [mitml::with.mitml.list()] #' @author Stef van Buuren #' @export as.mitml.result <- function(x) { diff --git a/R/auxiliary.R b/R/auxiliary.R index 4637f5d37..4c374f6e0 100644 --- a/R/auxiliary.R +++ b/R/auxiliary.R @@ -1,6 +1,6 @@ #' Conditional imputation helper #' -#' Sorry, the \code{ifdo()} function is not yet implemented. +#' Sorry, the `ifdo()` function is not yet implemented. #' @aliases ifdo #' @param cond a condition #' @param action the action to do @@ -16,11 +16,11 @@ ifdo <- function(cond, action) { #' #' A custom function to insert rows in long data with new pseudo-observations #' that are being done on the specified break ages. There should be a -#' column called \code{first} in \code{data} with logical data that codes whether -#' the current row is the first for subject \code{id}. Furthermore, -#' the function assumes that columns \code{age}, \code{occ}, -#' \code{hgt.z}, \code{wgt.z} and -#' \code{bmi.z} are available. This function is used on the \code{tbc} +#' column called `first` in `data` with logical data that codes whether +#' the current row is the first for subject `id`. Furthermore, +#' the function assumes that columns `age`, `occ`, +#' `hgt.z`, `wgt.z` and +#' `bmi.z` are available. This function is used on the `tbc` #' data in FIMD chapter 9. Check that out to see it in action. #' @aliases appendbreak #' @param data A data frame in the long long format @@ -62,9 +62,9 @@ appendbreak <- function(data, brk, warp.model = warp.model, id = NULL, typ = "pr app[order(app$id, app$age), ] } -#' Extract broken stick estimates from a \code{lmer} object +#' Extract broken stick estimates from a `lmer` object #' -#' @param fit An object of class \code{lmer} +#' @param fit An object of class `lmer` #' @return A matrix containing broken stick estimates #' @author Stef van Buuren, 2012 #' @export diff --git a/R/blocks.R b/R/blocks.R index 10dd6f107..161bf6828 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -1,40 +1,40 @@ -#' Creates a \code{blocks} argument +#' Creates a `blocks` argument #' #' This helper function generates a list of the type needed for -#' \code{blocks} argument in the \code{[=mice]{mice}} function. -#' @param data A \code{data.frame}, character vector with -#' variable names, or \code{list} with variable names. +#' `blocks` argument in the [mice()] function. +#' @param data A `data.frame`, character vector with +#' variable names, or `list` with variable names. #' @param partition A character vector of length 1 used to assign -#' variables to blocks when \code{data} is a \code{data.frame}. Value -#' \code{"scatter"} (default) will assign each column to it own -#' block. Value \code{"collect"} assigns all variables to one block, -#' whereas \code{"void"} produces an empty list. -#' @param calltype A character vector of \code{length(block)} elements +#' variables to blocks when `data` is a `data.frame`. Value +#' `"scatter"` (default) will assign each column to it own +#' block. Value `"collect"` assigns all variables to one block, +#' whereas `"void"` produces an empty list. +#' @param calltype A character vector of `length(block)` elements #' that indicates how the imputation model is specified. If -#' \code{calltype = "pred"} (the default), the underlying imputation -#' model is called by means of the \code{type} argument. The -#' \code{type} argument for block \code{h} is equivalent to -#' row \code{h} in the \code{predictorMatrix}. -#' The alternative is \code{calltype = "formula"}. This will pass -#' \code{formulas[[h]]} to the underlying imputation -#' function for block \code{h}, together with the current data. -#' The \code{calltype} of a block is set automatically during +#' `calltype = "pred"` (the default), the underlying imputation +#' model is called by means of the `type` argument. The +#' `type` argument for block `h` is equivalent to +#' row `h` in the `predictorMatrix`. +#' The alternative is `calltype = "formula"`. This will pass +#' `formulas[[h]]` to the underlying imputation +#' function for block `h`, together with the current data. +#' The `calltype` of a block is set automatically during #' initialization. Where a choice is possible, calltype -#' \code{"formula"} is preferred over \code{"pred"} since this is +#' `"formula"` is preferred over `"pred"` since this is #' more flexible and extendable. However, what precisely happens #' depends also on the capabilities of the imputation #' function that is called. #' @return A named list of character vectors with variables names. -#' @details Choices \code{"scatter"} and \code{"collect"} represent to two +#' @details Choices `"scatter"` and `"collect"` represent to two #' extreme scenarios for assigning variables to imputation blocks. -#' Use \code{"scatter"} to create an imputation model based on -#' \emph{fully conditionally specification} (FCS). Use \code{"collect"} to -#' gather all variables to be imputed by a \emph{joint model} (JM). +#' Use `"scatter"` to create an imputation model based on +#' *fully conditionally specification* (FCS). Use `"collect"` to +#' gather all variables to be imputed by a *joint model* (JM). #' Scenario's in-between these two extremes represent -#' \emph{hybrid} imputation models that combine FCS and JM. +#' *hybrid* imputation models that combine FCS and JM. #' #' Any variable not listed in will not be imputed. -#' Specification \code{"void"} represents the extreme scenario that +#' Specification `"void"` represents the extreme scenario that #' skips imputation of all variables. #' #' A variable may be a member of multiple blocks. The variable will be @@ -107,19 +107,19 @@ make.blocks <- function(data, #' Name imputation blocks #' -#' This helper function names any unnamed elements in the \code{blocks} +#' This helper function names any unnamed elements in the `blocks` #' specification. This is a convenience function. #' @inheritParams mice #' @param prefix A character vector of length 1 with the prefix to #' be using for naming any unnamed blocks with two or more variables. #' @return A named list of character vectors with variables names. -#' @seealso \code{\link{mice}} +#' @seealso [mice()] #' @details #' This function will name any unnamed list elements specified in -#' the optional argument \code{blocks}. Unnamed blocks +#' the optional argument `blocks`. Unnamed blocks #' consisting of just one variable will be named after this variable. #' Unnamed blocks containing more than one variables will be named -#' by the \code{prefix} argument, padded by an integer sequence +#' by the `prefix` argument, padded by an integer sequence #' stating at 1. #' @examples #' blocks <- list(c("hyp", "chl"), AGE = "age", c("bmi", "hyp"), "edu") @@ -170,21 +170,21 @@ check.blocks <- function(blocks, data, calltype = "formula") { blocks } -#' Construct blocks from \code{formulas} and \code{predictorMatrix} +#' Construct blocks from `formulas` and `predictorMatrix` #' #' This helper function attempts to find blocks of variables in the -#' specification of the \code{formulas} and/or \code{predictorMatrix} -#' objects. Blocks specified by \code{formulas} may consist of -#' multiple variables. Blocks specified by \code{predictorMatrix} are +#' specification of the `formulas` and/or `predictorMatrix` +#' objects. Blocks specified by `formulas` may consist of +#' multiple variables. Blocks specified by `predictorMatrix` are #' assumed to consist of single variables. Any duplicates in names are #' removed, and the formula specification is preferred. -#' \code{predictorMatrix} and \code{formulas}. When both arguments +#' `predictorMatrix` and `formulas`. When both arguments #' specify models for the same block, the model for the -#' \code{predictMatrix} is removed, and priority is given to the -#' specification given in \code{formulas}. +#' `predictMatrix` is removed, and priority is given to the +#' specification given in `formulas`. #' @inheritParams mice -#' @return A \code{blocks} object. -#' @seealso \code{\link{make.blocks}}, \code{\link{name.blocks}} +#' @return A `blocks` object. +#' @seealso [make.blocks()], [name.blocks()] #' @examples #' form <- list(bmi + hyp ~ chl + age, chl ~ bmi) #' pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) diff --git a/R/blots.R b/R/blots.R index 2762d8d95..66c385221 100644 --- a/R/blots.R +++ b/R/blots.R @@ -1,16 +1,16 @@ -#' Creates a \code{blots} argument +#' Creates a `blots` argument #' -#' This helper function creates a valid \code{blots} object. The -#' \code{blots} object is an argument to the \code{mice} function. -#' The name \code{blots} is a contraction of blocks-dots. -#' Through \code{blots}, the user can specify any additional +#' This helper function creates a valid `blots` object. The +#' `blots` object is an argument to the `mice` function. +#' The name `blots` is a contraction of blocks-dots. +#' Through `blots`, the user can specify any additional #' arguments that are specifically passed down to the lowest level #' imputation function. -#' @param data A \code{data.frame} with the source data +#' @param data A `data.frame` with the source data #' @param blocks An optional specification for blocks of variables in #' the rows. The default assigns each variable in its own block. #' @return A matrix -#' @seealso \code{\link{make.blocks}} +#' @seealso [make.blocks()] #' @examples #' make.predictorMatrix(nhanes) #' make.blots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) diff --git a/R/boys.R b/R/boys.R index 96863948d..ac1300883 100644 --- a/R/boys.R +++ b/R/boys.R @@ -3,8 +3,8 @@ #' Height, weight, head circumference and puberty of 748 Dutch boys. #' #' Random sample of 10\% from the cross-sectional data used to construct the -#' Dutch growth references 1997. Variables \code{gen} and \code{phb} are ordered -#' factors. \code{reg} is a factor. +#' Dutch growth references 1997. Variables `gen` and `phb` are ordered +#' factors. `reg` is a factor. #' #' @name boys #' @docType data @@ -21,11 +21,11 @@ #' @source Fredriks, A.M,, van Buuren, S., Burgmeijer, R.J., Meulmeester JF, #' Beuker, R.J., Brugman, E., Roede, M.J., Verloove-Vanhorick, S.P., Wit, J.M. #' (2000) Continuing positive secular growth change in The Netherlands -#' 1955-1997. \emph{Pediatric Research}, \bold{47}, 316-323. +#' 1955-1997. *Pediatric Research*, **47**, 316-323. #' #' Fredriks, A.M., van Buuren, S., Wit, J.M., Verloove-Vanhorick, S.P. (2000). -#' Body index measurements in 1996-7 compared with 1980. \emph{Archives of -#' Disease in Childhood}, \bold{82}, 107-112. +#' Body index measurements in 1996-7 compared with 1980. *Archives of +#' Disease in Childhood*, **82**, 107-112. #' @keywords datasets #' @examples #' diff --git a/R/brandsma.R b/R/brandsma.R index 73ee4addc..1d3dd81fd 100644 --- a/R/brandsma.R +++ b/R/brandsma.R @@ -6,22 +6,22 @@ #' #' @name brandsma #' @docType data -#' @format \code{brandsma} is a data frame with 4106 rows and 14 columns: +#' @format `brandsma` is a data frame with 4106 rows and 14 columns: #' \describe{ -#' \item{\code{sch}}{School number} -#' \item{\code{pup}}{Pupil ID} -#' \item{\code{iqv}}{IQ verbal} -#' \item{\code{iqp}}{IQ performal} -#' \item{\code{sex}}{Sex of pupil} -#' \item{\code{ses}}{SES score of pupil} -#' \item{\code{min}}{Minority member 0/1} -#' \item{\code{rpg}}{Number of repeated groups, 0, 1, 2} -#' \item{\code{lpr}}{language score PRE} -#' \item{\code{lpo}}{language score POST} -#' \item{\code{apr}}{Arithmetic score PRE} -#' \item{\code{apo}}{Arithmetic score POST} -#' \item{\code{den}}{Denomination classification 1-4 - at school level} -#' \item{\code{ssi}}{School SES indicator - at school level} +#' \item{`sch`}{School number} +#' \item{`pup`}{Pupil ID} +#' \item{`iqv`}{IQ verbal} +#' \item{`iqp`}{IQ performal} +#' \item{`sex`}{Sex of pupil} +#' \item{`ses`}{SES score of pupil} +#' \item{`min`}{Minority member 0/1} +#' \item{`rpg`}{Number of repeated groups, 0, 1, 2} +#' \item{`lpr`}{language score PRE} +#' \item{`lpo`}{language score POST} +#' \item{`apr`}{Arithmetic score PRE} +#' \item{`apo`}{Arithmetic score POST} +#' \item{`den`}{Denomination classification 1-4 - at school level} +#' \item{`ssi`}{School SES indicator - at school level} #' } #' #' @note This dataset is constructed from the raw data. There are @@ -29,11 +29,11 @@ #' of Snijders and Bosker: #' \enumerate{ #' \item All schools are included, including the five school with -#' missing values on \code{langpost}. -#' \item Missing \code{denomina} codes are left as missing. +#' missing values on `langpost`. +#' \item Missing `denomina` codes are left as missing. #' \item Aggregates are undefined in the presence of missing data #' in the underlying values. -#' Variables \code{ses}, \code{iqv} and \code{iqp} are in their +#' Variables `ses`, `iqv` and `iqp` are in their #' original scale, and not globally centered. #' No aggregate variables at the school level are included. #' \item There is a wider selection of original variables. Note @@ -41,9 +41,9 @@ #' variables. #' } #' -#' @source Constructed from \code{MLbook_2nded_total_4106-99.sav} from -#' \url{https://www.stats.ox.ac.uk/~snijders/mlbook.htm} by function -#' \code{data-raw/R/brandsma.R} +#' @source Constructed from `MLbook_2nded_total_4106-99.sav` from +#' by function +#' `data-raw/R/brandsma.R` #' #' @references #' Brandsma, HP and Knuver, JWM (1989), Effects of school and diff --git a/R/bwplot.R b/R/bwplot.R index 2163bbf12..7c28cff7b 100644 --- a/R/bwplot.R +++ b/R/bwplot.R @@ -1,130 +1,130 @@ #' Box-and-whisker plot of observed and imputed data #' -#' Plotting methods for imputed data using \pkg{lattice}. \code{bwplot} +#' Plotting methods for imputed data using \pkg{lattice}. `bwplot` #' produces box-and-whisker plots. The function #' automatically separates the observed and imputed data. The #' functions extend the usual features of \pkg{lattice}. #' -#' The argument \code{na.groups} may be used to specify (combinations of) -#' missingness in any of the variables. The argument \code{groups} can be used +#' The argument `na.groups` may be used to specify (combinations of) +#' missingness in any of the variables. The argument `groups` can be used #' to specify groups based on the variable values themselves. Only one of both -#' may be active at the same time. When both are specified, \code{na.groups} -#' takes precedence over \code{groups}. +#' may be active at the same time. When both are specified, `na.groups` +#' takes precedence over `groups`. #' -#' Use the \code{subset} and \code{na.groups} together to plots parts of the +#' Use the `subset` and `na.groups` together to plots parts of the #' data. For example, select the first imputed data set by by -#' \code{subset=.imp==1}. +#' `subset=.imp==1`. #' -#' Graphical parameters like \code{col}, \code{pch} and \code{cex} can be +#' Graphical parameters like `col`, `pch` and `cex` can be #' specified in the arguments list to alter the plotting symbols. If -#' \code{length(col)==2}, the color specification to define the observed and -#' missing groups. \code{col[1]} is the color of the 'observed' data, -#' \code{col[2]} is the color of the missing or imputed data. A convenient color -#' choice is \code{col=mdc(1:2)}, a transparent blue color for the observed +#' `length(col)==2`, the color specification to define the observed and +#' missing groups. `col[1]` is the color of the 'observed' data, +#' `col[2]` is the color of the missing or imputed data. A convenient color +#' choice is `col=mdc(1:2)`, a transparent blue color for the observed #' data, and a transparent red color for the imputed data. A good choice is -#' \code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the -#' duration of the session by running \code{mice.theme()}. +#' `col=mdc(1:2), pch=20, cex=1.5`. These choices can be set for the +#' duration of the session by running `mice.theme()`. #' #' @aliases bwplot -#' @param x A \code{mids} object, typically created by \code{mice()} or -#' \code{mice.mids()}. +#' @param x A `mids` object, typically created by `mice()` or +#' `mice.mids()`. #' @param data Formula that selects the data to be plotted. This argument -#' follows the \pkg{lattice} rules for \emph{formulas}, describing the primary +#' follows the \pkg{lattice} rules for *formulas*, describing the primary #' variables (used for the per-panel display) and the optional conditioning #' variables (which define the subsets plotted in different panels) to be used #' in the plot. #' -#' The formula is evaluated on the complete data set in the \code{long} form. -#' Legal variable names for the formula include \code{names(x$data)} plus the -#' two administrative factors \code{.imp} and \code{.id}. +#' The formula is evaluated on the complete data set in the `long` form. +#' Legal variable names for the formula include `names(x$data)` plus the +#' two administrative factors `.imp` and `.id`. #' -#' \bold{Extended formula interface:} The primary variable terms (both the LHS -#' \code{y} and RHS \code{x}) may consist of multiple terms separated by a -#' \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be -#' taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and -#' \code{y2 ~ x | a * b}, but with the \code{y1 ~ x} and \code{y2 ~ x} in -#' \emph{separate panels}. This behavior differs from standard \pkg{lattice}. -#' \emph{Only combine terms of the same type}, i.e. only factors or only +#' **Extended formula interface:** The primary variable terms (both the LHS +#' `y` and RHS `x`) may consist of multiple terms separated by a +#' \sQuote{+} sign, e.g., `y1 + y2 ~ x | a * b`. This formula would be +#' taken to mean that the user wants to plot both `y1 ~ x | a * b` and +#' `y2 ~ x | a * b`, but with the `y1 ~ x` and `y2 ~ x` in +#' *separate panels*. This behavior differs from standard \pkg{lattice}. +#' *Only combine terms of the same type*, i.e. only factors or only #' numerical variables. Mixing numerical and categorical data occasionally #' produces odds labeling of vertical axis. #' -#' For convenience, in \code{stripplot()} and \code{bwplot} the formula -#' \code{y~.imp} may be abbreviated as \code{y}. This applies only to a single -#' \code{y}, and does not (yet) work for \code{y1+y2~.imp}. +#' For convenience, in `stripplot()` and `bwplot` the formula +#' `y~.imp` may be abbreviated as `y`. This applies only to a single +#' `y`, and does not (yet) work for `y1+y2~.imp`. #' #' @param na.groups An expression evaluating to a logical vector indicating #' which two groups are distinguished (e.g. using different colors) in the #' display. The environment in which this expression is evaluated in the -#' response indicator \code{is.na(x$data)}. +#' response indicator `is.na(x$data)`. #' -#' The default \code{na.group = NULL} contrasts the observed and missing data -#' in the LHS \code{y} variable of the display, i.e. groups created by -#' \code{is.na(y)}. The expression \code{y} creates the groups according to -#' \code{is.na(y)}. The expression \code{y1 & y2} creates groups by -#' \code{is.na(y1) & is.na(y2)}, and \code{y1 | y2} creates groups as -#' \code{is.na(y1) | is.na(y2)}, and so on. -#' @param groups This is the usual \code{groups} arguments in \pkg{lattice}. It -#' differs from \code{na.groups} because it evaluates in the completed data -#' \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas -#' \code{na.groups} evaluates in the response indicator. See -#' \code{\link{xyplot}} for more details. When both \code{na.groups} and -#' \code{groups} are specified, \code{na.groups} takes precedence, and -#' \code{groups} is ignored. +#' The default `na.group = NULL` contrasts the observed and missing data +#' in the LHS `y` variable of the display, i.e. groups created by +#' `is.na(y)`. The expression `y` creates the groups according to +#' `is.na(y)`. The expression `y1 & y2` creates groups by +#' `is.na(y1) & is.na(y2)`, and `y1 | y2` creates groups as +#' `is.na(y1) | is.na(y2)`, and so on. +#' @param groups This is the usual `groups` arguments in \pkg{lattice}. It +#' differs from `na.groups` because it evaluates in the completed data +#' `data.frame(complete(x, "long", inc=TRUE))` (as usual), whereas +#' `na.groups` evaluates in the response indicator. See +#' [xyplot()] for more details. When both `na.groups` and +#' `groups` are specified, `na.groups` takes precedence, and +#' `groups` is ignored. #' @param theme A named list containing the graphical parameters. The default -#' function \code{mice.theme} produces a short list of default colors, line +#' function `mice.theme` produces a short list of default colors, line #' width, and so on. The extensive list may be obtained from -#' \code{trellis.par.get()}. Global graphical parameters like \code{col} or -#' \code{cex} in high-level calls are still honored, so first experiment with +#' `trellis.par.get()`. Global graphical parameters like `col` or +#' `cex` in high-level calls are still honored, so first experiment with #' the global parameters. Many setting consists of a pair. For example, -#' \code{mice.theme} defines two symbol colors. The first is for the observed +#' `mice.theme` defines two symbol colors. The first is for the observed #' data, the second for the imputed data. The theme settings only exist during #' the call, and do not affect the trellis graphical parameters. #' @param mayreplicate A logical indicating whether color, line widths, and so #' on, may be replicated. The graphical functions attempt to choose #' "intelligent" graphical parameters. For example, the same color can be #' replicated for different element, e.g. use all reds for the imputed data. -#' Replication may be switched off by setting the flag to \code{FALSE}, in order +#' Replication may be switched off by setting the flag to `FALSE`, in order #' to allow the user to gain full control. -#' @param as.table See \code{\link[lattice:xyplot]{xyplot}}. -#' @param outer See \code{\link[lattice:xyplot]{xyplot}}. -#' @param allow.multiple See \code{\link[lattice:xyplot]{xyplot}}. -#' @param drop.unused.levels See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subscripts See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subset See \code{\link[lattice:xyplot]{xyplot}}. +#' @param as.table See [lattice::xyplot()]. +#' @param outer See [lattice::xyplot()]. +#' @param allow.multiple See [lattice::xyplot()]. +#' @param drop.unused.levels See [lattice::xyplot()]. +#' @param subscripts See [lattice::xyplot()]. +#' @param subset See [lattice::xyplot()]. #' @param \dots Further arguments, usually not directly processed by the #' high-level functions documented here, but instead passed on to other #' functions. #' @return The high-level functions documented here, as well as other high-level -#' Lattice functions, return an object of class \code{"trellis"}. The -#' \code{\link[lattice:update.trellis]{update}} method can be used to +#' Lattice functions, return an object of class `"trellis"`. The +#' [`update()`][lattice::update.trellis] method can be used to #' subsequently update components of the object, and the -#' \code{\link[lattice:print.trellis]{print}} method (usually called by default) +#' [`print()`][lattice::print.trellis] method (usually called by default) #' will plot it on an appropriate plotting device. -#' @note The first two arguments (\code{x} and \code{data}) are reversed +#' @note The first two arguments (`x` and `data`) are reversed #' compared to the standard Trellis syntax implemented in \pkg{lattice}. This #' reversal was necessary in order to benefit from automatic method dispatch. #' -#' In \pkg{mice} the argument \code{x} is always a \code{mids} object, whereas -#' in \pkg{lattice} the argument \code{x} is always a formula. +#' In \pkg{mice} the argument `x` is always a `mids` object, whereas +#' in \pkg{lattice} the argument `x` is always a formula. #' -#' In \pkg{mice} the argument \code{data} is always a formula object, whereas in -#' \pkg{lattice} the argument \code{data} is usually a data frame. +#' In \pkg{mice} the argument `data` is always a formula object, whereas in +#' \pkg{lattice} the argument `data` is usually a data frame. #' #' All other arguments have identical interpretation. #' #' @author Stef van Buuren -#' @seealso \code{\link{mice}}, \code{\link{xyplot}}, \code{\link{densityplot}}, -#' \code{\link{stripplot}}, \code{\link{lattice}} for an overview of the -#' package, as well as \code{\link[lattice:xyplot]{bwplot}}, -#' \code{\link[lattice:panel.xyplot]{panel.bwplot}}, -#' \code{\link[lattice:print.trellis]{print.trellis}}, -#' \code{\link[lattice:trellis.par.get]{trellis.par.set}} -#' @references Sarkar, Deepayan (2008) \emph{Lattice: Multivariate Data -#' Visualization with R}, Springer. +#' @seealso [mice()], [xyplot()], [densityplot()], +#' [stripplot()], [lattice()] for an overview of the +#' package, as well as [`bwplot()`][lattice::xyplot], +#' [`panel.bwplot()`][lattice::panel.xyplot], +#' [lattice::print.trellis()], +#' [`trellis.par.set()`][lattice::trellis.par.get] +#' @references Sarkar, Deepayan (2008) *Lattice: Multivariate Data +#' Visualization with R*, Springer. #' -#' van Buuren S and 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} +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords hplot #' @examples #' diff --git a/R/bwplot.mads.R b/R/bwplot.mads.R index c268d565c..d02dc1d9d 100644 --- a/R/bwplot.mads.R +++ b/R/bwplot.mads.R @@ -4,8 +4,8 @@ #' the amputed data. The function shows how the amputed values are related #' to the variable values. #' -#' @param x A \code{mads} (\code{\link{mads-class}}) object, typically created by -#' \code{\link{ampute}}. +#' @param x A `mads` ([mads-class()]) object, typically created by +#' [ampute()]. #' @param data A string or vector of variable names that needs to be plotted. As #' a default, all variables will be plotted. #' @param which.pat A scalar or vector indicating which patterns need to be plotted. @@ -16,19 +16,19 @@ #' need to be printed. This is useful to examine the effect of the amputation. #' Default is TRUE. #' @param layout A vector of two values indicating how the boxplots of one pattern -#' should be divided over the plot. For example, \code{c(2, 3)} indicates that the +#' should be divided over the plot. For example, `c(2, 3)` indicates that the #' boxplots of six variables need to be placed on 3 rows and 2 columns. Default #' is 1 row and an amount of columns equal to #variables. Note that for more than #' 6 variables, multiple plots will be created automatically. #' @param \dots Not used, but for consistency with generic #' @return A list containing the box-and-whisker plots. Note that a new pattern #' will always be shown in a new plot. -#' @note The \code{mads} object contains all the information you need to -#' make any desired plots. Check \code{\link{mads-class}} or the vignette \emph{Multivariate -#' Amputation using Ampute} to understand the contents of class object \code{mads}. +#' @note The `mads` object contains all the information you need to +#' make any desired plots. Check [mads-class()] or the vignette *Multivariate +#' Amputation using Ampute* to understand the contents of class object `mads`. #' @author Rianne Schouten, 2016 -#' @seealso \code{\link{ampute}}, \code{\link{bwplot}}, \code{\link{Lattice}} for -#' an overview of the package, \code{\link{mads-class}} +#' @seealso [ampute()], [bwplot()], [Lattice()] for +#' an overview of the package, [mads-class()] #' @export bwplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, descriptives = TRUE, layout = NULL, ...) { diff --git a/R/cc.R b/R/cc.R index 2cf413c09..81762cf4b 100644 --- a/R/cc.R +++ b/R/cc.R @@ -1,17 +1,17 @@ #' Select complete cases #' -#' Extracts the complete cases, also known as \emph{listwise deletion}. -#' \code{cc(x)} is similar to -#' \code{na.omit(x)}, but returns an object of the same class +#' Extracts the complete cases, also known as *listwise deletion*. +#' `cc(x)` is similar to +#' `na.omit(x)`, but returns an object of the same class #' as the input data. Dimensions are not dropped. For extracting -#' incomplete cases, use \code{\link{ici}}. +#' incomplete cases, use [ici()]. #' -#' @param x An \code{R} object. Methods are available for classes -#' \code{mids}, \code{data.frame} and \code{matrix}. Also, \code{x} +#' @param x An `R` object. Methods are available for classes +#' `mids`, `data.frame` and `matrix`. Also, `x` #' could be a vector. -#' @return A \code{vector}, \code{matrix} or \code{data.frame} containing the data of the complete cases. +#' @return A `vector`, `matrix` or `data.frame` containing the data of the complete cases. #' @author Stef van Buuren, 2017. -#' @seealso \code{\link{na.omit}}, \code{\link{cci}}, \code{\link{ici}} +#' @seealso [na.omit()], [cci()], [ici()] #' @keywords univar #' @examples #' @@ -44,14 +44,14 @@ cc.default <- function(x) { #' Select incomplete cases #' #' Extracts incomplete cases from a data set. -#' The companion function for selecting the complete cases is \code{\link{cc}}. +#' The companion function for selecting the complete cases is [cc()]. #' -#' @param x An \code{R} object. Methods are available for classes -#' \code{mids}, \code{data.frame} and \code{matrix}. Also, \code{x} +#' @param x An `R` object. Methods are available for classes +#' `mids`, `data.frame` and `matrix`. Also, `x` #' could be a vector. -#' @return A \code{vector}, \code{matrix} or \code{data.frame} containing the data of the complete cases. +#' @return A `vector`, `matrix` or `data.frame` containing the data of the complete cases. #' @author Stef van Buuren, 2017. -#' @seealso \code{\link{cc}}, \code{\link{ici}} +#' @seealso [cc()], [ici()] #' @keywords univar #' @examples #' diff --git a/R/cci.R b/R/cci.R index f4144f371..606e35503 100644 --- a/R/cci.R +++ b/R/cci.R @@ -2,15 +2,15 @@ #' #' #' The complete case indicator is useful for extracting the subset of complete cases. The function -#' \code{cci(x)} calls \code{complete.cases(x)}. -#' The companion function \code{ici()} selects the incomplete cases. +#' `cci(x)` calls `complete.cases(x)`. +#' The companion function `ici()` selects the incomplete cases. #' #' @name cci -#' @param x An \code{R} object. Currently supported are methods for the -#' following classes: \code{mids}. +#' @param x An `R` object. Currently supported are methods for the +#' following classes: `mids`. #' @return Logical vector indicating the complete cases. #' @author Stef van Buuren, 2017. -#' @seealso \code{\link{complete.cases}}, \code{\link{ici}}, \code{\link{cc}} +#' @seealso [complete.cases()], [ici()], [cc()] #' @keywords univar #' @examples #' cci(nhanes) # indicator for 13 complete cases @@ -33,15 +33,15 @@ cci.default <- function(x) { #' Incomplete case indicator #' #' This array is useful for extracting the subset of incomplete cases. -#' The companion function \code{cci()} selects the complete cases. +#' The companion function `cci()` selects the complete cases. #' #' @name ici #' @aliases ici ici,data.frame-method ici,matrix-method ici,mids-method -#' @param x An \code{R} object. Currently supported are methods for the -#' following classes: \code{mids}. +#' @param x An `R` object. Currently supported are methods for the +#' following classes: `mids`. #' @return Logical vector indicating the incomplete cases, #' @author Stef van Buuren, 2017. -#' @seealso \code{\link{cci}}, \code{\link{ic}} +#' @seealso [cci()], [ic()] #' @keywords univar #' @examples #' diff --git a/R/complete.R b/R/complete.R index 2c1cfe242..f7f047687 100644 --- a/R/complete.R +++ b/R/complete.R @@ -1,58 +1,58 @@ -#' Extracts the completed data from a \code{mids} object +#' Extracts the completed data from a `mids` object #' -#' Takes an object of class \code{mids}, fills in the missing data, and returns +#' Takes an object of class `mids`, fills in the missing data, and returns #' the completed data in a specified format. #' #' @aliases complete -#' @param data An object of class \code{mids} as created by the function -#' \code{mice()}. +#' @param data An object of class `mids` as created by the function +#' `mice()`. #' @param action A numeric vector or a keyword. Numeric -#' values between 1 and \code{data$m} return the data with -#' imputation number \code{action} filled in. The value of \code{action = 0} -#' return the original data, with missing values. \code{action} can -#' also be one of the following keywords: \code{"all"}, \code{"long"}, -#' \code{"broad"} and \code{"repeated"}. See the Details section +#' values between 1 and `data$m` return the data with +#' imputation number `action` filled in. The value of `action = 0` +#' return the original data, with missing values. `action` can +#' also be one of the following keywords: `"all"`, `"long"`, +#' `"broad"` and `"repeated"`. See the Details section #' for the interpretation. -#' The default is \code{action = 1L} returns the first imputed data set. +#' The default is `action = 1L` returns the first imputed data set. #' @param include A logical to indicate whether the original data with the missing #' values should be included. #' @param mild A logical indicating whether the return value should -#' always be an object of class \code{mild}. Setting \code{mild = TRUE} -#' overrides \code{action} keywords \code{"long"}, \code{"broad"} -#' and \code{"repeated"}. The default is \code{FALSE}. -#' @param order Either \code{"first"} or \code{"last"}. Only relevant when -#' \code{action == "long"}. Writes the \code{".imp"} and \code{".id"} -#' in columns 1 and 2. The default is \code{order = "last"}. -#' Included for backward compatibility with \code{"< mice 3.16.0"}. +#' always be an object of class `mild`. Setting `mild = TRUE` +#' overrides `action` keywords `"long"`, `"broad"` +#' and `"repeated"`. The default is `FALSE`. +#' @param order Either `"first"` or `"last"`. Only relevant when +#' `action == "long"`. Writes the `".imp"` and `".id"` +#' in columns 1 and 2. The default is `order = "last"`. +#' Included for backward compatibility with `"< mice 3.16.0"`. #' @param \dots Additional arguments. Not used. #' @return Complete data set with missing values replaced by imputations. -#' A \code{data.frame}, or a list of data frames of class \code{mild}. +#' A `data.frame`, or a list of data frames of class `mild`. #' @details -#' The argument \code{action} can be length-1 character, which is +#' The argument `action` can be length-1 character, which is #' matched to one of the following keywords: #' \describe{ -#' \item{\code{"all"}}{produces a \code{mild} object of imputed data sets. When -#' \code{include = TRUE}, then the original data are appended as the first list +#' \item{`"all"`}{produces a `mild` object of imputed data sets. When +#' `include = TRUE`, then the original data are appended as the first list #' element;} -#' \item{\code{"long"}}{ produces a data set where imputed data sets -#' are stacked vertically. The columns are added: 1) \code{.imp}, integer, -#' referring the imputation number, and 2) \code{.id}, character, the row -#' names of \code{data$data};} -#' \item{\code{"stacked"}}{ same as \code{"long"} but without the two +#' \item{`"long"`}{ produces a data set where imputed data sets +#' are stacked vertically. The columns are added: 1) `.imp`, integer, +#' referring the imputation number, and 2) `.id`, character, the row +#' names of `data$data`;} +#' \item{`"stacked"`}{ same as `"long"` but without the two #' additional columns;} -#' \item{\code{"broad"}}{ produces a data set with where imputed data sets +#' \item{`"broad"`}{ produces a data set with where imputed data sets #' are stacked horizontally. Columns are ordered as in the original data. #' The imputation number is appended to each column name;} -#' \item{\code{"repeated"}}{ same as \code{"broad"}, but with +#' \item{`"repeated"`}{ same as `"broad"`, but with #' columns in a different order.} #' } #' @note -#' Technical note: \code{mice 3.7.5} renamed the \code{complete()} function -#' to \code{complete.mids()} and exported it as an S3 method of the -#' generic \code{tidyr::complete()}. Name clashes between -#' \code{mice::complete()} and \code{tidyr::complete()} should no +#' Technical note: `mice 3.7.5` renamed the `complete()` function +#' to `complete.mids()` and exported it as an S3 method of the +#' generic `tidyr::complete()`. Name clashes between +#' `mice::complete()` and `tidyr::complete()` should no #' longer occur. -#' @seealso \code{\link{mice}}, \code{\link[=mids-class]{mids}} +#' @seealso [mice()], [`mids()`][mids-class] #' @keywords manip #' @examples #' diff --git a/R/convergence.R b/R/convergence.R index 922cc51f5..ab9adbde3 100644 --- a/R/convergence.R +++ b/R/convergence.R @@ -1,33 +1,33 @@ -#' Computes convergence diagnostics for a \code{mids} object +#' Computes convergence diagnostics for a `mids` object #' -#' Takes an object of class \code{mids}, computes the autocorrelation -#' and/or potential scale reduction factor, and returns a \code{data.frame} +#' Takes an object of class `mids`, computes the autocorrelation +#' and/or potential scale reduction factor, and returns a `data.frame` #' with the specified diagnostic(s) per iteration. #' -#' @param data An object of class \code{mids} as created by the function -#' \code{mice()}. -#' @param diagnostic A keyword. One of the following keywords: \code{"ac"}, -#' \code{"all"}, \code{"gr"} and \code{"psrf"}. See the Details section +#' @param data An object of class `mids` as created by the function +#' `mice()`. +#' @param diagnostic A keyword. One of the following keywords: `"ac"`, +#' `"all"`, `"gr"` and `"psrf"`. See the Details section #' for the interpretation. -#' The default is \code{diagnostic = "all"} which returns both the +#' The default is `diagnostic = "all"` which returns both the #' autocorrelation and potential scale reduction factor per iteration. -#' @param parameter A keyword. One of the following keywords: \code{"mean"} -#' or \code{"sd"} to evaluate chain means or chain standard deviations, +#' @param parameter A keyword. One of the following keywords: `"mean"` +#' or `"sd"` to evaluate chain means or chain standard deviations, #' respectively. #' @param \dots Additional arguments. Not used. -#' @return A \code{data.frame} with the autocorrelation and/or potential +#' @return A `data.frame` with the autocorrelation and/or potential #' scale reduction factor per iteration of the MICE algorithm. #' @details -#' The argument \code{diagnostic} can be length-1 character, which is +#' The argument `diagnostic` can be length-1 character, which is #' matched to one of the following keywords: #' \describe{ -#' \item{\code{"all"}}{computes both the lag-1 autocorrelation as well as +#' \item{`"all"`}{computes both the lag-1 autocorrelation as well as #' the potential scale reduction factor (cf. Vehtari et al., 2021) per #' iteration of the MICE algorithm;} -#' \item{\code{"ac"}}{computes only the autocorrelation per iteration;} -#' \item{\code{"psrf"}}{computes only the potential scale reduction factor +#' \item{`"ac"`}{computes only the autocorrelation per iteration;} +#' \item{`"psrf"`}{computes only the potential scale reduction factor #' per iteration;} -#' \item{\code{"gr"}}{same as \code{psrf}, the potential scale reduction +#' \item{`"gr"`}{same as `psrf`, the potential scale reduction #' factor is colloquially called the Gelman-Rubin diagnostic.} #' } #' In the unlikely event of perfect convergence, the autocorrelation equals @@ -37,7 +37,7 @@ #' iteration number (.it) per imputed variable (vrb). A persistently #' decreasing trend across iterations indicates potential non-convergence. #' -#' @seealso \code{\link{mice}}, \code{\link[=mids-class]{mids}} +#' @seealso [mice()], [`mids()`][mids-class] #' @keywords none #' @references Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Burkner, #' P.-C. (2021). Rank-Normalization, Folding, and Localization: An Improved diff --git a/R/convert.R b/R/convert.R index cd43a4513..911668ea0 100644 --- a/R/convert.R +++ b/R/convert.R @@ -36,7 +36,6 @@ p2f <- function(predictorMatrix, blocks = NULL, silent = TRUE) { #' Convert predictorMatrix into roles #' #' @rdname convertmodels -#' @export p2c <- function(predictorMatrix) { # exports special predictorMatrix roles, not 0 or 1 blks <- row.names(predictorMatrix) @@ -56,10 +55,10 @@ p2c <- function(predictorMatrix) { #' Convert formulas into predictorMatrix #' #' @rdname convertmodels -#' @param roles A list with \code{ncol(data)} elements, each with a row of the -#' \code{predictorMatrix} when it contains values other than 0 or 1. +#' @param roles A list with `ncol(data)` elements, each with a row of the +#' `predictorMatrix` when it contains values other than 0 or 1. #' The argument is only needed if the model contains non-standard -#'values in the \code{predictorMatrix}. +#'values in the `predictorMatrix`. #' @export f2p <- function(formulas, blocks = NULL, roles = NULL) { # converts formulas and roles into predictorMatrix diff --git a/R/densityplot.R b/R/densityplot.R index 6f12ed56f..e60cef6a3 100644 --- a/R/densityplot.R +++ b/R/densityplot.R @@ -1,141 +1,141 @@ #' Density plot of observed and imputed data #' -#' Plotting methods for imputed data using \pkg{lattice}. \code{densityplot} +#' Plotting methods for imputed data using \pkg{lattice}. `densityplot` #' produces plots of the densities. The function #' automatically separates the observed and imputed data. The #' functions extend the usual features of \pkg{lattice}. #' -#' The argument \code{na.groups} may be used to specify (combinations of) -#' missingness in any of the variables. The argument \code{groups} can be used +#' The argument `na.groups` may be used to specify (combinations of) +#' missingness in any of the variables. The argument `groups` can be used #' to specify groups based on the variable values themselves. Only one of both -#' may be active at the same time. When both are specified, \code{na.groups} -#' takes precedence over \code{groups}. +#' may be active at the same time. When both are specified, `na.groups` +#' takes precedence over `groups`. #' -#' Use the \code{subset} and \code{na.groups} together to plots parts of the +#' Use the `subset` and `na.groups` together to plots parts of the #' data. For example, select the first imputed data set by by -#' \code{subset=.imp==1}. +#' `subset=.imp==1`. #' -#' Graphical parameters like \code{col}, \code{pch} and \code{cex} can be +#' Graphical parameters like `col`, `pch` and `cex` can be #' specified in the arguments list to alter the plotting symbols. If -#' \code{length(col)==2}, the color specification to define the observed and -#' missing groups. \code{col[1]} is the color of the 'observed' data, -#' \code{col[2]} is the color of the missing or imputed data. A convenient color -#' choice is \code{col=mdc(1:2)}, a transparent blue color for the observed +#' `length(col)==2`, the color specification to define the observed and +#' missing groups. `col[1]` is the color of the 'observed' data, +#' `col[2]` is the color of the missing or imputed data. A convenient color +#' choice is `col=mdc(1:2)`, a transparent blue color for the observed #' data, and a transparent red color for the imputed data. A good choice is -#' \code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the -#' duration of the session by running \code{mice.theme()}. +#' `col=mdc(1:2), pch=20, cex=1.5`. These choices can be set for the +#' duration of the session by running `mice.theme()`. #' #' @aliases densityplot -#' @param x A \code{mids} object, typically created by \code{mice()} or -#' \code{mice.mids()}. +#' @param x A `mids` object, typically created by `mice()` or +#' `mice.mids()`. #' @param data Formula that selects the data to be plotted. This argument -#' follows the \pkg{lattice} rules for \emph{formulas}, describing the primary +#' follows the \pkg{lattice} rules for *formulas*, describing the primary #' variables (used for the per-panel display) and the optional conditioning #' variables (which define the subsets plotted in different panels) to be used #' in the plot. #' -#' The formula is evaluated on the complete data set in the \code{long} form. -#' Legal variable names for the formula include \code{names(x$data)} plus the -#' two administrative factors \code{.imp} and \code{.id}. +#' The formula is evaluated on the complete data set in the `long` form. +#' Legal variable names for the formula include `names(x$data)` plus the +#' two administrative factors `.imp` and `.id`. #' -#' \bold{Extended formula interface:} The primary variable terms (both the LHS -#' \code{y} and RHS \code{x}) may consist of multiple terms separated by a -#' \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be -#' taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and -#' \code{y2 ~ x | a * b}, but with the \code{y1 ~ x} and \code{y2 ~ x} in -#' \emph{separate panels}. This behavior differs from standard \pkg{lattice}. -#' \emph{Only combine terms of the same type}, i.e. only factors or only +#' **Extended formula interface:** The primary variable terms (both the LHS +#' `y` and RHS `x`) may consist of multiple terms separated by a +#' \sQuote{+} sign, e.g., `y1 + y2 ~ x | a * b`. This formula would be +#' taken to mean that the user wants to plot both `y1 ~ x | a * b` and +#' `y2 ~ x | a * b`, but with the `y1 ~ x` and `y2 ~ x` in +#' *separate panels*. This behavior differs from standard \pkg{lattice}. +#' *Only combine terms of the same type*, i.e. only factors or only #' numerical variables. Mixing numerical and categorical data occasionally #' produces odds labeling of vertical axis. #' -#' The function \code{densityplot} does not use the \code{y} terms in the -#' formula. Density plots for \code{x1} and \code{x2} are requested as \code{~ -#' x1 + x2}. +#' The function `densityplot` does not use the `y` terms in the +#' formula. Density plots for `x1` and `x2` are requested as `~ +#' x1 + x2`. #' @param na.groups An expression evaluating to a logical vector indicating #' which two groups are distinguished (e.g. using different colors) in the #' display. The environment in which this expression is evaluated in the -#' response indicator \code{is.na(x$data)}. +#' response indicator `is.na(x$data)`. #' -#' The default \code{na.group = NULL} contrasts the observed and missing data -#' in the LHS \code{y} variable of the display, i.e. groups created by -#' \code{is.na(y)}. The expression \code{y} creates the groups according to -#' \code{is.na(y)}. The expression \code{y1 & y2} creates groups by -#' \code{is.na(y1) & is.na(y2)}, and \code{y1 | y2} creates groups as -#' \code{is.na(y1) | is.na(y2)}, and so on. -#' @param groups This is the usual \code{groups} arguments in \pkg{lattice}. It -#' differs from \code{na.groups} because it evaluates in the completed data -#' \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas -#' \code{na.groups} evaluates in the response indicator. See -#' \code{\link{xyplot}} for more details. When both \code{na.groups} and -#' \code{groups} are specified, \code{na.groups} takes precedence, and -#' \code{groups} is ignored. -#' @param plot.points A logical used in \code{densityplot} that signals whether +#' The default `na.group = NULL` contrasts the observed and missing data +#' in the LHS `y` variable of the display, i.e. groups created by +#' `is.na(y)`. The expression `y` creates the groups according to +#' `is.na(y)`. The expression `y1 & y2` creates groups by +#' `is.na(y1) & is.na(y2)`, and `y1 | y2` creates groups as +#' `is.na(y1) | is.na(y2)`, and so on. +#' @param groups This is the usual `groups` arguments in \pkg{lattice}. It +#' differs from `na.groups` because it evaluates in the completed data +#' `data.frame(complete(x, "long", inc=TRUE))` (as usual), whereas +#' `na.groups` evaluates in the response indicator. See +#' [xyplot()] for more details. When both `na.groups` and +#' `groups` are specified, `na.groups` takes precedence, and +#' `groups` is ignored. +#' @param plot.points A logical used in `densityplot` that signals whether #' the points should be plotted. #' @param theme A named list containing the graphical parameters. The default -#' function \code{mice.theme} produces a short list of default colors, line +#' function `mice.theme` produces a short list of default colors, line #' width, and so on. The extensive list may be obtained from -#' \code{trellis.par.get()}. Global graphical parameters like \code{col} or -#' \code{cex} in high-level calls are still honored, so first experiment with +#' `trellis.par.get()`. Global graphical parameters like `col` or +#' `cex` in high-level calls are still honored, so first experiment with #' the global parameters. Many setting consists of a pair. For example, -#' \code{mice.theme} defines two symbol colors. The first is for the observed +#' `mice.theme` defines two symbol colors. The first is for the observed #' data, the second for the imputed data. The theme settings only exist during #' the call, and do not affect the trellis graphical parameters. #' @param mayreplicate A logical indicating whether color, line widths, and so #' on, may be replicated. The graphical functions attempt to choose #' "intelligent" graphical parameters. For example, the same color can be #' replicated for different element, e.g. use all reds for the imputed data. -#' Replication may be switched off by setting the flag to \code{FALSE}, in order +#' Replication may be switched off by setting the flag to `FALSE`, in order #' to allow the user to gain full control. -#' @param thicker Used in \code{densityplot}. Multiplication factor of the line -#' width of the observed density. \code{thicker=1} uses the same thickness for +#' @param thicker Used in `densityplot`. Multiplication factor of the line +#' width of the observed density. `thicker=1` uses the same thickness for #' the observed and imputed data. -#' @param as.table See \code{\link[lattice:xyplot]{xyplot}}. -#' @param panel See \code{\link{xyplot}}. -#' @param default.prepanel See \code{\link[lattice:xyplot]{xyplot}}. -#' @param outer See \code{\link[lattice:xyplot]{xyplot}}. -#' @param allow.multiple See \code{\link[lattice:xyplot]{xyplot}}. -#' @param drop.unused.levels See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subscripts See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subset See \code{\link[lattice:xyplot]{xyplot}}. +#' @param as.table See [lattice::xyplot()]. +#' @param panel See [xyplot()]. +#' @param default.prepanel See [lattice::xyplot()]. +#' @param outer See [lattice::xyplot()]. +#' @param allow.multiple See [lattice::xyplot()]. +#' @param drop.unused.levels See [lattice::xyplot()]. +#' @param subscripts See [lattice::xyplot()]. +#' @param subset See [lattice::xyplot()]. #' @param \dots Further arguments, usually not directly processed by the #' high-level functions documented here, but instead passed on to other #' functions. #' @return The high-level functions documented here, as well as other high-level -#' Lattice functions, return an object of class \code{"trellis"}. The -#' \code{\link[lattice:update.trellis]{update}} method can be used to +#' Lattice functions, return an object of class `"trellis"`. The +#' [`update()`][lattice::update.trellis] method can be used to #' subsequently update components of the object, and the -#' \code{\link[lattice:print.trellis]{print}} method (usually called by default) +#' [`print()`][lattice::print.trellis] method (usually called by default) #' will plot it on an appropriate plotting device. -#' @note The first two arguments (\code{x} and \code{data}) are reversed +#' @note The first two arguments (`x` and `data`) are reversed #' compared to the standard Trellis syntax implemented in \pkg{lattice}. This #' reversal was necessary in order to benefit from automatic method dispatch. #' -#' In \pkg{mice} the argument \code{x} is always a \code{mids} object, whereas -#' in \pkg{lattice} the argument \code{x} is always a formula. +#' In \pkg{mice} the argument `x` is always a `mids` object, whereas +#' in \pkg{lattice} the argument `x` is always a formula. #' -#' In \pkg{mice} the argument \code{data} is always a formula object, whereas in -#' \pkg{lattice} the argument \code{data} is usually a data frame. +#' In \pkg{mice} the argument `data` is always a formula object, whereas in +#' \pkg{lattice} the argument `data` is usually a data frame. #' #' All other arguments have identical interpretation. #' -#' \code{densityplot} errs on empty groups, which occurs if all observations in -#' the subgroup contain \code{NA}. The relevant error message is: \code{Error in +#' `densityplot` errs on empty groups, which occurs if all observations in +#' the subgroup contain `NA`. The relevant error message is: `Error in #' density.default: ... need at least 2 points to select a bandwidth -#' automatically}. There is yet no workaround for this problem. Use the more -#' robust \code{bwplot} or \code{stripplot} as a replacement. +#' automatically`. There is yet no workaround for this problem. Use the more +#' robust `bwplot` or `stripplot` as a replacement. #' @author Stef van Buuren -#' @seealso \code{\link{mice}}, \code{\link{xyplot}}, \code{\link{stripplot}}, -#' \code{\link{bwplot}}, \code{\link{lattice}} for an overview of the -#' package, as well as \code{\link[lattice:histogram]{densityplot}}, -#' \code{\link[lattice:panel.densityplot]{panel.densityplot}}, -#' \code{\link[lattice:print.trellis]{print.trellis}}, -#' \code{\link[lattice:trellis.par.get]{trellis.par.set}} -#' @references Sarkar, Deepayan (2008) \emph{Lattice: Multivariate Data -#' Visualization with R}, Springer. +#' @seealso [mice()], [xyplot()], [stripplot()], +#' [bwplot()], [lattice()] for an overview of the +#' package, as well as [`densityplot()`][lattice::histogram], +#' [lattice::panel.densityplot()], +#' [lattice::print.trellis()], +#' [`trellis.par.set()`][lattice::trellis.par.get] +#' @references Sarkar, Deepayan (2008) *Lattice: Multivariate Data +#' Visualization with R*, Springer. #' -#' van Buuren S and 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} +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords hplot #' @examples #' imp <- mice(boys, maxit = 1) diff --git a/R/employee.R b/R/employee.R index 88e0e1d86..08ad25e54 100644 --- a/R/employee.R +++ b/R/employee.R @@ -17,7 +17,7 @@ #' is inadvertently lost. #' #' A larger version of this data set in present as -#' \code{\link[miceadds:data.enders]{data.enders.employee}}. +#' [`data.enders.employee()`][miceadds::data.enders]. #' #' @format A data frame with 20 rows and 3 variables: #' \describe{ diff --git a/R/fdd.R b/R/fdd.R index 51c40c3f3..73c8ae7a8 100644 --- a/R/fdd.R +++ b/R/fdd.R @@ -14,7 +14,7 @@ #' @name fdd #' @aliases fdd fdd.pred #' @docType data -#' @format \code{fdd} is a data frame with 52 rows and 65 columns: +#' @format `fdd` is a data frame with 52 rows and 65 columns: #' \describe{ #' \item{id}{Client number} #' \item{trt}{Treatment (E=EMDR, C=CBT)} @@ -82,16 +82,16 @@ #' \item{bir2}{Birlison T2} #' \item{bir3}{Birlison T3} #' } -#' \code{fdd.pred} is the 65 by 65 binary -#' predictor matrix used to impute \code{fdd}. +#' `fdd.pred` is the 65 by 65 binary +#' predictor matrix used to impute `fdd`. #' @source de Roos, C., Greenwald, R., den Hollander-Gijsman, M., Noorthoorn, #' E., van Buuren, S., de Jong, A. (2011). A Randomised Comparison of Cognitive #' Behavioral Therapy (CBT) and Eye Movement Desensitisation and Reprocessing -#' (EMDR) in disaster-exposed children. \emph{European Journal of -#' Psychotraumatology}, \emph{2}, 5694. +#' (EMDR) in disaster-exposed children. *European Journal of +#' Psychotraumatology*, *2*, 5694. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-fdd.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-fdd.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' Boca Raton, FL.: Chapman & Hall/CRC Press. #' @keywords datasets diff --git a/R/fdgs.R b/R/fdgs.R index b662cd77c..37b851d4e 100644 --- a/R/fdgs.R +++ b/R/fdgs.R @@ -16,7 +16,7 @@ #' @name fdgs #' @aliases fdgs #' @docType data -#' @format \code{fdgs} is a data frame with 10030 rows and 8 columns: +#' @format `fdgs` is a data frame with 10030 rows and 8 columns: #' \describe{ #' \item{id}{Person number} #' \item{reg}{Region (factor, 5 levels)} @@ -30,16 +30,16 @@ #' @source Schonbeck, Y., Talma, H., van Dommelen, P., Bakker, B., Buitendijk, #' S. E., Hirasing, R. A., van Buuren, S. (2011). Increase in prevalence of #' overweight in Dutch children and adolescents: A comparison of nationwide -#' growth studies in 1980, 1997 and 2009. \emph{PLoS ONE}, \emph{6}(11), +#' growth studies in 1980, 1997 and 2009. *PLoS ONE*, *6*(11), #' e27608. #' #' Schonbeck, Y., Talma, H., van Dommelen, P., Bakker, B., Buitendijk, S. E., #' Hirasing, R. A., van Buuren, S. (2013). The world's tallest nation has #' stopped growing taller: the height of Dutch children from 1955 to 2009. -#' \emph{Pediatric Research}, \emph{73}(3), 371-377. +#' *Pediatric Research*, *73*(3), 371-377. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-nonresponse.html#fifth-dutch-growth-study}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-nonresponse.html#fifth-dutch-growth-study) #' Boca Raton, FL.: Chapman & Hall/CRC Press. #' @keywords datasets #' @examples diff --git a/R/filter.R b/R/filter.R index e520b9bcb..b875d6729 100644 --- a/R/filter.R +++ b/R/filter.R @@ -2,46 +2,46 @@ #' @export dplyr::filter -#' Subset rows of a \code{mids} object +#' Subset rows of a `mids` object #' -#' This function takes a \code{mids} object and returns a new -#' \code{mids} object that pertains to the subset of the data +#' This function takes a `mids` object and returns a new +#' `mids` object that pertains to the subset of the data #' identified by the expression in \dots. The expression may use -#' column values from the incomplete data in \code{.data$data}. +#' column values from the incomplete data in `.data$data`. #' -#' @param .data A \code{mids} object. +#' @param .data A `mids` object. #' @param ... Expressions that return a -#' logical value, and are defined in terms of the variables in \code{.data$data}. -#' If multiple expressions are specified, they are combined with the \code{&} operator. -#' Only rows for which all conditions evaluate to \code{TRUE} are kept. +#' logical value, and are defined in terms of the variables in `.data$data`. +#' If multiple expressions are specified, they are combined with the `&` operator. +#' Only rows for which all conditions evaluate to `TRUE` are kept. #' @inheritParams dplyr::filter -#' @seealso \code{\link[dplyr]{filter}} -#' @return An S3 object of class \code{mids} -#' @note The function calculates a logical vector \code{include} of length \code{nrow(.data$data)}. -#' The function constructs the elements of the filtered \code{mids} object as follows: +#' @seealso [dplyr::filter()] +#' @return An S3 object of class `mids` +#' @note The function calculates a logical vector `include` of length `nrow(.data$data)`. +#' The function constructs the elements of the filtered `mids` object as follows: #' \tabular{ll}{ -#' \code{data} \tab Select rows in \code{.data$data} for which \code{include == TRUE}\cr -#' \code{imp} \tab Select rows each imputation \code{data.frame} in \code{.data$imp} for which \code{include == TRUE}\cr -#' \code{m} \tab Equals \code{.data$m}\cr -#' \code{where} \tab Select rows in \code{.data$where} for which \code{include == TRUE}\cr -#' \code{blocks} \tab Equals \code{.data$blocks}\cr -#' \code{call} \tab Equals \code{.data$call}\cr -#' \code{nmis} \tab Recalculate \code{nmis} based on the selected \code{data} rows\cr -#' \code{method} \tab Equals \code{.data$method}\cr -#' \code{predictorMatrix} \tab Equals \code{.data$predictorMatrix}\cr -#' \code{visitSequence} \tab Equals \code{.data$visitSequence}\cr -#' \code{formulas} \tab Equals \code{.data$formulas}\cr -#' \code{post} \tab Equals \code{.data$post}\cr -#' \code{blots} \tab Equals \code{.data$blots}\cr -#' \code{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$iteration}\cr -#' \code{lastSeedValue} \tab Equals \code{.data$lastSeedValue}\cr -#' \code{chainMean} \tab Set to \code{NULL}\cr -#' \code{chainVar} \tab Set to \code{NULL}\cr -#' \code{loggedEvents} \tab Equals \code{.data$loggedEvents}\cr -#' \code{version} \tab Replaced with current version\cr -#' \code{date} \tab Replaced with current date +#' `data` \tab Select rows in `.data$data` for which `include == TRUE`\cr +#' `imp` \tab Select rows each imputation `data.frame` in `.data$imp` for which `include == TRUE`\cr +#' `m` \tab Equals `.data$m`\cr +#' `where` \tab Select rows in `.data$where` for which `include == TRUE`\cr +#' `blocks` \tab Equals `.data$blocks`\cr +#' `call` \tab Equals `.data$call`\cr +#' `nmis` \tab Recalculate `nmis` based on the selected `data` rows\cr +#' `method` \tab Equals `.data$method`\cr +#' `predictorMatrix` \tab Equals `.data$predictorMatrix`\cr +#' `visitSequence` \tab Equals `.data$visitSequence`\cr +#' `formulas` \tab Equals `.data$formulas`\cr +#' `post` \tab Equals `.data$post`\cr +#' `blots` \tab Equals `.data$blots`\cr +#' `ignore` \tab Select positions in `.data$ignore` for which `include == TRUE`\cr +#' `seed` \tab Equals `.data$seed`\cr +#' `iteration` \tab Equals `.data$iteration`\cr +#' `lastSeedValue` \tab Equals `.data$lastSeedValue`\cr +#' `chainMean` \tab Set to `NULL`\cr +#' `chainVar` \tab Set to `NULL`\cr +#' `loggedEvents` \tab Equals `.data$loggedEvents`\cr +#' `version` \tab Replaced with current version\cr +#' `date` \tab Replaced with current date #' } #' @author Patrick Rockenschaub #' @keywords manip diff --git a/R/fix.coef.R b/R/fix.coef.R index 16e745001..ec547d6c4 100644 --- a/R/fix.coef.R +++ b/R/fix.coef.R @@ -2,20 +2,20 @@ #' #' Refits a model with a specified set of coefficients. #' -#' @param model An R model, e.g., produced by \code{lm} or \code{glm} -#' @param beta A numeric vector with \code{length(coef)} model coefficients. +#' @param model An R model, e.g., produced by `lm` or `glm` +#' @param beta A numeric vector with `length(coef)` model coefficients. #' If the vector is not named, the coefficients should be -#' given in the same order as in \code{coef(model)}. If the vector is named, +#' given in the same order as in `coef(model)`. If the vector is named, #' the procedure attempts to match on names. #' @return An updated R model object #' @author Stef van Buuren, 2018 #' @details #' The function calculates the linear predictor using the new coefficients, -#' and reformulates the model using the \code{offset} +#' and reformulates the model using the `offset` #' argument. The linear predictor is called -#' \code{offset}, and its coefficient will be \code{1} by definition. -#' The new model only fits the intercept, which should be \code{0} -#' if we set \code{beta = coef(model)}. +#' `offset`, and its coefficient will be `1` by definition. +#' The new model only fits the intercept, which should be `0` +#' if we set `beta = coef(model)`. #' @examples #' model0 <- lm(Volume ~ Girth + Height, data = trees) #' formula(model0) diff --git a/R/flux.R b/R/flux.R index 37ce57d96..6f34b1719 100644 --- a/R/flux.R +++ b/R/flux.R @@ -6,17 +6,17 @@ #' #' Infux and outflux have been proposed by Van Buuren (2018), chapter 4. #' -#' Influx is equal to the number of variable pairs \code{(Yj , Yk)} with -#' \code{Yj} missing and \code{Yk} observed, divided by the total number of +#' Influx is equal to the number of variable pairs `(Yj , Yk)` with +#' `Yj` missing and `Yk` observed, divided by the total number of #' observed data cells. Influx depends on the proportion of missing data of the #' variable. Influx of a completely observed variable is equal to 0, whereas for #' completely missing variables we have influx = 1. For two variables with the #' same proportion of missing data, the variable with higher influx is better #' connected to the observed data, and might thus be easier to impute. #' -#' Outflux is equal to the number of variable pairs with \code{Yj} observed and -#' \code{Yk} missing, divided by the total number of incomplete data cells. -#' Outflux is an indicator of the potential usefulness of \code{Yj} for imputing +#' Outflux is equal to the number of variable pairs with `Yj` observed and +#' `Yk` missing, divided by the total number of incomplete data cells. +#' Outflux is an indicator of the potential usefulness of `Yj` for imputing #' other variables. Outflux depends on the proportion of missing data of the #' variable. Outflux of a completely observed variable is equal to 1, whereas #' outflux of a completely missing variable is equal to 0. For two variables @@ -25,30 +25,30 @@ #' imputing other variables. #' #' FICO is an outbound statistic defined by the fraction of incomplete cases -#' among cases with \code{Yj} observed (White and Carlin, 2010). +#' among cases with `Yj` observed (White and Carlin, 2010). #' #' @aliases flux #' @param data A data frame or a matrix containing the incomplete data. Missing #' values are coded as NA's. -#' @param local A vector of names of columns of \code{data}. The default is to +#' @param local A vector of names of columns of `data`. The default is to #' include all columns in the calculations. -#' @return A data frame with \code{ncol(data)} rows and six columns: +#' @return A data frame with `ncol(data)` rows and six columns: #' pobs = Proportion observed, #' influx = Influx #' outflux = Outflux #' ainb = Average inbound statistic #' aout = Average outbound statistic -#' fico = Fraction of incomplete cases among cases with \code{Yj} observed -#' @seealso \code{\link{fluxplot}}, \code{\link{md.pattern}}, \code{\link{fico}} +#' fico = Fraction of incomplete cases among cases with `Yj` observed +#' @seealso [fluxplot()], [md.pattern()], [fico()] #' @author Stef van Buuren, 2012 #' @references #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/missing-data-pattern.html#sec:flux}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/missing-data-pattern.html#sec:flux) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' White, I.R., Carlin, J.B. (2010). Bias and efficiency of multiple imputation #' compared with complete-case analysis for missing covariate values. -#' \emph{Statistics in Medicine}, \emph{29}, 2920-2931. +#' *Statistics in Medicine*, *29*, 2920-2931. #' @keywords misc #' @export flux <- function(data, local = names(data)) { @@ -78,17 +78,17 @@ flux <- function(data, local = names(data)) { #' #' Infux and outflux have been proposed by Van Buuren (2012), chapter 4. #' -#' Influx is equal to the number of variable pairs \code{(Yj , Yk)} with -#' \code{Yj} missing and \code{Yk} observed, divided by the total number of +#' Influx is equal to the number of variable pairs `(Yj , Yk)` with +#' `Yj` missing and `Yk` observed, divided by the total number of #' observed data cells. Influx depends on the proportion of missing data of the #' variable. Influx of a completely observed variable is equal to 0, whereas for #' completely missing variables we have influx = 1. For two variables with the #' same proportion of missing data, the variable with higher influx is better #' connected to the observed data, and might thus be easier to impute. #' -#' Outflux is equal to the number of variable pairs with \code{Yj} observed and -#' \code{Yk} missing, divided by the total number of incomplete data cells. -#' Outflux is an indicator of the potential usefulness of \code{Yj} for imputing +#' Outflux is equal to the number of variable pairs with `Yj` observed and +#' `Yk` missing, divided by the total number of incomplete data cells. +#' Outflux is an indicator of the potential usefulness of `Yj` for imputing #' other variables. Outflux depends on the proportion of missing data of the #' variable. Outflux of a completely observed variable is equal to 1, whereas #' outflux of a completely missing variable is equal to 0. For two variables @@ -99,37 +99,37 @@ flux <- function(data, local = names(data)) { #' @aliases fluxplot #' @param data A data frame or a matrix containing the incomplete data. Missing #' values are coded as NA's. -#' @param local A vector of names of columns of \code{data}. The default is to +#' @param local A vector of names of columns of `data`. The default is to #' include all columns in the calculations. #' @param plot Should a graph be produced? #' @param labels Should the points be labeled? -#' @param xlim See \code{par}. -#' @param ylim See \code{par}. -#' @param las See \code{par}. -#' @param xlab See \code{par}. -#' @param ylab See \code{par}. -#' @param main See \code{par}. +#' @param xlim See `par`. +#' @param ylim See `par`. +#' @param las See `par`. +#' @param xlab See `par`. +#' @param ylab See `par`. +#' @param main See `par`. #' @param eqscplot Should a square plot be produced? -#' @param pty See \code{par}. -#' @param lwd See \code{par}. Controls axis line thickness and diagonal -#' @param \dots Further arguments passed to \code{plot()} or \code{eqscplot()}. -#' @return An invisible data frame with \code{ncol(data)} rows and six columns: +#' @param pty See `par`. +#' @param lwd See `par`. Controls axis line thickness and diagonal +#' @param \dots Further arguments passed to `plot()` or `eqscplot()`. +#' @return An invisible data frame with `ncol(data)` rows and six columns: #' pobs = Proportion observed, #' influx = Influx #' outflux = Outflux #' ainb = Average inbound statistic #' aout = Average outbound statistic -#' fico = Fraction of incomplete cases among cases with \code{Yj} observed -#' @seealso \code{\link{flux}}, \code{\link{md.pattern}}, \code{\link{fico}} +#' fico = Fraction of incomplete cases among cases with `Yj` observed +#' @seealso [flux()], [md.pattern()], [fico()] #' @author Stef van Buuren, 2012 #' @references #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/missing-data-pattern.html#sec:flux}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/missing-data-pattern.html#sec:flux) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' White, I.R., Carlin, J.B. (2010). Bias and efficiency of multiple imputation #' compared with complete-case analysis for missing covariate values. -#' \emph{Statistics in Medicine}, \emph{29}, 2920-2931. +#' *Statistics in Medicine*, *29*, 2920-2931. #' @keywords misc #' @export fluxplot <- function(data, local = names(data), @@ -175,22 +175,22 @@ fluxplot <- function(data, local = names(data), #' Fraction of incomplete cases among cases with observed #' #' FICO is an outbound statistic defined by the fraction of incomplete cases -#' among cases with \code{Yj} observed (White and Carlin, 2010). +#' among cases with `Yj` observed (White and Carlin, 2010). #' #' @aliases fico #' @param data A data frame or a matrix containing the incomplete data. Missing #' values are coded as NA's. -#' @return A vector of length \code{ncol(data)} of FICO statistics. -#' @seealso \code{\link{fluxplot}}, \code{\link{flux}}, \code{\link{md.pattern}} +#' @return A vector of length `ncol(data)` of FICO statistics. +#' @seealso [fluxplot()], [flux()], [md.pattern()] #' @author Stef van Buuren, 2012 #' @references #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/missing-data-pattern.html#sec:flux}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/missing-data-pattern.html#sec:flux) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' White, I.R., Carlin, J.B. (2010). Bias and efficiency of multiple imputation #' compared with complete-case analysis for missing covariate values. -#' \emph{Statistics in Medicine}, \emph{29}, 2920-2931. +#' *Statistics in Medicine*, *29*, 2920-2931. #' @keywords misc #' @export fico <- function(data) { diff --git a/R/formula.R b/R/formula.R index 72c0ee18d..279ab8299 100644 --- a/R/formula.R +++ b/R/formula.R @@ -1,15 +1,15 @@ -#' Creates a \code{formulas} argument +#' Creates a `formulas` argument #' -#' This helper function creates a valid \code{formulas} object. The -#' \code{formulas} object is an argument to the \code{mice} function. +#' This helper function creates a valid `formulas` object. The +#' `formulas` object is an argument to the `mice` function. #' It is a list of formula's that specifies the target variables and -#' the predictors by means of the standard \code{~} operator. -#' @param data A \code{data.frame} with the source data +#' the predictors by means of the standard `~` operator. +#' @param data A `data.frame` with the source data #' @param blocks An optional specification for blocks of variables in #' the rows. The default assigns each variable in its own block. -#' @param predictorMatrix A \code{predictorMatrix} specified by the user. +#' @param predictorMatrix A `predictorMatrix` specified by the user. #' @return A list of formula's. -#' @seealso \code{\link{make.blocks}}, \code{\link{make.predictorMatrix}} +#' @seealso [make.blocks()], [make.predictorMatrix()] #' @examples #' f1 <- make.formulas(nhanes) #' f1 @@ -54,19 +54,19 @@ make.formulas <- function(data, blocks = make.blocks(data), #' Name formula list elements #' -#' This helper function names any unnamed elements in the \code{formula} +#' This helper function names any unnamed elements in the `formula` #' list. This is a convenience function. #' @inheritParams mice #' @param prefix A character vector of length 1 with the prefix to #' be using for naming any unnamed blocks with two or more variables. #' @return Named list of formulas -#' @seealso \code{\link{mice}} +#' @seealso [mice()] #' @details #' This function will name any unnamed list elements specified in -#' the optional argument \code{formula}. Unnamed formula's +#' the optional argument `formula`. Unnamed formula's #' consisting with just one response variable will be named #' after this variable. Unnamed formula's containing more -#' than one variable will be named by the \code{prefix} +#' than one variable will be named by the `prefix` #' argument, padded by an integer sequence stating at 1. #' @examples #' # fully conditionally specified main effects model @@ -142,8 +142,8 @@ check.formulas <- function(formulas, data) { #' @inheritParams mice #' @return A list of formula's #' @param auxiliary A logical that indicates whether the variables -#' listed in \code{predictors} should be added to the formula as main -#' effects. The default is \code{TRUE}. +#' listed in `predictors` should be added to the formula as main +#' effects. The default is `TRUE`. #' @param include.intercept A logical that indicated whether the intercept #' should be included in the result. #' @keywords internal @@ -172,11 +172,11 @@ extend.formulas <- function(formulas, data, blocks, predictorMatrix = NULL, #' Extends a formula with predictors #' #' @param formula A formula. If it is -#' not a formula, the formula is internally reset to \code{~0}. +#' not a formula, the formula is internally reset to `~0`. #' @param predictors A character vector of variable names. #' @param auxiliary A logical that indicates whether the variables -#' listed in \code{predictors} should be added to the formula as main -#' effects. The default is \code{TRUE}. +#' listed in `predictors` should be added to the formula as main +#' effects. The default is `TRUE`. #' @param include.intercept A logical that indicated whether the intercept #' should be included in the result. #' @return A formula diff --git a/R/futuremice.R b/R/futuremice.R index 677a7ce2c..6357c4181 100644 --- a/R/futuremice.R +++ b/R/futuremice.R @@ -1,68 +1,68 @@ #' Wrapper function that runs MICE in parallel #' -#' This is a wrapper function for \code{\link{mice}}, using multiple cores to -#' execute \code{\link{mice}} in parallel. As a result, the imputation +#' This is a wrapper function for [mice()], using multiple cores to +#' execute [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} +#' [futuremice()] distributes the number of imputations `m` #' about equally over the cores. #' -#' This function relies on package \code{\link[furrr]{furrr}}, which is a +#' This function relies on package [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 +#' `future_map` to allow the use of `futuremice` on Mac, Linux and #' Windows systems. #' #' -#' This wrapper function combines the output of \code{\link[furrr]{future_map}} with -#' function \code{\link{ibind}} from the \code{\link{mice}} package. A -#' \code{mids} object is returned and can be used for further analyses. +#' This wrapper function combines the output of [furrr::future_map()] with +#' function [ibind()] from the [mice()] package. A +#' `mids` object is returned and can be used for further analyses. #' #' A seed value can be specified in the global environment, which will yield #' reproducible results. A seed value can also be specified within the -#' \code{\link{futuremice}} call, through specifying the argument -#' \code{parallelseed}. If \code{parallelseed} is not specified, a seed value is -#' drawn randomly by default, and accessible through \code{$parallelseed} in the +#' [futuremice()] call, through specifying the argument +#' `parallelseed`. If `parallelseed` is not specified, a seed value is +#' drawn randomly by default, and accessible through `$parallelseed` in the #' output object. Hence, results will always be reproducible, regardless of #' whether the seed is specified in the global environment, or by setting the #' same seed within the function (potentially by extracting the seed from the -#' \code{futuremice} output object. +#' `futuremice` output object. #' #' @aliases futuremice #' @param data A data frame or matrix containing the incomplete data. Similar to -#' the first argument of \code{\link{mice}}. +#' the first argument of [mice()]. #' @param m The number of desired imputated datasets. By default $m=5$ as with -#' \code{mice} +#' `mice` #' @param parallelseed A scalar to be used to obtain reproducible results over -#' the futures. The default \code{parallelseed = NA} will result in a seed value +#' the futures. The default `parallelseed = NA` will result in a seed value #' that is randomly drawn between -999999999 and 999999999. #' @param n.core A scalar indicating the number of cores that should be used. #' @param seed A scalar to be used as the seed value for the mice algorithm #' within each parallel stream. Please note that the imputations will be the #' same for all streams and, hence, this should be used if and only if -#' \code{n.core = 1} and if it is desired to obtain the same output as under -#' \code{mice}. -#' @param use.logical A logical indicating whether logical (\code{TRUE}) or -#' physical (\code{FALSE}) CPU's on machine should be used. -#' @param future.plan A character indicating how \code{future}s are resolved. -#' 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. +#' `n.core = 1` and if it is desired to obtain the same output as under +#' `mice`. +#' @param use.logical A logical indicating whether logical (`TRUE`) or +#' physical (`FALSE`) CPU's on machine should be used. +#' @param future.plan A character indicating how `future`s are resolved. +#' The default `multisession` resolves futures asynchronously (in parallel) +#' in separate `R` sessions running in the background. See +#' [future::plan()] for more information on future plans. #' @param packages A character vector with additional packages to be used in -#' \code{mice} (e.g., for using external imputation functions). +#' `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). -#' @param ... Named arguments that are passed down to function \code{\link{mice}}. +#' @param ... Named arguments that are passed down to function [mice()]. #' -#' @return A mids object as defined by \code{\link{mids-class}} +#' @return A mids object as defined by [mids-class()] #' #' @author Thom Benjamin Volker, Gerko Vink -#' @seealso \code{\link[future]{future}}, \code{\link[furrr]{furrr}}, \code{\link[furrr]{future_map}}, -#' \code{\link[future]{plan}}, \code{\link{mice}}, \code{\link{mids-class}} +#' @seealso [future::future()], [furrr::furrr()], [furrr::future_map()], +#' [future::plan()], [mice()], [mids-class()] #' @references #' Volker, T.B. and Vink, G. (2022). futuremice: The future starts today. -#' \url{https://www.gerkovink.com/miceVignettes/futuremice/Vignette_futuremice.html} +#' #' #' #'Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/parallel-computation.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/parallel-computation.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' @examples @@ -196,4 +196,4 @@ check.cores <- function(n.core, available, m) { n.core <- min(available - 1, m, n.core) } n.core -} \ No newline at end of file +} diff --git a/R/generics.R b/R/generics.R index 8361ba094..98f05c99a 100644 --- a/R/generics.R +++ b/R/generics.R @@ -1,117 +1,117 @@ #' Combine R objects by rows and columns #' -#' Functions \code{cbind()} and \code{rbind()} are defined in -#' the \code{mice} package in order to -#' enable dispatch to \code{cbind.mids()} and \code{rbind.mids()} -#' when one of the arguments is a \code{data.frame}. +#' Functions `cbind()` and `rbind()` are defined in +#' the `mice` package in order to +#' enable dispatch to `cbind.mids()` and `rbind.mids()` +#' when one of the arguments is a `data.frame`. #' -#' The standard \code{base::cbind()} and \code{base::rbind()} +#' The standard `base::cbind()` and `base::rbind()` #' always dispatch to -#' \code{base::cbind.data.frame()} or \code{base::rbind.data.frame()} +#' `base::cbind.data.frame()` or `base::rbind.data.frame()` #' if one of the arguments is a -#' \code{data.frame}. The versions defined in the \code{mice} +#' `data.frame`. The versions defined in the `mice` #' package intercept the user command -#' and test whether the first argument has class \code{"mids"}. If so, -#' function calls \code{cbind.mids()}, respectively \code{rbind.mids()}. In +#' and test whether the first argument has class `"mids"`. If so, +#' function calls `cbind.mids()`, respectively `rbind.mids()`. In #' all other cases, the call is forwarded to standard functions in the -#' \code{base} package. +#' `base` package. #' #' @inheritDotParams base::cbind #' @details -#' The \code{cbind.mids()} function combines two \code{mids} objects +#' The `cbind.mids()` function combines two `mids` objects #' columnwise into a single -#' object of class \code{mids}, or combines a single \code{mids} object with -#' a \code{vector}, \code{matrix}, \code{factor} or \code{data.frame} -#' columnwise into a \code{mids} object. -#' -#' If both arguments of \code{cbind.mids()} are \code{mids}-objects, the -#' \code{data} list components should have the same number of rows. Also, the -#' number of imputations (\code{m}) should be identical. -#' If the second argument is a \code{matrix}, -#' \code{factor} or \code{vector}, it is transformed into a -#' \code{data.frame}. The number of rows should match with the \code{data} +#' object of class `mids`, or combines a single `mids` object with +#' a `vector`, `matrix`, `factor` or `data.frame` +#' columnwise into a `mids` object. +#' +#' If both arguments of `cbind.mids()` are `mids`-objects, the +#' `data` list components should have the same number of rows. Also, the +#' number of imputations (`m`) should be identical. +#' If the second argument is a `matrix`, +#' `factor` or `vector`, it is transformed into a +#' `data.frame`. The number of rows should match with the `data` #' component of the first argument. #' -#' The \code{cbind.mids()} function renames any duplicated variable or block names by -#' appending \code{".1"}, \code{".2"} to duplicated names. +#' The `cbind.mids()` function renames any duplicated variable or block names by +#' appending `".1"`, `".2"` to duplicated names. #' -#' The \code{rbind.mids()} function combines two \code{mids} objects rowwise into a single -#' \code{mids} object, or combines a \code{mids} object with a vector, matrix, -#' factor or data frame rowwise into a \code{mids} object. +#' The `rbind.mids()` function combines two `mids` objects rowwise into a single +#' `mids` object, or combines a `mids` object with a vector, matrix, +#' factor or data frame rowwise into a `mids` object. #' -#' If both arguments of \code{rbind.mids()} are \code{mids} objects, -#' then \code{rbind.mids()} requires that both have the same number of multiple -#' imputations. In addition, their \code{data} components should match. +#' If both arguments of `rbind.mids()` are `mids` objects, +#' then `rbind.mids()` requires that both have the same number of multiple +#' imputations. In addition, their `data` components should match. #' -#' If the second argument of \code{rbind.mids()} is not a \code{mids} object, -#' the columns of the arguments should match. The \code{where} matrix for the -#' second argument is set to \code{FALSE}, signalling that any missing values in -#' that argument were not imputed. The \code{ignore} vector for the second argument is -#' set to \code{FALSE}. Rows inherited from the second argument will therefore +#' If the second argument of `rbind.mids()` is not a `mids` object, +#' the columns of the arguments should match. The `where` matrix for the +#' second argument is set to `FALSE`, signalling that any missing values in +#' that argument were not imputed. The `ignore` vector for the second argument is +#' set to `FALSE`. Rows inherited from the second argument will therefore #' influence the parameter estimation of the imputation model in any future #' iterations. # #' @note -#' The \code{cbind.mids()} function constructs the elements of the new \code{mids} object as follows: +#' The `cbind.mids()` function constructs the elements of the new `mids` object as follows: #' \tabular{ll}{ -#' \code{data} \tab Columnwise combination of the data in \code{x} and \code{y}\cr -#' \code{imp} \tab Combines the imputed values from \code{x} and \code{y}\cr -#' \code{m} \tab Taken from \code{x$m}\cr -#' \code{where} \tab Columnwise combination of \code{x$where} and \code{y$where}\cr -#' \code{blocks} \tab Combines \code{x$blocks} and \code{y$blocks}\cr -#' \code{call} \tab Vector, \code{call[1]} creates \code{x}, \code{call[2]} -#' is call to \code{cbind.mids()}\cr -#' \code{nmis} \tab Equals \code{c(x$nmis, y$nmis)}\cr -#' \code{method} \tab Combines \code{x$method} and \code{y$method}\cr -#' \code{predictorMatrix} \tab Combination with zeroes on the off-diagonal blocks\cr -#' \code{visitSequence} \tab Combined as \code{c(x$visitSequence, y$visitSequence)}\cr -#' \code{formulas} \tab Combined as \code{c(x$formulas, y$formulas)}\cr -#' \code{post} \tab Combined as \code{c(x$post, y$post)}\cr -#' \code{blots} \tab Combined as \code{c(x$blots, y$blots)}\cr -#' \code{ignore} \tab Taken from \code{x$ignore}\cr -#' \code{seed} \tab Taken from \code{x$seed}\cr -#' \code{iteration} \tab Taken from \code{x$iteration}\cr -#' \code{lastSeedValue} \tab Taken from \code{x$lastSeedValue}\cr -#' \code{chainMean} \tab Combined from \code{x$chainMean} and \code{y$chainMean}\cr -#' \code{chainVar} \tab Combined from \code{x$chainVar} and \code{y$chainVar}\cr -#' \code{loggedEvents} \tab Taken from \code{x$loggedEvents}\cr -#' \code{version} \tab Current package version\cr -#' \code{date} \tab Current date\cr +#' `data` \tab Columnwise combination of the data in `x` and `y`\cr +#' `imp` \tab Combines the imputed values from `x` and `y`\cr +#' `m` \tab Taken from `x$m`\cr +#' `where` \tab Columnwise combination of `x$where` and `y$where`\cr +#' `blocks` \tab Combines `x$blocks` and `y$blocks`\cr +#' `call` \tab Vector, `call[1]` creates `x`, `call[2]` +#' is call to `cbind.mids()`\cr +#' `nmis` \tab Equals `c(x$nmis, y$nmis)`\cr +#' `method` \tab Combines `x$method` and `y$method`\cr +#' `predictorMatrix` \tab Combination with zeroes on the off-diagonal blocks\cr +#' `visitSequence` \tab Combined as `c(x$visitSequence, y$visitSequence)`\cr +#' `formulas` \tab Combined as `c(x$formulas, y$formulas)`\cr +#' `post` \tab Combined as `c(x$post, y$post)`\cr +#' `blots` \tab Combined as `c(x$blots, y$blots)`\cr +#' `ignore` \tab Taken from `x$ignore`\cr +#' `seed` \tab Taken from `x$seed`\cr +#' `iteration` \tab Taken from `x$iteration`\cr +#' `lastSeedValue` \tab Taken from `x$lastSeedValue`\cr +#' `chainMean` \tab Combined from `x$chainMean` and `y$chainMean`\cr +#' `chainVar` \tab Combined from `x$chainVar` and `y$chainVar`\cr +#' `loggedEvents` \tab Taken from `x$loggedEvents`\cr +#' `version` \tab Current package version\cr +#' `date` \tab Current date\cr #' } #' -#' The \code{rbind.mids()} function constructs the elements of the new \code{mids} object as follows: +#' The `rbind.mids()` function constructs the elements of the new `mids` object as follows: #' \tabular{ll}{ -#' \code{data} \tab Rowwise combination of the (incomplete) data in \code{x} and \code{y}\cr -#' \code{imp} \tab Equals \code{rbind(x$imp[[j]], y$imp[[j]])} if \code{y} is \code{mids} object; otherwise -#' the data of \code{y} will be copied\cr -#' \code{m} \tab Equals \code{x$m}\cr -#' \code{where} \tab Rowwise combination of \code{where} arguments\cr -#' \code{blocks} \tab Equals \code{x$blocks}\cr -#' \code{call} \tab Vector, \code{call[1]} creates \code{x}, \code{call[2]} is call to \code{rbind.mids}\cr -#' \code{nmis} \tab \code{x$nmis} + \code{y$nmis}\cr -#' \code{method} \tab Taken from \code{x$method}\cr -#' \code{predictorMatrix} \tab Taken from \code{x$predictorMatrix}\cr -#' \code{visitSequence} \tab Taken from \code{x$visitSequence}\cr -#' \code{formulas} \tab Taken from \code{x$formulas}\cr -#' \code{post} \tab Taken from \code{x$post}\cr -#' \code{blots} \tab Taken from \code{x$blots}\cr -#' \code{ignore} \tab Concatenate \code{x$ignore} and \code{y$ignore}\cr -#' \code{seed} \tab Taken from \code{x$seed}\cr -#' \code{iteration} \tab Taken from \code{x$iteration}\cr -#' \code{lastSeedValue} \tab Taken from \code{x$lastSeedValue}\cr -#' \code{chainMean} \tab Set to \code{NA}\cr -#' \code{chainVar} \tab Set to \code{NA}\cr -#' \code{loggedEvents} \tab Taken from \code{x$loggedEvents}\cr -#' \code{version} \tab Taken from \code{x$version}\cr -#' \code{date} \tab Taken from \code{x$date} +#' `data` \tab Rowwise combination of the (incomplete) data in `x` and `y`\cr +#' `imp` \tab Equals `rbind(x$imp[[j]], y$imp[[j]])` if `y` is `mids` object; otherwise +#' the data of `y` will be copied\cr +#' `m` \tab Equals `x$m`\cr +#' `where` \tab Rowwise combination of `where` arguments\cr +#' `blocks` \tab Equals `x$blocks`\cr +#' `call` \tab Vector, `call[1]` creates `x`, `call[2]` is call to `rbind.mids`\cr +#' `nmis` \tab `x$nmis` + `y$nmis`\cr +#' `method` \tab Taken from `x$method`\cr +#' `predictorMatrix` \tab Taken from `x$predictorMatrix`\cr +#' `visitSequence` \tab Taken from `x$visitSequence`\cr +#' `formulas` \tab Taken from `x$formulas`\cr +#' `post` \tab Taken from `x$post`\cr +#' `blots` \tab Taken from `x$blots`\cr +#' `ignore` \tab Concatenate `x$ignore` and `y$ignore`\cr +#' `seed` \tab Taken from `x$seed`\cr +#' `iteration` \tab Taken from `x$iteration`\cr +#' `lastSeedValue` \tab Taken from `x$lastSeedValue`\cr +#' `chainMean` \tab Set to `NA`\cr +#' `chainVar` \tab Set to `NA`\cr +#' `loggedEvents` \tab Taken from `x$loggedEvents`\cr +#' `version` \tab Taken from `x$version`\cr +#' `date` \tab Taken from `x$date` #' } -#' @return An S3 object of class \code{mids} +#' @return An S3 object of class `mids` #' @author Karin Groothuis-Oudshoorn, Stef van Buuren -#' @seealso \code{\link[base:cbind]{cbind}}, \code{\link{ibind}}, -#' \code{\link[=mids-class]{mids}} -#' @references van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: -#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -#' Statistical Software}, \bold{45}(3), 1-67. +#' @seealso [base::cbind()], [ibind()], +#' [`mids()`][mids-class] +#' @references van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords manip #' @examples diff --git a/R/getfit.R b/R/getfit.R index 54e18e931..3a1737eb2 100644 --- a/R/getfit.R +++ b/R/getfit.R @@ -1,24 +1,24 @@ #' Extract list of fitted models #' -#' Function \code{getfit()} returns the list of objects containing the repeated analysis +#' Function `getfit()` returns the list of objects containing the repeated analysis #' results, or optionally, one of these fitted objects. The function looks for -#' a list element called \code{analyses}, and return this component as a list with -#' \code{mira} class. If element \code{analyses} is not found in \code{x}, then -#' it returns \code{x} as a \code{mira} object. +#' a list element called `analyses`, and return this component as a list with +#' `mira` class. If element `analyses` is not found in `x`, then +#' it returns `x` as a `mira` object. #' #' No checking is done for validity of objects. The function also processes -#' objects of class \code{mitml.result} from the \code{mitml} package. +#' objects of class `mitml.result` from the `mitml` package. #' -#' @param x An object of class \code{mira}, typically produced by a call -#' to \code{with()}. -#' @param i An integer between 1 and \code{x$m} signalling the index of the -#' repeated analysis. The default \code{i= -1} return a list with all analyses. +#' @param x An object of class `mira`, typically produced by a call +#' to `with()`. +#' @param i An integer between 1 and `x$m` signalling the index of the +#' repeated analysis. The default `i= -1` return a list with all analyses. #' @param simplify Should the return value be unlisted? -#' @return If \code{i = -1} an object of class \code{mira} containing -#' all analyses. If \code{i} selects one of the analyses, then it return +#' @return If `i = -1` an object of class `mira` containing +#' all analyses. If `i` selects one of the analyses, then it return #' an object whose with class inherited from that element. #' @author Stef van Buuren, 2012, 2020 -#' @seealso \code{\link[=mira-class]{mira}}, \code{\link{with.mids}} +#' @seealso [`mira()`][mira-class], [with.mids()] #' @keywords manip #' @examples #' imp <- mice(nhanes, print = FALSE, seed = 21443) @@ -42,11 +42,11 @@ getfit <- function(x, i = -1L, simplify = FALSE) { ra } -#' Extract estimate from \code{mipo} object +#' Extract estimate from `mipo` object #' -#' \code{getqbar} returns a named vector of pooled estimates. +#' `getqbar` returns a named vector of pooled estimates. #' -#' @param x An object of class \code{mipo} +#' @param x An object of class `mipo` #' @export getqbar <- function(x) { if (!is.mipo(x)) stop("Not a mipo object") diff --git a/R/ibind.R b/R/ibind.R index d23e33c21..1044d367f 100644 --- a/R/ibind.R +++ b/R/ibind.R @@ -1,20 +1,20 @@ -#' Enlarge number of imputations by combining \code{mids} objects +#' Enlarge number of imputations by combining `mids` objects #' -#' This function combines two \code{mids} objects \code{x} and \code{y} into a -#' single \code{mids} object, with the objective of increasing the number of -#' imputed data sets. If the number of imputations in \code{x} and \code{y} are -#' \code{m(x)} and \code{m(y)}, then the combined object will have -#' \code{m(x)+m(y)} imputations. +#' This function combines two `mids` objects `x` and `y` into a +#' single `mids` object, with the objective of increasing the number of +#' imputed data sets. If the number of imputations in `x` and `y` are +#' `m(x)` and `m(y)`, then the combined object will have +#' `m(x)+m(y)` imputations. #' -#' The two \code{mids} objects are required to +#' The two `mids` objects are required to #' have the same underlying multiple imputation model and should #' be fitted on the same data. #' -#' @param x A \code{mids} object. -#' @param y A \code{mids} object. -#' @return An S3 object of class \code{mids} +#' @param x A `mids` object. +#' @param y A `mids` object. +#' @return An S3 object of class `mids` #' @author Karin Groothuis-Oudshoorn, Stef van Buuren -#' @seealso \code{\link[=mids-class]{mids}} +#' @seealso [`mids()`][mids-class] #' @keywords manip #' @examples #' data(nhanes) diff --git a/R/is.R b/R/is.R index 86bd36a7d..00ed115bb 100644 --- a/R/is.R +++ b/R/is.R @@ -1,40 +1,40 @@ -#' Check for \code{mids} object +#' Check for `mids` object #' #' @aliases is.mids #' @param x An object -#' @return A logical indicating whether \code{x} is an object of class \code{mids} +#' @return A logical indicating whether `x` is an object of class `mids` #' @export is.mids <- function(x) { inherits(x, "mids") } -#' Check for \code{mira} object +#' Check for `mira` object #' #' @aliases is.mira #' @param x An object -#' @return A logical indicating whether \code{x} is an object of class \code{mira} +#' @return A logical indicating whether `x` is an object of class `mira` #' @export is.mira <- function(x) { inherits(x, "mira") } -#' Check for \code{mipo} object +#' Check for `mipo` object #' #' @aliases is.mipo #' @param x An object -#' @return A logical indicating whether \code{x} is an object of class \code{mipo} +#' @return A logical indicating whether `x` is an object of class `mipo` #' @export is.mipo <- function(x) { inherits(x, "mipo") } -#' Check for \code{mitml.result} object +#' Check for `mitml.result` object #' #' @aliases is.mitml.result #' @param x An object -#' @return A logical indicating whether \code{x} is an object of class \code{mitml.result} +#' @return A logical indicating whether `x` is an object of class `mitml.result` #' @export is.mitml.result <- function(x) { inherits(x, "mitml.result") @@ -46,11 +46,11 @@ is.passive <- function(string) { } -#' Check for \code{mads} object +#' Check for `mads` object #' #' @aliases is.mads #' @param x An object -#' @return A logical indicating whether \code{x} is an object of class \code{mads} +#' @return A logical indicating whether `x` is an object of class `mads` #' @export is.mads <- function(x) { inherits(x, "mads") diff --git a/R/leiden85.R b/R/leiden85.R index b3f359abe..2ba88c457 100644 --- a/R/leiden85.R +++ b/R/leiden85.R @@ -8,32 +8,32 @@ #' Multiple imputation of this data set has been described in Boshuizen et al #' (1998), Van Buuren et al (1999) and Van Buuren (2012), chapter 7. #' -#' The data set is not available as part of \code{mice}. +#' The data set is not available as part of `mice`. #' #' @name leiden85 #' @docType data -#' @format \code{leiden85} is a data frame with 956 rows and 336 columns. +#' @format `leiden85` is a data frame with 956 rows and 336 columns. #' @source #' #' Lagaay, A. M., van der Meij, J. C., Hijmans, W. (1992). Validation of #' medical history taking as part of a population based survey in subjects aged -#' 85 and over. \emph{Brit. Med. J.}, \emph{304}(6834), 1091-1092. +#' 85 and over. *Brit. Med. J.*, *304*(6834), 1091-1092. #' #' Izaks, G. J., van Houwelingen, H. C., Schreuder, G. M., Ligthart, G. J. #' (1997). The association between human leucocyte antigens (HLA) and mortality -#' in community residents aged 85 and older. \emph{Journal of the American -#' Geriatrics Society}, \emph{45}(1), 56-60. +#' in community residents aged 85 and older. *Journal of the American +#' Geriatrics Society*, *45*(1), 56-60. #' #' Boshuizen, H. C., Izaks, G. J., van Buuren, S., Ligthart, G. J. (1998). #' Blood pressure and mortality in elderly people aged 85 and older: Community -#' based study. \emph{Brit. Med. J.}, \emph{316}(7147), 1780-1784. +#' based study. *Brit. Med. J.*, *316*(7147), 1780-1784. #' #' Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of -#' missing blood pressure covariates in survival analysis. \emph{Statistics in -#' Medicine}, \bold{18}, 681--694. +#' missing blood pressure covariates in survival analysis. *Statistics in +#' Medicine*, **18**, 681--694. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-toomany.html#sec:leiden85cohort}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-toomany.html#sec:leiden85cohort) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets NULL diff --git a/R/lm.R b/R/lm.R index 6bfc74544..4f7ed5aa4 100644 --- a/R/lm.R +++ b/R/lm.R @@ -1,24 +1,24 @@ -#' Linear regression for \code{mids} object +#' Linear regression for `mids` object #' -#' Applies \code{lm()} to multiply imputed data set +#' Applies `lm()` to multiply imputed data set #' #' This function is included for backward compatibility with V1.0. The function -#' is superseded by \code{\link{with.mids}}. +#' is superseded by [with.mids()]. #' #' @param formula a formula object, with the response on the left of a ~ #' operator, and the terms, separated by + operators, on the right. See the -#' documentation of \code{\link{lm}} and \code{\link{formula}} for details. +#' documentation of [lm()] and [formula()] for details. #' @param data An object of type 'mids', which stands for 'multiply imputed data -#' set', typically created by a call to function \code{mice()}. -#' @param \dots Additional parameters passed to \code{\link{lm}} -#' @return An objects of class \code{mira}, which stands for 'multiply imputed -#' repeated analysis'. This object contains \code{data$m} distinct -#' \code{lm.objects}, plus some descriptive information. +#' set', typically created by a call to function `mice()`. +#' @param \dots Additional parameters passed to [lm()] +#' @return An objects of class `mira`, which stands for 'multiply imputed +#' repeated analysis'. This object contains `data$m` distinct +#' `lm.objects`, plus some descriptive information. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{lm}}, \code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}} -#' @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. +#' @seealso [lm()], [`mids()`][mids-class], [`mira()`][mira-class] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords multivariate #' @examples @@ -43,28 +43,28 @@ lm.mids <- function(formula, data, ...) { } -#' Generalized linear model for \code{mids} object +#' Generalized linear model for `mids` object #' -#' Applies \code{glm()} to a multiply imputed data set +#' Applies `glm()` to a multiply imputed data set #' #' This function is included for backward compatibility with V1.0. The function -#' is superseded by \code{\link{with.mids}}. +#' is superseded by [with.mids()]. #' #' @param formula a formula expression as for other regression models, of the -#' form response ~ predictors. See the documentation of \code{\link{lm}} and -#' \code{\link{formula}} for details. +#' form response ~ predictors. See the documentation of [lm()] and +#' [formula()] for details. #' @param family The family of the glm model -#' @param data An object of type \code{mids}, which stands for 'multiply imputed -#' data set', typically created by function \code{mice()}. -#' @param \dots Additional parameters passed to \code{\link{glm}}. -#' @return An objects of class \code{mira}, which stands for 'multiply imputed -#' repeated analysis'. This object contains \code{data$m} distinct -#' \code{glm.objects}, plus some descriptive information. +#' @param data An object of type `mids`, which stands for 'multiply imputed +#' data set', typically created by function `mice()`. +#' @param \dots Additional parameters passed to [glm()]. +#' @return An objects of class `mira`, which stands for 'multiply imputed +#' repeated analysis'. This object contains `data$m` distinct +#' `glm.objects`, plus some descriptive information. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{with.mids}}, \code{\link{glm}}, \code{\link[=mids-class]{mids}}, -#' \code{\link[=mira-class]{mira}} +#' @seealso [with.mids()], [glm()], [`mids()`][mids-class], +#' [`mira()`][mira-class] #' @references Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) -#' \emph{Multivariate Imputation by Chained Equations: MICE V1.0 User's manual.} +#' *Multivariate Imputation by Chained Equations: MICE V1.0 User's manual.* #' Leiden: TNO Quality of Life. #' @keywords multivariate #' @examples diff --git a/R/mads.R b/R/mads.R index 290b7c874..6cc857416 100644 --- a/R/mads.R +++ b/R/mads.R @@ -1,58 +1,58 @@ -#' Multivariate amputed data set (\code{mads}) +#' Multivariate amputed data set (`mads`) #' -#' The \code{mads} object contains an amputed data set. The \code{mads} object is -#' generated by the \code{ampute} function. The \code{mads} class of objects has -#' methods for the following generic functions: \code{print}, \code{summary}, -#' \code{bwplot} and \code{xyplot}. +#' The `mads` object contains an amputed data set. The `mads` object is +#' generated by the `ampute` function. The `mads` class of objects has +#' methods for the following generic functions: `print`, `summary`, +#' `bwplot` and `xyplot`. #' #' @section Contents: #' \describe{ -#' \item{\code{call}:}{The function call.} -#' \item{\code{prop}:}{Proportion of cases with missing values. Note: even when +#' \item{`call`:}{The function call.} +#' \item{`prop`:}{Proportion of cases with missing values. Note: even when #' the proportion is entered as the proportion of missing cells (when -#' \code{bycases == TRUE}), this object contains the proportion of missing cases.} -#' \item{\code{patterns}:}{A data frame of size #patterns by #variables where \code{0} -#' indicates a variable has missing values and \code{1} indicates a variable remains +#' `bycases == TRUE`), this object contains the proportion of missing cases.} +#' \item{`patterns`:}{A data frame of size #patterns by #variables where `0` +#' indicates a variable has missing values and `1` indicates a variable remains #' complete.} -#' \item{\code{freq}:}{A vector of length #patterns containing the relative +#' \item{`freq`:}{A vector of length #patterns containing the relative #' frequency with which the patterns occur. For example, if the vector is -#' \code{c(0.4, 0.4, 0.2)}, this means that of all cases with missing values, +#' `c(0.4, 0.4, 0.2)`, this means that of all cases with missing values, #' 40 percent is candidate for pattern 1, 40 percent for pattern 2 and 20 #' percent for pattern 3. The vector sums to 1.} -#' \item{\code{mech}:}{A string specifying the missingness mechanism, either -#' \code{"MCAR"}, \code{"MAR"} or \code{"MNAR"}.} -#' \item{\code{weights}:}{A data frame of size #patterns by #variables. It contains +#' \item{`mech`:}{A string specifying the missingness mechanism, either +#' `"MCAR"`, `"MAR"` or `"MNAR"`.} +#' \item{`weights`:}{A data frame of size #patterns by #variables. It contains #' the weights that were used to calculate the weighted sum scores. The weights #' may differ between patterns and between variables.} -#' \item{\code{cont}:}{Logical, whether probabilities are based on continuous logit +#' \item{`cont`:}{Logical, whether probabilities are based on continuous logit #' functions or on discrete odds distributions.} -#' \item{\code{type}:}{A vector of strings containing the type of missingness -#' for each pattern. Either \code{"LEFT"}, \code{"MID"}, \code{"TAIL"} or -#' \code{"RIGHT"}. The first type refers to the first pattern, the second type +#' \item{`type`:}{A vector of strings containing the type of missingness +#' for each pattern. Either `"LEFT"`, `"MID"`, `"TAIL"` or +#' `"RIGHT"`. The first type refers to the first pattern, the second type #' to the second pattern, etc.} -#' \item{\code{odds}:}{A matrix where #patterns defines the #rows. Each row contains +#' \item{`odds`:}{A matrix where #patterns defines the #rows. Each row contains #' the odds of being missing for the corresponding pattern. The amount of odds values #' defines in how many quantiles the sum scores were divided. The values are #' relative probabilities: a quantile with odds value 4 will have a probability of #' being missing that is four times higher than a quantile with odds 1. The #' #quantiles may differ between patterns, NA is used for cells remaining empty.} -#' \item{\code{amp}:}{A data frame containing the input data with NAs for the +#' \item{`amp`:}{A data frame containing the input data with NAs for the #' amputed values.} -#' \item{\code{cand}:}{A vector that contains the pattern number for each case. +#' \item{`cand`:}{A vector that contains the pattern number for each case. #' A value between 1 and #patterns is given. For example, a case with value 2 is #' candidate for missing data pattern 2.} -#' \item{\code{scores}:}{A list containing vectors with weighted sum scores of the +#' \item{`scores`:}{A list containing vectors with weighted sum scores of the #' candidates. The first vector refers to the candidates of the first pattern, the #' second vector refers to the candidates of the second pattern, etc. The length #' of the vectors differ because the number of candidates is different for each #' pattern.} -#' \item{\code{data}:}{The complete data set that was entered in \code{ampute}.} +#' \item{`data`:}{The complete data set that was entered in `ampute`.} #' } -#' @note Many of the functions of the \code{mice} package do not use the S4 class +#' @note Many of the functions of the `mice` package do not use the S4 class #' definitions, and instead rely on the S3 list equivalent -#' \code{oldClass(obj) <- "mads"}. +#' `oldClass(obj) <- "mads"`. #' @author Rianne Schouten, 2016 -#' @seealso \code{\link{ampute}}, Vignette titled "Multivariate Amputation using +#' @seealso [ampute()], Vignette titled "Multivariate Amputation using #' Ampute". #' @export setClass("mads", diff --git a/R/mammalsleep.R b/R/mammalsleep.R index a7ee1b92f..766fb8dc2 100644 --- a/R/mammalsleep.R +++ b/R/mammalsleep.R @@ -16,7 +16,7 @@ #' @name mammalsleep #' @aliases mammalsleep sleep #' @docType data -#' @format \code{mammalsleep} is a data frame with 62 rows and 11 columns: +#' @format `mammalsleep` is a data frame with 62 rows and 11 columns: #' \describe{ #' \item{species}{Species of animal} #' \item{bw}{Body weight (kg)} diff --git a/R/md.pairs.R b/R/md.pairs.R index 0b9fd7b97..1af7b96a4 100644 --- a/R/md.pairs.R +++ b/R/md.pairs.R @@ -3,20 +3,21 @@ #' Number of observations per variable pair. #' #' The four components in the output value is have the following interpretation: -#' \describe{ \item{list('rr')}{response-response, both variables are observed} +#' \describe{ +#' \item{list('rr')}{response-response, both variables are observed} #' \item{list('rm')}{response-missing, row observed, column missing} #' \item{list('mr')}{missing -response, row missing, column observed} #' \item{list('mm')}{missing -missing, both variables are missing} } #' #' @param data A data frame or a matrix containing the incomplete data. Missing -#' values are coded as \code{NA}. -#' @return A list of four components named \code{rr}, \code{rm}, \code{mr} and -#' \code{mm}. Each component is square numerical matrix containing the number +#' values are coded as `NA`. +#' @return A list of four components named `rr`, `rm`, `mr` and +#' `mm`. Each component is square numerical matrix containing the number #' observations within four missing data pattern. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2009 -#' @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. +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords univar #' @examples diff --git a/R/md.pattern.R b/R/md.pattern.R index c00a061dc..b5dd8654e 100644 --- a/R/md.pattern.R +++ b/R/md.pattern.R @@ -14,7 +14,7 @@ #' `plot = TRUE`. #' @param rotate.names Whether the variable names in the plot should be placed #' horizontally or vertically. Default is `rotate.names = FALSE`. -#' @return A matrix with \code{ncol(x)+1} columns, in which each row corresponds +#' @return A matrix with `ncol(x)+1` columns, in which each row corresponds #' to a missing data pattern (1=observed, 0=missing). Rows and columns are #' sorted in increasing amounts of missing information. The last column and row #' contain row and column counts, respectively. @@ -23,9 +23,9 @@ #' @references Schafer, J.L. (1997), Analysis of multivariate incomplete data. #' London: Chapman&Hall. #' -#' 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} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords univar #' @examples #' md.pattern(nhanes) diff --git a/R/mdc.R b/R/mdc.R index a8cd4fe42..50e9de7e1 100644 --- a/R/mdc.R +++ b/R/mdc.R @@ -1,9 +1,9 @@ #' Graphical parameter for missing data plots #' -#' \code{mdc} returns colors used to distinguish observed, missing and combined -#' data in plotting. \code{mice.theme} return a partial list of named objects -#' that can be used as a theme in \code{stripplot}, \code{bwplot}, -#' \code{densityplot} and \code{xyplot}. +#' `mdc` returns colors used to distinguish observed, missing and combined +#' data in plotting. `mice.theme` return a partial list of named objects +#' that can be used as a theme in `stripplot`, `bwplot`, +#' `densityplot` and `xyplot`. #' #' This function eases consistent use of colors in plots. The default follows #' the Abayomi convention, which uses blue for observed data, red for missing or @@ -11,14 +11,14 @@ #' #' @aliases mdc #' @param r A numerical or character vector. The numbers 1-6 request colors as -#' follows: 1=\code{cso}, 2=\code{csi}, 3=\code{csc}, 4=\code{clo}, 5=\code{cli} -#' and 6=\code{clc}. Alternatively, \code{r} may contain the strings -#'' \code{observed}', '\code{missing}', or '\code{both}', or abbreviations +#' follows: 1=`cso`, 2=`csi`, 3=`csc`, 4=`clo`, 5=`cli` +#' and 6=`clc`. Alternatively, `r` may contain the strings +#'' `observed`', '`missing`', or '`both`', or abbreviations #' thereof. -#' @param s A character vector containing the strings '\code{symbol}' or -#'' \code{line}', or abbreviations thereof. +#' @param s A character vector containing the strings '`symbol`' or +#'' `line`', or abbreviations thereof. #' @param transparent A logical indicating whether alpha-transparency is -#' allowed. The default is \code{TRUE}. +#' allowed. The default is `TRUE`. #' @param cso The symbol color for the observed data. The default is a #' transparent blue. #' @param csi The symbol color for the missing or imputed data. The default is a @@ -31,15 +31,15 @@ #' slightly darker transparent red. #' @param clc The line color for the combined observed and imputed data. The #' default is a grey color. -#' @return \code{mdc()} returns a vector containing color definitions. The length -#' of the output vector is calculate from the length of \code{r} and \code{s}. +#' @return `mdc()` returns a vector containing color definitions. The length +#' of the output vector is calculate from the length of `r` and `s`. #' Elements of the input vectors are repeated if needed. #' @author Stef van Buuren, sept 2012. -#' @seealso \code{\link{hcl}}, \code{\link{rgb}}, -#' \code{\link{xyplot.mids}}, \code{\link[lattice:xyplot]{xyplot}}, -#' \code{\link[lattice:trellis.par.get]{trellis.par.set}} -#' @references Sarkar, Deepayan (2008) \emph{Lattice: Multivariate Data -#' Visualization with R}, Springer. +#' @seealso [hcl()], [rgb()], +#' [xyplot.mids()], [lattice::xyplot()], +#' [`trellis.par.set()`][lattice::trellis.par.get] +#' @references Sarkar, Deepayan (2008) *Lattice: Multivariate Data +#' Visualization with R*, Springer. #' @keywords hplot #' @examples #' # all six colors diff --git a/R/method.R b/R/method.R index 00ded5bd8..ff09ea420 100644 --- a/R/method.R +++ b/R/method.R @@ -1,11 +1,11 @@ -#' Creates a \code{method} argument +#' Creates a `method` argument #' -#' This helper function creates a valid \code{method} vector. The -#' \code{method} vector is an argument to the \code{mice} function that +#' This helper function creates a valid `method` vector. The +#' `method` vector is an argument to the `mice` function that #' specifies the method for each block. #' @inheritParams mice -#' @return Vector of \code{length(blocks)} element with method names -#' @seealso \code{\link{mice}} +#' @return Vector of `length(blocks)` element with method names +#' @seealso [mice()] #' @examples #' make.method(nhanes2) #' @export diff --git a/R/mice-package.R b/R/mice-package.R index f0440cc74..e18cfb9b5 100644 --- a/R/mice-package.R +++ b/R/mice-package.R @@ -13,7 +13,7 @@ #' The \pkg{mice} package contains functions to #' \itemize{ #' \item Inspect the missing data pattern -#' \item Impute the missing data \emph{m} times, resulting in \emph{m} completed data sets +#' \item Impute the missing data *m* times, resulting in *m* completed data sets #' \item Diagnose the quality of the imputed values #' \item Analyze each completed data set #' \item Pool the results of the repeated analyses @@ -26,11 +26,11 @@ #' #' The main functions are: #' \tabular{ll}{ -#' \code{mice()} \tab Impute the missing data *m* times\cr -#' \code{with()} \tab Analyze completed data sets\cr -#' \code{pool()} \tab Combine parameter estimates\cr -#' \code{complete()} \tab Export imputed data\cr -#' \code{ampute()} \tab Generate missing data\cr} +#' `mice()` \tab Impute the missing data *m* times\cr +#' `with()` \tab Analyze completed data sets\cr +#' `pool()` \tab Combine parameter estimates\cr +#' `complete()` \tab Export imputed data\cr +#' `ampute()` \tab Generate missing data\cr} #' #' @section Vignettes: #' @@ -40,11 +40,11 @@ #' #' We suggest going through these vignettes in the following order #' \enumerate{ -#' \item \href{https://www.gerkovink.com/miceVignettes/Ad_hoc_and_mice/Ad_hoc_methods.html}{Ad hoc methods and the MICE algorithm} -#' \item \href{https://www.gerkovink.com/miceVignettes/Convergence_pooling/Convergence_and_pooling.html}{Convergence and pooling} -#' \item \href{https://www.gerkovink.com/miceVignettes/Missingness_inspection/Missingness_inspection.html}{Inspecting how the observed data and missingness are related} -#' \item \href{https://www.gerkovink.com/miceVignettes/Passive_Post_processing/Passive_imputation_post_processing.html}{Passive imputation and post-processing} -#' \item \href{https://www.gerkovink.com/miceVignettes/Multi_level/Multi_level_data.html}{Imputing multilevel data} +#' \item [Ad hoc methods and the MICE algorithm](https://www.gerkovink.com/miceVignettes/Ad_hoc_and_mice/Ad_hoc_methods.html) +#' \item [Convergence and pooling](https://www.gerkovink.com/miceVignettes/Convergence_pooling/Convergence_and_pooling.html) +#' \item [Inspecting how the observed data and missingness are related](https://www.gerkovink.com/miceVignettes/Missingness_inspection/Missingness_inspection.html) +#' \item [Passive imputation and post-processing](https://www.gerkovink.com/miceVignettes/Passive_Post_processing/Passive_imputation_post_processing.html) +#' \item [Imputing multilevel data](https://www.gerkovink.com/miceVignettes/Multi_level/Multi_level_data.html) #' \item \href{https://www.gerkovink.com/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html}{Sensitivity analysis with \pkg{mice}} #' } #' @@ -52,16 +52,16 @@ #' Boca Raton, FL.: Chapman & Hall/CRC Press. #' The book -#' \href{https://stefvanbuuren.name/fimd/}{\emph{Flexible Imputation of Missing Data. Second Edition.}} -#' contains a lot of \href{https://github.com/stefvanbuuren/fimdbook/tree/master/R}{example code}. +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/) +#' contains a lot of [example code](https://github.com/stefvanbuuren/fimdbook/tree/master/R). #' #' @section Methodology: #' #' The \pkg{mice} software was published in the {Journal of Statistical Software} (Van Buuren and Groothuis-Oudshoorn, 2011). \doi{10.18637/jss.v045.i03} #' The first application of the method #' concerned missing blood pressure data (Van Buuren et. al., 1999). -#' The term \emph{Fully Conditional Specification} was introduced in 2006 to describe a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (Van Buuren et. al., 2006). Further details on mixes of variables and applications can be found in the book -#' \href{https://stefvanbuuren.name/fimd/}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' The term *Fully Conditional Specification* was introduced in 2006 to describe a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (Van Buuren et. al., 2006). Further details on mixes of variables and applications can be found in the book +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' @section Enhanced linear algebra: @@ -72,23 +72,23 @@ #' #' @docType package #' @name mice -#' @seealso \code{\link{mice}}, \code{\link{with.mids}}, -#' \code{\link{pool}}, \code{\link{complete}}, \code{\link{ampute}} +#' @seealso [mice()], [with.mids()], +#' [pool()], [complete()], [ampute()] #' @references #' van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple #' imputation of missing blood pressure covariates in survival analysis. -#' \emph{Statistics in Medicine}, \bold{18}, 681--694. +#' *Statistics in Medicine*, **18**, 681--694. #' #' van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) -#' Fully conditional specification in multivariate imputation. \emph{Journal of -#' Statistical Computation and Simulation}, \bold{76}, 12, 1049--1064. +#' Fully conditional specification in multivariate imputation. *Journal of +#' Statistical Computation and Simulation*, **76**, 12, 1049--1064. #' -#' 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} +#' van Buuren, S., Groothuis-Oudshoorn, K. (2011). {`mice`: +#' Multivariate Imputation by Chained Equations in `R`}. *Journal of +#' Statistical Software*, **45**(3), 1--67. \doi{10.18637/jss.v045.i03} #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/) #' Chapman & Hall/CRC. Boca Raton, FL. #' @useDynLib mice, .registration = TRUE NULL diff --git a/R/mice.R b/R/mice.R index fd45925a8..1cf11f66c 100644 --- a/R/mice.R +++ b/R/mice.R @@ -26,60 +26,60 @@ #' Built-in univariate imputation methods are: #' #' \tabular{lll}{ -#' \code{pmm} \tab any \tab Predictive mean matching\cr -#' \code{midastouch} \tab any \tab Weighted predictive mean matching\cr -#' \code{sample} \tab any \tab Random sample from observed values\cr -#' \code{cart} \tab any \tab Classification and regression trees\cr -#' \code{rf} \tab any \tab Random forest imputations\cr -#' \code{mean} \tab numeric \tab Unconditional mean imputation\cr -#' \code{norm} \tab numeric \tab Bayesian linear regression\cr -#' \code{norm.nob} \tab numeric \tab Linear regression ignoring model error\cr -#' \code{norm.boot} \tab numeric \tab Linear regression using bootstrap\cr -#' \code{norm.predict} \tab numeric \tab Linear regression, predicted values\cr -#' \code{lasso.norm} \tab numeric \tab Lasso linear regression\cr -#' \code{lasso.select.norm} \tab numeric \tab Lasso select + linear regression\cr -#' \code{quadratic} \tab numeric \tab Imputation of quadratic terms\cr -#' \code{ri} \tab numeric \tab Random indicator for nonignorable data\cr -#' \code{logreg} \tab binary \tab Logistic regression\cr -#' \code{logreg.boot} \tab binary \tab Logistic regression with bootstrap\cr -#' \code{lasso.logreg} \tab binary \tab Lasso logistic regression\cr -#' \code{lasso.select.logreg}\tab binary \tab Lasso select + logistic regression\cr -#' \code{polr} \tab ordered \tab Proportional odds model\cr -#' \code{polyreg} \tab unordered\tab Polytomous logistic regression\cr -#' \code{lda} \tab unordered\tab Linear discriminant analysis\cr -#' \code{2l.norm} \tab numeric \tab Level-1 normal heteroscedastic\cr -#' \code{2l.lmer} \tab numeric \tab Level-1 normal homoscedastic, lmer\cr -#' \code{2l.pan} \tab numeric \tab Level-1 normal homoscedastic, pan\cr -#' \code{2l.bin} \tab binary \tab Level-1 logistic, glmer\cr -#' \code{2lonly.mean} \tab numeric \tab Level-2 class mean\cr -#' \code{2lonly.norm} \tab numeric \tab Level-2 class normal\cr -#' \code{2lonly.pmm} \tab any \tab Level-2 class predictive mean matching +#' `pmm` \tab any \tab Predictive mean matching\cr +#' `midastouch` \tab any \tab Weighted predictive mean matching\cr +#' `sample` \tab any \tab Random sample from observed values\cr +#' `cart` \tab any \tab Classification and regression trees\cr +#' `rf` \tab any \tab Random forest imputations\cr +#' `mean` \tab numeric \tab Unconditional mean imputation\cr +#' `norm` \tab numeric \tab Bayesian linear regression\cr +#' `norm.nob` \tab numeric \tab Linear regression ignoring model error\cr +#' `norm.boot` \tab numeric \tab Linear regression using bootstrap\cr +#' `norm.predict` \tab numeric \tab Linear regression, predicted values\cr +#' `lasso.norm` \tab numeric \tab Lasso linear regression\cr +#' `lasso.select.norm` \tab numeric \tab Lasso select + linear regression\cr +#' `quadratic` \tab numeric \tab Imputation of quadratic terms\cr +#' `ri` \tab numeric \tab Random indicator for nonignorable data\cr +#' `logreg` \tab binary \tab Logistic regression\cr +#' `logreg.boot` \tab binary \tab Logistic regression with bootstrap\cr +#' `lasso.logreg` \tab binary \tab Lasso logistic regression\cr +#' `lasso.select.logreg`\tab binary \tab Lasso select + logistic regression\cr +#' `polr` \tab ordered \tab Proportional odds model\cr +#' `polyreg` \tab unordered\tab Polytomous logistic regression\cr +#' `lda` \tab unordered\tab Linear discriminant analysis\cr +#' `2l.norm` \tab numeric \tab Level-1 normal heteroscedastic\cr +#' `2l.lmer` \tab numeric \tab Level-1 normal homoscedastic, lmer\cr +#' `2l.pan` \tab numeric \tab Level-1 normal homoscedastic, pan\cr +#' `2l.bin` \tab binary \tab Level-1 logistic, glmer\cr +#' `2lonly.mean` \tab numeric \tab Level-2 class mean\cr +#' `2lonly.norm` \tab numeric \tab Level-2 class normal\cr +#' `2lonly.pmm` \tab any \tab Level-2 class predictive mean matching #' } #' -#' These corresponding functions are coded in the \code{mice} library under -#' names \code{mice.impute.method}, where \code{method} is a string with the -#' name of the univariate imputation method name, for example \code{norm}. The -#' \code{method} argument specifies the methods to be used. For the \code{j}'th -#' column, \code{mice()} calls the first occurrence of -#' \code{paste('mice.impute.', method[j], sep = '')} in the search path. The +#' These corresponding functions are coded in the `mice` library under +#' names `mice.impute.method`, where `method` is a string with the +#' name of the univariate imputation method name, for example `norm`. The +#' `method` argument specifies the methods to be used. For the `j`'th +#' column, `mice()` calls the first occurrence of +#' `paste('mice.impute.', method[j], sep = '')` in the search path. The #' mechanism allows uses to write customized imputation function, -#' \code{mice.impute.myfunc}. To call it for all columns specify -#' \code{method='myfunc'}. To call it only for, say, column 2 specify +#' `mice.impute.myfunc`. To call it for all columns specify +#' `method='myfunc'`. To call it only for, say, column 2 specify #' \code{method=c('norm','myfunc','logreg',\dots{})}. #' -#' \emph{Skipping imputation:} The user may skip imputation of a column by -#' setting its entry to the empty method: \code{""}. For complete columns without -#' missing data \code{mice} will automatically set the empty method. Setting t +#' *Skipping imputation:* The user may skip imputation of a column by +#' setting its entry to the empty method: `""`. For complete columns without +#' missing data `mice` will automatically set the empty method. Setting t #' he empty method does not produce imputations for the column, so any missing -#' cells remain \code{NA}. If column A contains \code{NA}'s and is used as -#' predictor in the imputation model for column B, then \code{mice} produces no +#' cells remain `NA`. If column A contains `NA`'s and is used as +#' predictor in the imputation model for column B, then `mice` produces no #' imputations for the rows in B where A is missing. The imputed data -#' for B may thus contain \code{NA}'s. The remedy is to remove column A from +#' for B may thus contain `NA`'s. The remedy is to remove column A from #' the imputation model for the other columns in the data. This can be done -#' by setting the entire column for variable A in the \code{predictorMatrix} +#' by setting the entire column for variable A in the `predictorMatrix` #' equal to zero. #' -#' \emph{Passive imputation:} \code{mice()} supports a special built-in method, +#' *Passive imputation:* `mice()` supports a special built-in method, #' called passive imputation. This method can be used to ensure that a data #' transform always depends on the most recently generated imputations. In some #' cases, an imputation model may need transformed data in addition to the @@ -87,97 +87,97 @@ #' on). #' #' Passive imputation maintains consistency among different transformations of -#' the same data. Passive imputation is invoked if \code{~} is specified as the +#' the same data. Passive imputation is invoked if `~` is specified as the #' first character of the string that specifies the univariate method. -#' \code{mice()} interprets the entire string, including the \code{~} character, -#' as the formula argument in a call to \code{model.frame(formula, -#' data[!r[,j],])}. This provides a simple mechanism for specifying deterministic +#' `mice()` interprets the entire string, including the `~` character, +#' as the formula argument in a call to `model.frame(formula, +#' data[!r[,j],])`. This provides a simple mechanism for specifying deterministic #' dependencies among the columns. For example, suppose that the missing entries -#' in variables \code{data$height} and \code{data$weight} are imputed. The body -#' mass index (BMI) can be calculated within \code{mice} by specifying the -#' string \code{'~I(weight/height^2)'} as the univariate imputation method for -#' the target column \code{data$bmi}. Note that the \code{~} mechanism works +#' in variables `data$height` and `data$weight` are imputed. The body +#' mass index (BMI) can be calculated within `mice` by specifying the +#' string `'~I(weight/height^2)'` as the univariate imputation method for +#' the target column `data$bmi`. Note that the `~` mechanism works #' only on those entries which have missing values in the target column. You #' should make sure that the combined observed and imputed parts of the target #' column make sense. An easy way to create consistency is by coding all entries -#' in the target as \code{NA}, but for large data sets, this could be +#' in the target as `NA`, but for large data sets, this could be #' inefficient. Note that you may also need to adapt the default -#' \code{predictorMatrix} to evade linear dependencies among the predictors that -#' could cause errors like \code{Error in solve.default()} or \code{Error: -#' system is exactly singular}. Though not strictly needed, it is often useful -#' to specify \code{visitSequence} such that the column that is imputed by the -#' \code{~} mechanism is visited each time after one of its predictors was +#' `predictorMatrix` to evade linear dependencies among the predictors that +#' could cause errors like `Error in solve.default()` or `Error: +#' system is exactly singular`. Though not strictly needed, it is often useful +#' to specify `visitSequence` such that the column that is imputed by the +#' `~` mechanism is visited each time after one of its predictors was #' visited. In that way, deterministic relation between columns will always be #' synchronized. #' -#' #'A new argument \code{ls.meth} can be parsed to the lower level -#' \code{.norm.draw} to specify the method for generating the least squares -#' estimates and any subsequently derived estimates. Argument \code{ls.meth} -#' takes one of three inputs: \code{"qr"} for QR-decomposition, \code{"svd"} for -#' singular value decomposition and \code{"ridge"} for ridge regression. -#' \code{ls.meth} defaults to \code{ls.meth = "qr"}. +#' #'A new argument `ls.meth` can be parsed to the lower level +#' `.norm.draw` to specify the method for generating the least squares +#' estimates and any subsequently derived estimates. Argument `ls.meth` +#' takes one of three inputs: `"qr"` for QR-decomposition, `"svd"` for +#' singular value decomposition and `"ridge"` for ridge regression. +#' `ls.meth` defaults to `ls.meth = "qr"`. #' -#' \emph{Auxiliary predictors in formulas specification: } -#' For a given block, the \code{formulas} specification takes precedence over -#' the corresponding row in the \code{predictMatrix} argument. This +#' *Auxiliary predictors in formulas specification: * +#' For a given block, the `formulas` specification takes precedence over +#' the corresponding row in the `predictMatrix` argument. This #' precedence is, however, restricted to the subset of variables #' specified in the terms of the block formula. Any -#' variables not specified by \code{formulas} are imputed -#' according to the \code{predictMatrix} specification. Variables with -#' non-zero \code{type} values in the \code{predictMatrix} will -#' be added as main effects to the \code{formulas}, which will +#' variables not specified by `formulas` are imputed +#' according to the `predictMatrix` specification. Variables with +#' non-zero `type` values in the `predictMatrix` will +#' be added as main effects to the `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}. +#' argument `auxiliary = FALSE`. #' #' @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}. +#' values are coded as `NA`. +#' @param m Number of multiple imputations. The default is `m=5`. #' @param method Can be either a single string, or a vector of strings with -#' length \code{length(blocks)}, specifying the imputation method to be +#' length `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. -#' @param predictorMatrix A numeric matrix of \code{length(blocks)} rows -#' and \code{ncol(data)} columns, containing 0/1 data specifying +#' as regulated by the `defaultMethod` argument. Columns that need +#' not be imputed have the empty method `""`. See details. +#' @param predictorMatrix A numeric matrix of `length(blocks)` rows +#' and `ncol(data)` columns, containing 0/1 data specifying #' the set of predictors to be used for each target column. #' Each row corresponds to a variable block, i.e., a set of variables -#' to be imputed. A value of \code{1} means that the column +#' to be imputed. A value of `1` means that the column #' variable is used as a predictor for the target block (in the rows). -#' By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} +#' By default, the `predictorMatrix` is a square matrix of `ncol(data)` #' rows and columns with all 1's, except for the diagonal. -#' Note: For two-level imputation models (which have \code{"2l"} in their names) -#' other roles (e.g, \code{2} or \code{-2}) are also allowed. -#' @param ignore A logical vector of \code{nrow(data)} elements indicating +#' Note: For two-level imputation models (which have `"2l"` in their names) +#' other roles (e.g, `2` or `-2`) are also allowed. +#' @param ignore A logical vector of `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 -#' to imputed. Rows with \code{ignore} set to \code{TRUE} do not influence the +#' `NULL` includes all rows that have an observed value of the variable +#' to imputed. Rows with `ignore` set to `TRUE` do not influence the #' parameters of the imputation model, but are still imputed. We may use the -#' \code{ignore} argument to split \code{data} into a training set (on which the +#' `ignore` argument to split `data` into a training set (on which the #' imputation model is built) and a test set (that does not influence the #' imputation model estimates). -#' Note: Multivariate imputation methods, like \code{mice.impute.jomoImpute()} -#' or \code{mice.impute.panImpute()}, do not honour the \code{ignore} argument. +#' Note: Multivariate imputation methods, like `mice.impute.jomoImpute()` +#' or `mice.impute.panImpute()`, do not honour the `ignore` argument. #' @param where A data frame or matrix with logicals of the same dimensions -#' as \code{data} indicating where in the data the imputations should be -#' created. The default, \code{where = is.na(data)}, specifies that the -#' missing data should be imputed. The \code{where} argument may be used to +#' as `data` indicating where in the data the imputations should be +#' created. The default, `where = is.na(data)`, specifies that the +#' missing data should be imputed. The `where` argument may be used to #' overimpute observed data, or to skip imputations for selected missing values. #' Note: Imputation methods that generate imptutations outside of -#' \code{mice}, like \code{mice.impute.panImpute()} may depend on a complete -#' predictor space. In that case, a custom \code{where} matrix can not be +#' `mice`, like `mice.impute.panImpute()` may depend on a complete +#' predictor space. In that case, a custom `where` matrix can not be #' specified. #' @param 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 +#' (see `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. +#' `blocks` are imputed. The relevant columns in the `where` +#' matrix are set to `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. #' @param visitSequence A vector of block names of arbitrary length, specifying the @@ -186,89 +186,89 @@ #' members of the same block are imputed #' when the block is visited. A variable that is a member of multiple blocks #' is re-imputed within the same iteration. -#' The default \code{visitSequence = "roman"} visits the blocks (left to right) -#' in the order in which they appear in \code{blocks}. -#' One may also use one of the following keywords: \code{"arabic"} -#' (right to left), \code{"monotone"} (ordered low to high proportion -#' of missing data) and \code{"revmonotone"} (reverse of monotone). -#' \emph{Special case}: If you specify both \code{visitSequence = "monotone"} and -#' \code{maxit = 1}, then the procedure will edit the \code{predictorMatrix} +#' The default `visitSequence = "roman"` visits the blocks (left to right) +#' in the order in which they appear in `blocks`. +#' One may also use one of the following keywords: `"arabic"` +#' (right to left), `"monotone"` (ordered low to high proportion +#' of missing data) and `"revmonotone"` (reverse of monotone). +#' *Special case*: If you specify both `visitSequence = "monotone"` and +#' `maxit = 1`, then the procedure will edit the `predictorMatrix` #' to conform to the monotone pattern. Realize that convergence in one #' iteration is only guaranteed if the missing data pattern is actually #' monotone. The procedure does not check this. #' @param formulas A named list of formula's, or expressions that -#' can be converted into formula's by \code{as.formula}. List elements +#' can be converted into formula's by `as.formula`. List elements #' correspond to blocks. The block to which the list element applies is #' identified by its name, so list names must correspond to block names. -#' The \code{formulas} argument is an alternative to the -#' \code{predictorMatrix} argument that allows for more flexibility in +#' The `formulas` argument is an alternative to the +#' `predictorMatrix` argument that allows for more flexibility in #' specifying imputation models, e.g., for specifying interaction terms. -#' @param blots A named \code{list} of \code{alist}'s that can be used +#' @param blots A named `list` of `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 -#' called for block \code{blockname}. -#' @param post A vector of strings with length \code{ncol(data)} specifying +#' of element `blots[[blockname]]` are passed down to the function +#' called for block `blockname`. +#' @param post A vector of strings with length `ncol(data)` specifying #' expressions as strings. Each string is parsed and -#' executed within the \code{sampler()} function to post-process +#' executed within the `sampler()` function to post-process #' imputed values during the iterations. #' The default is a vector of empty strings, indicating no post-processing. -#' Multivariate (block) imputation methods ignore the \code{post} parameter. +#' Multivariate (block) imputation methods ignore the `post` parameter. #' @param 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 #' ordered levels. By default, the method uses -#' \code{pmm}, predictive mean matching (numeric data) \code{logreg}, logistic -#' regression imputation (binary data, factor with 2 levels) \code{polyreg}, +#' `pmm`, predictive mean matching (numeric data) `logreg`, logistic +#' regression imputation (binary data, factor with 2 levels) `polyreg`, #' polytomous regression imputation for unordered categorical data (factor > 2 -#' levels) \code{polr}, proportional odds model for (ordered, > 2 levels). +#' levels) `polr`, proportional odds model for (ordered, > 2 levels). #' @param maxit A scalar giving the number of iterations. The default is 5. -#' @param printFlag If \code{TRUE}, \code{mice} will print history on console. -#' Use \code{print=FALSE} for silent computation. -#' @param seed An integer that is used as argument by the \code{set.seed()} for +#' @param printFlag If `TRUE`, `mice` will print history on console. +#' Use `print=FALSE` for silent computation. +#' @param seed An integer that is used as argument by the `set.seed()` for #' offsetting the random number generator. Default is to leave the random number #' generator alone. -#' @param data.init A data frame of the same size and type as \code{data}, +#' @param data.init A data frame of the same size and type as `data`, #' without missing data, used to initialize imputations before the start of the -#' iterative process. The default \code{NULL} implies that starting imputation +#' iterative process. The default `NULL` implies that starting imputation #' 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 +#' `data.init` will start all `m` Gibbs sampling streams from the same #' imputation. #' @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}} +#' @return Returns an S3 object of class [`mids()`][mids-class] #' (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}} -#' @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. +#' @seealso [`mids()`][mids-class], [with.mids()], +#' [set.seed()], [complete()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-FCS.html#sec:MICE}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-FCS.html#sec:MICE) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) -#' Fully conditional specification in multivariate imputation. \emph{Journal of -#' Statistical Computation and Simulation}, \bold{76}, 12, 1049--1064. +#' Fully conditional specification in multivariate imputation. *Journal of +#' Statistical Computation and Simulation*, **76**, 12, 1049--1064. #' #' Van Buuren, S. (2007) Multiple imputation of discrete and continuous data by -#' fully conditional specification. \emph{Statistical Methods in Medical -#' Research}, \bold{16}, 3, 219--242. +#' fully conditional specification. *Statistical Methods in Medical +#' Research*, **16**, 3, 219--242. #' #' Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of -#' missing blood pressure covariates in survival analysis. \emph{Statistics in -#' Medicine}, \bold{18}, 681--694. +#' missing blood pressure covariates in survival analysis. *Statistics in +#' Medicine*, **18**, 681--694. #' -#' Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of +#' Brand, J.P.L. (1999) *Development, implementation and evaluation of #' multiple imputation strategies for the statistical analysis of incomplete -#' data sets.} Dissertation. Rotterdam: Erasmus University. +#' data sets.* Dissertation. Rotterdam: Erasmus University. #' @keywords iteration #' @examples #' # do default multiple imputation on a numeric matrix diff --git a/R/mice.impute.2l.bin.R b/R/mice.impute.2l.bin.R index 3b1f62d2d..f7512f4a5 100644 --- a/R/mice.impute.2l.bin.R +++ b/R/mice.impute.2l.bin.R @@ -1,7 +1,7 @@ -#' Imputation by a two-level logistic model using \code{glmer} +#' Imputation by a two-level logistic model using `glmer` #' #' Imputes univariate systematically and sporadically missing data -#' using a two-level logistic model using \code{lme4::glmer()} +#' using a two-level logistic model using `lme4::glmer()` #' #' Data are missing systematically if they have not been measured, e.g., in the #' case where we combine data from different sources. Data are missing sporadically @@ -10,15 +10,15 @@ #' @inheritParams mice.impute.2l.lmer #' @param intercept Logical determining whether the intercept is automatically #' added. -#' @param \dots Arguments passed down to \code{glmer} -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @param \dots Arguments passed down to `glmer` +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Shahab Jolani, 2015; adapted to mice, SvB, 2018 #' @references #' Jolani S., Debray T.P.A., Koffijberg H., van Buuren S., Moons K.G.M. (2015). #' Imputation of systematically missing predictors in an individual #' participant data meta-analysis: a generalized approach using MICE. -#' \emph{Statistics in Medicine}, 34:1841-1863. +#' *Statistics in Medicine*, 34:1841-1863. #' @family univariate-2l #' @keywords datagen #' @examples diff --git a/R/mice.impute.2l.lmer.R b/R/mice.impute.2l.lmer.R index a1f2f2e5a..679f2f66e 100644 --- a/R/mice.impute.2l.lmer.R +++ b/R/mice.impute.2l.lmer.R @@ -1,7 +1,7 @@ -#' Imputation by a two-level normal model using \code{lmer} +#' Imputation by a two-level normal model using `lmer` #' #' Imputes univariate systematically and sporadically missing data using a -#' two-level normal model using \code{lme4::lmer()}. +#' two-level normal model using `lme4::lmer()`. #' #' Data are missing systematically if they have not been measured, e.g., in the #' case where we combine data from different sources. Data are missing sporadically @@ -12,22 +12,22 @@ #' value in cases where creating draws from the posterior is not #' possible. The procedure throws a warning when this happens. #' -#' If \code{lme4::lmer()} fails, the procedure prints the warning -#' \code{"lmer does not run. Simplify imputation model"} and returns the +#' If `lme4::lmer()` fails, the procedure prints the warning +#' `"lmer does not run. Simplify imputation model"` and returns the #' current imputation. If that happens we see flat lines in the #' trace line plots. Thus, the appearance of flat trace lines should be taken #' as an additional alert to a problem with imputation model fitting. #' @name mice.impute.2l.lmer #' @inheritParams mice.impute.pmm -#' @param type Vector of length \code{ncol(x)} identifying random and class +#' @param type Vector of length `ncol(x)` identifying random and class #' variables. Random variables are identified by a '2'. The class variable #' (only one is allowed) is coded as '-2'. Fixed effects are indicated by #' a '1'. #' @param intercept Logical determining whether the intercept is automatically #' added. -#' @param \dots Arguments passed down to \code{lmer} -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @param \dots Arguments passed down to `lmer` +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Shahab Jolani, 2017 #' @references #' Jolani S. (2017) Hierarchical imputation of systematically and @@ -37,11 +37,11 @@ #' Jolani S., Debray T.P.A., Koffijberg H., van Buuren S., Moons K.G.M. (2015). #' Imputation of systematically missing predictors in an individual #' participant data meta-analysis: a generalized approach using MICE. -#' \emph{Statistics in Medicine}, 34:1841-1863. +#' *Statistics in Medicine*, 34:1841-1863. #' #' Van Buuren, S. (2011) Multiple imputation of multilevel data. In Hox, J.J. -#' and and Roberts, J.K. (Eds.), \emph{The Handbook of Advanced Multilevel -#' Analysis}, Chapter 10, pp. 173--196. Milton Park, UK: Routledge. +#' and and Roberts, J.K. (Eds.), *The Handbook of Advanced Multilevel +#' Analysis*, Chapter 10, pp. 173--196. Milton Park, UK: Routledge. #' @family univariate-2l #' @keywords datagen #' @export diff --git a/R/mice.impute.2l.norm.R b/R/mice.impute.2l.norm.R index 0d468eb0f..2b79c4311 100644 --- a/R/mice.impute.2l.norm.R +++ b/R/mice.impute.2l.norm.R @@ -7,28 +7,28 @@ #' are drawn as an extra step to the algorithm. For simulation work see Van #' Buuren (2011). #' -#' The random intercept is automatically added in \code{mice.impute.2L.norm()}. -#' A model within a random intercept can be specified by \code{mice(..., -#' intercept = FALSE)}. +#' The random intercept is automatically added in `mice.impute.2L.norm()`. +#' A model within a random intercept can be specified by `mice(..., +#' intercept = FALSE)`. #' #' @name mice.impute.2l.norm #' @inheritParams mice.impute.pmm -#' @param type Vector of length \code{ncol(x)} identifying random and class +#' @param type Vector of length `ncol(x)` identifying random and class #' variables. Random variables are identified by a '2'. The class variable #' (only one is allowed) is coded as '-2'. Random variables also include the #' fixed effect. #' @param intercept Logical determining whether the intercept is automatically #' added. #' @param ... Other named arguments. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @note Added June 25, 2012: The currently implemented algorithm does not #' handle predictors that are specified as fixed effects (type=1). When using -#' \code{mice.impute.2l.norm()}, the current advice is to specify all predictors +#' `mice.impute.2l.norm()`, the current advice is to specify all predictors #' as random effects (type=2). #' #' Warning: The assumption of heterogeneous variances requires that in every -#' class at least one observation has a response in \code{y}. +#' class at least one observation has a response in `y`. #' @author Roel de Jong, 2008 #' @references #' @@ -36,13 +36,13 @@ #' variance components models with heterogeneous within-group variance. Journal #' of Educational and Behavioral Statistics, 23(2), 93--116. #' -#' 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} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' #' Van Buuren, S. (2011) Multiple imputation of multilevel data. In Hox, J.J. -#' and and Roberts, J.K. (Eds.), \emph{The Handbook of Advanced Multilevel -#' Analysis}, Chapter 10, pp. 173--196. Milton Park, UK: Routledge. +#' and and Roberts, J.K. (Eds.), *The Handbook of Advanced Multilevel +#' Analysis*, Chapter 10, pp. 173--196. Milton Park, UK: Routledge. #' @family univariate-2l #' @keywords datagen #' @export diff --git a/R/mice.impute.2l.pan.R b/R/mice.impute.2l.pan.R index 0c5cd4c60..caa8d466c 100644 --- a/R/mice.impute.2l.pan.R +++ b/R/mice.impute.2l.pan.R @@ -6,30 +6,30 @@ # 4 ... fixed, random and aggregated effects -#' Imputation by a two-level normal model using \code{pan} +#' Imputation by a two-level normal model using `pan` #' #' Imputes univariate missing data using a two-level normal model with #' homogeneous within group variances. Aggregated group effects (i.e. group #' means) can be automatically created and included as predictors in the -#' two-level regression (see argument \code{type}). This function needs the -#' \code{pan} package. +#' two-level regression (see argument `type`). This function needs the +#' `pan` package. #' #' Implements the Gibbs sampler for the linear two-level model with homogeneous #' within group variances which is a special case of a multivariate linear mixed #' effects model (Schafer & Yucel, 2002). For a two-level imputation with -#' heterogeneous within-group variances see \code{\link{mice.impute.2l.norm}}. % +#' heterogeneous within-group variances see [mice.impute.2l.norm()]. % #' The random intercept is automatically added in % -#' \code{mice.impute.2l.norm()}. +#' `mice.impute.2l.norm()`. #' #' @aliases mice.impute.2l.pan 2l.pan #' @author Alexander Robitzsch (IPN - Leibniz Institute for Science and #' Mathematics Education, Kiel, Germany), \email{robitzsch@@ipn.uni-kiel.de} #' @name mice.impute.2l.pan -#' @param y Incomplete data vector of length \code{n} -#' @param ry Vector of missing data pattern (\code{FALSE}=missing, -#' \code{TRUE}=observed) -#' @param x Matrix (\code{n} x \code{p}) of complete covariates. -#' @param type Vector of length \code{ncol(x)} identifying random and class +#' @param y Incomplete data vector of length `n` +#' @param ry Vector of missing data pattern (`FALSE`=missing, +#' `TRUE`=observed) +#' @param x Matrix (`n` x `p`) of complete covariates. +#' @param type Vector of length `ncol(x)` identifying random and class #' variables. Random effects are identified by a '2'. The group variable (only #' one is allowed) is coded as '-2'. Random effects also include the fixed #' effect. If for a covariates X1 group means shall be calculated and included @@ -37,27 +37,27 @@ #' specification '4' also includes random effects of X1. #' @param intercept Logical determining whether the intercept is automatically #' added. -#' @param paniter Number of iterations in \code{pan}. Default is 500. -#' @param groupcenter.slope If \code{TRUE}, in case of group means (\code{type} +#' @param paniter Number of iterations in `pan`. Default is 500. +#' @param groupcenter.slope If `TRUE`, in case of group means (`type` #' is '3' or'4') group mean centering for these predictors are conducted before -#' doing imputations. Default is \code{FALSE}. +#' doing imputations. Default is `FALSE`. #' @param ... Other named arguments. -#' @return A vector of length \code{nmis} with imputations. +#' @return A vector of length `nmis` with imputations. #' @author Alexander Robitzsch (IPN - Leibniz Institute for Science and #' Mathematics Education, Kiel, Germany), \email{robitzsch@@ipn.uni-kiel.de}. -#' @note This function does not implement the \code{where} functionality. It -#' always produces \code{nmis} imputation, irrespective of the \code{where} -#' argument of the \code{mice} function. +#' @note This function does not implement the `where` functionality. It +#' always produces `nmis` imputation, irrespective of the `where` +#' argument of the `mice` function. #' @family univariate-2l #' @references #' #' Schafer J L, Yucel RM (2002). Computational strategies for multivariate -#' linear mixed-effects models with missing values. \emph{Journal of -#' Computational and Graphical Statistics}. \bold{11}, 437-457. +#' linear mixed-effects models with missing values. *Journal of +#' Computational and Graphical Statistics*. **11**, 437-457. #' -#' 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} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @examples #' # simulate some data #' # two-level regression model with fixed slope diff --git a/R/mice.impute.2lonly.mean.R b/R/mice.impute.2lonly.mean.R index 4048be4e2..8e4b34143 100644 --- a/R/mice.impute.2lonly.mean.R +++ b/R/mice.impute.2lonly.mean.R @@ -1,43 +1,43 @@ #' Imputation of most likely value within the class #' -#' Method \code{2lonly.mean} replicates the most likely value within +#' Method `2lonly.mean` replicates the most likely value within #' a class of a second-level variable. It works for numeric and #' factor data. The function is primarily useful as a quick fixup for #' data in which the second-level variable is inconsistent. #' #' @aliases 2lonly.mean #' @inheritParams mice.impute.pmm -#' @param type Vector of length \code{ncol(x)} identifying random and class -#' variables. The class variable (only one is allowed) is coded as \code{-2}. +#' @param type Vector of length `ncol(x)` identifying random and class +#' variables. The class variable (only one is allowed) is coded as `-2`. #' @param ... Other named arguments. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details -#' Observed values in \code{y} are averaged within the class, and -#' replicated to the missing \code{y} within that class. +#' Observed values in `y` are averaged within the class, and +#' replicated to the missing `y` within that class. #' This function is primarily useful for repairing incomplete data #' that are constant within the class, but vary over classes. #' -#' For numeric variables, \code{mice.impute.2lonly.mean()} imputes the -#' class mean of \code{y}. If \code{y} is a second-level variable, then -#' conventionally all observed \code{y} will be identical within the +#' For numeric variables, `mice.impute.2lonly.mean()` imputes the +#' class mean of `y`. If `y` is a second-level variable, then +#' conventionally all observed `y` will be identical within the #' class, and the function just provides a quick fix for any -#' missing \code{y} by filling in the class mean. +#' missing `y` by filling in the class mean. #' -#' For factor variables, \code{mice.impute.2lonly.mean()} imputes the +#' For factor variables, `mice.impute.2lonly.mean()` imputes the #' most frequently occuring category within the class. #' -#' If there are no observed \code{y} in the class, all entries of the -#' class are set to \code{NA}. Note that this may produce problems -#' later on in \code{mice} if imputation routines are called that +#' If there are no observed `y` in the class, all entries of the +#' class are set to `NA`. Note that this may produce problems +#' later on in `mice` if imputation routines are called that #' expects predictor data to be complete. Methods designed for #' imputing this type of second-level variables include -#' \code{\link{mice.impute.2lonly.norm}} and -#' \code{\link{mice.impute.2lonly.pmm}}. +#' [mice.impute.2lonly.norm()] and +#' [mice.impute.2lonly.pmm()]. #' #' @references #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-level2pred.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-level2pred.html) #' Boca Raton, FL.: Chapman & Hall/CRC Press. #' @author Gerko Vink, Stef van Buuren, 2019 #' @family univariate-2lonly diff --git a/R/mice.impute.2lonly.norm.R b/R/mice.impute.2lonly.norm.R index 0e86f00a1..f72b06656 100644 --- a/R/mice.impute.2lonly.norm.R +++ b/R/mice.impute.2lonly.norm.R @@ -2,22 +2,22 @@ #' #' Imputes univariate missing data at level 2 using Bayesian linear regression #' analysis. Variables are level 1 are aggregated at level 2. The group -#' identifier at level 2 must be indicated by \code{type = -2} in the -#' \code{predictorMatrix}. +#' identifier at level 2 must be indicated by `type = -2` in the +#' `predictorMatrix`. #' #' @aliases 2lonly.norm #' @inheritParams mice.impute.pmm #' @param type Group identifier must be specified by '-2'. Predictors must be #' specified by '1'. #' @param ... Other named arguments. -#' @return A vector of length \code{nmis} with imputations. +#' @return A vector of length `nmis` with imputations. #' @author Alexander Robitzsch (IPN - Leibniz Institute for Science and #' Mathematics Education, Kiel, Germany), \email{robitzsch@@ipn.uni-kiel.de} -#' @seealso \code{\link{mice.impute.norm}}, -#' \code{\link{mice.impute.2lonly.pmm}}, \code{\link{mice.impute.2l.pan}}, -#' \code{\link{mice.impute.2lonly.mean}} +#' @seealso [mice.impute.norm()], +#' [mice.impute.2lonly.pmm()], [mice.impute.2l.pan()], +#' [mice.impute.2lonly.mean()] #' @details -#' This function allows in combination with \code{\link{mice.impute.2l.pan}} +#' This function allows in combination with [mice.impute.2l.pan()] #' switching regression imputation between level 1 and level 2 as described in #' Yucel (2008) or Gelman and Hill (2007, p. 541). #' @@ -26,23 +26,23 @@ #' entries are missing, then the procedure aborts with an error #' message that identifies the cluster with incomplete level-2 data. #' In such cases, one may first fill in the cluster mean (or mode) by -#' the \code{2lonly.mean} method to remove inconsistencies. +#' the `2lonly.mean` method to remove inconsistencies. #' -#' @references Gelman, A. and Hill, J. (2007). \emph{Data analysis using -#' regression and multilevel/hierarchical models}. Cambridge, Cambridge +#' @references Gelman, A. and Hill, J. (2007). *Data analysis using +#' regression and multilevel/hierarchical models*. Cambridge, Cambridge #' University Press. #' #' Yucel, RM (2008). Multiple imputation inference for multivariate multilevel -#' continuous data with ignorable non-response. \emph{Philosophical -#' Transactions of the Royal Society A}, \bold{366}, 2389-2404. +#' continuous data with ignorable non-response. *Philosophical +#' Transactions of the Royal Society A*, **366**, 2389-2404. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-level2pred.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-level2pred.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' @family univariate-2lonly #' @note #' For a more general approach, see -#' \code{miceadds::mice.impute.2lonly.function()}. +#' `miceadds::mice.impute.2lonly.function()`. #' @examples #' # simulate some data #' # x,y ... level 1 variables diff --git a/R/mice.impute.2lonly.pmm.R b/R/mice.impute.2lonly.pmm.R index 5455b5990..e35cdf722 100644 --- a/R/mice.impute.2lonly.pmm.R +++ b/R/mice.impute.2lonly.pmm.R @@ -2,21 +2,21 @@ #' #' Imputes univariate missing data at level 2 using predictive mean matching. #' Variables are level 1 are aggregated at level 2. The group identifier at -#' level 2 must be indicated by \code{type = -2} in the \code{predictorMatrix}. +#' level 2 must be indicated by `type = -2` in the `predictorMatrix`. #' #' @aliases 2lonly.pmm #' @inheritParams mice.impute.pmm #' @param type Group identifier must be specified by '-2'. Predictors must be #' specified by '1'. #' @param ... Other named arguments. -#' @return A vector of length \code{nmis} with imputations. +#' @return A vector of length `nmis` with imputations. #' @author Alexander Robitzsch (IPN - Leibniz Institute for Science and #' Mathematics Education, Kiel, Germany), \email{robitzsch@@ipn.uni-kiel.de} -#' @seealso \code{\link{mice.impute.pmm}}, -#' \code{\link{mice.impute.2lonly.norm}}, \code{\link{mice.impute.2l.pan}}, -#' \code{\link{mice.impute.2lonly.mean}} +#' @seealso [mice.impute.pmm()], +#' [mice.impute.2lonly.norm()], [mice.impute.2l.pan()], +#' [mice.impute.2lonly.mean()] #' @details -#' This function allows in combination with \code{\link{mice.impute.2l.pan}} +#' This function allows in combination with [mice.impute.2l.pan()] #' switching regression imputation between level 1 and level 2 as described in #' Yucel (2008) or Gelman and Hill (2007, p. 541). #' @@ -25,26 +25,26 @@ #' entries are missing, then the procedure aborts with an error #' message that identifies the cluster with incomplete level-2 data. #' In such cases, one may first fill in the cluster mean (or mode) by -#' the \code{2lonly.mean} method to remove inconsistencies. -#' @references Gelman, A. and Hill, J. (2007). \emph{Data analysis using -#' regression and multilevel/hierarchical models}. Cambridge, Cambridge +#' the `2lonly.mean` method to remove inconsistencies. +#' @references Gelman, A. and Hill, J. (2007). *Data analysis using +#' regression and multilevel/hierarchical models*. Cambridge, Cambridge #' University Press. #' #' Yucel, RM (2008). Multiple imputation inference for multivariate multilevel -#' continuous data with ignorable non-response. \emph{Philosophical -#' Transactions of the Royal Society A}, \bold{366}, 2389-2404. +#' continuous data with ignorable non-response. *Philosophical +#' Transactions of the Royal Society A*, **366**, 2389-2404. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-level2pred.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-level2pred.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' @note The extension to categorical variables transforms -#' a dependent factor variable by means of the \code{as.integer()} +#' a dependent factor variable by means of the `as.integer()` #' function. This may make sense for categories that are #' approximately ordered, but less so for pure nominal measures. #' #' For a more general approach, see -#' \code{miceadds::mice.impute.2lonly.function()}. +#' `miceadds::mice.impute.2lonly.function()`. #' @family univariate-2lonly #' @examples #' # simulate some data diff --git a/R/mice.impute.cart.R b/R/mice.impute.cart.R index 91c1f342b..6c0c3a2c2 100644 --- a/R/mice.impute.cart.R +++ b/R/mice.impute.cart.R @@ -5,26 +5,26 @@ #' @aliases mice.impute.cart cart #' #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @param minbucket The minimum number of observations in any terminal node used. -#' See \code{\link{rpart.control}} for details. +#' See [rpart.control()] for details. #' @param cp Complexity parameter. Any split that does not decrease the overall -#' lack of fit by a factor of cp is not attempted. See \code{\link{rpart.control}} +#' lack of fit by a factor of cp is not attempted. See [rpart.control()] #' for details. -#' @param ... Other named arguments passed down to \code{rpart()}. -#' @return Numeric vector of length \code{sum(!ry)} with imputations +#' @param ... Other named arguments passed down to `rpart()`. +#' @return Numeric vector of length `sum(!ry)` with imputations #' @details -#' Imputation of \code{y} by classification and regression trees. The procedure +#' Imputation of `y` by classification and regression trees. The procedure #' is as follows: #' \enumerate{ #' \item Fit a classification or regression tree by recursive partitioning; -#' \item For each \code{ymis}, find the terminal node they end up according to the fitted tree; +#' \item For each `ymis`, find the terminal node they end up according to the fitted tree; #' \item Make a random draw among the member in the node, and take the observed value from that #' draw as the imputation. #' } -#' @seealso \code{\link{mice}}, \code{\link{mice.impute.rf}}, -#' \code{\link[rpart]{rpart}}, \code{\link[rpart]{rpart.control}} +#' @seealso [mice()], [mice.impute.rf()], +#' [rpart::rpart()], [rpart::rpart.control()] #' @author Lisa Doove, Stef van Buuren, Elise Dusseldorp, 2012 #' @references #' @@ -37,7 +37,7 @@ #' Brooks/Cole Advanced Books & Software. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-cart.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-cart.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' @family univariate imputation functions diff --git a/R/mice.impute.jomoImpute.R b/R/mice.impute.jomoImpute.R index 80ec6ded7..b8985d0d6 100644 --- a/R/mice.impute.jomoImpute.R +++ b/R/mice.impute.jomoImpute.R @@ -1,12 +1,12 @@ -#' Multivariate multilevel imputation using \code{jomo} +#' Multivariate multilevel imputation using `jomo` #' -#' This function is a wrapper around the \code{jomoImpute} function -#' from the \code{mitml} package so that it can be called to -#' impute blocks of variables in \code{mice}. The \code{mitml::jomoImpute} -#' function provides an interface to the \code{jomo} package for +#' This function is a wrapper around the `jomoImpute` function +#' from the `mitml` package so that it can be called to +#' impute blocks of variables in `mice`. The `mitml::jomoImpute` +#' function provides an interface to the `jomo` package for #' multiple imputation of multilevel data -#' \url{https://CRAN.R-project.org/package=jomo}. -#' Imputations can be generated using \code{type} or \code{formula}, +#' . +#' Imputations can be generated using `type` or `formula`, #' which offer different options for model specification. #' #' @name mice.impute.jomoImpute @@ -15,33 +15,33 @@ #' the cluster indicator variable, and any other variables that should be #' present in the imputed datasets. #' @param type An integer vector specifying the role of each variable -#' in the imputation model (see \code{\link[mitml]{jomoImpute}}) +#' in the imputation model (see [mitml::jomoImpute()]) #' @param formula A formula specifying the role of each variable #' in the imputation model. The basic model is constructed -#' by \code{model.matrix}, thus allowing to include derived variables -#' in the imputation model using \code{I()}. See -#' \code{\link[mitml]{jomoImpute}}. +#' by `model.matrix`, thus allowing to include derived variables +#' in the imputation model using `I()`. See +#' [mitml::jomoImpute()]. #' @param format A character vector specifying the type of object that should -#' be returned. The default is \code{format = "list"}. No other formats are +#' be returned. The default is `format = "list"`. No other formats are #' currently supported. -#' @param ... Other named arguments: \code{n.burn}, \code{n.iter}, -#' \code{group}, \code{prior}, \code{silent} and others. +#' @param ... Other named arguments: `n.burn`, `n.iter`, +#' `group`, `prior`, `silent` and others. #' @return A list of imputations for all incomplete variables in the model, -#' that can be stored in the the \code{imp} component of the \code{mids} +#' that can be stored in the the `imp` component of the `mids` #' object. -#' @seealso \code{\link[mitml]{jomoImpute}} -#' @note The number of imputations \code{m} is set to 1, and the function -#' is called \code{m} times so that it fits within the \code{mice} +#' @seealso [mitml::jomoImpute()] +#' @note The number of imputations `m` is set to 1, and the function +#' is called `m` times so that it fits within the `mice` #' iteration scheme. #' #' This is a multivariate imputation function using a joint model. #' @author Stef van Buuren, 2018, building on work of Simon Grund, -#' Alexander Robitzsch and Oliver Luedtke (authors of \code{mitml} package) -#' and Quartagno and Carpenter (authors of \code{jomo} package). +#' Alexander Robitzsch and Oliver Luedtke (authors of `mitml` package) +#' and Quartagno and Carpenter (authors of `jomo` package). #' @references #' Grund S, Luedtke O, Robitzsch A (2016). Multiple #' Imputation of Multilevel Missing Data: An Introduction to the R -#' Package \code{pan}. SAGE Open. +#' Package `pan`. SAGE Open. #' #' Quartagno M and Carpenter JR (2015). #' Multiple imputation for IPD meta-analysis: allowing for heterogeneity diff --git a/R/mice.impute.lasso.logreg.R b/R/mice.impute.lasso.logreg.R index 21eebb2b5..f8a21d064 100644 --- a/R/mice.impute.lasso.logreg.R +++ b/R/mice.impute.lasso.logreg.R @@ -6,14 +6,14 @@ #' @inheritParams mice.impute.pmm #' @param nfolds The number of folds for the cross-validation of the lasso penalty. #' The default is 10. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' The method consists of the following steps: #' \enumerate{ #' \item For a given y variable under imputation, draw a bootstrap version y* -#' with replacement from the observed cases \code{y[ry]}, and stores in x* the -#' corresponding values from \code{x[ry, ]}. +#' with replacement from the observed cases `y[ry]`, and stores in x* the +#' corresponding values from `x[ry, ]`. #' \item Fit a regularised (lasso) logistic regression with y* as the outcome, #' and x* as predictors. #' A vector of regression coefficients bhat is obtained. diff --git a/R/mice.impute.lasso.norm.R b/R/mice.impute.lasso.norm.R index 12efcfa0b..0f050203c 100644 --- a/R/mice.impute.lasso.norm.R +++ b/R/mice.impute.lasso.norm.R @@ -6,14 +6,14 @@ #' @inheritParams mice.impute.norm.boot #' @param nfolds The number of folds for the cross-validation of the lasso penalty. #' The default is 10. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' The method consists of the following steps: #' \enumerate{ #' \item For a given y variable under imputation, draw a bootstrap version y* -#' with replacement from the observed cases \code{y[ry]}, and stores in x* the -#' corresponding values from \code{x[ry, ]}. +#' with replacement from the observed cases `y[ry]`, and stores in x* the +#' corresponding values from `x[ry, ]`. #' \item Fit a regularised (lasso) linear regression with y* as the outcome, #' and x* as predictors. #' A vector of regression coefficients bhat is obtained. diff --git a/R/mice.impute.lasso.select.logreg.R b/R/mice.impute.lasso.select.logreg.R index 42a864511..2f3f055d3 100644 --- a/R/mice.impute.lasso.select.logreg.R +++ b/R/mice.impute.lasso.select.logreg.R @@ -7,13 +7,13 @@ #' @inheritParams mice.impute.pmm #' @param nfolds The number of folds for the cross-validation of the lasso penalty. #' The default is 10. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' The method consists of the following steps: #' \enumerate{ -#' \item For a given \code{y} variable under imputation, fit a linear regression with lasso -#' penalty using \code{y[ry]} as dependent variable and \code{x[ry, ]} as predictors. +#' \item For a given `y` variable under imputation, fit a linear regression with lasso +#' penalty using `y[ry]` as dependent variable and `x[ry, ]` as predictors. #' The coefficients that are not shrunk to 0 define the active set of predictors #' that will be used for imputation. #' \item Fit a logit with the active set of predictors, and find (bhat, V(bhat)) @@ -21,12 +21,12 @@ #' \item Compute predicted scores for m.d., i.e. logit-1(X BETA) #' \item Compare the score to a random (0,1) deviate, and impute. #' } -#' The user can specify a \code{predictorMatrix} in the \code{mice} call +#' The user can specify a `predictorMatrix` in the `mice` call #' to define which predictors are provided to this univariate imputation method. #' The lasso regularization will select, among the variables indicated by #' the user, the ones that are important for imputation at any given iteration. #' Therefore, users may force the exclusion of a predictor from a given -#' imputation model by speficing a \code{0} entry. +#' imputation model by speficing a `0` entry. #' However, a non-zero entry does not guarantee the variable will be used, #' as this decision is ultimately made by the lasso variable selection #' procedure. diff --git a/R/mice.impute.lasso.select.norm.R b/R/mice.impute.lasso.select.norm.R index 53bbf4a4a..c237886af 100644 --- a/R/mice.impute.lasso.select.norm.R +++ b/R/mice.impute.lasso.select.norm.R @@ -7,28 +7,28 @@ #' @inheritParams mice.impute.pmm #' @param nfolds The number of folds for the cross-validation of the lasso penalty. #' The default is 10. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' The method consists of the following steps: #' \enumerate{ -#' \item For a given \code{y} variable under imputation, fit a linear regression with lasso -#' penalty using \code{y[ry]} as dependent variable and \code{x[ry, ]} as predictors. +#' \item For a given `y` variable under imputation, fit a linear regression with lasso +#' penalty using `y[ry]` as dependent variable and `x[ry, ]` as predictors. #' Coefficients that are not shrunk to 0 define an active set of predictors #' that will be used for imputation -#' \item Define a Bayesian linear model using \code{y[ry]} as the -#' dependent variable, the active set of \code{x[ry, ]} as predictors, and standard +#' \item Define a Bayesian linear model using `y[ry]` as the +#' dependent variable, the active set of `x[ry, ]` as predictors, and standard #' non-informative priors #' \item Draw parameter values for the intercept, regression weights, and error #' variance from their posterior distribution #' \item Draw imputations from the posterior predictive distribution #' } -#' The user can specify a \code{predictorMatrix} in the \code{mice} call +#' The user can specify a `predictorMatrix` in the `mice` call #' to define which predictors are provided to this univariate imputation method. #' The lasso regularization will select, among the variables indicated by #' the user, the ones that are important for imputation at any given iteration. #' Therefore, users may force the exclusion of a predictor from a given -#' imputation model by specifying a \code{0} entry. +#' imputation model by specifying a `0` entry. #' However, a non-zero entry does not guarantee the variable will be used, #' as this decision is ultimately made by the lasso variable selection #' procedure. diff --git a/R/mice.impute.lda.R b/R/mice.impute.lda.R index 73fd6ad20..5cdb11d22 100644 --- a/R/mice.impute.lda.R +++ b/R/mice.impute.lda.R @@ -5,30 +5,29 @@ #' @inheritParams mice.impute.pmm #' @param ... Other named arguments. Not used. #' @return Vector with imputed data, of type factor, and of length -#' \code{sum(wy)} +#' `sum(wy)` #' @details Imputation of categorical response variables by linear discriminant analysis. -#' This function uses the Venables/Ripley functions \code{lda()} and -#' \code{predict.lda()} to compute posterior probabilities for each incomplete +#' This function uses the Venables/Ripley functions `lda()` and +#' `predict.lda()` to compute posterior probabilities for each incomplete #' case, and draws the imputations from this posterior. #' #' This function can be called from within the Gibbs sampler by specifying -#' \code{"lda"} in the \code{method} argument of \code{mice()}. This method is usually +#' `"lda"` in the `method` argument of `mice()`. This method is usually #' faster and uses fewer resources than calling the function, but the statistical #' properties may not be as good (Brand, 1999). -#' \code{\link{mice.impute.polyreg}}. +#' [mice.impute.polyreg()]. #' @section Warning: The function does not incorporate the variability of the #' discriminant weight, so it is not 'proper' in the sense of Rubin. For small -#' samples and rare categories in the \code{y}, variability of the imputed data +#' samples and rare categories in the `y`, variability of the imputed data #' could therefore be underestimated. #' #' Added: SvB June 2009 Tried to include bootstrap, but disabled since #' bootstrapping may easily lead to constant variables within groups. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{mice}}, \code{link{mice.impute.polyreg}}, -#' \code{\link[MASS]{lda}} -#' @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. +#' @seealso [mice()], [mice.impute.polyreg()], [MASS::lda()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' #' Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple diff --git a/R/mice.impute.logreg.R b/R/mice.impute.logreg.R index fe1a7782e..b521d1646 100644 --- a/R/mice.impute.logreg.R +++ b/R/mice.impute.logreg.R @@ -5,8 +5,8 @@ #' @aliases mice.impute.logreg #' @inheritParams mice.impute.pmm #' @param ... Other named arguments. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Stef van Buuren, Karin Groothuis-Oudshoorn #' @details #' Imputation for binary response variables by the Bayesian logistic regression @@ -19,14 +19,14 @@ #' \item Compare the score to a random (0,1) deviate, and impute. #' } #' The method relies on the -#' standard \code{glm.fit} function. Warnings from \code{glm.fit} are +#' standard `glm.fit` function. Warnings from `glm.fit` are #' suppressed. Perfect prediction is handled by the data augmentation #' method. #' -#' @seealso \code{\link{mice}}, \code{\link{glm}}, \code{\link{glm.fit}} -#' @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. +#' @seealso [mice()], [glm()], [glm.fit()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' #' Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple @@ -84,22 +84,22 @@ mice.impute.logreg <- function(y, ry, x, wy = NULL, ...) { #' Imputes univariate missing data using logistic regression #' by a bootstrapped logistic regression model. #' The bootstrap method draws a simple bootstrap sample with replacement -#' from the observed data \code{y[ry]} and \code{x[ry, ]}. +#' from the observed data `y[ry]` and `x[ry, ]`. #' #' @aliases mice.impute.logreg.boot #' @inheritParams mice.impute.pmm #' @param ... Other named arguments. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000, 2011 -#' @seealso \code{\link{mice}}, \code{\link{glm}}, \code{\link{glm.fit}} -#' @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. +#' @seealso [mice()], [glm()], [glm.fit()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-categorical.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-categorical.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.mean.R b/R/mice.impute.mean.R index 6e41e4ce2..610905226 100644 --- a/R/mice.impute.mean.R +++ b/R/mice.impute.mean.R @@ -3,22 +3,22 @@ #' Imputes the arithmetic mean of the observed data #' #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @section Warning: Imputing the mean of a variable is almost never #' appropriate. See Little and Rubin (2002, p. 61-62) or #' Van Buuren (2012, p. 10-11) -#' @seealso \code{\link{mice}}, \code{\link{mean}} -#' @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. +#' @seealso [mice()], [mean()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' #' Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing #' Data. New York: John Wiley and Sons. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-simplesolutions.html#sec:meanimp}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-simplesolutions.html#sec:meanimp) #' Chapman & Hall/CRC. Boca Raton, FL. #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.midastouch.R b/R/mice.impute.midastouch.R index 44f59d575..91896da46 100644 --- a/R/mice.impute.midastouch.R +++ b/R/mice.impute.midastouch.R @@ -3,13 +3,13 @@ #' Imputes univariate missing data using predictive mean matching. #' @aliases mice.impute.midastouch #' @inheritParams mice.impute.pmm -#' @param midas.kappa Scalar. If \code{NULL} (default) then the -#' optimal \code{kappa} gets selected automatically. Alternatively, the user -#' may specify a scalar. Siddique and Belin 2008 find \code{midas.kappa = 3} +#' @param midas.kappa Scalar. If `NULL` (default) then the +#' optimal `kappa` gets selected automatically. Alternatively, the user +#' may specify a scalar. Siddique and Belin 2008 find `midas.kappa = 3` #' to be sensible. -#' @param outout Logical. If \code{TRUE} (default) one model is estimated +#' @param outout Logical. If `TRUE` (default) one model is estimated #' for each donor (leave-one-out principle). For speedup choose -#' \code{outout = FALSE}, which estimates one model for all observations +#' `outout = FALSE`, which estimates one model for all observations #' leading to in-sample predictions for the donors and out-of-sample #' predictions for the recipients. Mind the inappropriateness, though. #' @param neff FOR EXPERTS. Null or character string. The name of an existing @@ -17,23 +17,23 @@ #' loop (CE iterations times multiple imputations) is supposed to be written. #' The effective sample size is necessary to compute the correction for the #' total variance as originally suggested by Parzen, Lipsitz and -#' Fitzmaurice 2005. The objectname is \code{midastouch.neff}. +#' Fitzmaurice 2005. The objectname is `midastouch.neff`. #' @param debug FOR EXPERTS. Null or character string. The name of an existing #' environment in which the input is supposed to be written. The objectname -#' is \code{midastouch.inputlist}. -#' @return Vector with imputed data, same type as \code{y}, and of -#' length \code{sum(wy)} -#' @details Imputation of \code{y} by predictive mean matching, based on +#' is `midastouch.inputlist`. +#' @return Vector with imputed data, same type as `y`, and of +#' length `sum(wy)` +#' @details Imputation of `y` by predictive mean matching, based on #' Rubin (1987, p. 168, formulas a and b) and Siddique and Belin 2008. #' The procedure is as follows: #' \enumerate{ #' \item Draw a bootstrap sample from the donor pool. #' \item Estimate a beta matrix on the bootstrap sample by the leave one out principle. -#' \item Compute type II predicted values for \code{yobs} (nobs x 1) and \code{ymis} (nmis x nobs). -#' \item Calculate the distance between all \code{yobs} and the corresponding \code{ymis}. +#' \item Compute type II predicted values for `yobs` (nobs x 1) and `ymis` (nmis x nobs). +#' \item Calculate the distance between all `yobs` and the corresponding `ymis`. #' \item Convert the distances in drawing probabilities. #' \item For each recipient draw a donor from the entire pool while considering the probabilities from the model. -#' \item Take its observed value in \code{y} as the imputation. +#' \item Take its observed value in `y` as the imputation. #' } #' @examples #' # do default multiple imputation on a numeric matrix @@ -52,7 +52,7 @@ #' @references #' Gaffert, P., Meinfelder, F., Bosch V. (2015) Towards an MI-proper #' Predictive Mean Matching, Discussion Paper. -#' \url{https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/statistik/Personen/Dateien_Florian/properPMM.pdf} +#' #' #' Little, R.J.A. (1988), Missing data adjustments in large #' surveys (with discussion), Journal of Business Economics and @@ -60,22 +60,22 @@ #' #' Parzen, M., Lipsitz, S. R., Fitzmaurice, G. M. (2005), A note on reducing #' the bias of the approximate Bayesian bootstrap imputation variance estimator. -#' Biometrika \bold{92}, 4, 971--974. +#' Biometrika **92**, 4, 971--974. #' #' Rubin, D.B. (1987), Multiple imputation for nonresponse in surveys. New York: Wiley. #' #' Siddique, J., Belin, T.R. (2008), Multiple imputation using an iterative #' hot-deck with distance-based donor selection. Statistics in medicine, -#' \bold{27}, 1, 83--102 +#' **27**, 1, 83--102 #' #' Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006), #' Fully conditional specification in multivariate imputation. -#' \emph{Journal of Statistical Computation and Simulation}, \bold{76}, 12, +#' *Journal of Statistical Computation and Simulation*, **76**, 12, #' 1049--1064. #' -#' 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} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011), `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**, 3, 1--67. \doi{10.18637/jss.v045.i03} #' @family univariate imputation functions #' @keywords datagen #' @export diff --git a/R/mice.impute.mnar.norm.R b/R/mice.impute.mnar.norm.R index c1da2a3ca..dcfb882fe 100644 --- a/R/mice.impute.mnar.norm.R +++ b/R/mice.impute.mnar.norm.R @@ -16,10 +16,10 @@ #' corresponding deltas (sensitivity parameters). See details. #' @param umx An auxiliary data matrix containing variables that do #' not appear in the identifiable part of the imputation procedure -#' but that have been specified via \code{ums} as being predictors +#' but that have been specified via `ums` as being predictors #' in the unidentifiable part of the imputation model. See details. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' This function imputes data that are thought to be Missing Not at #' Random (MNAR) by the NARFCS method. The NARFCS procedure @@ -28,79 +28,79 @@ #' Boshuizen & Knook (1999) to the case with multiple incomplete #' variables within the FCS framework. In practical terms, the #' NARFCS procedure shifts the imputations drawn at each -#' iteration of \code{mice} by a user-specified quantity that can +#' iteration of `mice` by a user-specified quantity that can #' vary across subjects, to reflect systematic departures of the #' missing data from the data distribution imputed under MAR. #' -#' Specification of the NARFCS model is done by the \code{blots} -#' argument of \code{mice()}. The \code{blots} parameter is a named +#' Specification of the NARFCS model is done by the `blots` +#' argument of `mice()`. The `blots` parameter is a named #' list. For each variable to be imputed by -#' \code{mice.impute.mnar.norm()} or \code{mice.impute.mnar.logreg()} -#' the corresponding element in \code{blots} is a list with -#' at least one argument \code{ums} and, optionally, a second -#' argument \code{umx}. +#' `mice.impute.mnar.norm()` or `mice.impute.mnar.logreg()` +#' the corresponding element in `blots` is a list with +#' at least one argument `ums` and, optionally, a second +#' argument `umx`. #' For example, the high-level call might like something like -#' \code{mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), -#' blots = list(chl = list(ums = "-3+2*bmi")))}. +#' `mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), +#' blots = list(chl = list(ums = "-3+2*bmi")))`. #' -#' The \code{ums} parameter is required, and might look like this: -#' \code{"-4+1*Y"}. The \code{ums} specifcation must have the +#' The `ums` parameter is required, and might look like this: +#' `"-4+1*Y"`. The `ums` specifcation must have the #' following characteristics: #' \enumerate{ #' \item{A single term corresponding to the intercept (constant) term, #' not multiplied by any variable name, must be included in the #' expression;} #' \item{Each term in the expression (corresponding to the intercept -#' or a predictor variable) must be separated by either a \code{"+"} -#' or \code{"-"} sign, depending on the sign of the sensitivity +#' or a predictor variable) must be separated by either a `"+"` +#' or `"-"` sign, depending on the sign of the sensitivity #' parameter;} #' \item{Within each non-intercept term, the sensitivity parameter #' value comes first and the predictor variable comes second, and these -#' must be separated by a \code{"*"} sign;} -#' \item{For categorical predictors, for example a variable \code{Z} -#' with K + 1 categories \code{("Cat0","Cat1", ...,"CatK")}, K -#' category-specific terms are needed, and those not in \code{umx} +#' must be separated by a `"*"` sign;} +#' \item{For categorical predictors, for example a variable `Z` +#' with K + 1 categories `("Cat0","Cat1", ...,"CatK")`, K +#' category-specific terms are needed, and those not in `umx` #' (see below) must be specified by concatenating the variable name -#' with the name of the category (e.g. \code{ZCat1}) as this is how -#' they are named in the design matrix (argument \code{x}) passed +#' with the name of the category (e.g. `ZCat1`) as this is how +#' they are named in the design matrix (argument `x`) passed #' to the univariate imputation function. An example is -#' \code{"2+1*ZCat1-3*ZCat2"}.} +#' `"2+1*ZCat1-3*ZCat2"`.} #' } #' -#' If given, the \code{umx} specification must have the following +#' If given, the `umx` specification must have the following #' characteristics: #' \enumerate{ #' \item{It contains only complete variables, with no missing values;} #' \item{It is a numeric matrix. In particular, categorical variables #' must be represented as dummy indicators with names corresponding -#' to what is used in \code{ums} to refer to the category-specific terms +#' to what is used in `ums` to refer to the category-specific terms #' (see above);} -#' \item{It has the same number of rows as the \code{data} argument -#' passed on to the main \code{mice} function;} +#' \item{It has the same number of rows as the `data` argument +#' passed on to the main `mice` function;} #' \item{It does not contain variables that were already predictors #' in the identifiable part of the model for the variable under #' imputation.} #' } #' #' Limitation: The present implementation can only condition on variables -#' that appear in the identifiable part of the imputation model (\code{x}) or -#' in complete auxiliary variables passed on via the \code{umx} argument. +#' that appear in the identifiable part of the imputation model (`x`) or +#' in complete auxiliary variables passed on via the `umx` argument. #' It is not possible to specify models where the offset depends on #' incomplete auxiliary variables. #' -#' For an MNAR alternative see also \code{\link{mice.impute.ri}}. +#' For an MNAR alternative see also [mice.impute.ri()]. #' #' @author Margarita Moreno-Betancur, Stef van Buuren, Ian R. White, 2020. #' @references #' Tompsett, D. M., Leacy, F., Moreno-Betancur, M., Heron, J., & #' White, I. R. (2018). On the use of the not-at-random fully #' conditional specification (NARFCS) procedure in practice. -#' \emph{Statistics in Medicine}, \bold{37}(15), 2338-2353. +#' *Statistics in Medicine*, **37**(15), 2338-2353. #' \doi{10.1002/sim.7643}. #' #' Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple #' imputation of missing blood pressure covariates in survival analysis. -#' \emph{Statistics in Medicine}, \bold{18}, 681--694. +#' *Statistics in Medicine*, **18**, 681--694. #' #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.mpmm.R b/R/mice.impute.mpmm.R index 601fd2cc1..fda9c2cb8 100644 --- a/R/mice.impute.mpmm.R +++ b/R/mice.impute.mpmm.R @@ -5,10 +5,10 @@ #' @aliases mice.impute.mpmm mpmm #' @param data matrix with exactly two missing data patterns #' @param format A character vector specifying the type of object that should -#' be returned. The default is \code{format = "imputes"}. +#' be returned. The default is `format = "imputes"`. #' @param ... Other named arguments. -#' @return A matrix with imputed data, which has \code{ncol(y)} columns and -#' \code{sum(wy)} rows. +#' @return A matrix with imputed data, which has `ncol(y)` columns and +#' `sum(wy)` rows. #' @details #' This function implements the predictive mean matching and applies canonical #' regression analysis to select donors fora set of missing variables. In general, @@ -25,9 +25,9 @@ #' @author Mingyang Cai and Gerko Vink # @author Mingyang Cai (University of Utrecht), \email{g.vink#uu.nl} -#' @seealso \code{\link{mice.impute.pmm}} +#' @seealso [mice.impute.pmm()] #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-knowledge.html#sec:quadratic}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-knowledge.html#sec:quadratic) #' Chapman & Hall/CRC. Boca Raton, FL. #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.norm.R b/R/mice.impute.norm.R index 664e1d4e5..e96dad977 100644 --- a/R/mice.impute.norm.R +++ b/R/mice.impute.norm.R @@ -5,11 +5,11 @@ #' #' @aliases mice.impute.norm norm #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Stef van Buuren, Karin Groothuis-Oudshoorn #' @details -#' Imputation of \code{y} by the normal model by the method defined by +#' Imputation of `y` by the normal model by the method defined by #' Rubin (1987, p. 167). The procedure is as follows: #' #' \enumerate{ @@ -26,7 +26,7 @@ #' \item{Calculate the \eqn{n_0} values \eqn{y_{imp} = X_{mis}\dot\beta + \dot z_2\dot\sigma}.} #' } #' -#' Using \code{mice.impute.norm} for all columns emulates Schafer's NORM method (Schafer, 1997). +#' Using `mice.impute.norm` for all columns emulates Schafer's NORM method (Schafer, 1997). #' @references #' Rubin, D.B (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons. #' @@ -49,19 +49,19 @@ mice.impute.norm <- function(y, ry, x, wy = NULL, ...) { #' can be called by user-specified imputation functions. #' #' @aliases norm.draw .norm.draw -#' @param y Incomplete data vector of length \code{n} -#' @param ry Vector of missing data pattern (\code{FALSE}=missing, -#' \code{TRUE}=observed) -#' @param x Matrix (\code{n} x \code{p}) of complete covariates. -#' @param rank.adjust Argument that specifies whether \code{NA}'s in the -#' coefficients need to be set to zero. Only relevant when \code{ls.meth = "qr"} +#' @param y Incomplete data vector of length `n` +#' @param ry Vector of missing data pattern (`FALSE`=missing, +#' `TRUE`=observed) +#' @param x Matrix (`n` x `p`) of complete covariates. +#' @param rank.adjust Argument that specifies whether `NA`'s in the +#' coefficients need to be set to zero. Only relevant when `ls.meth = "qr"` #' AND the predictor matrix is rank-deficient. #' @param ... Other named arguments. -#' @return A \code{list} containing components \code{coef} (least squares estimate), -#' \code{beta} (drawn regression weights) and \code{sigma} (drawn value of the +#' @return A `list` containing components `coef` (least squares estimate), +#' `beta` (drawn regression weights) and `sigma` (drawn value of the #' residual standard deviation). #' @references -#' Rubin, D.B. (1987). \emph{Multiple imputation for nonresponse in surveys}. New York: Wiley. +#' Rubin, D.B. (1987). *Multiple imputation for nonresponse in surveys*. New York: Wiley. #' @author Gerko Vink, 2018, for this version, based on earlier versions written #' by Stef van Buuren, Karin Groothuis-Oudshoorn, 2017 #' @export @@ -102,20 +102,20 @@ norm.draw <- function(y, ry, x, rank.adjust = TRUE, ...) { #' @note #' This functions adds a star to variable names in the mice iteration #' history to signal that a ridge penalty was added. In that case, it -#' also adds an entry to \code{loggedEvents}. +#' also adds an entry to `loggedEvents`. #' #' @aliases estimice -#' @param x Matrix (\code{n} x \code{p}) of complete covariates. -#' @param y Incomplete data vector of length \code{n} +#' @param x Matrix (`n` x `p`) of complete covariates. +#' @param y Incomplete data vector of length `n` #' @param ls.meth the method to use for obtaining the least squares estimates. By #' default parameters are drawn by means of QR decomposition. #' @param ridge A small numerical value specifying the size of the ridge used. -#' The default value \code{ridge = 1e-05} represents a compromise between stability -#' and unbiasedness. Decrease \code{ridge} if the data contain many junk variables. -#' Increase \code{ridge} for highly collinear data. +#' The default value `ridge = 1e-05` represents a compromise between stability +#' and unbiasedness. Decrease `ridge` if the data contain many junk variables. +#' Increase `ridge` for highly collinear data. #' @param ... Other named arguments. -#' @return A \code{list} containing components \code{c} (least squares estimate), -#' \code{r} (residuals), \code{v} (variance/covariance matrix) and \code{df} +#' @return A `list` containing components `c` (least squares estimate), +#' `r` (residuals), `v` (variance/covariance matrix) and `df` #' (degrees of freedom). #' @author Gerko Vink, 2018 #' @export diff --git a/R/mice.impute.norm.boot.R b/R/mice.impute.norm.boot.R index 3d7bd034c..3286331d3 100644 --- a/R/mice.impute.norm.boot.R +++ b/R/mice.impute.norm.boot.R @@ -4,15 +4,15 @@ #' #' @aliases mice.impute.norm.boot norm.boot #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details -#' Draws a bootstrap sample from \code{x[ry,]} and \code{y[ry]}, calculates +#' Draws a bootstrap sample from `x[ry,]` and `y[ry]`, calculates #' regression weights and imputes with normal residuals. #' @author Gerko Vink, Stef van Buuren, 2018 -#' @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. +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.norm.nob.R b/R/mice.impute.norm.nob.R index a265d9a08..a4a007fda 100644 --- a/R/mice.impute.norm.nob.R +++ b/R/mice.impute.norm.nob.R @@ -5,28 +5,28 @@ #' #' @aliases mice.impute.norm.nob norm.nob #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' This function creates imputations using the spread around the -#' fitted linear regression line of \code{y} given \code{x}, as +#' fitted linear regression line of `y` given `x`, as #' fitted on the observed data. #' #' This function is provided mainly to allow comparison between proper (e.g., -#' as implemented in \code{mice.impute.norm} and improper (this function) +#' as implemented in `mice.impute.norm` and improper (this function) #' normal imputation methods. #' #' For large data, having many rows, differences between proper and improper #' methods are small, and in those cases one may opt for speed by using -#' \code{mice.impute.norm.nob}. +#' `mice.impute.norm.nob`. #' @section Warning: The function does not incorporate the variability of the #' regression weights, so it is not 'proper' in the sense of Rubin. For small #' samples, variability of the imputed data is therefore underestimated. #' @author Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn, 2018 -#' @seealso \code{\link{mice}}, \code{\link{mice.impute.norm}} -#' @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. +#' @seealso [mice()], [mice.impute.norm()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' #' Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple diff --git a/R/mice.impute.norm.predict.R b/R/mice.impute.norm.predict.R index 4c273376c..6c1d1d714 100644 --- a/R/mice.impute.norm.predict.R +++ b/R/mice.impute.norm.predict.R @@ -1,24 +1,24 @@ #' Imputation by linear regression through prediction #' #' Imputes the "best value" according to the linear regression model, also -#' known as \emph{regression imputation}. +#' known as *regression imputation*. #' #' @aliases mice.impute.norm.predict norm.predict #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' Calculates regression weights from the observed data and returns predicted #' values to as imputations. This -#' method is known as \emph{regression imputation}. +#' method is known as *regression imputation*. #' @section Warning: THIS METHOD SHOULD NOT BE USED FOR DATA ANALYSIS. #' This method is seductive because it imputes the most #' likely value according to the model. However, it ignores the uncertainty #' of the missing values and artificially #' amplifies the relations between the columns of the data. Application of #' richer models having more parameters does not help to evade these issues. -#' Stochastic regression methods, like \code{\link{mice.impute.pmm}} or -#' \code{\link{mice.impute.norm}}, are generally preferred. +#' Stochastic regression methods, like [mice.impute.pmm()] or +#' [mice.impute.norm()], are generally preferred. #' #' At best, prediction can give reasonable estimates of the mean, especially #' if normality assumptions are plausible. See Little and Rubin (2002, p. 62-64) @@ -29,7 +29,7 @@ #' Data. New York: John Wiley and Sons. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-linearnormal.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-linearnormal.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.panImpute.R b/R/mice.impute.panImpute.R index 01ac47d2c..485d6aad9 100644 --- a/R/mice.impute.panImpute.R +++ b/R/mice.impute.panImpute.R @@ -1,11 +1,11 @@ -#' Impute multilevel missing data using \code{pan} +#' Impute multilevel missing data using `pan` #' -#' This function is a wrapper around the \code{panImpute} function -#' from the \code{mitml} package so that it can be called to -#' impute blocks of variables in \code{mice}. The \code{mitml::panImpute} -#' function provides an interface to the \code{pan} package for +#' This function is a wrapper around the `panImpute` function +#' from the `mitml` package so that it can be called to +#' impute blocks of variables in `mice`. The `mitml::panImpute` +#' function provides an interface to the `pan` package for #' multiple imputation of multilevel data (Schafer & Yucel, 2002). -#' Imputations can be generated using \code{type} or \code{formula}, +#' Imputations can be generated using `type` or `formula`, #' which offer different options for model specification. #' #' @name mice.impute.panImpute @@ -14,33 +14,33 @@ #' the cluster indicator variable, and any other variables that should be #' present in the imputed datasets. #' @param type An integer vector specifying the role of each variable -#' in the imputation model (see \code{\link[mitml]{panImpute}}) +#' in the imputation model (see [mitml::panImpute()]) #' @param formula A formula specifying the role of each variable #' in the imputation model. The basic model is constructed -#' by \code{model.matrix}, thus allowing to include derived variables -#' in the imputation model using \code{I()}. See -#' \code{\link[mitml]{panImpute}}. +#' by `model.matrix`, thus allowing to include derived variables +#' in the imputation model using `I()`. See +#' [mitml::panImpute()]. #' @param format A character vector specifying the type of object that should -#' be returned. The default is \code{format = "list"}. No other formats are +#' be returned. The default is `format = "list"`. No other formats are #' currently supported. -#' @param ... Other named arguments: \code{n.burn}, \code{n.iter}, -#' \code{group}, \code{prior}, \code{silent} and others. +#' @param ... Other named arguments: `n.burn`, `n.iter`, +#' `group`, `prior`, `silent` and others. #' @return A list of imputations for all incomplete variables in the model, -#' that can be stored in the the \code{imp} component of the \code{mids} +#' that can be stored in the the `imp` component of the `mids` #' object. -#' @seealso \code{\link[mitml]{panImpute}} -#' @note The number of imputations \code{m} is set to 1, and the function -#' is called \code{m} times so that it fits within the \code{mice} +#' @seealso [mitml::panImpute()] +#' @note The number of imputations `m` is set to 1, and the function +#' is called `m` times so that it fits within the `mice` #' iteration scheme. #' #' This is a multivariate imputation function using a joint model. #' @author Stef van Buuren, 2018, building on work of Simon Grund, -#' Alexander Robitzsch and Oliver Luedtke (authors of \code{mitml} package) -#' and Joe Schafer (author of \code{pan} package). +#' Alexander Robitzsch and Oliver Luedtke (authors of `mitml` package) +#' and Joe Schafer (author of `pan` package). #' @references #' Grund S, Luedtke O, Robitzsch A (2016). Multiple #' Imputation of Multilevel Missing Data: An Introduction to the R -#' Package \code{pan}. SAGE Open. +#' Package `pan`. SAGE Open. #' #' Schafer JL (1997). Analysis of Incomplete Multivariate Data. London: #' Chapman & Hall. diff --git a/R/mice.impute.passive.R b/R/mice.impute.passive.R index 61b32462b..2fabfede8 100644 --- a/R/mice.impute.passive.R +++ b/R/mice.impute.passive.R @@ -3,22 +3,22 @@ #' Calculate new variable during imputation #' #' @param data A data frame -#' @param func A \code{formula} specifying the transformations on data -#' @return The result of applying \code{formula} +#' @param func A `formula` specifying the transformations on data +#' @return The result of applying `formula` #' @details #' Passive imputation is a special internal imputation function. Using this -#' facility, the user can specify, at any point in the \code{mice} Gibbs +#' facility, the user can specify, at any point in the `mice` Gibbs #' sampling algorithm, a function on the imputed data. This is useful, for #' example, to compute a cubic version of a variable, a transformation like -#' \code{Q = W/H^2} based on two variables, or a mean variable like -#' \code{(x_1+x_2+x_3)/3}. The so derived variables might be used in other +#' `Q = W/H^2` based on two variables, or a mean variable like +#' `(x_1+x_2+x_3)/3`. The so derived variables might be used in other #' places in the imputation model. The function allows to dynamically derive #' virtually any function of the imputed data at virtually any time. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{mice}} -#' @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. +#' @seealso [mice()] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords datagen #' @export diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index 0122338e2..afd4de41e 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -2,53 +2,53 @@ #' #' @aliases mice.impute.pmm 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 ry Logical vector of length `length(y)` indicating the +#' the subset `y[ry]` of elements in `y` to which the imputation +#' model is fitted. The `ry` generally distinguishes the observed +#' (`TRUE`) and missing values (`FALSE`) in `y`. +#' @param x Numeric design matrix with `length(y)` rows with predictors for +#' `y`. Matrix `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 +#' @param quantify Logical. If `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. +#' Relevant only of `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. +#' Relevant only of `y` is a factor. +#' @param wy Logical vector of length `length(y)`. A `TRUE` value +#' indicates locations in `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 default is `donors = 5L`. Setting `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} +#' (`matchtype = 1L`) calculates the distance between +#' the *predicted* value of `yobs` and +#' the *drawn* values of `ymis` (called type-1 matching). +#' Other choices are `matchtype = 0L` +#' (distance between predicted values) and `matchtype = 2L` #' (distance between drawn values). -#' @param ridge The ridge penalty used in \code{.norm.draw()} to prevent -#' problems with multicollinearity. The default is \code{ridge = 1e-05}, +#' @param ridge The ridge penalty used in `.norm.draw()` to prevent +#' problems with multicollinearity. The default is `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. -#' @param use.matcher Logical. Set \code{use.matcher = TRUE} to specify -#' the C function \code{matcher()}, the now deprecated matching function that +#' (e.g. many junk variables), set `ridge = 1e-06` or even lower to +#' reduce bias. For highly collinear data, set `ridge = 1e-04` or higher. +#' @param use.matcher Logical. Set `use.matcher = TRUE` to specify +#' the C function `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. +#' `2.22` (June 2014) to `3.11.7` (Oct 2020). Since version `3.12.0` +#' `mice()` uses the much faster `matchindex` C function. Use +#' the deprecated `matcher` function only for exact reproduction. #' @param \dots Other named arguments. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Gerko Vink, Stef van Buuren, Karin Groothuis-Oudshoorn #' @details -#' Imputation of \code{y} by predictive mean matching, based on +#' Imputation of `y` by predictive mean matching, based on #' van Buuren (2012, p. 73). The procedure is as follows: #' #' \enumerate{ @@ -66,7 +66,7 @@ #' \item{Calculate imputations \eqn{\dot y_j = y_{i_j}} for \eqn{j=1,\dots,n_0}.} #' } #' -#' The name \emph{predictive mean matching} was proposed by Little (1988). +#' The name *predictive mean matching* was proposed by Little (1988). #' #' @references Little, R.J.A. (1988), Missing data adjustments in large surveys #' (with discussion), Journal of Business Economics and Statistics, 6, 287--301. @@ -75,12 +75,12 @@ #' 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.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-pmm.html) #' 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} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @family univariate imputation functions #' @keywords datagen #' @examples @@ -215,23 +215,23 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, #' 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 +#' mean metric. It selects the `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()}. +#' used up to `mice 2.21`. The current `mice.impute.pmm()` +#' function calls the faster `C` function `matcher` instead of +#' `.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 y A vector of `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 +#' `donors = 5`. Setting `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). @@ -240,10 +240,10 @@ mice.impute.pmm <- function(y, ry, x, wy = NULL, donors = 5L, #' @rdname pmm.match #' @references #' Schenker N & Taylor JMG (1996) Partially parametric techniques -#' for multiple imputation. \emph{Computational Statistics and Data Analysis}, 22, 425-446. +#' for multiple imputation. *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. +#' *Journal of Business Economics and Statistics*, 6, 287-301. #' #' @export .pmm.match <- function(z, yhat = yhat, y = y, donors = 5, ...) { diff --git a/R/mice.impute.polr.R b/R/mice.impute.polr.R index ef354f18d..39bc15c1a 100644 --- a/R/mice.impute.polr.R +++ b/R/mice.impute.polr.R @@ -3,62 +3,62 @@ #' 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 nnet.maxit Tuning parameter for `nnet()`. +#' @param nnet.trace Tuning parameter for `nnet()`. +#' @param nnet.MaxNWts Tuning parameter for `nnet()`. #' @param polr.to.loggedEvents A logical indicating whether each fallback -#' 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)} +#' to the `multinom()` function should be written to `loggedEvents`. +#' The default is `FALSE`. +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details -#' The function \code{mice.impute.polr()} imputes for ordered categorical response +#' The function `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}. +#' `mice.impute.polr`. #' -#' The algorithm of \code{mice.impute.polr} uses the function \code{polr()} from -#' the \code{MASS} package. +#' The algorithm of `mice.impute.polr` uses the function `polr()` from +#' the `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. -#' In that case, \code{multinom} is tried as a fallback. -#' If the local flag \code{polr.to.loggedEvents} is set to TRUE, +#' The call to `polr` might fail, usually because the data are very sparse. +#' In that case, `multinom` is tried as a fallback. +#' If the local flag `polr.to.loggedEvents` is set to TRUE, #' 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. +#' to the `loggedEvents` component of the [mids()] object. +#' Use `mice(data, polr.to.loggedEvents = TRUE)` to set the flag. #' #' @note #' In December 2019 Simon White alerted that the -#' \code{polr} could always fail silently. I can confirm this behaviour for -#' versions \code{mice 3.0.0 - mice 3.6.6}, so any method requests -#' for \code{polr} in these versions were in fact handled by \code{multinom}. -#' See \url{https://github.com/amices/mice/issues/206} for details. +#' `polr` could always fail silently. I can confirm this behaviour for +#' versions `mice 3.0.0 - mice 3.6.6`, so any method requests +#' for `polr` in these versions were in fact handled by `multinom`. +#' See for details. #' #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 -#' @seealso \code{\link{mice}}, \code{\link[nnet]{multinom}}, -#' \code{\link[MASS]{polr}} +#' @seealso [mice()], [nnet::multinom()], +#' [MASS::polr()] #' @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. \doi{10.18637/jss.v045.i03} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' -#' Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of +#' Brand, J.P.L. (1999) *Development, implementation and evaluation of #' multiple imputation strategies for the statistical analysis of incomplete -#' data sets.} Dissertation. Rotterdam: Erasmus University. +#' 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. +#' *Computational Statistics and Data Analysis*, 54, 2267-2275. #' -#' Venables, W.N. & Ripley, B.D. (2002). \emph{Modern applied statistics with -#' S-Plus (4th ed)}. Springer, Berlin. +#' Venables, W.N. & Ripley, B.D. (2002). *Modern applied statistics with +#' S-Plus (4th ed)*. Springer, Berlin. #' @family univariate imputation functions #' @keywords datagen #' @export diff --git a/R/mice.impute.polyreg.R b/R/mice.impute.polyreg.R index 4d3055f23..416f59a09 100644 --- a/R/mice.impute.polyreg.R +++ b/R/mice.impute.polyreg.R @@ -4,19 +4,19 @@ #' #' @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()}. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @param nnet.maxit Tuning parameter for `nnet()`. +#' @param nnet.trace Tuning parameter for `nnet()`. +#' @param nnet.MaxNWts Tuning parameter for `nnet()`. +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000-2010 #' @details -#' The function \code{mice.impute.polyreg()} imputes categorical response +#' The function `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()}. +#' `mice.impute.polyreg()`. #' #' The method consists of the following steps: #' \enumerate{ @@ -25,29 +25,29 @@ #' \item Add appropriate noise to predictions #' } #' -#' The algorithm of \code{mice.impute.polyreg} uses the function -#' \code{multinom()} from the \code{nnet} package. +#' The algorithm of `mice.impute.polyreg` uses the function +#' `multinom()` from the `nnet` 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). -#' @seealso \code{\link{mice}}, \code{\link[nnet]{multinom}}, -#' \code{\link[MASS]{polr}} +#' @seealso [mice()], [nnet::multinom()], +#' [MASS::polr()] #' @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. \doi{10.18637/jss.v045.i03} +#' Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' -#' Brand, J.P.L. (1999) \emph{Development, implementation and evaluation of +#' Brand, J.P.L. (1999) *Development, implementation and evaluation of #' multiple imputation strategies for the statistical analysis of incomplete -#' data sets.} Dissertation. Rotterdam: Erasmus University. +#' 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. +#' *Computational Statistics and Data Analysis*, 54, 2267-2275. #' -#' Venables, W.N. & Ripley, B.D. (2002). \emph{Modern applied statistics with -#' S-Plus (4th ed)}. Springer, Berlin. +#' Venables, W.N. & Ripley, B.D. (2002). *Modern applied statistics with +#' S-Plus (4th ed)*. Springer, Berlin. #' @family univariate imputation functions #' @keywords datagen #' @export diff --git a/R/mice.impute.quadratic.R b/R/mice.impute.quadratic.R index 54f02458c..7f275cd5c 100644 --- a/R/mice.impute.quadratic.R +++ b/R/mice.impute.quadratic.R @@ -7,9 +7,9 @@ #' @inheritParams mice.impute.pmm #' @param quad.outcome The name of the outcome in the quadratic analysis as a #' character string. For example, if the substantive model of interest is -#' \code{y ~ x + xx}, then \code{"y"} would be the \code{quad.outcome} -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' `y ~ x + xx`, then `"y"` would be the `quad.outcome` +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details #' This function implements the "polynomial combination" method. #' First, the polynomial @@ -24,23 +24,23 @@ #' estimates of the regression weights in a complete-data linear regression that #' use both \eqn{Y} and \eqn{Y^2}. #' -#' @note There are two situations to consider. If only the linear term \code{Y} -#' is present in the data, calculate the quadratic term \code{YY} after -#' imputation. If both the linear term \code{Y} and the the quadratic term -#' \code{YY} are variables in the data, then first impute \code{Y} by calling -#' \code{mice.impute.quadratic()} on \code{Y}, and then impute \code{YY} by -#' passive imputation as \code{meth["YY"] <- "~I(Y^2)"}. See example section -#' for details. Generally, we would like \code{YY} to be present in the data if -#' we need to preserve quadratic relations between \code{YY} and any third +#' @note There are two situations to consider. If only the linear term `Y` +#' is present in the data, calculate the quadratic term `YY` after +#' imputation. If both the linear term `Y` and the the quadratic term +#' `YY` are variables in the data, then first impute `Y` by calling +#' `mice.impute.quadratic()` on `Y`, and then impute `YY` by +#' passive imputation as `meth["YY"] <- "~I(Y^2)"`. See example section +#' for details. Generally, we would like `YY` to be present in the data if +#' we need to preserve quadratic relations between `YY` and any third #' variables in the multivariate incomplete data that we might wish to impute. #' @author Mingyang Cai and Gerko Vink -#' @seealso \code{\link{mice.impute.pmm}} +#' @seealso [mice.impute.pmm()] #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-knowledge.html#sec:quadratic}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-knowledge.html#sec:quadratic) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' Vink, G., van Buuren, S. (2013). Multiple Imputation of Squared Terms. -#' \emph{Sociological Methods & Research}, 42:598-607. +#' *Sociological Methods & Research*, 42:598-607. #' @family univariate imputation functions #' @keywords datagen #' @examples diff --git a/R/mice.impute.rf.R b/R/mice.impute.rf.R index 3125d1710..afe32e755 100644 --- a/R/mice.impute.rf.R +++ b/R/mice.impute.rf.R @@ -6,28 +6,28 @@ #' @inheritParams mice.impute.pmm #' @param ntree The number of trees to grow. The default is 10. #' @param rfPackage A single string specifying the backend for estimating the -#' random forest. The default backend is the \code{ranger} package. The only -#' alternative currently implemented is the \code{randomForest} package, which +#' random forest. The default backend is the `ranger` package. The only +#' alternative currently implemented is the `randomForest` package, which #' used to be the default in mice 3.13.10 and earlier. #' @param \dots Other named arguments passed down to -#' \code{mice:::install.on.demand()}, \code{randomForest::randomForest()}, -#' \code{randomForest:::randomForest.default()}, and \code{ranger::ranger()}. -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' `mice:::install.on.demand()`, `randomForest::randomForest()`, +#' `randomForest:::randomForest.default()`, and `ranger::ranger()`. +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @details -#' Imputation of \code{y} by random forests. The method -#' calls \code{randomForrest()} which implements Breiman's random forest +#' Imputation of `y` by random forests. The method +#' calls `randomForrest()` which implements Breiman's random forest #' algorithm (based on Breiman and Cutler's original Fortran code) #' for classification and regression. See Appendix A.1 of Doove et al. #' (2014) for the definition of the algorithm used. #' @note An alternative implementation was independently #' developed by Shah et al (2014). This were available as -#' functions \code{CALIBERrfimpute::mice.impute.rfcat} and -#' \code{CALIBERrfimpute::mice.impute.rfcont} (now archived). +#' functions `CALIBERrfimpute::mice.impute.rfcat` and +#' `CALIBERrfimpute::mice.impute.rfcont` (now archived). #' Simulations by Shah (Feb 13, 2014) suggested that #' the quality of the imputation for 10 and 100 trees was identical, -#' so mice 2.22 changed the default number of trees from \code{ntree = 100} to -#' \code{ntree = 10}. +#' so mice 2.22 changed the default number of trees from `ntree = 100` to +#' `ntree = 10`. #' @author Lisa Doove, Stef van Buuren, Elise Dusseldorp, 2012; Patrick Rockenschaub, 2021 #' @references #' @@ -41,11 +41,11 @@ #' of Epidemiology, \doi{10.1093/aje/kwt312}. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-cart.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-cart.html) #' Chapman & Hall/CRC. Boca Raton, FL. -#' @seealso \code{\link{mice}}, \code{\link{mice.impute.cart}}, -#' \code{\link[randomForest]{randomForest}} -#' \code{\link[ranger]{ranger}} +#' @seealso [mice()], [mice.impute.cart()], +#' [randomForest::randomForest()] +#' [ranger::ranger()] #' @family univariate imputation functions #' @keywords datagen #' @examples diff --git a/R/mice.impute.ri.R b/R/mice.impute.ri.R index c1b2327ce..596057edb 100755 --- a/R/mice.impute.ri.R +++ b/R/mice.impute.ri.R @@ -5,8 +5,8 @@ #' @aliases mice.impute.ri ri #' @inheritParams mice.impute.pmm #' @param ri.maxit Number of inner iterations -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Shahab Jolani (University of Utrecht) #' @details #' The random indicator method estimates an offset between the @@ -16,9 +16,9 @@ #' This routine assumes that the response model and imputation model #' have same predictors. #' -#' For an MNAR alternative see also \code{\link{mice.impute.mnar.logreg}}. +#' For an MNAR alternative see also [mice.impute.mnar.logreg()]. #' @references Jolani, S. (2012). -#' \emph{Dual Imputation Strategies for Analyzing Incomplete Data}. +#' *Dual Imputation Strategies for Analyzing Incomplete Data*. #' Dissertation. University of Utrecht, Dec 7 2012. #' @family univariate imputation functions #' @keywords datagen diff --git a/R/mice.impute.sample.R b/R/mice.impute.sample.R index 4a49e7737..7409f505f 100644 --- a/R/mice.impute.sample.R +++ b/R/mice.impute.sample.R @@ -1,17 +1,17 @@ #' Imputation by simple random sampling #' -#' Imputes a random sample from the observed \code{y} data +#' Imputes a random sample from the observed `y` data #' #' This function takes a simple random sample from the observed values in -#' \code{y}, and returns these as imputations. +#' `y`, and returns these as imputations. #' #' @inheritParams mice.impute.pmm -#' @return Vector with imputed data, same type as \code{y}, and of length -#' \code{sum(wy)} +#' @return Vector with imputed data, same type as `y`, and of length +#' `sum(wy)` #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000, 2017 -#' @references van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: -#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -#' Statistical Software}, \bold{45}(3), 1-67. +#' @references van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords datagen #' @export diff --git a/R/mice.mids.R b/R/mice.mids.R index 89c9f7646..7f831b006 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -1,6 +1,6 @@ #' Multivariate Imputation by Chained Equations (Iteration Step) #' -#' Takes a \code{mids} object, and produces a new object of class \code{mids}. +#' Takes a `mids` object, and produces a new object of class `mids`. #' #' This function enables the user to split up the computations of the Gibbs #' sampler into smaller parts. This is useful for the following reasons: @@ -9,26 +9,26 @@ #' problems. \item The user can compute customized convergence statistics at #' specific points, e.g. after each iteration, for monitoring convergence. - #' For computing a 'few extra iterations'. } Note: The imputation model itself -#' is specified in the \code{mice()} function and cannot be changed with -#' \code{mice.mids}. The state of the random generator is saved with the -#' \code{mids} object. +#' is specified in the `mice()` function and cannot be changed with +#' `mice.mids`. The state of the random generator is saved with the +#' `mids` object. #' -#' @param obj An object of class \code{mids}, typically produces by a previous -#' call to \code{mice()} or \code{mice.mids()} -#' @param newdata An optional \code{data.frame} for which multiple imputations -#' are generated according to the model in \code{obj}. +#' @param obj An object of class `mids`, typically produces by a previous +#' call to `mice()` or `mice.mids()` +#' @param newdata An optional `data.frame` for which multiple imputations +#' are generated according to the model in `obj`. #' @param maxit The number of additional Gibbs sampling iterations. -#' @param printFlag A Boolean flag. If \code{TRUE}, diagnostic information +#' @param printFlag A Boolean flag. If `TRUE`, diagnostic information #' during the Gibbs sampling iterations will be written to the command window. -#' The default is \code{TRUE}. +#' The default is `TRUE`. #' @param ... Named arguments that are passed down to the univariate imputation #' functions. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{complete}}, \code{\link{mice}}, \code{\link{set.seed}}, -#' \code{\link[=mids-class]{mids}} -#' @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. +#' @seealso [complete()], [mice()], [set.seed()], +#' [`mids()`][mids-class] +#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords iteration #' @examples diff --git a/R/mice.theme.R b/R/mice.theme.R index 058772b9e..0ab8e1737 100644 --- a/R/mice.theme.R +++ b/R/mice.theme.R @@ -1,16 +1,16 @@ #' Set the theme for the plotting Trellis functions #' -#' The \code{mice.theme()} function sets default choices for +#' The `mice.theme()` function sets default choices for #' Trellis plots that are built into \pkg{mice}. #' #' @aliases mice.theme #' @param transparent A logical indicating whether alpha-transparency is -#' allowed. The default is \code{TRUE}. +#' allowed. The default is `TRUE`. #' @param alpha.fill A numerical values between 0 and 1 that indicates the #' default alpha value for fills. -#' @return \code{mice.theme()} returns a named list that can be used as a theme in the functions in -#' \pkg{lattice}. By default, the \code{mice.theme()} function sets -#' \code{transparent <- TRUE} if the current device \code{.Device} supports +#' @return `mice.theme()` returns a named list that can be used as a theme in the functions in +#' \pkg{lattice}. By default, the `mice.theme()` function sets +#' `transparent <- TRUE` if the current device `.Device` supports #' semi-transparent colors. #' @author Stef van Buuren 2011 #' @export diff --git a/R/mids.R b/R/mids.R index eee7e552e..b8abe44d5 100644 --- a/R/mids.R +++ b/R/mids.R @@ -1,68 +1,68 @@ -#' Multiply imputed data set (\code{mids}) +#' Multiply imputed data set (`mids`) #' -#' The \code{mids} object contains a multiply imputed data set. The \code{mids} object is -#' generated by functions \code{mice()}, \code{mice.mids()}, \code{cbind.mids()}, -#' \code{rbind.mids()} and \code{ibind.mids()}. +#' The `mids` object contains a multiply imputed data set. The `mids` object is +#' generated by functions `mice()`, `mice.mids()`, `cbind.mids()`, +#' `rbind.mids()` and `ibind.mids()`. #' -#' The \code{mids} +#' The `mids` #' class of objects has methods for the following generic functions: -#' \code{print}, \code{summary}, \code{plot}. +#' `print`, `summary`, `plot`. #' #' @section Slots: #' \describe{ -#' \item{\code{.Data}:}{Object of class \code{"list"} containing the +#' \item{`.Data`:}{Object of class `"list"` containing the #' following slots:} -#' \item{\code{data}:}{Original (incomplete) data set.} -#' \item{\code{imp}:}{A list of \code{ncol(data)} components with +#' \item{`data`:}{Original (incomplete) data set.} +#' \item{`imp`:}{A list of `ncol(data)` components with #' the generated multiple imputations. Each list component is a -#' \code{data.frame} (\code{nmis[j]} by \code{m}) of imputed values -#' for variable \code{j}. A \code{NULL} component is used for +#' `data.frame` (`nmis[j]` by `m`) of imputed values +#' for variable `j`. A `NULL` component is used for #' variables for which not imputations are generated.} -#' \item{\code{m}:}{Number of imputations.} -#' \item{\code{where}:}{The \code{where} argument of the -#' \code{mice()} function.} -#' \item{\code{blocks}:}{The \code{blocks} argument of the -#' \code{mice()} function.} -#' \item{\code{call}:}{Call that created the object.} -#' \item{\code{nmis}:}{An array containing the number of missing +#' \item{`m`:}{Number of imputations.} +#' \item{`where`:}{The `where` argument of the +#' `mice()` function.} +#' \item{`blocks`:}{The `blocks` argument of the +#' `mice()` function.} +#' \item{`call`:}{Call that created the object.} +#' \item{`nmis`:}{An array containing the number of missing #' observations per column.} -#' \item{\code{method}:}{A vector of strings of \code{length(blocks} +#' \item{`method`:}{A vector of strings of `length(blocks` #' specifying the imputation method per block.} -#' \item{\code{predictorMatrix}:}{A numerical matrix of containing +#' \item{`predictorMatrix`:}{A numerical matrix of containing #' integers specifying the predictor set.} -#' \item{\code{visitSequence}:}{A vector of variable and block names that +#' \item{`visitSequence`:}{A vector of variable and block names that #' specifies how variables and blocks are visited in one iteration throuh #' the data.} -#' \item{\code{formulas}:}{A named list of formula's, or expressions that -#' can be converted into formula's by \code{as.formula}. List elements +#' \item{`formulas`:}{A named list of formula's, or expressions that +#' can be converted into formula's by `as.formula`. List elements #' correspond to blocks. The block to which the list element applies is #' identified by its name, so list names must correspond to block names.} -#' \item{\code{post}:}{A vector of strings of length \code{length(blocks)} +#' \item{`post`:}{A vector of strings of length `length(blocks)` #' with commands for post-processing.} -#' \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} +#' \item{`blots`:}{"Block dots". The `blots` argument to the `mice()` #' function.} -#' \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.} -#' \item{\code{iteration}:}{Last Gibbs sampling iteration number.} -#' \item{\code{lastSeedValue}:}{The most recent seed value.} -#' \item{\code{chainMean}:}{An array of dimensions \code{ncol} by -#' \code{maxit} by \code{m} elements containing the mean of +#' \item{`ignore`:}{A logical vector of length `nrow(data)` indicating +#' the rows in `data` used to build the imputation model. (new in `mice 3.12.0`)} +#' \item{`seed`:}{The seed value of the solution.} +#' \item{`iteration`:}{Last Gibbs sampling iteration number.} +#' \item{`lastSeedValue`:}{The most recent seed value.} +#' \item{`chainMean`:}{An array of dimensions `ncol` by +#' `maxit` by `m` elements containing the mean of #' the generated multiple imputations. #' The array can be used for monitoring convergence. #' 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 +#' \item{`chainVar`:}{An array with similar structure as +#' `chainMean`, containing the variance of the imputed values.} +#' \item{`loggedEvents`:}{A `data.frame` with five columns #' containing warnings, corrective actions, and other inside info.} -#' \item{\code{version}:}{Version number of \code{mice} package that +#' \item{`version`:}{Version number of `mice` package that #' created the object.} -#' \item{\code{date}:}{Date at which the object was created.} +#' \item{`date`:}{Date at which the object was created.} #' } #' #' @details -#' 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 +#' The `loggedEvents` entry is a matrix with five columns containing a +#' record of automatic removal actions. It is `NULL` is no action was #' made. At initialization the program does the following three actions: #' \describe{ #' \item{1}{A variable that contains missing values, that is not imputed @@ -76,32 +76,32 @@ #' \item{1}{One or more variables that are linearly dependent are removed #' (for categorical data, a 'variable' corresponds to a dummy variable)} #' \item{2}{Proportional odds regression imputation that does not converge -#' and is replaced by \code{polyreg}.} +#' and is replaced by `polyreg`.} #' } #' -#' Explanation of elements in \code{loggedEvents}: +#' Explanation of elements in `loggedEvents`: #' \describe{ -#' \item{\code{it}}{iteration number at which the record was added,} -#' \item{\code{im}}{imputation number,} -#' \item{\code{dep}}{name of the dependent variable,} -#' \item{\code{meth}}{imputation method used,} -#' \item{\code{out}}{a (possibly long) character vector with the +#' \item{`it`}{iteration number at which the record was added,} +#' \item{`im`}{imputation number,} +#' \item{`dep`}{name of the dependent variable,} +#' \item{`meth`}{imputation method used,} +#' \item{`out`}{a (possibly long) character vector with the #' names of the altered or removed predictors.} #' } #' -#' @note The \code{mice} package does not use +#' @note The `mice` package does not use #' the S4 class definitions, and instead relies on the S3 list -#' equivalent \code{oldClass(obj) <- "mids"}. +#' equivalent `oldClass(obj) <- "mids"`. #' #' @name mids-class #' @rdname mids-class #' @aliases mids-class mids #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{mice}}, \code{\link[=mira-class]{mira}}, -#' \code{\link{mipo}} -#' @references van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: -#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -#' Statistical Software}, \bold{45}(3), 1-67. +#' @seealso [mice()], [`mira()`][mira-class], +#' [mipo()] +#' @references van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords classes NULL diff --git a/R/mids2mplus.R b/R/mids2mplus.R index 6d6029e06..fc985f7a5 100644 --- a/R/mids2mplus.R +++ b/R/mids2mplus.R @@ -1,29 +1,29 @@ -#' Export \code{mids} object to Mplus +#' Export `mids` object to Mplus #' -#' Converts a \code{mids} object into a format recognized by Mplus, and writes +#' Converts a `mids` object into a format recognized by Mplus, and writes #' the data and the Mplus input files #' -#' This function automates most of the work needed to export a \code{mids} -#' object to \code{Mplus}. The function writes the multiple imputation datasets, +#' This function automates most of the work needed to export a `mids` +#' object to `Mplus`. The function writes the multiple imputation datasets, #' the file that contains the names of the multiple imputation data sets and an -#' \code{Mplus} input file. The \code{Mplus} input file has the proper file +#' `Mplus` input file. The `Mplus` input file has the proper file #' names, so in principle it should run and read the data without alteration. -#' \code{Mplus} will recognize the data set as a multiply imputed data set, and +#' `Mplus` will recognize the data set as a multiply imputed data set, and #' do automatic pooling in procedures where that is supported. #' -#' @param imp The \code{imp} argument is an object of class \code{mids}, -#' typically produced by the \code{mice()} function. +#' @param imp The `imp` argument is an object of class `mids`, +#' typically produced by the `mice()` function. #' @param file.prefix A character string describing the prefix of the output #' data files. #' @param path A character string containing the path of the output file. By -#' default, files are written to the current \code{R} working directory. +#' default, files are written to the current `R` working directory. #' @param sep The separator between the data fields. #' @param dec The decimal separator for numerical data. #' @param silent A logical flag stating whether the names of the files should be #' printed. -#' @return The return value is \code{NULL}. +#' @return The return value is `NULL`. #' @author Gerko Vink, 2011. -#' @seealso \code{\link[=mids-class]{mids}}, \code{\link{mids2spss}} +#' @seealso [`mids()`][mids-class], [mids2spss()] #' @keywords manip #' @export mids2mplus <- function(imp, file.prefix = "imp", path = getwd(), sep = "\t", dec = ".", silent = FALSE) { diff --git a/R/mids2spss.R b/R/mids2spss.R index dbbddfaa0..ba66a4416 100644 --- a/R/mids2spss.R +++ b/R/mids2spss.R @@ -1,47 +1,47 @@ -#' Export \code{mids} object to SPSS +#' Export `mids` object to SPSS #' -#' Converts a \code{mids} object into a format recognized by SPSS, and writes +#' Converts a `mids` object into a format recognized by SPSS, and writes #' the data and the SPSS syntax files. #' -#' This function automates most of the work needed to export a \code{mids} -#' object to SPSS. It uses \code{haven::write_sav()} to facilitate the export to an -#' SPSS \code{.sav} or \code{.zsav} file. +#' This function automates most of the work needed to export a `mids` +#' object to SPSS. It uses `haven::write_sav()` to facilitate the export to an +#' SPSS `.sav` or `.zsav` file. #' #' Below are some things to pay attention to. #' -#' The \code{SPSS} syntax file has the proper file names and separators set, so -#' in principle it should run and read the data without alteration. \code{SPSS} -#' is more strict than \code{R} with respect to the paths. Always use the full -#' path, otherwise \code{SPSS} may not be able to find the data file. +#' The `SPSS` syntax file has the proper file names and separators set, so +#' in principle it should run and read the data without alteration. `SPSS` +#' is more strict than `R` with respect to the paths. Always use the full +#' path, otherwise `SPSS` may not be able to find the data file. #' -#' Factors in \code{R} translate into categorical variables in \code{SPSS}. The -#' internal coding of factor levels used in \code{R} is exported. This is -#' generally acceptable for \code{SPSS}. However, when the data are to be -#' combined with existing \code{SPSS} data, watch out for any changes in the +#' Factors in `R` translate into categorical variables in `SPSS`. The +#' internal coding of factor levels used in `R` is exported. This is +#' generally acceptable for `SPSS`. However, when the data are to be +#' combined with existing `SPSS` data, watch out for any changes in the #' factor levels codes. #' -#' \code{SPSS} will recognize the data set as a multiply imputed data set, and +#' `SPSS` will recognize the data set as a multiply imputed data set, and #' do automatic pooling in procedures where that is supported. Note however that #' pooling is an extra option only available to those who license the -#' \code{MISSING VALUES} module. Without this license, \code{SPSS} will still +#' `MISSING VALUES` module. Without this license, `SPSS` will still #' recognize the structure of the data, but it will not pool the multiply imputed #' estimates into a single inference. #' -#' @param imp The \code{imp} argument is an object of class \code{mids}, -#' typically produced by the \code{mice()} function. +#' @param imp The `imp` argument is an object of class `mids`, +#' typically produced by the `mice()` function. #' @param filename A character string describing the name of the output data #' file and its extension. #' @param path A character string containing the path of the output file. The -#' value in \code{path} is appended to \code{filedat}. By -#' default, files are written to the current \code{R} working directory. If -#' \code{path=NULL} then no file path appending is done. +#' value in `path` is appended to `filedat`. By +#' default, files are written to the current `R` working directory. If +#' `path=NULL` then no file path appending is done. #' @param compress A logical flag stating whether the resulting SPSS set should -#' be a compressed \code{.zsav} file. +#' be a compressed `.zsav` file. #' @param silent A logical flag stating whether the location of the saved file should be #' printed. -#' @return The return value is \code{NULL}. +#' @return The return value is `NULL`. #' @author Gerko Vink, dec 2020. -#' @seealso \code{\link[=mids-class]{mids}} +#' @seealso [`mids()`][mids-class] #' @keywords manip #' @export mids2spss <- function(imp, filename = "midsdata", diff --git a/R/mipo.R b/R/mipo.R index 975865048..4369df761 100644 --- a/R/mipo.R +++ b/R/mipo.R @@ -1,50 +1,50 @@ -#' \code{mipo}: Multiple imputation pooled object +#' `mipo`: Multiple imputation pooled object #' -#' The \code{mipo} object contains the results of the pooling step. -#' The function \code{\link{pool}} generates an object of class \code{mipo}. +#' The `mipo` object contains the results of the pooling step. +#' The function [pool()] generates an object of class `mipo`. #' -#' @param x An object of class \code{mipo} -#' @param object An object of class \code{mipo} -#' @param mira.obj An object of class \code{mira} +#' @param x An object of class `mipo` +#' @param object An object of class `mipo` +#' @param mira.obj An object of class `mira` #' @inheritParams broom::lm_tidiers #' @param z Data frame with a tidied version of a coefficient matrix #' @param conf.int Logical indicating whether to include #' a confidence interval. #' @param conf.level Confidence level of the interval, used only if -#' \code{conf.int = TRUE}. Number between 0 and 1. +#' `conf.int = TRUE`. Number between 0 and 1. #' @param exponentiate Flag indicating whether to exponentiate the #' coefficient estimates and confidence intervals (typical for #' logistic regression). #' @param \dots Arguments passed down -#' @details An object class \code{mipo} is a \code{list} with -#' elements: \code{call}, \code{m}, \code{pooled} and \code{glanced}. +#' @details An object class `mipo` is a `list` with +#' elements: `call`, `m`, `pooled` and `glanced`. #' -#' The \code{pooled} elements is a data frame with columns: +#' The `pooled` elements is a data frame with columns: #' \tabular{ll}{ -#' \code{estimate}\tab Pooled complete data estimate\cr -#' \code{ubar} \tab Within-imputation variance of \code{estimate}\cr -#' \code{b} \tab Between-imputation variance of \code{estimate}\cr -#' \code{t} \tab Total variance, of \code{estimate}\cr -#' \code{dfcom} \tab Degrees of freedom in complete data\cr -#' \code{df} \tab Degrees of freedom of $t$-statistic\cr -#' \code{riv} \tab Relative increase in variance\cr -#' \code{lambda} \tab Proportion attributable to the missingness\cr -#' \code{fmi} \tab Fraction of missing information\cr +#' `estimate`\tab Pooled complete data estimate\cr +#' `ubar` \tab Within-imputation variance of `estimate`\cr +#' `b` \tab Between-imputation variance of `estimate`\cr +#' `t` \tab Total variance, of `estimate`\cr +#' `dfcom` \tab Degrees of freedom in complete data\cr +#' `df` \tab Degrees of freedom of $t$-statistic\cr +#' `riv` \tab Relative increase in variance\cr +#' `lambda` \tab Proportion attributable to the missingness\cr +#' `fmi` \tab Fraction of missing information\cr #' } -#' The names of the terms are stored as \code{row.names(pooled)}. +#' The names of the terms are stored as `row.names(pooled)`. #' -#' The \code{glanced} elements is a \code{data.frame} with \code{m} rows. +#' The `glanced` elements is a `data.frame` with `m` rows. #' The precise composition depends on the class of the complete-data analysis. -#' At least field \code{nobs} is expected to be present. +#' At least field `nobs` is expected to be present. #' -#' The \code{process_mipo} is a helper function to process a +#' The `process_mipo` is a helper function to process a #' tidied mipo object, and is normally not called directly. #' It adds a confidence interval, and optionally exponentiates, the result. -#' @seealso \code{\link{pool}}, -#' \code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}} -#' @references van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: -#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -#' Statistical Software}, \bold{45}(3), 1-67. +#' @seealso [pool()], +#' [`mids()`][mids-class], [`mira()`][mira-class] +#' @references van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords classes #' @name mipo @@ -57,7 +57,7 @@ mipo <- function(mira.obj, ...) { structure(pool(mira.obj, ...), class = c("mipo")) } -#' @return The \code{summary} method returns a data frame with summary statistics of the pooled analysis. +#' @return The `summary` method returns a data frame with summary statistics of the pooled analysis. #' @rdname mipo #' @export summary.mipo <- function(object, type = c("tests", "all"), diff --git a/R/mira.R b/R/mira.R index 18d826fe5..05511ddbb 100644 --- a/R/mira.R +++ b/R/mira.R @@ -1,52 +1,52 @@ -#' Multiply imputed repeated analyses (\code{mira}) +#' Multiply imputed repeated analyses (`mira`) #' -#' The \code{mira} object is generated by the \code{with.mids()} function. -#' The \code{as.mira()} +#' The `mira` object is generated by the `with.mids()` function. +#' The `as.mira()` #' function takes the results of repeated complete-data analysis stored as a -#' list, and turns it into a \code{mira} object that can be pooled. +#' list, and turns it into a `mira` object that can be pooled. #' #' @section Slots: #' \describe{ -#' #' \item{\code{.Data}:}{Object of class \code{"list"} containing the +#' #' \item{`.Data`:}{Object of class `"list"` containing the #' following slots:} -#' \item{\code{call}:}{The call that created the object.} -#' \item{\code{call1}:}{The call that created the \code{mids} object that was used -#' in \code{call}.} -#' \item{\code{nmis}:}{An array containing the number of missing observations per +#' \item{`call`:}{The call that created the object.} +#' \item{`call1`:}{The call that created the `mids` object that was used +#' in `call`.} +#' \item{`nmis`:}{An array containing the number of missing observations per #' column.} -#' \item{\code{analyses}:}{A list of \code{m} components containing the individual -#' fit objects from each of the \code{m} complete data analyses.} +#' \item{`analyses`:}{A list of `m` components containing the individual +#' fit objects from each of the `m` complete data analyses.} #' } #' #' @details -#' In versions prior to \code{mice 3.0} pooling required only that -#' \code{coef()} and \code{vcov()} methods were available for fitted -#' objects. \emph{This feature is no longer supported}. The reason is that \code{vcov()} +#' In versions prior to `mice 3.0` pooling required only that +#' `coef()` and `vcov()` methods were available for fitted +#' objects. *This feature is no longer supported*. The reason is that `vcov()` #' methods are inconsistent across packages, leading to buggy behaviour -#' of the \code{pool()} function. Since \code{mice 3.0+}, the \code{broom} +#' of the `pool()` function. Since `mice 3.0+`, the `broom` #' package takes care of filtering out the relevant parts of the #' complete-data analysis. It may happen that you'll see the messages -#' like \code{No method for tidying an S3 object of class ...} or -#' \code{Error: No glance method for objects of class ...}. The royal -#' way to solve this problem is to write your own \code{glance()} and \code{tidy()} -#' methods and add these to \code{broom} according to the specifications -#' given in \url{https://broom.tidymodels.org}. +#' like `No method for tidying an S3 object of class ...` or +#' `Error: No glance method for objects of class ...`. The royal +#' way to solve this problem is to write your own `glance()` and `tidy()` +#' methods and add these to `broom` according to the specifications +#' given in . #' -#' #'The \code{mira} class of objects has methods for the -#' following generic functions: \code{print}, \code{summary}. +#' #'The `mira` class of objects has methods for the +#' following generic functions: `print`, `summary`. #' -#' Many of the functions of the \code{mice} package do not use the +#' Many of the functions of the `mice` package do not use the #' S4 class definitions, and instead rely on the S3 list equivalent -#' \code{oldClass(obj) <- "mira"}. +#' `oldClass(obj) <- "mira"`. #' #' @name mira-class #' @rdname mira-class #' @aliases mira-class mira #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 -#' @seealso \code{\link{with.mids}}, \code{\link[=mids-class]{mids}}, \code{\link{mipo}} -#' @references van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: -#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -#' Statistical Software}, \bold{45}(3), 1-67. +#' @seealso [with.mids()], [`mids()`][mids-class], [mipo()] +#' @references van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords classes #' @export diff --git a/R/mnar_demo_data.R b/R/mnar_demo_data.R index b9ddf0d1e..574da75ca 100644 --- a/R/mnar_demo_data.R +++ b/R/mnar_demo_data.R @@ -3,5 +3,5 @@ #' A toy example from Margarita Moreno-Betancur for checking NARFCS. #' #' A small dataset with just three columns. -#' @source \url{https://github.com/moreno-betancur/NARFCS/blob/master/datmis.csv} +#' @source "mnar_demo_data" diff --git a/R/ncc.R b/R/ncc.R index 4fbe13779..83beafeea 100644 --- a/R/ncc.R +++ b/R/ncc.R @@ -2,12 +2,12 @@ #' #' Calculates the number of complete cases. #' -#' @param x An \code{R} object. Currently supported are methods for the -#' following classes: \code{mids}, \code{data.frame} and \code{matrix}. Also, -#' \code{x} can be a vector. -#' @return Number of elements in \code{x} with complete data. +#' @param x An `R` object. Currently supported are methods for the +#' following classes: `mids`, `data.frame` and `matrix`. Also, +#' `x` can be a vector. +#' @return Number of elements in `x` with complete data. #' @author Stef van Buuren, 2017 -#' @seealso \code{\link{nic}}, \code{\link{cci}} +#' @seealso [nic()], [cci()] #' @examples #' ncc(nhanes) # 13 complete cases #' @export @@ -17,12 +17,12 @@ ncc <- function(x) sum(cci(x)) #' #' Calculates the number of incomplete cases. #' -#' @param x An \code{R} object. Currently supported are methods for the -#' following classes: \code{mids}, \code{data.frame} and \code{matrix}. Also, -#' \code{x} can be a vector. -#' @return Number of elements in \code{x} with incomplete data. +#' @param x An `R` object. Currently supported are methods for the +#' following classes: `mids`, `data.frame` and `matrix`. Also, +#' `x` can be a vector. +#' @return Number of elements in `x` with incomplete data. #' @author Stef van Buuren, 2017 -#' @seealso \code{\link{ncc}}, \code{\link{cci}} +#' @seealso [ncc()], [cci()] #' @examples #' nic(nhanes) # the remaining 12 rows #' nic(nhanes[, c("bmi", "hyp")]) # number of cases with incomplete bmi and hyp diff --git a/R/nelsonaalen.R b/R/nelsonaalen.R index 5f8974b80..73b374e94 100644 --- a/R/nelsonaalen.R +++ b/R/nelsonaalen.R @@ -9,17 +9,17 @@ #' #' @aliases nelsonaalen hazard #' @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}. -#' @return A vector with \code{nrow(data)} elements containing the Nelson-Aalen +#' @param timevar The name of the time variable in `data`. +#' @param statusvar The name of the event variable, e.g. death in `data`. +#' @return A vector with `nrow(data)` elements containing the Nelson-Aalen #' estimates of the cumulative hazard function. #' @author Stef van Buuren, 2012 #' @references White, I. R., Royston, P. (2009). Imputing missing covariate -#' values for the Cox model. \emph{Statistics in Medicine}, \emph{28}(15), +#' values for the Cox model. *Statistics in Medicine*, *28*(15), #' 1982-1998. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-toomany.html#a-further-improvement-survival-as-predictor-variable}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-toomany.html#a-further-improvement-survival-as-predictor-variable) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords misc #' @examples diff --git a/R/nhanes.R b/R/nhanes.R index 917d9b5be..3d76f2ee6 100644 --- a/R/nhanes.R +++ b/R/nhanes.R @@ -2,8 +2,8 @@ #' #' A small data set with non-monotone missing values. #' -#' A small data set with all numerical variables. The data set \code{nhanes2} is -#' the same data set, but with \code{age} and \code{hyp} treated as factors. +#' A small data set with all numerical variables. The data set `nhanes2` is +#' the same data set, but with `age` and `hyp` treated as factors. #' #' @name nhanes #' @docType data @@ -13,9 +13,9 @@ #' \item{bmi}{Body mass index (kg/m**2)} #' \item{hyp}{Hypertensive (1=no,2=yes)} #' \item{chl}{Total serum cholesterol (mg/dL)} } -#' @seealso \code{\link{nhanes2}} -#' @source Schafer, J.L. (1997). \emph{Analysis of Incomplete Multivariate -#' Data.} London: Chapman & Hall. Table 6.14. +#' @seealso [nhanes2()] +#' @source Schafer, J.L. (1997). *Analysis of Incomplete Multivariate +#' Data.* London: Chapman & Hall. Table 6.14. #' @keywords datasets #' @examples #' # create 5 imputed data sets diff --git a/R/nhanes2.R b/R/nhanes2.R index d67cebe15..681276f08 100644 --- a/R/nhanes2.R +++ b/R/nhanes2.R @@ -3,7 +3,7 @@ #' A small data set with non-monotone missing values. #' #' A small data set with missing data and mixed numerical and discrete -#' variables. The data set \code{nhanes} is the same data set, but with all data +#' variables. The data set `nhanes` is the same data set, but with all data #' treated as numerical. #' #' @name nhanes2 @@ -14,9 +14,9 @@ #' \item{bmi}{Body mass index (kg/m**2)} #' \item{hyp}{Hypertensive (1=no,2=yes)} #' \item{chl}{Total serum cholesterol (mg/dL)} } -#' @seealso \code{\link{nhanes}} -#' @source Schafer, J.L. (1997). \emph{Analysis of Incomplete Multivariate -#' Data.} London: Chapman & Hall. Table 6.14. +#' @seealso [nhanes()] +#' @source Schafer, J.L. (1997). *Analysis of Incomplete Multivariate +#' Data.* London: Chapman & Hall. Table 6.14. #' @keywords datasets #' @examples #' # create 5 imputed data sets diff --git a/R/nimp.R b/R/nimp.R index 11eed7901..caaac812d 100644 --- a/R/nimp.R +++ b/R/nimp.R @@ -3,9 +3,9 @@ #' Calculates the number of cells within a block for which imputation #' is requested. #' @inheritParams mice -#' @return A numeric vector of length \code{length(blocks)} containing +#' @return A numeric vector of length `length(blocks)` containing #' the number of cells that need to be imputed within a block. -#' @seealso \code{\link{mice}} +#' @seealso [mice()] #' @export #' @examples #' where <- is.na(nhanes) diff --git a/R/parlmice.R b/R/parlmice.R index b78748991..364173f0c 100644 --- a/R/parlmice.R +++ b/R/parlmice.R @@ -1,57 +1,57 @@ #' Wrapper function that runs MICE in parallel #' #' This function is included for backward compatibility. The function -#' is superseded by \code{\link{futuremice}}. +#' is superseded by [futuremice()]. #' -#' This function relies on package \code{\link{parallel}}, which is a base +#' This function relies on package [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 +#' `parLapply` to allow the use of `parlmice` on Mac, Linux and Windows #' systems. For the same reason, we use the Parallel Socket Cluster (PSOCK) type by default. #' #' On systems other than Windows, it can be hugely beneficial to change the cluster type to -#' \code{FORK}, as it generally results in improved memory handling. When memory issues +#' `FORK`, as it generally results in improved memory handling. When memory issues #' arise on a Windows system, we advise to store the multiply imputed datasets, -#' clean the memory by using \code{\link{rm}} and \code{\link{gc}} and make another +#' clean the memory by using [rm()] and [gc()] and make another #' run using the same settings. #' -#' This wrapper function combines the output of \code{\link{parLapply}} with -#' function \code{\link{ibind}} in \code{\link{mice}}. A \code{mids} object is returned +#' This wrapper function combines the output of [parLapply()] with +#' function [ibind()] in [mice()]. A `mids` object is returned #' and can be used for further analyses. #' #' Note that if a seed value is desired, the seed should be entered to this function -#' with argument \code{seed}. Seed values outside the wrapper function (in an -#' R-script or passed to \code{\link{mice}}) will not result to reproducible results. -#' We refer to the manual of \code{\link{parallel}} for an explanation on this matter. +#' with argument `seed`. Seed values outside the wrapper function (in an +#' R-script or passed to [mice()]) will not result to reproducible results. +#' We refer to the manual of [parallel()] for an explanation on this matter. #' #' @aliases parlmice #' @param data A data frame or matrix containing the incomplete data. Similar to -#' the first argument of \code{\link{mice}}. -#' @param m The number of desired imputated datasets. By default $m=5$ as with \code{mice} +#' the first argument of [mice()]. +#' @param m The number of desired imputated datasets. By default $m=5$ as with `mice` #' @param seed A scalar to be used as the seed value for the mice algorithm within #' each parallel stream. Please note that the imputations will be the same for all -#' streams and, hence, this should be used if and only if \code{n.core = 1} and -#' if it is desired to obtain the same output as under \code{mice}. +#' streams and, hence, this should be used if and only if `n.core = 1` and +#' if it is desired to obtain the same output as under `mice`. #' @param n.core A scalar indicating the number of cores that should be used. #' @param n.imp.core A scalar indicating the number of imputations per core. #' @param cluster.seed A scalar to be used as the seed value. It is recommended to put the #' seed value here and not outside this function, as otherwise the parallel processes #' will be performed with separate, random seeds. -#' @param cl.type The cluster type. Default value is \code{"PSOCK"}. Posix machines (linux, Mac) -#' generally benefit from much faster cluster computation if \code{type} is set to \code{type = "FORK"}. -#' @param ... Named arguments that are passed down to function \code{\link{mice}} or -#' \code{\link{makeCluster}}. +#' @param cl.type The cluster type. Default value is `"PSOCK"`. Posix machines (linux, Mac) +#' generally benefit from much faster cluster computation if `type` is set to `type = "FORK"`. +#' @param ... Named arguments that are passed down to function [mice()] or +#' [makeCluster()]. #' -#' @return A mids object as defined by \code{\link{mids-class}} +#' @return A mids object as defined by [mids-class()] #' #' @author Gerko Vink, Rianne Schouten -#' @seealso \code{\link{parallel}}, \code{\link{parLapply}}, \code{\link{makeCluster}}, -#' \code{\link{mice}}, \code{\link{mids-class}} +#' @seealso [parallel()], [parLapply()], [makeCluster()], +#' [mice()], [mids-class()] #' @references #' Schouten, R. and Vink, G. (2017). parlmice: faster, paraleller, micer. -#' \url{https://www.gerkovink.com/parlMICE/Vignette_parlMICE.html} +#' #' #' #'Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/parallel-computation.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/parallel-computation.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' #' @examples diff --git a/R/pattern1.R b/R/pattern1.R index 9f4e3ce69..1f9d1b28d 100644 --- a/R/pattern1.R +++ b/R/pattern1.R @@ -13,7 +13,7 @@ #' pattern} \item{list("pattern3")}{Data with a file matching missing data #' pattern} \item{list("pattern4")}{Data with a general missing data pattern} } #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/missing-data-pattern.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/missing-data-pattern.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets #' @examples diff --git a/R/plot.R b/R/plot.R index 1dceb4260..1e7672a4f 100644 --- a/R/plot.R +++ b/R/plot.R @@ -2,26 +2,26 @@ #' #' Trace line plots portray the value of an estimate #' against the iteration number. The estimate can be anything that you can calculate, but -#' typically are chosen as parameter of scientific interest. The \code{plot} method for -#' a \code{mids} object plots the mean and standard deviation of the imputed (not observed) +#' typically are chosen as parameter of scientific interest. The `plot` method for +#' a `mids` object plots the mean and standard deviation of the imputed (not observed) #' values against the iteration number for each of the $m$ replications. By default, #' the function plot the development of the mean and standard deviation for each incomplete #' variable. On convergence, the streams should intermingle and be free of any trend. #' -#' @param x An object of class \code{mids} +#' @param x An object of class `mids` #' @param y A formula that specifies which variables, stream and iterations are plotted. #' If omitted, all streams, variables and iterations are plotted. -#' @param theme The trellis theme to applied to the graphs. The default is \code{mice.theme()}. +#' @param theme The trellis theme to applied to the graphs. The default is `mice.theme()`. #' @param layout A vector of length 2 given the number of columns and rows in the plot. -#' The default is \code{c(2, 3)}. -#' @param type Parameter \code{type} of \code{\link{panel.xyplot}}. -#' @param col Parameter \code{col} of \code{\link{panel.xyplot}}. -#' @param lty Parameter \code{lty} of \code{\link{panel.xyplot}}. -#' @param ... Extra arguments for \code{\link{xyplot}}. -#' @return An object of class \code{"trellis"}. +#' The default is `c(2, 3)`. +#' @param type Parameter `type` of [panel.xyplot()]. +#' @param col Parameter `col` of [panel.xyplot()]. +#' @param lty Parameter `lty` of [panel.xyplot()]. +#' @param ... Extra arguments for [xyplot()]. +#' @return An object of class `"trellis"`. #' @author Stef van Buuren 2011 -#' @seealso \code{\link{mice}}, \code{\link[=mids-class]{mids}}, -#' \code{\link{xyplot}} +#' @seealso [mice()], [`mids()`][mids-class], +#' [xyplot()] #' @method plot mids #' @examples #' imp <- mice(nhanes, print = FALSE) diff --git a/R/pool.R b/R/pool.R index 55c48e05c..da92f14b2 100644 --- a/R/pool.R +++ b/R/pool.R @@ -1,39 +1,39 @@ #' Combine estimates by pooling rules #' -#' The \code{pool()} function combines the estimates from \code{m} +#' The `pool()` function combines the estimates from `m` #' repeated complete data analyses. The typical sequence of steps to #' perform a multiple imputation analysis is: #' \enumerate{ -#' \item Impute the missing data by the \code{mice()} function, resulting in -#' a multiple imputed data set (class \code{mids}); +#' \item Impute the missing data by the `mice()` function, resulting in +#' a multiple imputed data set (class `mids`); #' \item Fit the model of interest (scientific model) on each imputed data set -#' by the \code{with()} function, resulting an object of class \code{mira}; +#' by the `with()` function, resulting an object of class `mira`; #' \item Pool the estimates from each model into a single set of estimates -#' and standard errors, resulting in an object of class \code{mipo}; +#' and standard errors, resulting in an object of class `mipo`; #' \item Optionally, compare pooled estimates from different scientific models -#' by the \code{D1()} or \code{D3()} functions. +#' by the `D1()` or `D3()` functions. #' } #' A common error is to reverse steps 2 and 3, i.e., to pool the #' multiply-imputed data instead of the estimates. Doing so may severely bias #' the estimates of scientific interest and yield incorrect statistical -#' intervals and p-values. The \code{pool()} function will detect +#' intervals and p-values. The `pool()` function will detect #' this case. #' #' @details -#' The \code{pool()} function averages the estimates of the complete +#' The `pool()` function averages the estimates of the complete #' data model, computes the total variance over the repeated analyses #' by Rubin's rules (Rubin, 1987, p. 76), and computes the following #' diagnostic statistics per estimate: #' \enumerate{ -#' \item Relative increase in variance due to nonresponse {\code{r}}; -#' \item Residual degrees of freedom for hypothesis testing {\code{df}}; -#' \item Proportion of total variance due to missingness {\code{lambda}}; -#' \item Fraction of missing information {\code{fmi}}. +#' \item Relative increase in variance due to nonresponse {`r`}; +#' \item Residual degrees of freedom for hypothesis testing {`df`}; +#' \item Proportion of total variance due to missingness {`lambda`}; +#' \item Fraction of missing information {`fmi`}. #' } #' The degrees of freedom calculation for the pooled estimates uses the #' Barnard-Rubin adjustment for small samples (Barnard and Rubin, 1999). #' -#' The \code{pool.syn()} function combines estimates by Reiter's partially +#' The `pool.syn()` function combines estimates by Reiter's partially #' synthetic data pooling rules (Reiter, 2003). This combination rule #' assumes that the data that is synthesised is completely observed. #' Pooling differs from Rubin's method in the calculation of the total @@ -45,88 +45,88 @@ #' \item the standard error of each estimate; #' \item the residual degrees of freedom of the model. #' } -#' The \code{pool()} and \code{pool.syn()} functions rely on the -#' \code{broom::tidy} and \code{broom::glance} for extracting these +#' The `pool()` and `pool.syn()` functions rely on the +#' `broom::tidy` and `broom::glance` for extracting these #' parameters. #' -#' Since \code{mice 3.0+}, the \code{broom} +#' Since `mice 3.0+`, the `broom` #' package takes care of filtering out the relevant parts of the #' complete-data analysis. It may happen that you'll see the messages -#' like \code{Error: No tidy method for objects of class ...} or -#' \code{Error: No glance method for objects of class ...}. The message -#' means that your complete-data method used in \code{with(imp, ...)} has -#' no \code{tidy} or \code{glance} method defined in the \code{broom} package. +#' like `Error: No tidy method for objects of class ...` or +#' `Error: No glance method for objects of class ...`. The message +#' means that your complete-data method used in `with(imp, ...)` has +#' no `tidy` or `glance` method defined in the `broom` package. #' -#' The \code{broom.mixed} package contains \code{tidy} and \code{glance} methods +#' The `broom.mixed` package contains `tidy` and `glance` methods #' for mixed models. If you are using a mixed model, first run -#' \code{library(broom.mixed)} before calling \code{pool()}. +#' `library(broom.mixed)` before calling `pool()`. #' -#' If no \code{tidy} or \code{glance} methods are defined for your analysis -#' tabulate the \code{m} parameter estimates and their variance -#' estimates (the square of the standard errors) from the \code{m} fitted -#' models stored in \code{fit$analyses}. For each parameter, run -#' \code{\link{pool.scalar}} to obtain the pooled parameters estimate, its variance, the +#' If no `tidy` or `glance` methods are defined for your analysis +#' tabulate the `m` parameter estimates and their variance +#' estimates (the square of the standard errors) from the `m` fitted +#' models stored in `fit$analyses`. For each parameter, run +#' [pool.scalar()] to obtain the pooled parameters estimate, its variance, the #' degrees of freedom, the relative increase in variance and the fraction of missing #' information. #' -#' An alternative is to write your own \code{glance()} and \code{tidy()} -#' methods and add these to \code{broom} according to the specifications -#' given in \url{https://broom.tidymodels.org}. +#' An alternative is to write your own `glance()` and `tidy()` +#' methods and add these to `broom` according to the specifications +#' given in . -#' In versions prior to \code{mice 3.0} pooling required that -#' \code{coef()} and \code{vcov()} methods were available for fitted -#' objects. \emph{This feature is no longer supported}. The reason is that -#' \code{vcov()} methods are inconsistent across packages, leading to -#' buggy behaviour of the \code{pool()} function. +#' In versions prior to `mice 3.0` pooling required that +#' `coef()` and `vcov()` methods were available for fitted +#' objects. *This feature is no longer supported*. The reason is that +#' `vcov()` methods are inconsistent across packages, leading to +#' buggy behaviour of the `pool()` function. #' -#' Since \code{mice 3.13.2} function \code{pool()} uses the robust +#' Since `mice 3.13.2` function `pool()` uses the robust #' the standard error estimate for pooling when it can extract -#' \code{robust.se} from the \code{tidy()} object. +#' `robust.se` from the `tidy()` object. #' -#' @param object An object of class \code{mira} (produced by \code{with.mids()} -#' or \code{as.mira()}), or a \code{list} with model fits. +#' @param object An object of class `mira` (produced by `with.mids()` +#' or `as.mira()`), or a `list` with model fits. #' @param dfcom A positive number representing the degrees of freedom in the #' complete-data analysis. Normally, this would be the number of independent #' observation minus the number of fitted parameters. The default -#' (\code{dfcom = NULL}) extract this information in the following +#' (`dfcom = NULL`) extract this information in the following #' order: 1) the component -#' \code{residual.df} returned by \code{glance()} if a \code{glance()} -#' function is found, 2) the result of \code{df.residual(} applied to -#' the first fitted model, and 3) as \code{999999}. -#' In the last case, the warning \code{"Large sample assumed"} is printed. +#' `residual.df` returned by `glance()` if a `glance()` +#' function is found, 2) the result of `df.residual(` applied to +#' the first fitted model, and 3) as `999999`. +#' In the last case, the warning `"Large sample assumed"` is printed. #' If the degrees of freedom is incorrect, specify the appropriate value #' manually. #' @param rule A string indicating the pooling rule. Currently supported are -#' \code{"rubin1987"} (default, for missing data) and \code{"reiter2003"} +#' `"rubin1987"` (default, for missing data) and `"reiter2003"` #' (for synthetic data created from a complete data set). #' @param custom.t A custom character string to be parsed as a calculation rule -#' for the total variance \code{t}. The custom rule can use the other calculated -#' pooling statistics where the dimensions must come from \code{.data$}. The -#' default \code{t} calculation would have the form -#' \code{".data$ubar + (1 + 1 / .data$m) * .data$b"}. +#' for the total variance `t`. The custom rule can use the other calculated +#' pooling statistics where the dimensions must come from `.data$`. The +#' default `t` calculation would have the form +#' `".data$ubar + (1 + 1 / .data$m) * .data$b"`. #' See examples for an example. -#' @return An object of class \code{mipo}, which stands for 'multiple imputation +#' @return An object of class `mipo`, which stands for 'multiple imputation #' pooled outcome'. -#' For rule \code{"reiter2003"} values for \code{lambda} and \code{fmi} are +#' For rule `"reiter2003"` values for `lambda` and `fmi` are #' set to `NA`, as these statistics do not apply for data synthesised from #' fully observed data. -#' @seealso \code{\link{with.mids}}, \code{\link{as.mira}}, \code{\link{pool.scalar}}, -#' \code{\link[broom:reexports]{glance}}, \code{\link[broom:reexports]{tidy}} -#' \url{https://github.com/amices/mice/issues/142}, -#' \url{https://github.com/amices/mice/issues/274} +#' @seealso [with.mids()], [as.mira()], [pool.scalar()], +#' [`glance()`][broom::reexports], [`tidy()`][broom::reexports] +#' , +#' #' @references #' Barnard, J. and Rubin, D.B. (1999). Small sample degrees of -#' freedom with multiple imputation. \emph{Biometrika}, 86, 948-955. +#' freedom with multiple imputation. *Biometrika*, 86, 948-955. #' -#' Rubin, D.B. (1987). \emph{Multiple Imputation for Nonresponse in Surveys}. +#' Rubin, D.B. (1987). *Multiple Imputation for Nonresponse in Surveys*. #' New York: John Wiley and Sons. #' #' Reiter, J.P. (2003). Inference for Partially Synthetic, -#' Public Use Microdata Sets. \emph{Survey Methodology}, \bold{29}, 181-189. +#' Public Use Microdata Sets. *Survey Methodology*, **29**, 181-189. #' -#' van Buuren S and 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} +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @examples #' # impute missing data, analyse and pool using the classic MICE workflow #' imp <- mice(nhanes, maxit = 2, m = 2) diff --git a/R/pool.compare.R b/R/pool.compare.R index 614569c81..4689cdfbf 100644 --- a/R/pool.compare.R +++ b/R/pool.compare.R @@ -1,59 +1,59 @@ #' Compare two nested models fitted to imputed data #' -#' This function is deprecated in V3. Use \code{\link{D1}} or -#' \code{\link{D3}} instead. +#' This function is deprecated in V3. Use [D1()] or +#' [D3()] instead. #' #' Compares two nested models after m repeated complete data analysis #' #' The function is based on the article of Meng and Rubin (1992). The #' Wald-method can be found in paragraph 2.2 and the likelihood method can be #' found in paragraph 3. One could use the Wald method for comparison of linear -#' models obtained with e.g. \code{lm} (in \code{with.mids()}). The likelihood +#' models obtained with e.g. `lm` (in `with.mids()`). The likelihood #' method should be used in case of logistic regression models obtained with -#' \code{glm()} in \code{with.mids()}. +#' `glm()` in `with.mids()`. #' -#' The function assumes that \code{fit1} is the -#' larger model, and that model \code{fit0} is fully contained in \code{fit1}. -#' In case of \code{method='wald'}, the null hypothesis is tested that the extra +#' The function assumes that `fit1` is the +#' larger model, and that model `fit0` is fully contained in `fit1`. +#' In case of `method='wald'`, the null hypothesis is tested that the extra #' parameters are all zero. #' -#' @param fit1 An object of class 'mira', produced by \code{with.mids()}. -#' @param fit0 An object of class 'mira', produced by \code{with.mids()}. The -#' model in \code{fit0} is a nested fit0 of \code{fit1}. -#' @param method Either \code{"wald"} or \code{"likelihood"} specifying -#' the type of comparison. The default is \code{"wald"}. +#' @param fit1 An object of class 'mira', produced by `with.mids()`. +#' @param fit0 An object of class 'mira', produced by `with.mids()`. The +#' model in `fit0` is a nested fit0 of `fit1`. +#' @param method Either `"wald"` or `"likelihood"` specifying +#' the type of comparison. The default is `"wald"`. #' @param data No longer used. -#' @return A list containing several components. Component \code{call} is -#' the call to the \code{pool.compare} function. Component \code{call11} is -#' the call that created \code{fit1}. Component \code{call12} is the -#' call that created the imputations. Component \code{call01} is the -#' call that created \code{fit0}. Component \code{call02} is the -#' call that created the imputations. Components \code{method} is the +#' @return A list containing several components. Component `call` is +#' the call to the `pool.compare` function. Component `call11` is +#' the call that created `fit1`. Component `call12` is the +#' call that created the imputations. Component `call01` is the +#' call that created `fit0`. Component `call02` is the +#' call that created the imputations. Components `method` is the #' method used to compare two models: 'Wald' or 'likelihood'. Component -#' \code{nmis} is the number of missing entries for each variable. -#' Component \code{m} is the number of imputations. -#' Component \code{qhat1} is a matrix, containing the estimated coefficients of the -#' \emph{m} repeated complete data analyses from \code{fit1}. -#' Component \code{qhat0} is a matrix, containing the estimated coefficients of the -#' \emph{m} repeated complete data analyses from \code{fit0}. -#' Component \code{ubar1} is the mean of the variances of \code{fit1}, +#' `nmis` is the number of missing entries for each variable. +#' Component `m` is the number of imputations. +#' Component `qhat1` is a matrix, containing the estimated coefficients of the +#' *m* repeated complete data analyses from `fit1`. +#' Component `qhat0` is a matrix, containing the estimated coefficients of the +#' *m* repeated complete data analyses from `fit0`. +#' Component `ubar1` is the mean of the variances of `fit1`, #' formula (3.1.3), Rubin (1987). -#' Component \code{ubar0} is the mean of the variances of \code{fit0}, +#' Component `ubar0` is the mean of the variances of `fit0`, #' formula (3.1.3), Rubin (1987). -#' Component \code{qbar1} is the pooled estimate of \code{fit1}, formula (3.1.2) Rubin +#' Component `qbar1` is the pooled estimate of `fit1`, formula (3.1.2) Rubin #' (1987). -#' Component \code{qbar0} is the pooled estimate of \code{fit0}, formula (3.1.2) Rubin +#' Component `qbar0` is the pooled estimate of `fit0`, formula (3.1.2) Rubin #' (1987). -#' Component \code{Dm} is the test statistic. -#' Component \code{rm} is the relative increase in variance due to nonresponse, formula +#' Component `Dm` is the test statistic. +#' Component `rm` is the relative increase in variance due to nonresponse, formula #' (3.1.7), Rubin (1987). -#' Component \code{df1}: df1 = under the null hypothesis it is assumed that \code{Dm} has an F +#' Component `df1`: df1 = under the null hypothesis it is assumed that `Dm` has an F #' distribution with (df1,df2) degrees of freedom. -#' Component \code{df2}: df2. -#' Component \code{pvalue} is the P-value of testing whether the model \code{fit1} is -#' statistically different from the smaller \code{fit0}. +#' Component `df2`: df2. +#' Component `pvalue` is the P-value of testing whether the model `fit1` is +#' statistically different from the smaller `fit0`. #' @author Karin Groothuis-Oudshoorn and Stef van Buuren, 2009 -#' @seealso \code{\link{lm.mids}}, \code{\link{glm.mids}} +#' @seealso [lm.mids()], [glm.mids()] #' @references Li, K.H., Meng, X.L., Raghunathan, T.E. and Rubin, D. B. (1991). #' Significance levels from repeated p-values with multiply-imputed data. #' Statistica Sinica, 1, 65-92. @@ -61,9 +61,9 @@ #' Meng, X.L. and Rubin, D.B. (1992). Performing likelihood ratio tests with #' multiple-imputed data sets. Biometrika, 79, 103-111. #' -#' van Buuren S and 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} +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords htest #' @export pool.compare <- function(fit1, fit0, method = c("wald", "likelihood"), diff --git a/R/pool.r.squared.R b/R/pool.r.squared.R index 9ec7dda7c..00ca29156 100644 --- a/R/pool.r.squared.R +++ b/R/pool.r.squared.R @@ -1,19 +1,19 @@ #' Pools R^2 of m models fitted to multiply-imputed data #' #' The function pools the coefficients of determination R^2 or the adjusted -#' coefficients of determination (R^2_a) obtained with the \code{lm} modeling -#' function. For pooling it uses the Fisher \emph{z}-transformation. +#' coefficients of determination (R^2_a) obtained with the `lm` modeling +#' function. For pooling it uses the Fisher *z*-transformation. #' -#' @param object An object of class 'mira' or 'mipo', produced by \code{lm.mids}, -#' \code{with.mids}, or \code{pool} with \code{lm} as modeling function. +#' @param object An object of class 'mira' or 'mipo', produced by `lm.mids`, +#' `with.mids`, or `pool` with `lm` as modeling function. #' @param adjusted A logical value. If adjusted=TRUE then the adjusted R^2 is #' calculated. The default value is FALSE. -#' @return Returns a 1x4 table with components. Component \code{est} is the -#' pooled R^2 estimate. Component \code{lo95} is the 95 \% lower bound of the pooled R^2. -#' Component \code{hi95} is the 95 \% upper bound of the pooled R^2. -#' Component \code{fmi} is the fraction of missing information due to nonresponse. +#' @return Returns a 1x4 table with components. Component `est` is the +#' pooled R^2 estimate. Component `lo95` is the 95 \% lower bound of the pooled R^2. +#' Component `hi95` is the 95 \% upper bound of the pooled R^2. +#' Component `fmi` is the fraction of missing information due to nonresponse. #' @author Karin Groothuis-Oudshoorn and Stef van Buuren, 2009 -#' @seealso \code{\link{pool}},\code{\link{pool.scalar}} +#' @seealso [pool()],[pool.scalar()] #' @references Harel, O (2009). The estimation of R^2 and adjusted R^2 in #' incomplete data sets using multiple imputation, Journal of Applied Statistics, #' 36:1109-1118. @@ -21,9 +21,9 @@ #' Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New #' York: John Wiley and Sons. #' -#' van Buuren S and 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} +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' #' @keywords htest diff --git a/R/pool.scalar.R b/R/pool.scalar.R index 4dd469f05..b3a37ad41 100644 --- a/R/pool.scalar.R +++ b/R/pool.scalar.R @@ -7,41 +7,41 @@ #' relative increase in variance due to missing data or data synthesisation #' and the fraction of missing information. #' -#' @param Q A vector of univariate estimates of \code{m} repeated complete data +#' @param Q A vector of univariate estimates of `m` repeated complete data #' analyses. -#' @param U A vector containing the corresponding \code{m} variances of the univariate +#' @param U A vector containing the corresponding `m` variances of the univariate #' estimates. #' @param n A number providing the sample size. If nothing is specified, -#' an infinite sample \code{n = Inf} is assumed. +#' an infinite sample `n = Inf` is assumed. #' @param k A number indicating the number of parameters to be estimated. -#' By default, \code{k = 1} is assumed. +#' By default, `k = 1` is assumed. #' @inheritParams pool #' @return Returns a list with components. #' \describe{ -#' \item{\code{m}:}{Number of imputations.} -#' \item{\code{qhat}:}{The \code{m} univariate estimates of repeated complete-data analyses.} -#' \item{\code{u}:}{The corresponding \code{m} variances of the univariate estimates.} -#' \item{\code{qbar}:}{The pooled univariate estimate, formula (3.1.2) Rubin (1987).} -#' \item{\code{ubar}:}{The mean of the variances (i.e. the pooled within-imputation variance), +#' \item{`m`:}{Number of imputations.} +#' \item{`qhat`:}{The `m` univariate estimates of repeated complete-data analyses.} +#' \item{`u`:}{The corresponding `m` variances of the univariate estimates.} +#' \item{`qbar`:}{The pooled univariate estimate, formula (3.1.2) Rubin (1987).} +#' \item{`ubar`:}{The mean of the variances (i.e. the pooled within-imputation variance), #' formula (3.1.3) Rubin (1987).} -#' \item{\code{b}:}{The between-imputation variance, formula (3.1.4) Rubin (1987).} -#' \item{\code{t}:}{The total variance of the pooled estimated, formula (3.1.5) +#' \item{`b`:}{The between-imputation variance, formula (3.1.4) Rubin (1987).} +#' \item{`t`:}{The total variance of the pooled estimated, formula (3.1.5) #' Rubin (1987).} -#' \item{\code{r}:}{The relative increase in variance due to nonresponse, formula +#' \item{`r`:}{The relative increase in variance due to nonresponse, formula #' (3.1.7) Rubin (1987).} -#' \item{\code{df}:}{The degrees of freedom for t reference distribution by the +#' \item{`df`:}{The degrees of freedom for t reference distribution by the #' method of Barnard-Rubin (1999).} -#' \item{\code{fmi}:}{The fraction missing information due to nonresponse, +#' \item{`fmi`:}{The fraction missing information due to nonresponse, #' formula (3.1.10) Rubin (1987). (Not defined for synthetic data.)} #' } #' @author Karin Groothuis-Oudshoorn and Stef van Buuren, 2009; Thom Volker, 2021 -#' @seealso \code{\link{pool}} +#' @seealso [pool()] #' @references #' Rubin, D.B. (1987). Multiple Imputation for Nonresponse in #' Surveys. New York: John Wiley and Sons. #' #' Reiter, J.P. (2003). Inference for Partially Synthetic, -#' Public Use Microdata Sets. \emph{Survey Methodology}, \bold{29}, 181-189. +#' Public Use Microdata Sets. *Survey Methodology*, **29**, 181-189. #' @examples #' # missing data imputation with with manual pooling #' imp <- mice(nhanes, maxit = 2, m = 2, print = FALSE, seed = 18210) diff --git a/R/pool.table.R b/R/pool.table.R index 3b2e228c0..f2266aebe 100644 --- a/R/pool.table.R +++ b/R/pool.table.R @@ -1,76 +1,76 @@ #' Combines estimates from a tidy table #' -#' @param w A \code{data.frame} with parameter estimates +#' @param w A `data.frame` with parameter estimates #' in tidy format (see details). #' @param dfcom A positive number representing the degrees of freedom of the -#' residuals in the complete-data analysis. The \code{dfcom} argument is -#' used for the Barnard-Rubin adjustment. In a linear regression, \code{dfcom} +#' residuals in the complete-data analysis. The `dfcom` argument is +#' used for the Barnard-Rubin adjustment. In a linear regression, `dfcom` #' would be equivalent to the number of independent observation minus the number #' of fitted parameters, but the expression becomes more complex for #' regularized, proportional hazards, or other semi-parametric -#' techniques. Only used if \code{w} lacks a column named \code{"df.residual"}. +#' techniques. Only used if `w` lacks a column named `"df.residual"`. #' @param rule A string indicating the pooling rule. Currently supported are -#' \code{"rubin1987"} (default, for analyses applied to multiply-imputed -#' incomplete data) and \code{"reiter2003"} (for analyses applied to +#' `"rubin1987"` (default, for analyses applied to multiply-imputed +#' incomplete data) and `"reiter2003"` (for analyses applied to #' synthetic data created from complete data). #' @param custom.t A custom character string to be parsed as a calculation -#' rule for the total variance \code{t}. The custom rule can use the -#' other calculated pooling statistics. The default \code{t} calculation -#' has the form \code{".data$ubar + (1 + 1 / .data$m) * .data$b"}. -#' @param type A string, either \code{"minimal"}, \code{"tests"} or \code{"all"}. -#' Use minimal to mimick the output of \code{summary(pool(fit))}. The default -#' is \code{"all"}. +#' rule for the total variance `t`. The custom rule can use the +#' other calculated pooling statistics. The default `t` calculation +#' has the form `".data$ubar + (1 + 1 / .data$m) * .data$b"`. +#' @param type A string, either `"minimal"`, `"tests"` or `"all"`. +#' Use minimal to mimick the output of `summary(pool(fit))`. The default +#' is `"all"`. #' @param conf.int Logical indicating whether to include #' a confidence interval. #' @param conf.level Confidence level of the interval, used only if -#' \code{conf.int = TRUE}. Number between 0 and 1. +#' `conf.int = TRUE`. Number between 0 and 1. #' @param exponentiate Flag indicating whether to exponentiate the #' coefficient estimates and confidence intervals (typical for #' logistic regression). #' @param \dots Arguments passed down #' @details -#' The input data \code{w} is a \code{data.frame} with columns named: +#' The input data `w` is a `data.frame` with columns named: #' #' \tabular{ll}{ -#' \code{term} \tab a character or factor with the parameter names\cr -#' \code{estimate} \tab a numeric vector with parameter estimates\cr -#' \code{std.error} \tab a numeric vector with standard errors of \code{estimate}\cr -#' \code{residual.df} \tab a numeric vector with the degrees of freedom +#' `term` \tab a character or factor with the parameter names\cr +#' `estimate` \tab a numeric vector with parameter estimates\cr +#' `std.error` \tab a numeric vector with standard errors of `estimate`\cr +#' `residual.df` \tab a numeric vector with the degrees of freedom #' } #' #' Columns 1-3 are obligatory. Column 4 is optional. Usually, #' all entries in column 4 are the same. The user can omit column 4, -#' and specify argument \code{pool.table(..., dfcom = ...)} instead. -#' If both are given, then column \code{residual.df} takes precedence. -#' If neither are specified, then \code{mice} tries to calculate the +#' and specify argument `pool.table(..., dfcom = ...)` instead. +#' If both are given, then column `residual.df` takes precedence. +#' If neither are specified, then `mice` tries to calculate the #' residual degrees of freedom. If that fails (e.g. because there is -#' no information on sample size), \code{mice} sets \code{dfcom = Inf}. -#' The value \code{dfcom = Inf} is acceptable for large samples +#' no information on sample size), `mice` sets `dfcom = Inf`. +#' The value `dfcom = Inf` is acceptable for large samples #' (n > 1000) and relatively concise parametric models. #' #' @return #' -#' \code{pool.table()} returns a \code{data.frame} with aggregated +#' `pool.table()` returns a `data.frame` with aggregated #' estimates, standard errors, confidence intervals and statistical tests. #' #' The meaning of the columns is as follows: #' #' \tabular{ll}{ -#' \code{term} \tab Parameter name\cr -#' \code{m} \tab Number of multiple imputations\cr -#' \code{estimate} \tab Pooled complete data estimate\cr -#' \code{std.error} \tab Standard error of \code{estimate}\cr -#' \code{statistic} \tab t-statistic = \code{estimate} / \code{std.error}\cr -#' \code{df} \tab Degrees of freedom for \code{statistic}\cr -#' \code{p.value} \tab One-sided P-value under null hypothesis\cr -#' \code{conf.low} \tab Lower bound of c.i. (default 95 pct)\cr -#' \code{conf.high} \tab Upper bound of c.i. (default 95 pct)\cr -#' \code{riv} \tab Relative increase in variance\cr -#' \code{fmi} \tab Fraction of missing information\cr -#' \code{ubar} \tab Within-imputation variance of \code{estimate}\cr -#' \code{b} \tab Between-imputation variance of \code{estimate}\cr -#' \code{t} \tab Total variance, of \code{estimate}\cr -#' \code{dfcom} \tab Residual degrees of freedom in complete data\cr +#' `term` \tab Parameter name\cr +#' `m` \tab Number of multiple imputations\cr +#' `estimate` \tab Pooled complete data estimate\cr +#' `std.error` \tab Standard error of `estimate`\cr +#' `statistic` \tab t-statistic = `estimate` / `std.error`\cr +#' `df` \tab Degrees of freedom for `statistic`\cr +#' `p.value` \tab One-sided P-value under null hypothesis\cr +#' `conf.low` \tab Lower bound of c.i. (default 95 pct)\cr +#' `conf.high` \tab Upper bound of c.i. (default 95 pct)\cr +#' `riv` \tab Relative increase in variance\cr +#' `fmi` \tab Fraction of missing information\cr +#' `ubar` \tab Within-imputation variance of `estimate`\cr +#' `b` \tab Between-imputation variance of `estimate`\cr +#' `t` \tab Total variance, of `estimate`\cr +#' `dfcom` \tab Residual degrees of freedom in complete data\cr #' } #' #' @examples diff --git a/R/popmis.R b/R/popmis.R index 962a9ee25..6624745b1 100644 --- a/R/popmis.R +++ b/R/popmis.R @@ -17,8 +17,8 @@ #' \item{texp}{Teacher experience (years)} #' \item{const}{Constant intercept term} #' \item{teachpop}{Teacher popularity} } -#' @source Hox, J. J. (2002) \emph{Multilevel analysis. Techniques and -#' applications.} Mahwah, NJ: Lawrence Erlbaum. +#' @source Hox, J. J. (2002) *Multilevel analysis. Techniques and +#' applications.* Mahwah, NJ: Lawrence Erlbaum. #' @keywords datasets #' @examples #' diff --git a/R/pops.R b/R/pops.R index 7db507de8..641eb977f 100644 --- a/R/pops.R +++ b/R/pops.R @@ -20,12 +20,12 @@ #' #' Multiple imputation of this data set has been described in Hille et al (2007) #' and Van Buuren (2012), chapter 8. -#' @note This dataset is not part of \code{mice}. +#' @note This dataset is not part of `mice`. #' @name pops #' @aliases pops pops.pred #' @docType data -#' @format \code{pops} is a data frame with 959 rows and 86 columns. -#' \code{pops.pred} is the 86 by 86 binary predictor matrix used for specifying +#' @format `pops` is a data frame with 959 rows and 86 columns. +#' `pops.pred` is the 86 by 86 binary predictor matrix used for specifying #' the multiple imputation model. #' @source #' Hille, E. T. M., Elbertse, L., Bennebroek Gravenhorst, J., Brand, R., @@ -41,7 +41,7 @@ #' gestational age infants at 19 years of age. Pediatrics, 120(3):587595. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-selective.html#pops-study-19-years-follow-up}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-selective.html#pops-study-19-years-follow-up) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets #' @examples diff --git a/R/post.R b/R/post.R index 5e601151c..587a8f7ca 100644 --- a/R/post.R +++ b/R/post.R @@ -1,11 +1,11 @@ -#' Creates a \code{post} argument +#' Creates a `post` argument #' -#' This helper function creates a valid \code{post} vector. The -#' \code{post} vector is an argument to the \code{mice} function that +#' This helper function creates a valid `post` vector. The +#' `post` vector is an argument to the `mice` function that #' specifies post-processing for a variable after each iteration of imputation. #' @inheritParams mice -#' @return Character vector of \code{ncol(data)} element -#' @seealso \code{\link{mice}} +#' @return Character vector of `ncol(data)` element +#' @seealso [mice()] #' @examples #' make.post(nhanes2) #' @export diff --git a/R/potthoffroy.R b/R/potthoffroy.R index bd93e3153..b07178af7 100644 --- a/R/potthoffroy.R +++ b/R/potthoffroy.R @@ -17,7 +17,7 @@ #' #' @name potthoffroy #' @docType data -#' @format \code{tbs} is a data frame with 27 rows and 6 columns: +#' @format `tbs` is a data frame with 27 rows and 6 columns: #' \describe{ #' \item{id}{Person number} #' \item{sex}{Sex M/F} @@ -28,13 +28,13 @@ #' } #' @source Potthoff, R. F., Roy, S. N. (1964). A generalized multivariate #' analysis of variance model usefully especially for growth curve problems. -#' \emph{Biometrika}, \emph{51}(3), 313-326. +#' *Biometrika*, *51*(3), 313-326. #' -#' Little, R. J. A., Rubin, D. B. (1987). \emph{Statistical Analysis with -#' Missing Data.} New York: John Wiley & Sons. +#' Little, R. J. A., Rubin, D. B. (1987). *Statistical Analysis with +#' Missing Data.* New York: John Wiley & Sons. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/ex-ch-longitudinal.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/ex-ch-longitudinal.html) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets #' @examples diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index b22711d88..3abdf320b 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -1,18 +1,18 @@ -#' Creates a \code{predictorMatrix} argument +#' Creates a `predictorMatrix` argument #' -#' This helper function creates a valid \code{predictMatrix}. The -#' \code{predictorMatrix} is an argument to the \code{mice} function. +#' This helper function creates a valid `predictMatrix`. The +#' `predictorMatrix` is an argument to the `mice` function. #' It specifies the target variable or block in the rows, and the -#' predictor variables on the columns. An entry of \code{0} means that +#' predictor variables on the columns. An entry of `0` means that #' 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 data A `data.frame` with the source data #' @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 -#' @seealso \code{\link{make.blocks}} +#' @seealso [make.blocks()] #' @examples #' make.predictorMatrix(nhanes) #' make.predictorMatrix(nhanes, blocks = make.blocks(nhanes, "collect")) diff --git a/R/print.R b/R/print.R index 5bfe2a40d..d205bacbe 100644 --- a/R/print.R +++ b/R/print.R @@ -1,10 +1,10 @@ -#' Print a \code{mids} object +#' Print a `mids` object #' #' @rdname print -#' @param x Object of class \code{mids}, \code{mira} or \code{mipo} -#' @param ... Other parameters passed down to \code{print.default()} -#' @return \code{NULL} -#' @seealso \code{\link[=mids-class]{mids}} +#' @param x Object of class `mids`, `mira` or `mipo` +#' @param ... Other parameters passed down to `print.default()` +#' @return `NULL` +#' @seealso [`mids()`][mids-class] #' @method print mids #' @export print.mids <- function(x, ...) { @@ -22,11 +22,11 @@ print.mids <- function(x, ...) { } -#' Print a \code{mira} object +#' Print a `mira` object #' #' @rdname print -#' @return \code{NULL} -#' @seealso \code{\link[=mira-class]{mira}} +#' @return `NULL` +#' @seealso [`mira()`][mira-class] #' @method print mira #' @export print.mira <- function(x, ...) { @@ -39,11 +39,11 @@ print.mira <- function(x, ...) { } -#' Print a \code{mice.anova} object +#' Print a `mice.anova` object #' #' @rdname print -#' @return \code{NULL} -#' @seealso \code{\link{mipo}} +#' @return `NULL` +#' @seealso [mipo()] #' @method print mice.anova #' @export print.mice.anova <- function(x, ...) { @@ -53,11 +53,11 @@ print.mice.anova <- function(x, ...) { } -#' Print a \code{summary.mice.anova} object +#' Print a `summary.mice.anova` object #' #' @rdname print -#' @return \code{NULL} -#' @seealso \code{\link{mipo}} +#' @return `NULL` +#' @seealso [mipo()] #' @method print mice.anova.summary #' @export print.mice.anova.summary <- function(x, ...) { @@ -75,12 +75,12 @@ print.mice.anova.summary <- function(x, ...) { } -#' Print a \code{mads} object +#' Print a `mads` object #' -#' @param x Object of class \code{mads} -#' @param ... Other parameters passed down to \code{print.default()} -#' @return \code{NULL} -#' @seealso \code{\link[=mads-class]{mads}} +#' @param x Object of class `mads` +#' @param ... Other parameters passed down to `print.default()` +#' @return `NULL` +#' @seealso [`mads()`][mads-class] #' @method print mads #' @export print.mads <- function(x, ...) { diff --git a/R/quickpred.R b/R/quickpred.R index 9ac43bfb6..7d1c2e12a 100644 --- a/R/quickpred.R +++ b/R/quickpred.R @@ -12,20 +12,20 @@ #' The first correlation uses the values of the target and the predictor #' directly. The second correlation uses the (binary) response indicator of the #' target and the values of the predictor. If the largest (in absolute value) of -#' these correlations exceeds \code{mincor}, the predictor will be added to the -#' imputation set. The default value for \code{mincor} is 0.1. +#' these correlations exceeds `mincor`, the predictor will be added to the +#' imputation set. The default value for `mincor` is 0.1. #' #' In addition, the procedure eliminates predictors whose proportion of usable -#' cases fails to meet the minimum specified by \code{minpuc}. The default value +#' cases fails to meet the minimum specified by `minpuc`. The default value #' is 0, so predictors are retained even if they have no usable case. #' -#' Finally, the procedure includes any predictors named in the \code{include} +#' Finally, the procedure includes any predictors named in the `include` #' argument (which is useful for background variables like age and sex) and -#' eliminates any predictor named in the \code{exclude} argument. If a variable -#' is listed in both \code{include} and \code{exclude} arguments, the -#' \code{include} argument takes precedence. +#' eliminates any predictor named in the `exclude` argument. If a variable +#' is listed in both `include` and `exclude` arguments, the +#' `include` argument takes precedence. #' -#' Advanced topic: \code{mincor} and \code{minpuc} are typically specified as +#' Advanced topic: `mincor` and `minpuc` are typically specified as #' scalars, but vectors and squares matrices of appropriate size will also work. #' Each element of the vector corresponds to a row of the predictor matrix, so #' the procedure can effectively differentiate between different target @@ -34,36 +34,36 @@ #' relatively small. Using a square matrix extends the idea to the columns, so #' that one can also apply cellwise thresholds. #' -#' @note \code{quickpred()} uses \code{\link[base]{data.matrix}} to convert +#' @note `quickpred()` uses [base::data.matrix()] to convert #' factors to numbers through their internal codes. Especially for unordered #' factors the resulting quantification may not make sense. #' #' @param data Matrix or data frame with incomplete data. -#' @param mincor A scalar, numeric vector (of size \code{ncol(data))} or numeric -#' matrix (square, of size \code{ncol(data)} specifying the minimum +#' @param mincor A scalar, numeric vector (of size `ncol(data))` or numeric +#' matrix (square, of size `ncol(data)` specifying the minimum #' threshold(s) against which the absolute correlation in the data is compared. -#' @param minpuc A scalar, vector (of size \code{ncol(data))} or matrix (square, -#' of size \code{ncol(data)} specifying the minimum threshold(s) for the +#' @param minpuc A scalar, vector (of size `ncol(data))` or matrix (square, +#' of size `ncol(data)` specifying the minimum threshold(s) for the #' proportion of usable cases. #' @param include A string or a vector of strings containing one or more -#' variable names from \code{names(data)}. Variables specified are always +#' variable names from `names(data)`. Variables specified are always #' included as a predictor. #' @param exclude A string or a vector of strings containing one or more -#' variable names from \code{names(data)}. Variables specified are always +#' variable names from `names(data)`. Variables specified are always #' excluded as a predictor. #' @param method A string specifying the type of correlation. Use -#' \code{'pearson'} (default), \code{'kendall'} or \code{'spearman'}. Can be +#' `'pearson'` (default), `'kendall'` or `'spearman'`. Can be #' abbreviated. -#' @return A square binary matrix of size \code{ncol(data)}. +#' @return A square binary matrix of size `ncol(data)`. #' @author Stef van Buuren, Aug 2009 -#' @seealso \code{\link{mice}}, \code{\link[=mids-class]{mids}} +#' @seealso [mice()], [`mids()`][mids-class] #' @references van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple #' imputation of missing blood pressure covariates in survival analysis. -#' \emph{Statistics in Medicine}, \bold{18}, 681--694. +#' *Statistics in Medicine*, **18**, 681--694. #' -#' van Buuren, S. and 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} +#' van Buuren, S. and Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords misc #' @examples #' # default: include all predictors with absolute correlation over 0.1 diff --git a/R/selfreport.R b/R/selfreport.R index 67135d422..0ddc62d91 100644 --- a/R/selfreport.R +++ b/R/selfreport.R @@ -3,29 +3,29 @@ #' Dataset containing height and weight data (measured, self-reported) from two #' studies. #' -#' This dataset combines two datasets: \code{krul} data (Krul, 2010) (1257 -#' persons) and the \code{mgg} data (Van Keulen 2011; Van der Klauw 2011) (803 -#' persons). The \code{krul} dataset contains height and weight (both measures -#' and self-reported) from 1257 Dutch adults, whereas the \code{mgg} dataset +#' This dataset combines two datasets: `krul` data (Krul, 2010) (1257 +#' persons) and the `mgg` data (Van Keulen 2011; Van der Klauw 2011) (803 +#' persons). The `krul` dataset contains height and weight (both measures +#' and self-reported) from 1257 Dutch adults, whereas the `mgg` dataset #' contains self-reported height and weight for 803 Dutch adults. Section 7.3 in #' Van Buuren (2012) shows how the missing measured data can be imputed in the -#' \code{mgg} data, so corrected prevalence estimates can be calculated. +#' `mgg` data, so corrected prevalence estimates can be calculated. #' #' @name selfreport #' @aliases selfreport mgg #' @docType data #' @format A data frame with 2060 rows and 15 variables: #' \describe{ -#' \item{src}{Study, either \code{krul} or \code{mgg} (factor)} +#' \item{src}{Study, either `krul` or `mgg` (factor)} #' \item{id}{Person identification number} -#' \item{pop}{Population, all \code{NL} (factor)} +#' \item{pop}{Population, all `NL` (factor)} #' \item{age}{Age of respondent in years} #' \item{sex}{Sex of respondent (factor)} #' \item{hm}{Height measured (cm)} #' \item{wm}{Weight measured (kg)} #' \item{hr}{Height reported (cm)} #' \item{wr}{Weight reported (kg)} -#' \item{prg}{Pregnancy (factor), all \code{Not pregnant}} +#' \item{prg}{Pregnancy (factor), all `Not pregnant`} #' \item{edu}{Educational level (factor)} #' \item{etn}{Ethnicity (factor)} #' \item{web}{Obtained through web survey (factor)} @@ -34,21 +34,21 @@ #' } #' @source Krul, A., Daanen, H. A. M., Choi, H. (2010). Self-reported and #' measured weight, height and body mass index (BMI) in Italy, The Netherlands -#' and North America. \emph{European Journal of Public Health}, \emph{21}(4), +#' and North America. *European Journal of Public Health*, *21*(4), #' 414-419. #' -#' Van Keulen, H.M.,, Chorus, A.M.J., Verheijden, M.W. (2011). \emph{Monitor +#' Van Keulen, H.M.,, Chorus, A.M.J., Verheijden, M.W. (2011). *Monitor #' Convenant Gezond Gewicht Nulmeting (determinanten van) beweeg- en eetgedrag -#' van kinderen (4-11 jaar), jongeren (12-17 jaar) en volwassenen (18+ jaar)}. +#' van kinderen (4-11 jaar), jongeren (12-17 jaar) en volwassenen (18+ jaar)*. #' TNO/LS 2011.016. Leiden: TNO. #' -#' Van der Klauw, M., Van Keulen, H.M., Verheijden, M.W. (2011). \emph{Monitor +#' Van der Klauw, M., Van Keulen, H.M., Verheijden, M.W. (2011). *Monitor #' Convenant Gezond Gewicht Beweeg- en eetgedrag van kinderen (4-11 jaar), -#' jongeren (12-17 jaar) en volwassenen (18+ jaar) in 2010 en 2011.} TNO/LS +#' jongeren (12-17 jaar) en volwassenen (18+ jaar) in 2010 en 2011.* TNO/LS #' 2011.055. Leiden: TNO. (in Dutch) #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-prevalence.html#sec:srcdata}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-prevalence.html#sec:srcdata) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets #' @examples diff --git a/R/squeeze.R b/R/squeeze.R index 6cb4d7d8c..e6ab63e41 100644 --- a/R/squeeze.R +++ b/R/squeeze.R @@ -1,16 +1,16 @@ #' Squeeze the imputed values to be within specified boundaries. #' -#' This function replaces any values in \code{x} that are lower than -#' \code{bounds[1]} by \code{bounds[1]}, and replaces any values higher -#' than \code{bounds[2]} by \code{bounds[2]}. +#' This function replaces any values in `x` that are lower than +#' `bounds[1]` by `bounds[1]`, and replaces any values higher +#' than `bounds[2]` by `bounds[2]`. #' #' @aliases squeeze #' @param x A numerical vector with values #' @param bounds A numerical vector of length 2 containing the lower and upper bounds. -#' By default, the bounds are to the minimum and maximum values in \code{x}. -#' @param r A logical vector of length \code{length(x)} that is used to select a -#' subset in \code{x} before calculating automatic bounds. -#' @return A vector of length \code{length(x)}. +#' By default, the bounds are to the minimum and maximum values in `x`. +#' @param r A logical vector of length `length(x)` that is used to select a +#' subset in `x` before calculating automatic bounds. +#' @return A vector of length `length(x)`. #' @author Stef van Buuren, 2011. #' @export squeeze <- function(x, bounds = c(min(x[r]), max(x[r])), diff --git a/R/stripplot.R b/R/stripplot.R index 75de591d1..29338566f 100644 --- a/R/stripplot.R +++ b/R/stripplot.R @@ -1,129 +1,129 @@ #' Stripplot of observed and imputed data #' #' Plotting methods for imputed data using \pkg{lattice}. -#' \code{stripplot} produces one-dimensional +#' `stripplot` produces one-dimensional #' scatterplots. The function #' automatically separates the observed and imputed data. The #' functions extend the usual features of \pkg{lattice}. #' -#' The argument \code{na.groups} may be used to specify (combinations of) -#' missingness in any of the variables. The argument \code{groups} can be used +#' The argument `na.groups` may be used to specify (combinations of) +#' missingness in any of the variables. The argument `groups` can be used #' to specify groups based on the variable values themselves. Only one of both -#' may be active at the same time. When both are specified, \code{na.groups} -#' takes precedence over \code{groups}. +#' may be active at the same time. When both are specified, `na.groups` +#' takes precedence over `groups`. #' -#' Use the \code{subset} and \code{na.groups} together to plots parts of the +#' Use the `subset` and `na.groups` together to plots parts of the #' data. For example, select the first imputed data set by by -#' \code{subset=.imp==1}. +#' `subset=.imp==1`. #' -#' Graphical parameters like \code{col}, \code{pch} and \code{cex} can be +#' Graphical parameters like `col`, `pch` and `cex` can be #' specified in the arguments list to alter the plotting symbols. If -#' \code{length(col)==2}, the color specification to define the observed and -#' missing groups. \code{col[1]} is the color of the 'observed' data, -#' \code{col[2]} is the color of the missing or imputed data. A convenient color -#' choice is \code{col=mdc(1:2)}, a transparent blue color for the observed +#' `length(col)==2`, the color specification to define the observed and +#' missing groups. `col[1]` is the color of the 'observed' data, +#' `col[2]` is the color of the missing or imputed data. A convenient color +#' choice is `col=mdc(1:2)`, a transparent blue color for the observed #' data, and a transparent red color for the imputed data. A good choice is -#' \code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the -#' duration of the session by running \code{mice.theme()}. +#' `col=mdc(1:2), pch=20, cex=1.5`. These choices can be set for the +#' duration of the session by running `mice.theme()`. #' #' @aliases stripplot -#' @param x A \code{mids} object, typically created by \code{mice()} or -#' \code{mice.mids()}. +#' @param x A `mids` object, typically created by `mice()` or +#' `mice.mids()`. #' @param data Formula that selects the data to be plotted. This argument -#' follows the \pkg{lattice} rules for \emph{formulas}, describing the primary +#' follows the \pkg{lattice} rules for *formulas*, describing the primary #' variables (used for the per-panel display) and the optional conditioning #' variables (which define the subsets plotted in different panels) to be used #' in the plot. #' -#' The formula is evaluated on the complete data set in the \code{long} form. -#' Legal variable names for the formula include \code{names(x$data)} plus the -#' two administrative factors \code{.imp} and \code{.id}. +#' The formula is evaluated on the complete data set in the `long` form. +#' Legal variable names for the formula include `names(x$data)` plus the +#' two administrative factors `.imp` and `.id`. #' -#' \bold{Extended formula interface:} The primary variable terms (both the LHS -#' \code{y} and RHS \code{x}) may consist of multiple terms separated by a -#' \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be -#' taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and -#' \code{y2 ~ x | a * b}, but with the \code{y1 ~ x} and \code{y2 ~ x} in -#' \emph{separate panels}. This behavior differs from standard \pkg{lattice}. -#' \emph{Only combine terms of the same type}, i.e. only factors or only +#' **Extended formula interface:** The primary variable terms (both the LHS +#' `y` and RHS `x`) may consist of multiple terms separated by a +#' \sQuote{+} sign, e.g., `y1 + y2 ~ x | a * b`. This formula would be +#' taken to mean that the user wants to plot both `y1 ~ x | a * b` and +#' `y2 ~ x | a * b`, but with the `y1 ~ x` and `y2 ~ x` in +#' *separate panels*. This behavior differs from standard \pkg{lattice}. +#' *Only combine terms of the same type*, i.e. only factors or only #' numerical variables. Mixing numerical and categorical data occasionally #' produces odds labeling of vertical axis. #' -#' For convenience, in \code{stripplot()} and \code{bwplot} the formula -#' \code{y~.imp} may be abbreviated as \code{y}. This applies only to a single -#' \code{y}, and does not (yet) work for \code{y1+y2~.imp}. +#' For convenience, in `stripplot()` and `bwplot` the formula +#' `y~.imp` may be abbreviated as `y`. This applies only to a single +#' `y`, and does not (yet) work for `y1+y2~.imp`. #' #' @param na.groups An expression evaluating to a logical vector indicating #' which two groups are distinguished (e.g. using different colors) in the #' display. The environment in which this expression is evaluated in the -#' response indicator \code{is.na(x$data)}. +#' response indicator `is.na(x$data)`. #' -#' The default \code{na.group = NULL} contrasts the observed and missing data -#' in the LHS \code{y} variable of the display, i.e. groups created by -#' \code{is.na(y)}. The expression \code{y} creates the groups according to -#' \code{is.na(y)}. The expression \code{y1 & y2} creates groups by -#' \code{is.na(y1) & is.na(y2)}, and \code{y1 | y2} creates groups as -#' \code{is.na(y1) | is.na(y2)}, and so on. -#' @param groups This is the usual \code{groups} arguments in \pkg{lattice}. It -#' differs from \code{na.groups} because it evaluates in the completed data -#' \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas -#' \code{na.groups} evaluates in the response indicator. See -#' \code{\link{xyplot}} for more details. When both \code{na.groups} and -#' \code{groups} are specified, \code{na.groups} takes precedence, and -#' \code{groups} is ignored. +#' The default `na.group = NULL` contrasts the observed and missing data +#' in the LHS `y` variable of the display, i.e. groups created by +#' `is.na(y)`. The expression `y` creates the groups according to +#' `is.na(y)`. The expression `y1 & y2` creates groups by +#' `is.na(y1) & is.na(y2)`, and `y1 | y2` creates groups as +#' `is.na(y1) | is.na(y2)`, and so on. +#' @param groups This is the usual `groups` arguments in \pkg{lattice}. It +#' differs from `na.groups` because it evaluates in the completed data +#' `data.frame(complete(x, "long", inc=TRUE))` (as usual), whereas +#' `na.groups` evaluates in the response indicator. See +#' [xyplot()] for more details. When both `na.groups` and +#' `groups` are specified, `na.groups` takes precedence, and +#' `groups` is ignored. #' @param theme A named list containing the graphical parameters. The default -#' function \code{mice.theme} produces a short list of default colors, line +#' function `mice.theme` produces a short list of default colors, line #' width, and so on. The extensive list may be obtained from -#' \code{trellis.par.get()}. Global graphical parameters like \code{col} or -#' \code{cex} in high-level calls are still honored, so first experiment with +#' `trellis.par.get()`. Global graphical parameters like `col` or +#' `cex` in high-level calls are still honored, so first experiment with #' the global parameters. Many setting consists of a pair. For example, -#' \code{mice.theme} defines two symbol colors. The first is for the observed +#' `mice.theme` defines two symbol colors. The first is for the observed #' data, the second for the imputed data. The theme settings only exist during #' the call, and do not affect the trellis graphical parameters. -#' @param jitter.data See \code{\link[lattice:panel.xyplot]{panel.xyplot}}. -#' @param horizontal See \code{\link[lattice:xyplot]{xyplot}}. -#' @param as.table See \code{\link[lattice:xyplot]{xyplot}}. -#' @param panel See \code{\link{xyplot}}. -#' @param default.prepanel See \code{\link[lattice:xyplot]{xyplot}}. -#' @param outer See \code{\link[lattice:xyplot]{xyplot}}. -#' @param allow.multiple See \code{\link[lattice:xyplot]{xyplot}}. -#' @param drop.unused.levels See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subscripts See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subset See \code{\link[lattice:xyplot]{xyplot}}. +#' @param jitter.data See [lattice::panel.xyplot()]. +#' @param horizontal See [lattice::xyplot()]. +#' @param as.table See [lattice::xyplot()]. +#' @param panel See [xyplot()]. +#' @param default.prepanel See [lattice::xyplot()]. +#' @param outer See [lattice::xyplot()]. +#' @param allow.multiple See [lattice::xyplot()]. +#' @param drop.unused.levels See [lattice::xyplot()]. +#' @param subscripts See [lattice::xyplot()]. +#' @param subset See [lattice::xyplot()]. #' @param \dots Further arguments, usually not directly processed by the #' high-level functions documented here, but instead passed on to other #' functions. #' @return The high-level functions documented here, as well as other high-level -#' Lattice functions, return an object of class \code{"trellis"}. The -#' \code{\link[lattice:update.trellis]{update}} method can be used to +#' Lattice functions, return an object of class `"trellis"`. The +#' [`update()`][lattice::update.trellis] method can be used to #' subsequently update components of the object, and the -#' \code{\link[lattice:print.trellis]{print}} method (usually called by default) +#' [`print()`][lattice::print.trellis] method (usually called by default) #' will plot it on an appropriate plotting device. -#' @note The first two arguments (\code{x} and \code{data}) are reversed +#' @note The first two arguments (`x` and `data`) are reversed #' compared to the standard Trellis syntax implemented in \pkg{lattice}. This #' reversal was necessary in order to benefit from automatic method dispatch. #' -#' In \pkg{mice} the argument \code{x} is always a \code{mids} object, whereas -#' in \pkg{lattice} the argument \code{x} is always a formula. +#' In \pkg{mice} the argument `x` is always a `mids` object, whereas +#' in \pkg{lattice} the argument `x` is always a formula. #' -#' In \pkg{mice} the argument \code{data} is always a formula object, whereas in -#' \pkg{lattice} the argument \code{data} is usually a data frame. +#' In \pkg{mice} the argument `data` is always a formula object, whereas in +#' \pkg{lattice} the argument `data` is usually a data frame. #' #' All other arguments have identical interpretation. #' #' @author Stef van Buuren -#' @seealso \code{\link{mice}}, \code{\link{xyplot}}, \code{\link{densityplot}}, -#' \code{\link{bwplot}}, \code{\link{lattice}} for an overview of the -#' package, as well as \code{\link[lattice:xyplot]{stripplot}}, -#' \code{\link[lattice:panel.stripplot]{panel.stripplot}}, -#' \code{\link[lattice:print.trellis]{print.trellis}}, -#' \code{\link[lattice:trellis.par.get]{trellis.par.set}} -#' @references Sarkar, Deepayan (2008) \emph{Lattice: Multivariate Data -#' Visualization with R}, Springer. +#' @seealso [mice()], [xyplot()], [densityplot()], +#' [bwplot()], [lattice()] for an overview of the +#' package, as well as [`stripplot()`][lattice::xyplot], +#' [lattice::panel.stripplot()], +#' [lattice::print.trellis()], +#' [`trellis.par.set()`][lattice::trellis.par.get] +#' @references Sarkar, Deepayan (2008) *Lattice: Multivariate Data +#' Visualization with R*, Springer. #' -#' van Buuren S and 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} +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords hplot #' @examples #' imp <- mice(boys, maxit = 1) diff --git a/R/summary.R b/R/summary.R index 0c8059a86..0a08bf509 100644 --- a/R/summary.R +++ b/R/summary.R @@ -1,17 +1,17 @@ -#' Summary of a \code{mira} object +#' Summary of a `mira` object #' #' @rdname summary -#' @param object A \code{mira} object +#' @param object A `mira` object #' @param type A length-1 character vector indicating the -#' type of summary. There are three choices: \code{type = "tidy"} +#' type of summary. There are three choices: `type = "tidy"` #' return the parameters estimates of each analyses as a data frame. -#' \code{type = "glance"} return the fit statistics of each analysis -#' as a data frame. \code{type = "summary"} returns a list of -#' length \code{m} with the analysis results. The default is -#' \code{"tidy"}. -#' @param ... Other parameters passed down to \code{print()} and \code{summary()} -#' @return \code{NULL} -#' @seealso \code{\link[=mira-class]{mira}} +#' `type = "glance"` return the fit statistics of each analysis +#' as a data frame. `type = "summary"` returns a list of +#' length `m` with the analysis results. The default is +#' `"tidy"`. +#' @param ... Other parameters passed down to `print()` and `summary()` +#' @return `NULL` +#' @seealso [`mira()`][mira-class] #' @method summary mira #' @export summary.mira <- function(object, @@ -44,11 +44,11 @@ summary.mira <- function(object, } -#' Summary of a \code{mids} object +#' Summary of a `mids` object #' #' @rdname summary -#' @return \code{NULL} -#' @seealso \code{\link[=mids-class]{mids}} +#' @return `NULL` +#' @seealso [`mids()`][mids-class] #' @method summary mids #' @export summary.mids <- function(object, ...) { @@ -57,11 +57,11 @@ summary.mids <- function(object, ...) { } -#' Summary of a \code{mads} object +#' Summary of a `mads` object #' #' @rdname summary -#' @return \code{NULL} -#' @seealso \code{\link[=mads-class]{mads}} +#' @return `NULL` +#' @seealso [`mads()`][mads-class] #' @export summary.mads <- function(object, ...) { print(object, ...) @@ -69,11 +69,11 @@ summary.mads <- function(object, ...) { } -#' Print a \code{mice.anova} object +#' Print a `mice.anova` object #' #' @rdname summary -#' @return \code{NULL} -#' @seealso \code{\link{mipo}} +#' @return `NULL` +#' @seealso [mipo()] #' @method summary mice.anova #' @export summary.mice.anova <- function(object, ...) { diff --git a/R/supports.transparent.R b/R/supports.transparent.R index 608380c45..dccdce8bd 100644 --- a/R/supports.transparent.R +++ b/R/supports.transparent.R @@ -1,15 +1,15 @@ #' Supports semi-transparent foreground colors? #' -#' This function is used by \code{mdc()} to find out whether the current device +#' This function is used by `mdc()` to find out whether the current device #' supports semi-transparent foreground colors. #' -#' The function calls the function \code{dev.capabilities()} from the package -#' \code{grDevices}. The function return \code{FALSE} if the status of the +#' The function calls the function `dev.capabilities()` from the package +#' `grDevices`. The function return `FALSE` if the status of the #' current device is unknown. #' #' @aliases supports.transparent transparent -#' @return \code{TRUE} or \code{FALSE} -#' @seealso \code{\link{mdc}} \code{\link{dev.capabilities}} +#' @return `TRUE` or `FALSE` +#' @seealso [mdc()] [dev.capabilities()] #' @keywords hplot #' @examples #' diff --git a/R/tbc.R b/R/tbc.R index bd43c7215..bb43f94cf 100644 --- a/R/tbc.R +++ b/R/tbc.R @@ -2,10 +2,10 @@ #' #' Data of subset of the Terneuzen Birth Cohort data on child growth. #' -#' This \code{tbc} data set is a random subset of persons from a much larger +#' This `tbc` data set is a random subset of persons from a much larger #' collection of data from the Terneuzen Birth Cohort. The total cohort -#' comprises of 2604 unique persons, whereas the subset in \code{tbc} covers 306 -#' persons. The \code{tbc.target} is an auxiliary data set containing two +#' comprises of 2604 unique persons, whereas the subset in `tbc` covers 306 +#' persons. The `tbc.target` is an auxiliary data set containing two #' outcomes at adult age. For more details, see De Kroon et al (2008, 2010, #' 2011). The imputation methodology is explained in Chapter 9 of Van Buuren #' (2012). @@ -13,7 +13,7 @@ #' @name tbc #' @aliases tbc tbc.target terneuzen #' @docType data -#' @format \code{tbs} is a data frame with 3951 rows and 11 columns: +#' @format `tbs` is a data frame with 3951 rows and 11 columns: #' \describe{ #' \item{id}{Person number} #' \item{occ}{Occasion number} @@ -28,7 +28,7 @@ #' \item{ao}{Adult overweight (0=no, 1=yes)} #' } #' -#' \code{tbc.target} is a data frame with 2612 rows and 3 columns: +#' `tbc.target` is a data frame with 2612 rows and 3 columns: #' \describe{ #' \item{id}{Person number} #' \item{ao}{Adult overweight (0=no, 1=yes)} @@ -37,20 +37,20 @@ #' @source De Kroon, M. L. A., Renders, C. M., Kuipers, E. C., van Wouwe, J. P., #' van Buuren, S., de Jonge, G. A., Hirasing, R. A. (2008). Identifying #' metabolic syndrome without blood tests in young adults - The Terneuzen birth -#' cohort. \emph{European Journal of Public Health}, \emph{18}(6), 656-660. +#' cohort. *European Journal of Public Health*, *18*(6), 656-660. #' #' De Kroon, M. L. A., Renders, C. M., Van Wouwe, J. P., Van Buuren, S., #' Hirasing, R. A. (2010). The Terneuzen birth cohort: BMI changes between 2 -#' and 6 years correlate strongest with adult overweight. \emph{PLoS ONE}, -#' \emph{5}(2), e9155. +#' and 6 years correlate strongest with adult overweight. *PLoS ONE*, +#' *5*(2), e9155. #' -#' De Kroon, M. L. A. (2011). \emph{The Terneuzen Birth Cohort. Detection and -#' Prevention of Overweight and Cardiometabolic Risk from Infancy Onward.} +#' De Kroon, M. L. A. (2011). *The Terneuzen Birth Cohort. Detection and +#' Prevention of Overweight and Cardiometabolic Risk from Infancy Onward.* #' Dissertation, Vrije Universiteit, Amsterdam. -#' \url{https://research.vu.nl/en/publications/the-terneuzen-birth-cohort-detection-and-prevention-of-overweight} +#' #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-rastering.html#terneuzen-birth-cohort}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-rastering.html#terneuzen-birth-cohort) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets #' @examples diff --git a/R/tidiers.R b/R/tidiers.R index 4339b606c..bcb38cba3 100644 --- a/R/tidiers.R +++ b/R/tidiers.R @@ -8,7 +8,7 @@ generics::glance #' Tidy method to extract results from a `mipo` object #' -#' @param x An object of class \code{mipo} +#' @param x An object of class `mipo` #' @param conf.int Logical. Should confidence intervals be returned? #' @param conf.level Confidence level for intervals. Defaults to .95 #' @param ... extra arguments (not used) diff --git a/R/toenail.R b/R/toenail.R index a89b1235a..328de5bfb 100644 --- a/R/toenail.R +++ b/R/toenail.R @@ -11,13 +11,13 @@ #' @docType data #' @format A data frame with 1908 observations on the following 5 variables: #' \describe{ -#' \item{\code{ID}}{a numeric vector giving the ID of patient} -#' \item{\code{outcome}}{a numeric vector giving the response +#' \item{`ID`}{a numeric vector giving the ID of patient} +#' \item{`outcome`}{a numeric vector giving the response #' (0=none or mild seperation, 1=moderate or severe)} -#' \item{\code{treatment}}{a numeric vector giving the treatment group} -#' \item{\code{month}}{a numeric vector giving the time of the visit +#' \item{`treatment`}{a numeric vector giving the treatment group} +#' \item{`month`}{a numeric vector giving the time of the visit #' (not exactly monthly intervals hence not round numbers)} -#' \item{\code{visit}}{a numeric vector giving the number of the visit} +#' \item{`visit`}{a numeric vector giving the number of the visit} #' } #' @source #' De Backer, M., De Vroey, C., Lesaffre, E., Scheys, I., and De @@ -34,11 +34,11 @@ #' Wiley and Sons, New York, USA. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-catoutcome.html#example}{\emph{Flexible -#' Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. +#' [*Flexible +#' Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-catoutcome.html#example) Chapman & Hall/CRC. #' Boca Raton, FL. #' @keywords datasets -#' @seealso \code{\link{toenail2}} -#' @details This dataset was copied from the \code{DPpackage}, which is +#' @seealso [toenail2()] +#' @details This dataset was copied from the `DPpackage`, which is #' scheduled to be discontinued from CRAN in August 2019. NULL diff --git a/R/toenail2.R b/R/toenail2.R index 11e5b63f7..645295282 100644 --- a/R/toenail2.R +++ b/R/toenail2.R @@ -11,12 +11,12 @@ #' @docType data #' @format A data frame with 1908 observations on the following 5 variables: #' \describe{ -#' \item{\code{patientID}}{a numeric vector giving the ID of patient} -#' \item{\code{outcome}}{a factor with 2 levels giving the response} -#' \item{\code{treatment}}{a factor with 2 levels giving the treatment group} -#' \item{\code{time}}{a numeric vector giving the time of the visit +#' \item{`patientID`}{a numeric vector giving the ID of patient} +#' \item{`outcome`}{a factor with 2 levels giving the response} +#' \item{`treatment`}{a factor with 2 levels giving the treatment group} +#' \item{`time`}{a numeric vector giving the time of the visit #' (not exactly monthly intervals hence not round numbers)} -#' \item{\code{visit}}{an integer giving the number of the visit} +#' \item{`visit`}{an integer giving the number of the visit} #' } #' @source #' De Backer, M., De Vroey, C., Lesaffre, E., Scheys, I., and De @@ -33,12 +33,12 @@ #' Wiley and Sons, New York, USA. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-catoutcome.html#example}{\emph{Flexible -#' Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. +#' [*Flexible +#' Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-catoutcome.html#example) Chapman & Hall/CRC. #' Boca Raton, FL. #' @keywords datasets -#' @seealso \code{\link{toenail}} +#' @seealso [toenail()] #' @details Apart from formatting, this dataset is identical to -#' \code{toenail}. The formatting is taken identical to -#' \code{data("toenail", package = "HSAUR3")}. +#' `toenail`. The formatting is taken identical to +#' `data("toenail", package = "HSAUR3")`. NULL diff --git a/R/visitSequence.R b/R/visitSequence.R index dd0a1443a..c05870775 100644 --- a/R/visitSequence.R +++ b/R/visitSequence.R @@ -1,11 +1,11 @@ -#' Creates a \code{visitSequence} argument +#' Creates a `visitSequence` argument #' -#' This helper function creates a valid \code{visitSequence}. The -#' \code{visitSequence} is an argument to the \code{mice} function that +#' This helper function creates a valid `visitSequence`. The +#' `visitSequence` is an argument to the `mice` function that #' specifies the sequence in which blocks are imputed. #' @inheritParams mice #' @return Vector containing block names -#' @seealso \code{\link{mice}} +#' @seealso [mice()] #' @examples #' make.visitSequence(nhanes) #' @export diff --git a/R/walking.R b/R/walking.R index c43c308d4..f7c93cc1e 100644 --- a/R/walking.R +++ b/R/walking.R @@ -27,10 +27,10 @@ #' } #' @references van Buuren, S., Eyres, S., Tennant, A., Hopman-Rock, M. (2005). #' Improving comparability of existing data by Response Conversion. -#' \emph{Journal of Official Statistics}, \bold{21}(1), 53-72. +#' *Journal of Official Statistics*, **21**(1), 53-72. #' #' Van Buuren, S. (2018). -#' \href{https://stefvanbuuren.name/fimd/sec-codingsystems.html#sec:impbridge}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +#' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/sec-codingsystems.html#sec:impbridge) #' Chapman & Hall/CRC. Boca Raton, FL. #' @keywords datasets #' @examples diff --git a/R/where.R b/R/where.R index 856b90e0f..2e77d2ff2 100644 --- a/R/where.R +++ b/R/where.R @@ -1,16 +1,16 @@ -#' Creates a \code{where} argument +#' Creates a `where` argument #' -#' This helper function creates a valid \code{where} matrix. The -#' \code{where} matrix is an argument to the \code{mice} function. -#' It has the same size as \code{data} and specifies which values -#' are to be imputed (\code{TRUE}) or nor (\code{FALSE}). -#' @param data A \code{data.frame} with the source data -#' @param keyword An optional keyword, one of \code{"missing"} (missing -#' values are imputed), \code{"observed"} (observed values are imputed), -#' \code{"all"} and \code{"none"}. The default -#' is \code{keyword = "missing"} +#' This helper function creates a valid `where` matrix. The +#' `where` matrix is an argument to the `mice` function. +#' It has the same size as `data` and specifies which values +#' are to be imputed (`TRUE`) or nor (`FALSE`). +#' @param data A `data.frame` with the source data +#' @param keyword An optional keyword, one of `"missing"` (missing +#' values are imputed), `"observed"` (observed values are imputed), +#' `"all"` and `"none"`. The default +#' is `keyword = "missing"` #' @return A matrix with logical -#' @seealso \code{\link{make.blocks}}, \code{\link{make.predictorMatrix}} +#' @seealso [make.blocks()], [make.predictorMatrix()] #' @examples #' head(make.where(nhanes), 3) #' diff --git a/R/windspeed.R b/R/windspeed.R index 9172fc993..15bafac3d 100644 --- a/R/windspeed.R +++ b/R/windspeed.R @@ -18,15 +18,15 @@ #' \item{Dublin}{Dublin} #' \item{Clones}{Clones} #' \item{MalinHead}{Malin Head} } -#' @references Haslett, J. and Raftery, A. E. (1989). \emph{Space-time +#' @references Haslett, J. and Raftery, A. E. (1989). *Space-time #' Modeling with Long-memory Dependence: Assessing Ireland's Wind Power -#' Resource (with Discussion)}. Applied Statistics 38, 1-50. -#' \url{http://lib.stat.cmu.edu/datasets/wind.desc} and -#' \url{http://lib.stat.cmu.edu/datasets/wind.data} +#' Resource (with Discussion)*. Applied Statistics 38, 1-50. +#' and +#' #' #' van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) -#' Fully conditional specification in multivariate imputation. \emph{Journal of -#' Statistical Computation and Simulation}, \bold{76}, 12, 1049--1064. +#' Fully conditional specification in multivariate imputation. *Journal of +#' Statistical Computation and Simulation*, **76**, 12, 1049--1064. #' @keywords datasets #' @examples #' diff --git a/R/with.R b/R/with.R index 4e094b309..ce42f35a6 100644 --- a/R/with.R +++ b/R/with.R @@ -2,22 +2,22 @@ #' #' Performs a computation of each of imputed datasets in data. #' -#' @param data An object of type \code{mids}, which stands for 'multiply imputed -#' data set', typically created by a call to function \code{mice()}. +#' @param data An object of type `mids`, which stands for 'multiply imputed +#' data set', typically created by a call to function `mice()`. #' @param expr An expression to evaluate for each imputed data set. Formula's #' containing a dot (notation for "all other variables") do not work. #' @param \dots Not used -#' @return An object of S3 class \code{\link[=mira-class]{mira}} +#' @return An object of S3 class [`mira()`][mira-class] #' @note Version 3.11.10 changed to tidy evaluation on a quosure. This change #' should not affect any code that worked on previous versions. #' It turned out that the latter statement was not true (#292). -#' Version 3.12.2 reverts to the old \code{with()} function. +#' Version 3.12.2 reverts to the old `with()` function. #' @author Karin Oudshoorn, Stef van Buuren 2009, 2012, 2020 -#' @seealso \code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}}, \code{\link{pool}}, -#' \code{\link{D1}}, \code{\link{D3}}, \code{\link{pool.r.squared}} -#' @references van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: -#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -#' Statistical Software}, \bold{45}(3), 1-67. +#' @seealso [`mids()`][mids-class], [`mira()`][mira-class], [pool()], +#' [D1()], [D3()], [pool.r.squared()] +#' @references van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: +#' Multivariate Imputation by Chained Equations in `R`. *Journal of +#' Statistical Software*, **45**(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords multivariate #' @examples diff --git a/R/xyplot.R b/R/xyplot.R index 398e7211f..e2cf8fe92 100644 --- a/R/xyplot.R +++ b/R/xyplot.R @@ -1,120 +1,120 @@ #' Scatterplot of observed and imputed data #' #' Plotting methods for imputed data using \pkg{lattice}. -#' \code{xyplot()} produces a conditional scatterplots. The function +#' `xyplot()` produces a conditional scatterplots. The function #' automatically separates the observed (blue) and imputed (red) data. The #' function extends the usual features of \pkg{lattice}. #' -#' The argument \code{na.groups} may be used to specify (combinations of) -#' missingness in any of the variables. The argument \code{groups} can be used +#' The argument `na.groups` may be used to specify (combinations of) +#' missingness in any of the variables. The argument `groups` can be used #' to specify groups based on the variable values themselves. Only one of both -#' may be active at the same time. When both are specified, \code{na.groups} -#' takes precedence over \code{groups}. +#' may be active at the same time. When both are specified, `na.groups` +#' takes precedence over `groups`. #' -#' Use the \code{subset} and \code{na.groups} together to plots parts of the +#' Use the `subset` and `na.groups` together to plots parts of the #' data. For example, select the first imputed data set by by -#' \code{subset=.imp==1}. +#' `subset=.imp==1`. #' -#' Graphical parameters like \code{col}, \code{pch} and \code{cex} can be +#' Graphical parameters like `col`, `pch` and `cex` can be #' specified in the arguments list to alter the plotting symbols. If -#' \code{length(col)==2}, the color specification to define the observed and -#' missing groups. \code{col[1]} is the color of the 'observed' data, -#' \code{col[2]} is the color of the missing or imputed data. A convenient color -#' choice is \code{col=mdc(1:2)}, a transparent blue color for the observed +#' `length(col)==2`, the color specification to define the observed and +#' missing groups. `col[1]` is the color of the 'observed' data, +#' `col[2]` is the color of the missing or imputed data. A convenient color +#' choice is `col=mdc(1:2)`, a transparent blue color for the observed #' data, and a transparent red color for the imputed data. A good choice is -#' \code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the -#' duration of the session by running \code{mice.theme()}. +#' `col=mdc(1:2), pch=20, cex=1.5`. These choices can be set for the +#' duration of the session by running `mice.theme()`. #' #' @aliases xyplot -#' @param x A \code{mids} object, typically created by \code{mice()} or -#' \code{mice.mids()}. +#' @param x A `mids` object, typically created by `mice()` or +#' `mice.mids()`. #' @param data Formula that selects the data to be plotted. This argument -#' follows the \pkg{lattice} rules for \emph{formulas}, describing the primary +#' follows the \pkg{lattice} rules for *formulas*, describing the primary #' variables (used for the per-panel display) and the optional conditioning #' variables (which define the subsets plotted in different panels) to be used #' in the plot. #' -#' The formula is evaluated on the complete data set in the \code{long} form. -#' Legal variable names for the formula include \code{names(x$data)} plus the -#' two administrative factors \code{.imp} and \code{.id}. -#' -#' \bold{Extended formula interface:} The primary variable terms (both the LHS -#' \code{y} and RHS \code{x}) may consist of multiple terms separated by a -#' \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be -#' taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and -#' \code{y2 ~ x | a * b}, but with the \code{y1 ~ x} and \code{y2 ~ x} in -#' \emph{separate panels}. This behavior differs from standard \pkg{lattice}. -#' \emph{Only combine terms of the same type}, i.e. only factors or only +#' The formula is evaluated on the complete data set in the `long` form. +#' Legal variable names for the formula include `names(x$data)` plus the +#' two administrative factors `.imp` and `.id`. +#' +#' **Extended formula interface:** The primary variable terms (both the LHS +#' `y` and RHS `x`) may consist of multiple terms separated by a +#' \sQuote{+} sign, e.g., `y1 + y2 ~ x | a * b`. This formula would be +#' taken to mean that the user wants to plot both `y1 ~ x | a * b` and +#' `y2 ~ x | a * b`, but with the `y1 ~ x` and `y2 ~ x` in +#' *separate panels*. This behavior differs from standard \pkg{lattice}. +#' *Only combine terms of the same type*, i.e. only factors or only #' numerical variables. Mixing numerical and categorical data occasionally #' produces odds labeling of vertical axis. #' #' @param na.groups An expression evaluating to a logical vector indicating #' which two groups are distinguished (e.g. using different colors) in the #' display. The environment in which this expression is evaluated in the -#' response indicator \code{is.na(x$data)}. -#' -#' The default \code{na.group = NULL} contrasts the observed and missing data -#' in the LHS \code{y} variable of the display, i.e. groups created by -#' \code{is.na(y)}. The expression \code{y} creates the groups according to -#' \code{is.na(y)}. The expression \code{y1 & y2} creates groups by -#' \code{is.na(y1) & is.na(y2)}, and \code{y1 | y2} creates groups as -#' \code{is.na(y1) | is.na(y2)}, and so on. -#' @param groups This is the usual \code{groups} arguments in \pkg{lattice}. It -#' differs from \code{na.groups} because it evaluates in the completed data -#' \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas -#' \code{na.groups} evaluates in the response indicator. See -#' \code{\link{xyplot}} for more details. When both \code{na.groups} and -#' \code{groups} are specified, \code{na.groups} takes precedence, and -#' \code{groups} is ignored. +#' response indicator `is.na(x$data)`. +#' +#' The default `na.group = NULL` contrasts the observed and missing data +#' in the LHS `y` variable of the display, i.e. groups created by +#' `is.na(y)`. The expression `y` creates the groups according to +#' `is.na(y)`. The expression `y1 & y2` creates groups by +#' `is.na(y1) & is.na(y2)`, and `y1 | y2` creates groups as +#' `is.na(y1) | is.na(y2)`, and so on. +#' @param groups This is the usual `groups` arguments in \pkg{lattice}. It +#' differs from `na.groups` because it evaluates in the completed data +#' `data.frame(complete(x, "long", inc=TRUE))` (as usual), whereas +#' `na.groups` evaluates in the response indicator. See +#' [xyplot()] for more details. When both `na.groups` and +#' `groups` are specified, `na.groups` takes precedence, and +#' `groups` is ignored. #' @param theme A named list containing the graphical parameters. The default -#' function \code{mice.theme} produces a short list of default colors, line +#' function `mice.theme` produces a short list of default colors, line #' width, and so on. The extensive list may be obtained from -#' \code{trellis.par.get()}. Global graphical parameters like \code{col} or -#' \code{cex} in high-level calls are still honored, so first experiment with +#' `trellis.par.get()`. Global graphical parameters like `col` or +#' `cex` in high-level calls are still honored, so first experiment with #' the global parameters. Many setting consists of a pair. For example, -#' \code{mice.theme} defines two symbol colors. The first is for the observed +#' `mice.theme` defines two symbol colors. The first is for the observed #' data, the second for the imputed data. The theme settings only exist during #' the call, and do not affect the trellis graphical parameters. -#' @param as.table See \code{\link[lattice:xyplot]{xyplot}}. -#' @param outer See \code{\link[lattice:xyplot]{xyplot}}. -#' @param allow.multiple See \code{\link[lattice:xyplot]{xyplot}}. -#' @param drop.unused.levels See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subscripts See \code{\link[lattice:xyplot]{xyplot}}. -#' @param subset See \code{\link[lattice:xyplot]{xyplot}}. +#' @param as.table See [lattice::xyplot()]. +#' @param outer See [lattice::xyplot()]. +#' @param allow.multiple See [lattice::xyplot()]. +#' @param drop.unused.levels See [lattice::xyplot()]. +#' @param subscripts See [lattice::xyplot()]. +#' @param subset See [lattice::xyplot()]. #' @param \dots Further arguments, usually not directly processed by the #' high-level functions documented here, but instead passed on to other #' functions. #' @return The high-level functions documented here, as well as other high-level -#' Lattice functions, return an object of class \code{"trellis"}. The -#' \code{\link[lattice:update.trellis]{update}} method can be used to +#' Lattice functions, return an object of class `"trellis"`. The +#' [`update()`][lattice::update.trellis] method can be used to #' subsequently update components of the object, and the -#' \code{\link[lattice:print.trellis]{print}} method (usually called by default) +#' [`print()`][lattice::print.trellis] method (usually called by default) #' will plot it on an appropriate plotting device. -#' @note The first two arguments (\code{x} and \code{data}) are reversed +#' @note The first two arguments (`x` and `data`) are reversed #' compared to the standard Trellis syntax implemented in \pkg{lattice}. This #' reversal was necessary in order to benefit from automatic method dispatch. #' -#' In \pkg{mice} the argument \code{x} is always a \code{mids} object, whereas -#' in \pkg{lattice} the argument \code{x} is always a formula. +#' In \pkg{mice} the argument `x` is always a `mids` object, whereas +#' in \pkg{lattice} the argument `x` is always a formula. #' -#' In \pkg{mice} the argument \code{data} is always a formula object, whereas in -#' \pkg{lattice} the argument \code{data} is usually a data frame. +#' In \pkg{mice} the argument `data` is always a formula object, whereas in +#' \pkg{lattice} the argument `data` is usually a data frame. #' #' All other arguments have identical interpretation. #' #' @author Stef van Buuren -#' @seealso \code{\link{mice}}, \code{\link{stripplot}}, \code{\link{densityplot}}, -#' \code{\link{bwplot}}, \code{\link{lattice}} for an overview of the -#' package, as well as \code{\link[lattice:xyplot]{xyplot}}, -#' \code{\link[lattice:panel.xyplot]{panel.xyplot}}, -#' \code{\link[lattice:print.trellis]{print.trellis}}, -#' \code{\link[lattice:trellis.par.get]{trellis.par.set}} -#' @references Sarkar, Deepayan (2008) \emph{Lattice: Multivariate Data -#' Visualization with R}, Springer. -#' -#' van Buuren S and 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 [mice()], [stripplot()], [densityplot()], +#' [bwplot()], [lattice()] for an overview of the +#' package, as well as [lattice::xyplot()], +#' [lattice::panel.xyplot()], +#' [lattice::print.trellis()], +#' [`trellis.par.set()`][lattice::trellis.par.get] +#' @references Sarkar, Deepayan (2008) *Lattice: Multivariate Data +#' Visualization with R*, Springer. +#' +#' van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate +#' Imputation by Chained Equations in `R`. *Journal of Statistical +#' Software*, **45**(3), 1-67. \doi{10.18637/jss.v045.i03} #' @keywords hplot #' @examples #' imp <- mice(boys, maxit = 1) diff --git a/R/xyplot.mads.R b/R/xyplot.mads.R index bf961d258..1ad593b74 100644 --- a/R/xyplot.mads.R +++ b/R/xyplot.mads.R @@ -1,12 +1,12 @@ #' Scatterplot of amputed and non-amputed data against weighted sum scores #' #' Plotting method to investigate relation between amputed data and the weighted sum -#' scores. Based on \code{\link{lattice}}. \code{xyplot} produces scatterplots. +#' scores. Based on [lattice()]. `xyplot` produces scatterplots. #' The function plots the variables against the weighted sum scores. The function #' automatically separates the amputed and non-amputed data to see the relation between #' the amputation and the weighted sum scores. #' -#' @param x A \code{mads} object, typically created by \code{\link{ampute}}. +#' @param x A `mads` object, typically created by [ampute()]. #' @param data A string or vector of variable names that needs to be plotted. As #' a default, all variables will be plotted. #' @param which.pat A scalar or vector indicating which patterns need to be plotted. @@ -14,21 +14,21 @@ #' @param standardized Logical. Whether the scatterplots need to be created #' from standardized data or not. Default is TRUE. #' @param layout A vector of two values indicating how the scatterplots of one -#' pattern should be divided over the plot. For example, \code{c(2, 3)} indicates +#' pattern should be divided over the plot. For example, `c(2, 3)` indicates #' that the scatterplots of six variables need to be placed on 3 rows and 2 columns. #' There are several defaults for different #variables. Note that for more than #' 9 variables, multiple plots will be created automatically. #' @param colors A vector of two RGB values defining the colors of the non-amputed and -#' amputed data respectively. RGB values can be obtained with \code{\link{hcl}}. +#' amputed data respectively. RGB values can be obtained with [hcl()]. #' @param \dots Not used, but for consistency with generic #' @return A list containing the scatterplots. Note that a new pattern #' will always be shown in a new plot. -#' @note The \code{mads} object contains all the information you need to -#' make any desired plots. Check \code{\link{mads-class}} or the vignette \emph{Multivariate -#' Amputation using Ampute} to understand the contents of class object \code{mads}. +#' @note The `mads` object contains all the information you need to +#' make any desired plots. Check [mads-class()] or the vignette *Multivariate +#' Amputation using Ampute* to understand the contents of class object `mads`. #' @author Rianne Schouten, 2016 -#' @seealso \code{\link{ampute}}, \code{\link{bwplot}}, \code{\link{Lattice}} for -#' an overview of the package, \code{\link{mads-class}} +#' @seealso [ampute()], [bwplot()], [Lattice()] for +#' an overview of the package, [mads-class()] #' @export xyplot.mads <- function(x, data, which.pat = NULL, standardized = TRUE, layout = NULL, diff --git a/_pkgdown.yml b/_pkgdown.yml index 2d30f9ee2..aa8a5091f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -51,6 +51,8 @@ reference: - construct.blocks - name.blocks - name.formulas + - p2f + - f2p - 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. diff --git a/man/D1.Rd b/man/D1.Rd index 9a030b1dd..618ebff3f 100644 --- a/man/D1.Rd +++ b/man/D1.Rd @@ -24,7 +24,7 @@ be done, the procedure assumes (perhaps incorrectly) a large sample.} The D1-statistics is the multivariate Wald test. } \note{ -Warning: `D1()` assumes that the order of the variables is the +Warning: \code{D1()} assumes that the order of the variables is the same in different models. See \url{https://github.com/amices/mice/issues/420} for details. } @@ -51,5 +51,5 @@ Moment-Based Statistics and an F Reference Distribution. \url{https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:wald} } \seealso{ -\code{\link[mitml]{testModels}} +\code{\link[mitml:testModels]{mitml::testModels()}} } diff --git a/man/D2.Rd b/man/D2.Rd index fd00f1135..65480ecc1 100644 --- a/man/D2.Rd +++ b/man/D2.Rd @@ -20,7 +20,7 @@ The D2-statistic pools test statistics from the repeated analyses. The method is less powerful than the D1- and D3-statistics. } \note{ -Warning: `D2()` assumes that the order of the variables is the +Warning: \code{D2()} assumes that the order of the variables is the same in different models. See \url{https://github.com/amices/mice/issues/420} for details. } @@ -46,5 +46,5 @@ Significance Levels from Repeated p-Values with Multiply-Imputed Data. \url{https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:chi} } \seealso{ -\code{\link[mitml]{testModels}} +\code{\link[mitml:testModels]{mitml::testModels()}} } diff --git a/man/D3.Rd b/man/D3.Rd index 7c08102d5..ef79e2d47 100644 --- a/man/D3.Rd +++ b/man/D3.Rd @@ -68,5 +68,5 @@ Performing Likelihood Ratio Tests with Multiply-Imputed Data Sets. \url{http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#setting-residual-variances-to-a-fixed-value-zero-or-other} } \seealso{ -\code{\link{fix.coef}} +\code{\link[=fix.coef]{fix.coef()}} } diff --git a/man/ampute.Rd b/man/ampute.Rd index 0b14aab20..bfec72622 100644 --- a/man/ampute.Rd +++ b/man/ampute.Rd @@ -31,8 +31,8 @@ between 0 and 1. Default is a missingness proportion of 0.5.} that a variable should remain complete. The user may specify as many patterns as desired. One pattern (a vector) is possible as well. Default is a square matrix of size #variables where each pattern has missingness on one -variable only (created with \code{\link{ampute.default.patterns}}). After the -amputation procedure, \code{\link{md.pattern}} can be used to investigate the +variable only (created with \code{\link[=ampute.default.patterns]{ampute.default.patterns()}}). After the +amputation procedure, \code{\link[=md.pattern]{md.pattern()}} can be used to investigate the missing data patterns in the data.} \item{freq}{A vector of length #patterns containing the relative frequency with @@ -40,7 +40,7 @@ which the patterns should occur. For example, for three missing data patterns, the vector could be \code{c(0.4, 0.4, 0.2)}, meaning that of all cases with missing values, 40 percent should have pattern 1, 40 percent pattern 2 and 20 percent pattern 3. The vector should sum to 1. Default is an equal probability -for each pattern, created with \code{\link{ampute.default.freq}}.} +for each pattern, created with \code{\link[=ampute.default.freq]{ampute.default.freq()}}.} \item{mech}{A string specifying the missingness mechanism, either "MCAR" (Missing Completely At Random), "MAR" (Missing At Random) or "MNAR" (Missing Not At @@ -52,7 +52,7 @@ a MAR mechanism, the weights of the variables that will be made incomplete shoul zero. For a MNAR mechanism, these weights could have any possible value. Furthermore, the weights may differ between patterns and between variables. They may be negative as well. Within each pattern, the relative size of the values are of importance. -The default weights matrix is made with \code{\link{ampute.default.weights}} and +The default weights matrix is made with \code{\link[=ampute.default.weights]{ampute.default.weights()}} and returns a matrix with equal weights for all variables. In case of MAR, variables that will be amputed will be weighted with \code{0}. For MNAR, variables that will be observed will be weighted with \code{0}. If the mechanism is MCAR, the @@ -64,10 +64,10 @@ making use of train and test sets in order to prevent leakage.} \item{cont}{Logical. Whether the probabilities should be based on a continuous or a discrete distribution. If TRUE, the probabilities of being missing are based -on a continuous logistic distribution function. \code{\link{ampute.continuous}} +on a continuous logistic distribution function. \code{\link[=ampute.continuous]{ampute.continuous()}} will be used to calculate and assign the probabilities. These probabilities will then be based on the argument \code{type}. If FALSE, the probabilities of being missing are -based on a discrete distribution (\code{\link{ampute.discrete}}) based on the \code{odds} +based on a discrete distribution (\code{\link[=ampute.discrete]{ampute.discrete()}}) based on the \code{odds} argument. Default is TRUE.} \item{type}{A string or vector of strings containing the type of missingness for each @@ -75,7 +75,7 @@ pattern. Either \code{"LEFT"}, \code{"MID"}, \code{"TAIL"} or '\code{"RIGHT"}. If a single missingness type is given, all patterns will be created with the same type. If the missingness types should differ between patterns, a vector of missingness types should be given. Default is RIGHT for all patterns and is the result of -\code{\link{ampute.default.type}}.} +\code{\link[=ampute.default.type]{ampute.default.type()}}.} \item{odds}{A matrix where #patterns defines the #rows. Each row should contain the odds of being missing for the corresponding pattern. The number of odds values @@ -84,7 +84,7 @@ relative probabilities: a quantile with odds value 4 will have a probability of being missing that is four times higher than a quantile with odds 1. The number of quantiles may differ between the patterns, specify NA for cells remaining empty. Default is 4 quantiles with odds values 1, 2, 3 and 4 and is created by -\code{\link{ampute.default.odds}}.} +\code{\link[=ampute.default.odds]{ampute.default.odds()}}.} \item{bycases}{Logical. If TRUE, the proportion of missingness is defined in terms of cases. If FALSE, the proportion of missingness is defined in terms of @@ -94,18 +94,18 @@ cells. Default is TRUE.} return object will contain everything except for the amputed data set.} } \value{ -Returns an S3 object of class \code{\link{mads-class}} (multivariate +Returns an S3 object of class \code{\link[=mads-class]{mads-class()}} (multivariate amputed data set) } \description{ This function generates multivariate missing data under a MCAR, MAR or MNAR missing data mechanism. Imputation of data sets containing missing values can -be performed with \code{\link{mice}}. +be performed with \code{\link[=mice]{mice()}}. } \details{ This function generates missing values in complete data sets. Amputation of complete data sets is useful for the evaluation of imputation techniques, such as multiple -imputation (performed with function \code{\link{mice}} in this package). +imputation (performed with function \code{\link[=mice]{mice()}} in this package). The basic strategy underlying multivariate imputation was suggested by Don Rubin during discussions in the 90's. Brand (1997) created one particular @@ -126,7 +126,7 @@ The idea behind the function is the specification of several missingness patterns. Each pattern is a combination of variables with and without missing values (denoted by \code{0} and \code{1} respectively). For example, one might want to create two missingness patterns on a data set with four variables. The -patterns could be something like: \code{0,0,1,1} and \code{1,0,1,0}. +patterns could be something like: \verb{0,0,1,1} and \verb{1,0,1,0}. Each combination of zeros and ones may occur. Furthermore, the researcher specifies the proportion of missingness, either the @@ -140,7 +140,7 @@ mechanisms: the missingness depends completely on chance (MCAR), the missingness depends on the values of the observed variables (i.e. the variables that remain complete) (MAR) or on the values of the variables that will be made incomplete (MNAR). For a discussion on how missingness mechanisms are related to the observed data, -we refer to \doi{10.1177/0049124118799376}{Schouten and Vink, 2018}. +we refer to \doi{10.1177/0049124118799376}. When the user specifies the missingness mechanism to be \code{"MCAR"}, the candidates have an equal probability of becoming incomplete. For a \code{"MAR"} or \code{"MNAR"} mechanism, @@ -184,8 +184,7 @@ The user can specify the type of missingness, which, again, may differ between p For an example and more explanation about how the arguments interact with each other, we refer to the vignette \href{https://rianneschouten.github.io/mice_ampute/vignette/ampute.html}{Generate missing values with ampute} -The amputation methodology is published in -\doi{10.1080/00949655.2018.1491577}{Schouten, Lugtig and Vink, 2018}. +The amputation methodology is published in \doi{10.1080/00949655.2018.1491577}. } \examples{ # start with a complete data set @@ -229,15 +228,16 @@ Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn, C.G.M., Rubin, D.B. (2006) \emph{Journal of Statistical Computation and Simulation}, 76(12): 1049-1064. \doi{10.1080/10629360600810434} -Van Buuren, S. (2018) \href{https://stefvanbuuren.name/fimd/sec-FCS.html#sec:MICE}{\emph{Flexible Imputation of Missing Data. Second Edition.}} +Van Buuren, S. (2018) +\href{https://stefvanbuuren.name/fimd}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. Vink, G. (2016) Towards a standardized evaluation of multiple imputation routines. } \seealso{ -\code{\link{mads-class}}, \code{\link{bwplot}}, \code{\link{xyplot}}, -\code{\link{mice}} +\code{\link[=mads-class]{mads-class()}}, \code{\link[=bwplot]{bwplot()}}, \code{\link[=xyplot]{xyplot()}}, +\code{\link[=mice]{mice()}} } \author{ -Rianne Schouten [aut, cre], Gerko Vink [aut], Peter Lugtig [ctb], 2016 +Rianne Schouten (aut, cre), Gerko Vink (aut), Peter Lugtig (ctb), 2016 } diff --git a/man/ampute.continuous.Rd b/man/ampute.continuous.Rd index 3f0c72e9c..5df6c1a96 100644 --- a/man/ampute.continuous.Rd +++ b/man/ampute.continuous.Rd @@ -12,7 +12,7 @@ For each case, a value between 1 and #patterns is given. For example, a case with value 2 is candidate for missing data pattern 2.} \item{scores}{A list containing vectors with the candidates's weighted sum scores, -the result of an underlying function in \code{\link{ampute}}.} +the result of an underlying function in \code{\link[=ampute]{ampute()}}.} \item{prop}{A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.} @@ -22,7 +22,7 @@ pattern. Either \code{"LEFT"}, \code{"MID"}, \code{"TAIL"} or '\code{"RIGHT"}. If a single missingness type is entered, all patterns will be created by the same type. If missingness types should differ over patterns, a vector of missingness types should be entered. Default is RIGHT for all patterns and is the result of -\code{\link{ampute.default.type}}.} +\code{\link[=ampute.default.type]{ampute.default.type()}}.} } \value{ A list containing vectors with \code{0} if a case should be made missing @@ -33,7 +33,7 @@ first pattern, the second vector to the second pattern, etcetera. This function creates a missing data indicator for each pattern. The continuous probability distributions (Van Buuren, 2012, pp. 63, 64) will be induced on the weighted sum scores, calculated earlier in the multivariate amputation function -\code{\link{ampute}}. +\code{\link[=ampute]{ampute()}}. } \references{ #'Van Buuren, S. (2018). @@ -41,9 +41,9 @@ weighted sum scores, calculated earlier in the multivariate amputation function Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{ampute}}, \code{\link{ampute.default.type}} +\code{\link[=ampute]{ampute()}}, \code{\link[=ampute.default.type]{ampute.default.type()}} } \author{ -Rianne Schouten [aut, cre], Gerko Vink [aut], Peter Lugtig [ctb], 2016 +Rianne Schouten (aut, cre), Gerko Vink (aut), Peter Lugtig (ctb), 2016 } \keyword{internal} diff --git a/man/ampute.default.freq.Rd b/man/ampute.default.freq.Rd index c0c9eb409..9fe374372 100644 --- a/man/ampute.default.freq.Rd +++ b/man/ampute.default.freq.Rd @@ -9,7 +9,7 @@ ampute.default.freq(patterns) \arguments{ \item{patterns}{A matrix of size #patterns by #variables where \code{0} indicates a variable should have missing values and \code{1} indicates a variable should -remain complete. Could be the result of \code{\link{ampute.default.patterns}}.} +remain complete. Could be the result of \code{\link[=ampute.default.patterns]{ampute.default.patterns()}}.} } \value{ A vector of length #patterns containing the relative frequencies with @@ -20,7 +20,7 @@ Defines the default relative frequency vector for the multivariate amputation function \code{ampute}. } \seealso{ -\code{\link{ampute}}, \code{\link{ampute.default.patterns}} +\code{\link[=ampute]{ampute()}}, \code{\link[=ampute.default.patterns]{ampute.default.patterns()}} } \author{ Rianne Schouten, 2016 diff --git a/man/ampute.default.odds.Rd b/man/ampute.default.odds.Rd index 59b3bb81f..e29f2cf68 100644 --- a/man/ampute.default.odds.Rd +++ b/man/ampute.default.odds.Rd @@ -9,7 +9,7 @@ ampute.default.odds(patterns) \arguments{ \item{patterns}{A matrix of size #patterns by #variables where 0 indicates a variable should have missing values and 1 indicates a variable should remain -complete. Could be the result of \code{\link{ampute.default.patterns}}.} +complete. Could be the result of \code{\link[=ampute.default.patterns]{ampute.default.patterns()}}.} } \value{ A matrix where #rows equals #patterns. Default is 4 quantiles with odds @@ -20,7 +20,7 @@ Defines the default odds matrix for the multivariate amputation function \code{ampute}. } \seealso{ -\code{\link{ampute}}, \code{\link{ampute.default.patterns}} +\code{\link[=ampute]{ampute()}}, \code{\link[=ampute.default.patterns]{ampute.default.patterns()}} } \author{ Rianne Schouten, 2016 diff --git a/man/ampute.default.patterns.Rd b/man/ampute.default.patterns.Rd index 8deaf3f45..42bf92abc 100644 --- a/man/ampute.default.patterns.Rd +++ b/man/ampute.default.patterns.Rd @@ -17,7 +17,7 @@ This function creates a default pattern matrix for the multivariate amputation function \code{ampute()}. } \seealso{ -\code{\link{ampute}}, \code{\link{md.pattern}} +\code{\link[=ampute]{ampute()}}, \code{\link[=md.pattern]{md.pattern()}} } \author{ Rianne Schouten, 2016 diff --git a/man/ampute.default.type.Rd b/man/ampute.default.type.Rd index cc4d5bb26..aa026853f 100644 --- a/man/ampute.default.type.Rd +++ b/man/ampute.default.type.Rd @@ -9,7 +9,7 @@ ampute.default.type(patterns) \arguments{ \item{patterns}{A matrix of size #patterns by #variables where 0 indicates a variable should have missing values and 1 indicates a variable should remain -complete. Could be the result of \code{\link{ampute.default.patterns}}.} +complete. Could be the result of \code{\link[=ampute.default.patterns]{ampute.default.patterns()}}.} } \value{ A string vector of length #patterns containing the missingness types. @@ -20,7 +20,7 @@ Defines the default type vector for the multivariate amputation function \code{ampute}. } \seealso{ -\code{\link{ampute}}, \code{\link{ampute.default.patterns}} +\code{\link[=ampute]{ampute()}}, \code{\link[=ampute.default.patterns]{ampute.default.patterns()}} } \author{ Rianne Schouten, 2016 diff --git a/man/ampute.default.weights.Rd b/man/ampute.default.weights.Rd index 77c10155b..5b14ad54a 100644 --- a/man/ampute.default.weights.Rd +++ b/man/ampute.default.weights.Rd @@ -9,7 +9,7 @@ ampute.default.weights(patterns, mech) \arguments{ \item{patterns}{A matrix of size #patterns by #variables where \code{0} indicates a variable should have missing values and \code{1} indicates a variable should -remain complete. Could be the result of \code{\link{ampute.default.patterns}}.} +remain complete. Could be the result of \code{\link[=ampute.default.patterns]{ampute.default.patterns()}}.} \item{mech}{A string specifying the missingness mechanism.} } @@ -26,7 +26,7 @@ Defines the default weights matrix for the multivariate amputation function \code{ampute}. } \seealso{ -\code{\link{ampute}}, \code{\link{ampute.default.patterns}} +\code{\link[=ampute]{ampute()}}, \code{\link[=ampute.default.patterns]{ampute.default.patterns()}} } \author{ Rianne Schouten, 2016 diff --git a/man/ampute.discrete.Rd b/man/ampute.discrete.Rd index 4451fa53e..8f9d0c50c 100644 --- a/man/ampute.discrete.Rd +++ b/man/ampute.discrete.Rd @@ -12,7 +12,7 @@ For each case, a value between 1 and #patterns is given. For example, a case with value 2 is candidate for missing data pattern 2.} \item{scores}{A list containing vectors with the candidates's weighted sum scores, -the result of an underlying function in \code{\link{ampute}}.} +the result of an underlying function in \code{\link[=ampute]{ampute()}}.} \item{prop}{A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.} @@ -24,7 +24,7 @@ relative probabilities: a quantile with odds value 4 will have a probability of being missing that is four times higher than a quantile with odds 1. The #quantiles may differ between the patterns, specify NA for cells remaining empty. Default is 4 quantiles with odds values 1, 2, 3 and 4, the result of -\code{\link{ampute.default.odds}}.} +\code{\link[=ampute.default.odds]{ampute.default.odds()}}.} } \value{ A list containing vectors with \code{0} if a case should be made missing @@ -34,7 +34,7 @@ first pattern, the second vector to the second pattern, etcetera. \description{ This function creates a missing data indicator for each pattern. Odds probabilities (Brand, 1999, pp. 110-113) will be induced on the weighted sum scores, calculated earlier -in the multivariate amputation function \code{\link{ampute}}. +in the multivariate amputation function \code{\link[=ampute]{ampute()}}. } \references{ Brand, J.P.L. (1999). \emph{Development, implementation and @@ -42,7 +42,7 @@ evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets.} Dissertation. Rotterdam: Erasmus University. } \seealso{ -\code{\link{ampute}}, \code{\link{ampute.default.odds}} +\code{\link[=ampute]{ampute()}}, \code{\link[=ampute.default.odds]{ampute.default.odds()}} } \author{ Rianne Schouten, 2016 diff --git a/man/ampute.mcar.Rd b/man/ampute.mcar.Rd index e704d7a22..d7203fd75 100644 --- a/man/ampute.mcar.Rd +++ b/man/ampute.mcar.Rd @@ -14,7 +14,7 @@ case with value 2 is candidate for missing data pattern 2.} \item{patterns}{A matrix of size #patterns by #variables where \code{0} indicates a variable should have missing values and \code{1} indicates a variable should remain complete. The user may specify as many patterns as desired. One pattern -(a vector) is also possible. Could be the result of \code{\link{ampute.default.patterns}}, +(a vector) is also possible. Could be the result of \code{\link[=ampute.default.patterns]{ampute.default.patterns()}}, default will be a square matrix of size #variables where each pattern has missingness on one variable only.} @@ -29,10 +29,10 @@ first pattern, the second vector to the second pattern, etcetera. \description{ This function creates a missing data indicator for each pattern, based on a MCAR missingness mechanism. The function is used in the multivariate amputation function -\code{\link{ampute}}. +\code{\link[=ampute]{ampute()}}. } \seealso{ -\code{\link{ampute}} +\code{\link[=ampute]{ampute()}} } \author{ Rianne Schouten, 2016 diff --git a/man/as.mira.Rd b/man/as.mira.Rd index 18e6caf1b..6c71670a3 100644 --- a/man/as.mira.Rd +++ b/man/as.mira.Rd @@ -18,7 +18,7 @@ complete-data analysis stored as a list, and turns it into a \code{mira} object that can be pooled. } \seealso{ -\code{\link[=mira-class]{mira}} +\code{\link[=mira-class]{mira()}} } \author{ Stef van Buuren diff --git a/man/as.mitml.result.Rd b/man/as.mitml.result.Rd index a9b202a8c..80de6f538 100644 --- a/man/as.mitml.result.Rd +++ b/man/as.mitml.result.Rd @@ -19,7 +19,7 @@ complete-data analysis stored as a list, and turns it into an object of class \code{mitml.result}. } \seealso{ -\code{\link[mitml]{with.mitml.list}} +\code{\link[mitml:with.mitml.list]{mitml::with.mitml.list()}} } \author{ Stef van Buuren diff --git a/man/boys.Rd b/man/boys.Rd index 1008b54c2..57376eccb 100644 --- a/man/boys.Rd +++ b/man/boys.Rd @@ -20,17 +20,17 @@ A data frame with 748 rows on the following 9 variables: \describe{ Fredriks, A.M,, van Buuren, S., Burgmeijer, R.J., Meulmeester JF, Beuker, R.J., Brugman, E., Roede, M.J., Verloove-Vanhorick, S.P., Wit, J.M. (2000) Continuing positive secular growth change in The Netherlands -1955-1997. \emph{Pediatric Research}, \bold{47}, 316-323. +1955-1997. \emph{Pediatric Research}, \strong{47}, 316-323. Fredriks, A.M., van Buuren, S., Wit, J.M., Verloove-Vanhorick, S.P. (2000). Body index measurements in 1996-7 compared with 1980. \emph{Archives of -Disease in Childhood}, \bold{82}, 107-112. +Disease in Childhood}, \strong{82}, 107-112. } \description{ Height, weight, head circumference and puberty of 748 Dutch boys. } \details{ -Random sample of 10\% from the cross-sectional data used to construct the +Random sample of 10\\% from the cross-sectional data used to construct the Dutch growth references 1997. Variables \code{gen} and \code{phb} are ordered factors. \code{reg} is a factor. } diff --git a/man/brandsma.Rd b/man/brandsma.Rd index 78fefef51..c91d40a09 100644 --- a/man/brandsma.Rd +++ b/man/brandsma.Rd @@ -24,7 +24,7 @@ } } \source{ -Constructed from \code{MLbook_2nded_total_4106-99.sav} from +Constructed from \verb{MLbook_2nded_total_4106-99.sav} from \url{https://www.stats.ox.ac.uk/~snijders/mlbook.htm} by function \code{data-raw/R/brandsma.R} } diff --git a/man/bwplot.mads.Rd b/man/bwplot.mads.Rd index ab90db403..c9f392a93 100644 --- a/man/bwplot.mads.Rd +++ b/man/bwplot.mads.Rd @@ -15,8 +15,8 @@ ) } \arguments{ -\item{x}{A \code{mads} (\code{\link{mads-class}}) object, typically created by -\code{\link{ampute}}.} +\item{x}{A \code{mads} (\code{\link[=mads-class]{mads-class()}}) object, typically created by +\code{\link[=ampute]{ampute()}}.} \item{data}{A string or vector of variable names that needs to be plotted. As a default, all variables will be plotted.} @@ -50,12 +50,12 @@ to the variable values. } \note{ The \code{mads} object contains all the information you need to -make any desired plots. Check \code{\link{mads-class}} or the vignette \emph{Multivariate +make any desired plots. Check \code{\link[=mads-class]{mads-class()}} or the vignette \emph{Multivariate Amputation using Ampute} to understand the contents of class object \code{mads}. } \seealso{ -\code{\link{ampute}}, \code{\link{bwplot}}, \code{\link{Lattice}} for -an overview of the package, \code{\link{mads-class}} +\code{\link[=ampute]{ampute()}}, \code{\link[=bwplot]{bwplot()}}, \code{\link[=Lattice]{Lattice()}} for +an overview of the package, \code{\link[=mads-class]{mads-class()}} } \author{ Rianne Schouten, 2016 diff --git a/man/bwplot.mids.Rd b/man/bwplot.mids.Rd index 40943d2bd..39f986f13 100644 --- a/man/bwplot.mids.Rd +++ b/man/bwplot.mids.Rd @@ -35,7 +35,7 @@ The formula is evaluated on the complete data set in the \code{long} form. Legal variable names for the formula include \code{names(x$data)} plus the two administrative factors \code{.imp} and \code{.id}. -\bold{Extended formula interface:} The primary variable terms (both the LHS +\strong{Extended formula interface:} The primary variable terms (both the LHS \code{y} and RHS \code{x}) may consist of multiple terms separated by a \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and @@ -65,11 +65,11 @@ in the LHS \code{y} variable of the display, i.e. groups created by differs from \code{na.groups} because it evaluates in the completed data \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas \code{na.groups} evaluates in the response indicator. See -\code{\link{xyplot}} for more details. When both \code{na.groups} and +\code{\link[=xyplot]{xyplot()}} for more details. When both \code{na.groups} and \code{groups} are specified, \code{na.groups} takes precedence, and \code{groups} is ignored.} -\item{as.table}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{as.table}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{theme}{A named list containing the graphical parameters. The default function \code{mice.theme} produces a short list of default colors, line @@ -88,26 +88,26 @@ replicated for different element, e.g. use all reds for the imputed data. Replication may be switched off by setting the flag to \code{FALSE}, in order to allow the user to gain full control.} -\item{allow.multiple}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{allow.multiple}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{outer}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{outer}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{\dots}{Further arguments, usually not directly processed by the high-level functions documented here, but instead passed on to other functions.} -\item{subscripts}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subscripts}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{subset}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subset}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} } \value{ The high-level functions documented here, as well as other high-level Lattice functions, return an object of class \code{"trellis"}. The -\code{\link[lattice:update.trellis]{update}} method can be used to +\code{\link[lattice:update.trellis]{update()}} method can be used to subsequently update components of the object, and the -\code{\link[lattice:print.trellis]{print}} method (usually called by default) +\code{\link[lattice:print.trellis]{print()}} method (usually called by default) will plot it on an appropriate plotting device. } \description{ @@ -134,7 +134,7 @@ missing groups. \code{col[1]} is the color of the 'observed' data, \code{col[2]} is the color of the missing or imputed data. A convenient color choice is \code{col=mdc(1:2)}, a transparent blue color for the observed data, and a transparent red color for the imputed data. A good choice is -\code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the +\verb{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the duration of the session by running \code{mice.theme()}. } \note{ @@ -169,15 +169,15 @@ Visualization with R}, Springer. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}}, \code{\link{xyplot}}, \code{\link{densityplot}}, -\code{\link{stripplot}}, \code{\link{lattice}} for an overview of the -package, as well as \code{\link[lattice:xyplot]{bwplot}}, -\code{\link[lattice:panel.xyplot]{panel.bwplot}}, -\code{\link[lattice:print.trellis]{print.trellis}}, -\code{\link[lattice:trellis.par.get]{trellis.par.set}} +\code{\link[=mice]{mice()}}, \code{\link[=xyplot]{xyplot()}}, \code{\link[=densityplot]{densityplot()}}, +\code{\link[=stripplot]{stripplot()}}, \code{\link[=lattice]{lattice()}} for an overview of the +package, as well as \code{\link[lattice:xyplot]{bwplot()}}, +\code{\link[lattice:panel.xyplot]{panel.bwplot()}}, +\code{\link[lattice:print.trellis]{lattice::print.trellis()}}, +\code{\link[lattice:trellis.par.get]{trellis.par.set()}} } \author{ Stef van Buuren diff --git a/man/cbind.Rd b/man/cbind.Rd index 41c2af1bf..4a9c5066a 100644 --- a/man/cbind.Rd +++ b/man/cbind.Rd @@ -42,7 +42,6 @@ function calls \code{cbind.mids()}, respectively \code{rbind.mids()}. In all other cases, the call is forwarded to standard functions in the \code{base} package. - The \code{cbind.mids()} function combines two \code{mids} objects columnwise into a single object of class \code{mids}, or combines a single \code{mids} object with @@ -179,12 +178,12 @@ nrow(complete(rbind(imp1, complete(imp5)))) \references{ van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -Statistical Software}, \bold{45}(3), 1-67. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link[base:cbind]{cbind}}, \code{\link{ibind}}, -\code{\link[=mids-class]{mids}} +\code{\link[base:cbind]{base::cbind()}}, \code{\link[=ibind]{ibind()}}, +\code{\link[=mids-class]{mids()}} } \author{ Karin Groothuis-Oudshoorn, Stef van Buuren diff --git a/man/cc.Rd b/man/cc.Rd index d61d41575..483b521e6 100644 --- a/man/cc.Rd +++ b/man/cc.Rd @@ -19,7 +19,7 @@ Extracts the complete cases, also known as \emph{listwise deletion}. \code{cc(x)} is similar to \code{na.omit(x)}, but returns an object of the same class as the input data. Dimensions are not dropped. For extracting -incomplete cases, use \code{\link{ici}}. +incomplete cases, use \code{\link[=ici]{ici()}}. } \examples{ @@ -27,7 +27,7 @@ incomplete cases, use \code{\link{ici}}. # cc(nhanes$bmi) # extract complete bmi } \seealso{ -\code{\link{na.omit}}, \code{\link{cci}}, \code{\link{ici}} +\code{\link[=na.omit]{na.omit()}}, \code{\link[=cci]{cci()}}, \code{\link[=ici]{ici()}} } \author{ Stef van Buuren, 2017. diff --git a/man/cci.Rd b/man/cci.Rd index 8215b6901..96a545f5f 100644 --- a/man/cci.Rd +++ b/man/cci.Rd @@ -25,7 +25,7 @@ f <- cci(nhanes[, c("bmi", "hyp")]) # complete data for bmi and hyp nhanes[f, ] # obtain all data from those with complete bmi and hyp } \seealso{ -\code{\link{complete.cases}}, \code{\link{ici}}, \code{\link{cc}} +\code{\link[=complete.cases]{complete.cases()}}, \code{\link[=ici]{ici()}}, \code{\link[=cc]{cc()}} } \author{ Stef van Buuren, 2017. diff --git a/man/complete.mids.Rd b/man/complete.mids.Rd index 2d015b3a6..b31b9e7a4 100644 --- a/man/complete.mids.Rd +++ b/man/complete.mids.Rd @@ -71,7 +71,7 @@ columns in a different order.} } } \note{ -Technical note: \code{mice 3.7.5} renamed the \code{complete()} function +Technical note: \verb{mice 3.7.5} renamed the \code{complete()} function to \code{complete.mids()} and exported it as an S3 method of the generic \code{tidyr::complete()}. Name clashes between \code{mice::complete()} and \code{tidyr::complete()} should no @@ -104,6 +104,6 @@ dslist <- complete(imp, c(0, 3, 5), mild = TRUE) names(dslist) } \seealso{ -\code{\link{mice}}, \code{\link[=mids-class]{mids}} +\code{\link[=mice]{mice()}}, \code{\link[=mids-class]{mids()}} } \keyword{manip} diff --git a/man/construct.blocks.Rd b/man/construct.blocks.Rd index a97e84a63..87d8f3165 100644 --- a/man/construct.blocks.Rd +++ b/man/construct.blocks.Rd @@ -47,5 +47,5 @@ pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) construct.blocks(formulas = form, pred = pred) } \seealso{ -\code{\link{make.blocks}}, \code{\link{name.blocks}} +\code{\link[=make.blocks]{make.blocks()}}, \code{\link[=name.blocks]{name.blocks()}} } diff --git a/man/convergence.Rd b/man/convergence.Rd index 9e1dd7c68..9964fda4d 100644 --- a/man/convergence.Rd +++ b/man/convergence.Rd @@ -66,6 +66,6 @@ R for Assessing Convergence of MCMC. Bayesian Analysis, 1(1), 1-38. https://doi.org/10.1214/20-BA1221 } \seealso{ -\code{\link{mice}}, \code{\link[=mids-class]{mids}} +\code{\link[=mice]{mice()}}, \code{\link[=mids-class]{mids()}} } \keyword{none} diff --git a/man/densityplot.mids.Rd b/man/densityplot.mids.Rd index 2aa8bfff8..7b1d3c60f 100644 --- a/man/densityplot.mids.Rd +++ b/man/densityplot.mids.Rd @@ -39,7 +39,7 @@ The formula is evaluated on the complete data set in the \code{long} form. Legal variable names for the formula include \code{names(x$data)} plus the two administrative factors \code{.imp} and \code{.id}. -\bold{Extended formula interface:} The primary variable terms (both the LHS +\strong{Extended formula interface:} The primary variable terms (both the LHS \code{y} and RHS \code{x}) may consist of multiple terms separated by a \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and @@ -50,8 +50,7 @@ numerical variables. Mixing numerical and categorical data occasionally produces odds labeling of vertical axis. The function \code{densityplot} does not use the \code{y} terms in the -formula. Density plots for \code{x1} and \code{x2} are requested as \code{~ -x1 + x2}.} +formula. Density plots for \code{x1} and \code{x2} are requested as \code{~ x1 + x2}.} \item{na.groups}{An expression evaluating to a logical vector indicating which two groups are distinguished (e.g. using different colors) in the @@ -69,11 +68,11 @@ in the LHS \code{y} variable of the display, i.e. groups created by differs from \code{na.groups} because it evaluates in the completed data \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas \code{na.groups} evaluates in the response indicator. See -\code{\link{xyplot}} for more details. When both \code{na.groups} and +\code{\link[=xyplot]{xyplot()}} for more details. When both \code{na.groups} and \code{groups} are specified, \code{na.groups} takes precedence, and \code{groups} is ignored.} -\item{as.table}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{as.table}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{plot.points}{A logical used in \code{densityplot} that signals whether the points should be plotted.} @@ -99,30 +98,30 @@ to allow the user to gain full control.} width of the observed density. \code{thicker=1} uses the same thickness for the observed and imputed data.} -\item{allow.multiple}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{allow.multiple}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{outer}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{outer}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{panel}{See \code{\link{xyplot}}.} +\item{panel}{See \code{\link[=xyplot]{xyplot()}}.} -\item{default.prepanel}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{default.prepanel}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{\dots}{Further arguments, usually not directly processed by the high-level functions documented here, but instead passed on to other functions.} -\item{subscripts}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subscripts}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{subset}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subset}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} } \value{ The high-level functions documented here, as well as other high-level Lattice functions, return an object of class \code{"trellis"}. The -\code{\link[lattice:update.trellis]{update}} method can be used to +\code{\link[lattice:update.trellis]{update()}} method can be used to subsequently update components of the object, and the -\code{\link[lattice:print.trellis]{print}} method (usually called by default) +\code{\link[lattice:print.trellis]{print()}} method (usually called by default) will plot it on an appropriate plotting device. } \description{ @@ -149,7 +148,7 @@ missing groups. \code{col[1]} is the color of the 'observed' data, \code{col[2]} is the color of the missing or imputed data. A convenient color choice is \code{col=mdc(1:2)}, a transparent blue color for the observed data, and a transparent red color for the imputed data. A good choice is -\code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the +\verb{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the duration of the session by running \code{mice.theme()}. } \note{ @@ -166,9 +165,7 @@ In \pkg{mice} the argument \code{data} is always a formula object, whereas in All other arguments have identical interpretation. \code{densityplot} errs on empty groups, which occurs if all observations in -the subgroup contain \code{NA}. The relevant error message is: \code{Error in -density.default: ... need at least 2 points to select a bandwidth -automatically}. There is yet no workaround for this problem. Use the more +the subgroup contain \code{NA}. The relevant error message is: \verb{Error in density.default: ... need at least 2 points to select a bandwidth automatically}. There is yet no workaround for this problem. Use the more robust \code{bwplot} or \code{stripplot} as a replacement. } \examples{ @@ -187,15 +184,15 @@ Visualization with R}, Springer. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}}, \code{\link{xyplot}}, \code{\link{stripplot}}, -\code{\link{bwplot}}, \code{\link{lattice}} for an overview of the -package, as well as \code{\link[lattice:histogram]{densityplot}}, -\code{\link[lattice:panel.densityplot]{panel.densityplot}}, -\code{\link[lattice:print.trellis]{print.trellis}}, -\code{\link[lattice:trellis.par.get]{trellis.par.set}} +\code{\link[=mice]{mice()}}, \code{\link[=xyplot]{xyplot()}}, \code{\link[=stripplot]{stripplot()}}, +\code{\link[=bwplot]{bwplot()}}, \code{\link[=lattice]{lattice()}} for an overview of the +package, as well as \code{\link[lattice:histogram]{densityplot()}}, +\code{\link[lattice:panel.densityplot]{lattice::panel.densityplot()}}, +\code{\link[lattice:print.trellis]{lattice::print.trellis()}}, +\code{\link[lattice:trellis.par.get]{trellis.par.set()}} } \author{ Stef van Buuren diff --git a/man/employee.Rd b/man/employee.Rd index 7578470a7..cbb69fd43 100644 --- a/man/employee.Rd +++ b/man/employee.Rd @@ -37,6 +37,6 @@ mimic a situation where the applicant's well-being questionnaire is inadvertently lost. A larger version of this data set in present as -\code{\link[miceadds:data.enders]{data.enders.employee}}. +\code{\link[miceadds:data.enders]{data.enders.employee()}}. } \keyword{datasets} diff --git a/man/fico.Rd b/man/fico.Rd index be3b124cf..8e5ea7d21 100644 --- a/man/fico.Rd +++ b/man/fico.Rd @@ -27,7 +27,7 @@ compared with complete-case analysis for missing covariate values. \emph{Statistics in Medicine}, \emph{29}, 2920-2931. } \seealso{ -\code{\link{fluxplot}}, \code{\link{flux}}, \code{\link{md.pattern}} +\code{\link[=fluxplot]{fluxplot()}}, \code{\link[=flux]{flux()}}, \code{\link[=md.pattern]{md.pattern()}} } \author{ Stef van Buuren, 2012 diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index 61e419a03..c681db7fb 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -69,7 +69,7 @@ imp_f2 <- filter(imp, age >= 2 & hyp == 1) nrow(complete(imp_f2)) # should be 5 } \seealso{ -\code{\link[dplyr]{filter}} +\code{\link[dplyr:filter]{dplyr::filter()}} } \author{ Patrick Rockenschaub diff --git a/man/flux.Rd b/man/flux.Rd index 67ca66cc7..05b29cedc 100644 --- a/man/flux.Rd +++ b/man/flux.Rd @@ -30,7 +30,7 @@ imputation model. \details{ Infux and outflux have been proposed by Van Buuren (2018), chapter 4. -Influx is equal to the number of variable pairs \code{(Yj , Yk)} with +Influx is equal to the number of variable pairs \verb{(Yj , Yk)} with \code{Yj} missing and \code{Yk} observed, divided by the total number of observed data cells. Influx depends on the proportion of missing data of the variable. Influx of a completely observed variable is equal to 0, whereas for @@ -61,7 +61,7 @@ compared with complete-case analysis for missing covariate values. \emph{Statistics in Medicine}, \emph{29}, 2920-2931. } \seealso{ -\code{\link{fluxplot}}, \code{\link{md.pattern}}, \code{\link{fico}} +\code{\link[=fluxplot]{fluxplot()}}, \code{\link[=md.pattern]{md.pattern()}}, \code{\link[=fico]{fico()}} } \author{ Stef van Buuren, 2012 diff --git a/man/fluxplot.Rd b/man/fluxplot.Rd index 7361f518e..77d210717 100644 --- a/man/fluxplot.Rd +++ b/man/fluxplot.Rd @@ -69,7 +69,7 @@ imputation model. \details{ Infux and outflux have been proposed by Van Buuren (2012), chapter 4. -Influx is equal to the number of variable pairs \code{(Yj , Yk)} with +Influx is equal to the number of variable pairs \verb{(Yj , Yk)} with \code{Yj} missing and \code{Yk} observed, divided by the total number of observed data cells. Influx depends on the proportion of missing data of the variable. Influx of a completely observed variable is equal to 0, whereas for @@ -97,7 +97,7 @@ compared with complete-case analysis for missing covariate values. \emph{Statistics in Medicine}, \emph{29}, 2920-2931. } \seealso{ -\code{\link{flux}}, \code{\link{md.pattern}}, \code{\link{fico}} +\code{\link[=flux]{flux()}}, \code{\link[=md.pattern]{md.pattern()}}, \code{\link[=fico]{fico()}} } \author{ Stef van Buuren, 2012 diff --git a/man/futuremice.Rd b/man/futuremice.Rd index 238f9f8a9..ff649bf6e 100644 --- a/man/futuremice.Rd +++ b/man/futuremice.Rd @@ -19,7 +19,7 @@ futuremice( } \arguments{ \item{data}{A data frame or matrix containing the incomplete data. Similar to -the first argument of \code{\link{mice}}.} +the first argument of \code{\link[=mice]{mice()}}.} \item{m}{The number of desired imputated datasets. By default $m=5$ as with \code{mice}} @@ -42,42 +42,41 @@ physical (\code{FALSE}) CPU's on machine should be used.} \item{future.plan}{A character indicating how \code{future}s are resolved. 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.} +\code{\link[future:plan]{future::plan()}} for more information on future plans.} -\item{packages}{A character vector with additional packages to be used in +\item{packages}{A character vector with additional packages to be used in \code{mice} (e.g., for using external imputation functions).} \item{globals}{A character string with additional functions to be exported to each future (e.g., user-written imputation functions).} -\item{...}{Named arguments that are passed down to function \code{\link{mice}}.} +\item{...}{Named arguments that are passed down to function \code{\link[=mice]{mice()}}.} } \value{ -A mids object as defined by \code{\link{mids-class}} +A mids object as defined by \code{\link[=mids-class]{mids-class()}} } \description{ -This is a wrapper function for \code{\link{mice}}, using multiple cores to -execute \code{\link{mice}} in parallel. As a result, the imputation +This is a wrapper function for \code{\link[=mice]{mice()}}, using multiple cores to +execute \code{\link[=mice]{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} +\code{\link[=futuremice]{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 +This function relies on package \code{\link[furrr:furrr-package]{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 Windows systems. - -This wrapper function combines the output of \code{\link[furrr]{future_map}} with -function \code{\link{ibind}} from the \code{\link{mice}} package. A +This wrapper function combines the output of \code{\link[furrr:future_map]{furrr::future_map()}} with +function \code{\link[=ibind]{ibind()}} from the \code{\link[=mice]{mice()}} package. A \code{mids} object is returned and can be used for further analyses. A seed value can be specified in the global environment, which will yield reproducible results. A seed value can also be specified within the -\code{\link{futuremice}} call, through specifying the argument +\code{\link[=futuremice]{futuremice()}} call, through specifying the argument \code{parallelseed}. If \code{parallelseed} is not specified, a seed value is -drawn randomly by default, and accessible through \code{$parallelseed} in the +drawn randomly by default, and accessible through \verb{$parallelseed} in the output object. Hence, results will always be reproducible, regardless of whether the seed is specified in the global environment, or by setting the same seed within the function (potentially by extracting the seed from the @@ -104,8 +103,8 @@ Volker, T.B. and Vink, G. (2022). futuremice: The future starts today. Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link[future]{future}}, \code{\link[furrr]{furrr}}, \code{\link[furrr]{future_map}}, -\code{\link[future]{plan}}, \code{\link{mice}}, \code{\link{mids-class}} +\code{\link[future:future]{future::future()}}, \code{\link[furrr:furrr-package]{furrr::furrr()}}, \code{\link[furrr:future_map]{furrr::future_map()}}, +\code{\link[future:plan]{future::plan()}}, \code{\link[=mice]{mice()}}, \code{\link[=mids-class]{mids-class()}} } \author{ Thom Benjamin Volker, Gerko Vink diff --git a/man/getfit.Rd b/man/getfit.Rd index 9cb6105a9..817da5fae 100644 --- a/man/getfit.Rd +++ b/man/getfit.Rd @@ -40,7 +40,7 @@ f2 <- getfit(fit, 2) class(f2) } \seealso{ -\code{\link[=mira-class]{mira}}, \code{\link{with.mids}} +\code{\link[=mira-class]{mira()}}, \code{\link[=with.mids]{with.mids()}} } \author{ Stef van Buuren, 2012, 2020 diff --git a/man/glance.mipo.Rd b/man/glance.mipo.Rd index 12d19184f..215ffc80a 100644 --- a/man/glance.mipo.Rd +++ b/man/glance.mipo.Rd @@ -2,27 +2,27 @@ % Please edit documentation in R/tidiers.R \name{glance.mipo} \alias{glance.mipo} -\title{Glance method to extract information from a `mipo` object} +\title{Glance method to extract information from a \code{mipo} object} \usage{ \method{glance}{mipo}(x, ...) } \arguments{ -\item{x}{An object with multiply-imputed models from `mice` (class: `mipo`)} +\item{x}{An object with multiply-imputed models from \code{mice} (class: \code{mipo})} \item{...}{extra arguments (not used)} } \value{ a dataframe with one row and the following columns: \itemize{ - \item nimp - \item nobs +\item nimp +\item nobs } } \description{ -Glance method to extract information from a `mipo` object +Glance method to extract information from a \code{mipo} object } \note{ -If x contains `lm` models, R2 and Adj.R2 are included in the output +If x contains \code{lm} models, R2 and Adj.R2 are included in the output } \concept{tidiers} \keyword{internal} diff --git a/man/glm.mids.Rd b/man/glm.mids.Rd index 2f0625199..700a5a758 100644 --- a/man/glm.mids.Rd +++ b/man/glm.mids.Rd @@ -8,15 +8,15 @@ glm.mids(formula, family = gaussian, data, ...) } \arguments{ \item{formula}{a formula expression as for other regression models, of the -form response ~ predictors. See the documentation of \code{\link{lm}} and -\code{\link{formula}} for details.} +form response ~ predictors. See the documentation of \code{\link[=lm]{lm()}} and +\code{\link[=formula]{formula()}} for details.} \item{family}{The family of the glm model} \item{data}{An object of type \code{mids}, which stands for 'multiply imputed data set', typically created by function \code{mice()}.} -\item{\dots}{Additional parameters passed to \code{\link{glm}}.} +\item{\dots}{Additional parameters passed to \code{\link[=glm]{glm()}}.} } \value{ An objects of class \code{mira}, which stands for 'multiply imputed @@ -28,7 +28,7 @@ Applies \code{glm()} to a multiply imputed data set } \details{ This function is included for backward compatibility with V1.0. The function -is superseded by \code{\link{with.mids}}. +is superseded by \code{\link[=with.mids]{with.mids()}}. } \examples{ @@ -44,8 +44,8 @@ Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) Leiden: TNO Quality of Life. } \seealso{ -\code{\link{with.mids}}, \code{\link{glm}}, \code{\link[=mids-class]{mids}}, -\code{\link[=mira-class]{mira}} +\code{\link[=with.mids]{with.mids()}}, \code{\link[=glm]{glm()}}, \code{\link[=mids-class]{mids()}}, +\code{\link[=mira-class]{mira()}} } \author{ Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 diff --git a/man/ibind.Rd b/man/ibind.Rd index 28628fbe9..702759aac 100644 --- a/man/ibind.Rd +++ b/man/ibind.Rd @@ -39,7 +39,7 @@ imp12$m plot(imp12) } \seealso{ -\code{\link[=mids-class]{mids}} +\code{\link[=mids-class]{mids()}} } \author{ Karin Groothuis-Oudshoorn, Stef van Buuren diff --git a/man/ic.Rd b/man/ic.Rd index e5698fdbe..1d3bc3754 100644 --- a/man/ic.Rd +++ b/man/ic.Rd @@ -16,7 +16,7 @@ A \code{vector}, \code{matrix} or \code{data.frame} containing the data of the c } \description{ Extracts incomplete cases from a data set. -The companion function for selecting the complete cases is \code{\link{cc}}. +The companion function for selecting the complete cases is \code{\link[=cc]{cc()}}. } \examples{ @@ -25,7 +25,7 @@ ic(nhanes[1:10, ]) # incomplete cases within the first ten rows ic(nhanes[, c("bmi", "hyp")]) # restrict extraction to variables bmi and hyp } \seealso{ -\code{\link{cc}}, \code{\link{ici}} +\code{\link[=cc]{cc()}}, \code{\link[=ici]{ici()}} } \author{ Stef van Buuren, 2017. diff --git a/man/ici.Rd b/man/ici.Rd index b7d1d2656..bb860eb61 100644 --- a/man/ici.Rd +++ b/man/ici.Rd @@ -25,7 +25,7 @@ The companion function \code{cci()} selects the complete cases. ici(nhanes) # indicator for 12 rows with incomplete cases } \seealso{ -\code{\link{cci}}, \code{\link{ic}} +\code{\link[=cci]{cci()}}, \code{\link[=ic]{ic()}} } \author{ Stef van Buuren, 2017. diff --git a/man/leiden85.Rd b/man/leiden85.Rd index 585cbf192..a46bc682a 100644 --- a/man/leiden85.Rd +++ b/man/leiden85.Rd @@ -23,7 +23,7 @@ based study. \emph{Brit. Med. J.}, \emph{316}(7147), 1780-1784. Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of missing blood pressure covariates in survival analysis. \emph{Statistics in -Medicine}, \bold{18}, 681--694. +Medicine}, \strong{18}, 681--694. Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/sec-toomany.html#sec:leiden85cohort}{\emph{Flexible Imputation of Missing Data. Second Edition.}} diff --git a/man/lm.mids.Rd b/man/lm.mids.Rd index dd81163b2..3742efb32 100644 --- a/man/lm.mids.Rd +++ b/man/lm.mids.Rd @@ -9,12 +9,12 @@ lm.mids(formula, data, ...) \arguments{ \item{formula}{a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right. See the -documentation of \code{\link{lm}} and \code{\link{formula}} for details.} +documentation of \code{\link[=lm]{lm()}} and \code{\link[=formula]{formula()}} for details.} \item{data}{An object of type 'mids', which stands for 'multiply imputed data set', typically created by a call to function \code{mice()}.} -\item{\dots}{Additional parameters passed to \code{\link{lm}}} +\item{\dots}{Additional parameters passed to \code{\link[=lm]{lm()}}} } \value{ An objects of class \code{mira}, which stands for 'multiply imputed @@ -26,7 +26,7 @@ Applies \code{lm()} to multiply imputed data set } \details{ This function is included for backward compatibility with V1.0. The function -is superseded by \code{\link{with.mids}}. +is superseded by \code{\link[=with.mids]{with.mids()}}. } \examples{ imp <- mice(nhanes) @@ -36,11 +36,11 @@ fit \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{lm}}, \code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}} +\code{\link[=lm]{lm()}}, \code{\link[=mids-class]{mids()}}, \code{\link[=mira-class]{mira()}} } \author{ Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 diff --git a/man/mads-class.Rd b/man/mads-class.Rd index f20aba8c2..df34461a9 100644 --- a/man/mads-class.Rd +++ b/man/mads-class.Rd @@ -62,7 +62,7 @@ pattern.} } \seealso{ -\code{\link{ampute}}, Vignette titled "Multivariate Amputation using +\code{\link[=ampute]{ampute()}}, Vignette titled "Multivariate Amputation using Ampute". } \author{ diff --git a/man/make.blocks.Rd b/man/make.blocks.Rd index ab1276447..4becda4fe 100644 --- a/man/make.blocks.Rd +++ b/man/make.blocks.Rd @@ -41,7 +41,7 @@ A named list of character vectors with variables names. } \description{ This helper function generates a list of the type needed for -\code{blocks} argument in the \code{[=mice]{mice}} function. +\code{blocks} argument in the \code{\link[=mice]{mice()}} function. } \details{ Choices \code{"scatter"} and \code{"collect"} represent to two diff --git a/man/make.blots.Rd b/man/make.blots.Rd index 37229cf90..a9a747f0c 100644 --- a/man/make.blots.Rd +++ b/man/make.blots.Rd @@ -28,5 +28,5 @@ make.predictorMatrix(nhanes) make.blots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) } \seealso{ -\code{\link{make.blocks}} +\code{\link[=make.blocks]{make.blocks()}} } diff --git a/man/make.formulas.Rd b/man/make.formulas.Rd index 3f291ad47..2837ebe97 100644 --- a/man/make.formulas.Rd +++ b/man/make.formulas.Rd @@ -38,5 +38,5 @@ f3 <- name.formulas(lapply(c1, as.formula)) f3 } \seealso{ -\code{\link{make.blocks}}, \code{\link{make.predictorMatrix}} +\code{\link[=make.blocks]{make.blocks()}}, \code{\link[=make.predictorMatrix]{make.predictorMatrix()}} } diff --git a/man/make.method.Rd b/man/make.method.Rd index a4d9a843f..5a04efe0a 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -58,5 +58,5 @@ specifies the method for each block. make.method(nhanes2) } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } diff --git a/man/make.post.Rd b/man/make.post.Rd index 5a6f6acfb..5b0e94af2 100644 --- a/man/make.post.Rd +++ b/man/make.post.Rd @@ -22,5 +22,5 @@ specifies post-processing for a variable after each iteration of imputation. make.post(nhanes2) } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } diff --git a/man/make.predictorMatrix.Rd b/man/make.predictorMatrix.Rd index 6e064d59e..b888e21b4 100644 --- a/man/make.predictorMatrix.Rd +++ b/man/make.predictorMatrix.Rd @@ -31,5 +31,5 @@ make.predictorMatrix(nhanes) make.predictorMatrix(nhanes, blocks = make.blocks(nhanes, "collect")) } \seealso{ -\code{\link{make.blocks}} +\code{\link[=make.blocks]{make.blocks()}} } diff --git a/man/make.visitSequence.Rd b/man/make.visitSequence.Rd index 9696a66d2..d597100eb 100644 --- a/man/make.visitSequence.Rd +++ b/man/make.visitSequence.Rd @@ -34,5 +34,5 @@ specifies the sequence in which blocks are imputed. make.visitSequence(nhanes) } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } diff --git a/man/make.where.Rd b/man/make.where.Rd index 811ded901..77b39cf23 100644 --- a/man/make.where.Rd +++ b/man/make.where.Rd @@ -36,5 +36,5 @@ fit <- with(imp, lm(chl ~ bmi + age + hyp)) summary(pool.syn(fit)) } \seealso{ -\code{\link{make.blocks}}, \code{\link{make.predictorMatrix}} +\code{\link[=make.blocks]{make.blocks()}}, \code{\link[=make.predictorMatrix]{make.predictorMatrix()}} } diff --git a/man/matchindex.Rd b/man/matchindex.Rd index dafb1de4b..1e7aa519b 100644 --- a/man/matchindex.Rd +++ b/man/matchindex.Rd @@ -29,22 +29,19 @@ neighbours in \code{d}, randomly draws one of these neighbours, and returns its position in vector \code{d}. Fast predictive mean matching algorithm in seven steps: - -1. Shuffle records to remove effects of ties - -2. Obtain sorting order on shuffled data - -3. Calculate index on input data and sort it - -4. Pre-sample vector \code{h} with values between 1 and \code{k} +\enumerate{ +\item Shuffle records to remove effects of ties +\item Obtain sorting order on shuffled data +\item Calculate index on input data and sort it +\item Pre-sample vector \code{h} with values between 1 and \code{k} +} For each of the \code{n0} elements in \code{t}: - - 5. find the two adjacent neighbours - - 6. find the \code{h_i}'th nearest neighbour - - 7. store the index of that neighbour +\enumerate{ +\item find the two adjacent neighbours +\item find the \code{h_i}'th nearest neighbour +\item store the index of that neighbour +} Return vector of \code{n0} positions in \code{d}. diff --git a/man/md.pairs.Rd b/man/md.pairs.Rd index 6398900fe..80616a602 100644 --- a/man/md.pairs.Rd +++ b/man/md.pairs.Rd @@ -20,7 +20,8 @@ Number of observations per variable pair. } \details{ The four components in the output value is have the following interpretation: -\describe{ \item{list('rr')}{response-response, both variables are observed} +\describe{ +\item{list('rr')}{response-response, both variables are observed} \item{list('rm')}{response-missing, row observed, column missing} \item{list('mr')}{missing -response, row missing, column observed} \item{list('mm')}{missing -missing, both variables are missing} } @@ -39,7 +40,7 @@ round(100 * pat$mr / (pat$mr + pat$mm)) \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \author{ diff --git a/man/md.pattern.Rd b/man/md.pattern.Rd index 134c305ff..b18bfb2b7 100644 --- a/man/md.pattern.Rd +++ b/man/md.pattern.Rd @@ -11,10 +11,10 @@ md.pattern(x, plot = TRUE, rotate.names = FALSE) values are coded as NA's.} \item{plot}{Should the missing data pattern be made into a plot. Default is -`plot = TRUE`.} +\code{plot = TRUE}.} \item{rotate.names}{Whether the variable names in the plot should be placed -horizontally or vertically. Default is `rotate.names = FALSE`.} +horizontally or vertically. Default is \code{rotate.names = FALSE}.} } \value{ A matrix with \code{ncol(x)+1} columns, in which each row corresponds @@ -48,7 +48,7 @@ London: Chapman&Hall. 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \author{ Gerko Vink, 2018, based on an earlier version of the same function by diff --git a/man/mdc.Rd b/man/mdc.Rd index fd88f6973..d4b2b2c79 100644 --- a/man/mdc.Rd +++ b/man/mdc.Rd @@ -75,9 +75,9 @@ Sarkar, Deepayan (2008) \emph{Lattice: Multivariate Data Visualization with R}, Springer. } \seealso{ -\code{\link{hcl}}, \code{\link{rgb}}, -\code{\link{xyplot.mids}}, \code{\link[lattice:xyplot]{xyplot}}, -\code{\link[lattice:trellis.par.get]{trellis.par.set}} +\code{\link[=hcl]{hcl()}}, \code{\link[=rgb]{rgb()}}, +\code{\link[=xyplot.mids]{xyplot.mids()}}, \code{\link[lattice:xyplot]{lattice::xyplot()}}, +\code{\link[lattice:trellis.par.get]{trellis.par.set()}} } \author{ Stef van Buuren, sept 2012. diff --git a/man/mice.Rd b/man/mice.Rd index c2bbc17eb..1573b5b23 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -149,8 +149,8 @@ imputation.} functions.} } \value{ -Returns an S3 object of class \code{\link[=mids-class]{mids}} - (multiply imputed data set) +Returns an S3 object of class \code{\link[=mids-class]{mids()}} +(multiply imputed data set) } \description{ The \pkg{mice} package implements a method to deal with missing data. @@ -223,13 +223,13 @@ Built-in univariate imputation methods are: \code{polr} \tab ordered \tab Proportional odds model\cr \code{polyreg} \tab unordered\tab Polytomous logistic regression\cr \code{lda} \tab unordered\tab Linear discriminant analysis\cr -\code{2l.norm} \tab numeric \tab Level-1 normal heteroscedastic\cr -\code{2l.lmer} \tab numeric \tab Level-1 normal homoscedastic, lmer\cr -\code{2l.pan} \tab numeric \tab Level-1 normal homoscedastic, pan\cr -\code{2l.bin} \tab binary \tab Level-1 logistic, glmer\cr -\code{2lonly.mean} \tab numeric \tab Level-2 class mean\cr -\code{2lonly.norm} \tab numeric \tab Level-2 class normal\cr -\code{2lonly.pmm} \tab any \tab Level-2 class predictive mean matching +\verb{2l.norm} \tab numeric \tab Level-1 normal heteroscedastic\cr +\verb{2l.lmer} \tab numeric \tab Level-1 normal homoscedastic, lmer\cr +\verb{2l.pan} \tab numeric \tab Level-1 normal homoscedastic, pan\cr +\verb{2l.bin} \tab binary \tab Level-1 logistic, glmer\cr +\verb{2lonly.mean} \tab numeric \tab Level-2 class mean\cr +\verb{2lonly.norm} \tab numeric \tab Level-2 class normal\cr +\verb{2lonly.pmm} \tab any \tab Level-2 class predictive mean matching } These corresponding functions are coded in the \code{mice} library under @@ -266,8 +266,7 @@ Passive imputation maintains consistency among different transformations of the same data. Passive imputation is invoked if \code{~} is specified as the first character of the string that specifies the univariate method. \code{mice()} interprets the entire string, including the \code{~} character, -as the formula argument in a call to \code{model.frame(formula, -data[!r[,j],])}. This provides a simple mechanism for specifying deterministic +as the formula argument in a call to \code{model.frame(formula, data[!r[,j],])}. This provides a simple mechanism for specifying deterministic dependencies among the columns. For example, suppose that the missing entries in variables \code{data$height} and \code{data$weight} are imputed. The body mass index (BMI) can be calculated within \code{mice} by specifying the @@ -279,8 +278,7 @@ column make sense. An easy way to create consistency is by coding all entries in the target as \code{NA}, but for large data sets, this could be inefficient. Note that you may also need to adapt the default \code{predictorMatrix} to evade linear dependencies among the predictors that -could cause errors like \code{Error in solve.default()} or \code{Error: -system is exactly singular}. Though not strictly needed, it is often useful +could cause errors like \verb{Error in solve.default()} or \verb{Error: system is exactly singular}. Though not strictly needed, it is often useful to specify \code{visitSequence} such that the column that is imputed by the \code{~} mechanism is visited each time after one of its predictors was visited. In that way, deterministic relation between columns will always be @@ -293,7 +291,7 @@ takes one of three inputs: \code{"qr"} for QR-decomposition, \code{"svd"} for singular value decomposition and \code{"ridge"} for ridge regression. \code{ls.meth} defaults to \code{ls.meth = "qr"}. -\emph{Auxiliary predictors in formulas specification: } +*Auxiliary predictors in formulas specification: * For a given block, the \code{formulas} specification takes precedence over the corresponding row in the \code{predictMatrix} argument. This precedence is, however, restricted to the subset of variables @@ -311,11 +309,11 @@ argument \code{auxiliary = FALSE}. The main functions are: \tabular{ll}{ - \code{mice()} \tab Impute the missing data *m* times\cr - \code{with()} \tab Analyze completed data sets\cr - \code{pool()} \tab Combine parameter estimates\cr - \code{complete()} \tab Export imputed data\cr - \code{ampute()} \tab Generate missing data\cr} +\code{mice()} \tab Impute the missing data \emph{m} times\cr +\code{with()} \tab Analyze completed data sets\cr +\code{pool()} \tab Combine parameter estimates\cr +\code{complete()} \tab Export imputed data\cr +\code{ampute()} \tab Generate missing data\cr} } \section{Vignettes}{ @@ -400,15 +398,15 @@ complete(imp.test2, 2) \references{ van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of missing blood pressure covariates in survival analysis. -\emph{Statistics in Medicine}, \bold{18}, 681--694. +\emph{Statistics in Medicine}, \strong{18}, 681--694. van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. \emph{Journal of -Statistical Computation and Simulation}, \bold{76}, 12, 1049--1064. +Statistical Computation and Simulation}, \strong{76}, 12, 1049--1064. 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} +Statistical Software}, \strong{45}(3), 1--67. \doi{10.18637/jss.v045.i03} Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/}{\emph{Flexible Imputation of Missing Data. Second Edition.}} @@ -416,7 +414,7 @@ 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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Van Buuren, S. (2018). @@ -425,26 +423,26 @@ Chapman & Hall/CRC. Boca Raton, FL. Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. \emph{Journal of -Statistical Computation and Simulation}, \bold{76}, 12, 1049--1064. +Statistical Computation and Simulation}, \strong{76}, 12, 1049--1064. Van Buuren, S. (2007) Multiple imputation of discrete and continuous data by fully conditional specification. \emph{Statistical Methods in Medical -Research}, \bold{16}, 3, 219--242. +Research}, \strong{16}, 3, 219--242. Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of missing blood pressure covariates in survival analysis. \emph{Statistics in -Medicine}, \bold{18}, 681--694. +Medicine}, \strong{18}, 681--694. 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. } \seealso{ -\code{\link{mice}}, \code{\link{with.mids}}, -\code{\link{pool}}, \code{\link{complete}}, \code{\link{ampute}} +\code{\link[=mice]{mice()}}, \code{\link[=with.mids]{with.mids()}}, +\code{\link[=pool]{pool()}}, \code{\link[=complete]{complete()}}, \code{\link[=ampute]{ampute()}} -\code{\link[=mids-class]{mids}}, \code{\link{with.mids}}, -\code{\link{set.seed}}, \code{\link{complete}} +\code{\link[=mids-class]{mids()}}, \code{\link[=with.mids]{with.mids()}}, +\code{\link[=set.seed]{set.seed()}}, \code{\link[=complete]{complete()}} } \author{ Stef van Buuren \email{stef.vanbuuren@tno.nl}, Karin diff --git a/man/mice.impute.2l.norm.Rd b/man/mice.impute.2l.norm.Rd index db2be8d47..3846464fa 100644 --- a/man/mice.impute.2l.norm.Rd +++ b/man/mice.impute.2l.norm.Rd @@ -44,8 +44,7 @@ are drawn as an extra step to the algorithm. For simulation work see Van Buuren (2011). The random intercept is automatically added in \code{mice.impute.2L.norm()}. -A model within a random intercept can be specified by \code{mice(..., -intercept = FALSE)}. +A model within a random intercept can be specified by \code{mice(..., intercept = FALSE)}. } \note{ Added June 25, 2012: The currently implemented algorithm does not @@ -63,7 +62,7 @@ of Educational and Behavioral Statistics, 23(2), 93--116. 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Van Buuren, S. (2011) Multiple imputation of multilevel data. In Hox, J.J. and and Roberts, J.K. (Eds.), \emph{The Handbook of Advanced Multilevel diff --git a/man/mice.impute.2l.pan.Rd b/man/mice.impute.2l.pan.Rd index 7d84aefcd..441c28fe7 100644 --- a/man/mice.impute.2l.pan.Rd +++ b/man/mice.impute.2l.pan.Rd @@ -56,8 +56,8 @@ two-level regression (see argument \code{type}). This function needs the Implements the Gibbs sampler for the linear two-level model with homogeneous within group variances which is a special case of a multivariate linear mixed effects model (Schafer & Yucel, 2002). For a two-level imputation with -heterogeneous within-group variances see \code{\link{mice.impute.2l.norm}}. % -The random intercept is automatically added in % +heterogeneous within-group variances see \code{\link[=mice.impute.2l.norm]{mice.impute.2l.norm()}}. \% +The random intercept is automatically added in \% \code{mice.impute.2l.norm()}. } \note{ @@ -124,11 +124,11 @@ summary(mod) \references{ Schafer J L, Yucel RM (2002). Computational strategies for multivariate linear mixed-effects models with missing values. \emph{Journal of -Computational and Graphical Statistics}. \bold{11}, 437-457. +Computational and Graphical Statistics}. \strong{11}, 437-457. 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ Other univariate-2l: diff --git a/man/mice.impute.2lonly.mean.Rd b/man/mice.impute.2lonly.mean.Rd index deecb9dd8..bf1838034 100644 --- a/man/mice.impute.2lonly.mean.Rd +++ b/man/mice.impute.2lonly.mean.Rd @@ -31,7 +31,7 @@ Vector with imputed data, same type as \code{y}, and of length \code{sum(wy)} } \description{ -Method \code{2lonly.mean} replicates the most likely value within +Method \verb{2lonly.mean} replicates the most likely value within a class of a second-level variable. It works for numeric and factor data. The function is primarily useful as a quick fixup for data in which the second-level variable is inconsistent. @@ -56,8 +56,8 @@ class are set to \code{NA}. Note that this may produce problems later on in \code{mice} if imputation routines are called that expects predictor data to be complete. Methods designed for imputing this type of second-level variables include -\code{\link{mice.impute.2lonly.norm}} and -\code{\link{mice.impute.2lonly.pmm}}. +\code{\link[=mice.impute.2lonly.norm]{mice.impute.2lonly.norm()}} and +\code{\link[=mice.impute.2lonly.pmm]{mice.impute.2lonly.pmm()}}. } \references{ Van Buuren, S. (2018). diff --git a/man/mice.impute.2lonly.norm.Rd b/man/mice.impute.2lonly.norm.Rd index 17a0b6c9b..40e5f9577 100644 --- a/man/mice.impute.2lonly.norm.Rd +++ b/man/mice.impute.2lonly.norm.Rd @@ -36,7 +36,7 @@ identifier at level 2 must be indicated by \code{type = -2} in the \code{predictorMatrix}. } \details{ -This function allows in combination with \code{\link{mice.impute.2l.pan}} +This function allows in combination with \code{\link[=mice.impute.2l.pan]{mice.impute.2l.pan()}} switching regression imputation between level 1 and level 2 as described in Yucel (2008) or Gelman and Hill (2007, p. 541). @@ -45,7 +45,7 @@ are assumed to be constant within the same cluster. If one or more entries are missing, then the procedure aborts with an error message that identifies the cluster with incomplete level-2 data. In such cases, one may first fill in the cluster mean (or mode) by -the \code{2lonly.mean} method to remove inconsistencies. +the \verb{2lonly.mean} method to remove inconsistencies. } \note{ For a more general approach, see @@ -138,16 +138,16 @@ University Press. Yucel, RM (2008). Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. \emph{Philosophical -Transactions of the Royal Society A}, \bold{366}, 2389-2404. +Transactions of the Royal Society A}, \strong{366}, 2389-2404. Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/sec-level2pred.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{mice.impute.norm}}, -\code{\link{mice.impute.2lonly.pmm}}, \code{\link{mice.impute.2l.pan}}, -\code{\link{mice.impute.2lonly.mean}} +\code{\link[=mice.impute.norm]{mice.impute.norm()}}, +\code{\link[=mice.impute.2lonly.pmm]{mice.impute.2lonly.pmm()}}, \code{\link[=mice.impute.2l.pan]{mice.impute.2l.pan()}}, +\code{\link[=mice.impute.2lonly.mean]{mice.impute.2lonly.mean()}} Other univariate-2lonly: \code{\link{mice.impute.2lonly.mean}()}, diff --git a/man/mice.impute.2lonly.pmm.Rd b/man/mice.impute.2lonly.pmm.Rd index 0d0a12dc4..82aa40f40 100644 --- a/man/mice.impute.2lonly.pmm.Rd +++ b/man/mice.impute.2lonly.pmm.Rd @@ -35,7 +35,7 @@ Variables are level 1 are aggregated at level 2. The group identifier at level 2 must be indicated by \code{type = -2} in the \code{predictorMatrix}. } \details{ -This function allows in combination with \code{\link{mice.impute.2l.pan}} +This function allows in combination with \code{\link[=mice.impute.2l.pan]{mice.impute.2l.pan()}} switching regression imputation between level 1 and level 2 as described in Yucel (2008) or Gelman and Hill (2007, p. 541). @@ -44,7 +44,7 @@ are assumed to be constant within the same cluster. If one or more entries are missing, then the procedure aborts with an error message that identifies the cluster with incomplete level-2 data. In such cases, one may first fill in the cluster mean (or mode) by -the \code{2lonly.mean} method to remove inconsistencies. +the \verb{2lonly.mean} method to remove inconsistencies. } \note{ The extension to categorical variables transforms @@ -112,16 +112,16 @@ University Press. Yucel, RM (2008). Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. \emph{Philosophical -Transactions of the Royal Society A}, \bold{366}, 2389-2404. +Transactions of the Royal Society A}, \strong{366}, 2389-2404. Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/sec-level2pred.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{mice.impute.pmm}}, -\code{\link{mice.impute.2lonly.norm}}, \code{\link{mice.impute.2l.pan}}, -\code{\link{mice.impute.2lonly.mean}} +\code{\link[=mice.impute.pmm]{mice.impute.pmm()}}, +\code{\link[=mice.impute.2lonly.norm]{mice.impute.2lonly.norm()}}, \code{\link[=mice.impute.2l.pan]{mice.impute.2l.pan()}}, +\code{\link[=mice.impute.2lonly.mean]{mice.impute.2lonly.mean()}} Other univariate-2lonly: \code{\link{mice.impute.2lonly.mean}()}, diff --git a/man/mice.impute.cart.Rd b/man/mice.impute.cart.Rd index 3accdc7c5..cd72f8ff7 100644 --- a/man/mice.impute.cart.Rd +++ b/man/mice.impute.cart.Rd @@ -22,10 +22,10 @@ model is fitted. The \code{ry} generally distinguishes the observed indicates locations in \code{y} for which imputations are created.} \item{minbucket}{The minimum number of observations in any terminal node used. -See \code{\link{rpart.control}} for details.} +See \code{\link[=rpart.control]{rpart.control()}} for details.} \item{cp}{Complexity parameter. Any split that does not decrease the overall -lack of fit by a factor of cp is not attempted. See \code{\link{rpart.control}} +lack of fit by a factor of cp is not attempted. See \code{\link[=rpart.control]{rpart.control()}} for details.} \item{...}{Other named arguments passed down to \code{rpart()}.} @@ -67,8 +67,8 @@ Van Buuren, S. (2018). Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{mice}}, \code{\link{mice.impute.rf}}, -\code{\link[rpart]{rpart}}, \code{\link[rpart]{rpart.control}} +\code{\link[=mice]{mice()}}, \code{\link[=mice.impute.rf]{mice.impute.rf()}}, +\code{\link[rpart:rpart]{rpart::rpart()}}, \code{\link[rpart:rpart.control]{rpart::rpart.control()}} Other univariate imputation functions: \code{\link{mice.impute.lasso.logreg}()}, diff --git a/man/mice.impute.jomoImpute.Rd b/man/mice.impute.jomoImpute.Rd index b106c1f9f..003df3981 100644 --- a/man/mice.impute.jomoImpute.Rd +++ b/man/mice.impute.jomoImpute.Rd @@ -23,10 +23,10 @@ present in the imputed datasets.} in the imputation model. The basic model is constructed by \code{model.matrix}, thus allowing to include derived variables in the imputation model using \code{I()}. See -\code{\link[mitml]{jomoImpute}}.} +\code{\link[mitml:jomoImpute]{mitml::jomoImpute()}}.} \item{type}{An integer vector specifying the role of each variable -in the imputation model (see \code{\link[mitml]{jomoImpute}})} +in the imputation model (see \code{\link[mitml:jomoImpute]{mitml::jomoImpute()}})} \item{m}{The number of imputed data sets to generate. Default is 10.} @@ -83,7 +83,7 @@ and studies with missing covariates. Statistics in Medicine, 35:2938-2954, 2015. } \seealso{ -\code{\link[mitml]{jomoImpute}} +\code{\link[mitml:jomoImpute]{mitml::jomoImpute()}} Other multivariate-2l: \code{\link{mice.impute.panImpute}()} diff --git a/man/mice.impute.lda.Rd b/man/mice.impute.lda.Rd index 316c12687..e3a06e812 100644 --- a/man/mice.impute.lda.Rd +++ b/man/mice.impute.lda.Rd @@ -39,7 +39,7 @@ This function can be called from within the Gibbs sampler by specifying \code{"lda"} in the \code{method} argument of \code{mice()}. This method is usually faster and uses fewer resources than calling the function, but the statistical properties may not be as good (Brand, 1999). -\code{\link{mice.impute.polyreg}}. +\code{\link[=mice.impute.polyreg]{mice.impute.polyreg()}}. } \section{Warning}{ The function does not incorporate the variability of the @@ -54,7 +54,7 @@ bootstrapping may easily lead to constant variables within groups. \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple @@ -66,8 +66,7 @@ Venables, W.N. & Ripley, B.D. (1997). Modern applied statistics with S-PLUS (2nd ed). Springer, Berlin. } \seealso{ -\code{\link{mice}}, \code{link{mice.impute.polyreg}}, -\code{\link[MASS]{lda}} +\code{\link[=mice]{mice()}}, \code{\link[=mice.impute.polyreg]{mice.impute.polyreg()}}, \code{\link[MASS:lda]{MASS::lda()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index a64851401..2e562acf4 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -47,7 +47,7 @@ method. \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple @@ -63,7 +63,7 @@ prediction in multiple imputation of incomplete categorical variables. Computational Statistics and Data Analysis, 54:22672275. } \seealso{ -\code{\link{mice}}, \code{\link{glm}}, \code{\link{glm.fit}} +\code{\link[=mice]{mice()}}, \code{\link[=glm]{glm()}}, \code{\link[=glm.fit]{glm.fit()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.logreg.boot.Rd b/man/mice.impute.logreg.boot.Rd index 3fc94bedd..35d894033 100644 --- a/man/mice.impute.logreg.boot.Rd +++ b/man/mice.impute.logreg.boot.Rd @@ -35,7 +35,7 @@ from the observed data \code{y[ry]} and \code{x[ry, ]}. \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Van Buuren, S. (2018). @@ -43,7 +43,7 @@ Van Buuren, S. (2018). Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{mice}}, \code{\link{glm}}, \code{\link{glm.fit}} +\code{\link[=mice]{mice()}}, \code{\link[=glm]{glm()}}, \code{\link[=glm.fit]{glm.fit()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.mean.Rd b/man/mice.impute.mean.Rd index 66a8bf45e..b4ea18631 100644 --- a/man/mice.impute.mean.Rd +++ b/man/mice.impute.mean.Rd @@ -38,7 +38,7 @@ Van Buuren (2012, p. 10-11) \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing @@ -49,7 +49,7 @@ Van Buuren, S. (2018). Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{mice}}, \code{\link{mean}} +\code{\link[=mice]{mice()}}, \code{\link[=mean]{mean()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.midastouch.Rd b/man/mice.impute.midastouch.Rd index a8956bb93..f0a72adcb 100644 --- a/man/mice.impute.midastouch.Rd +++ b/man/mice.impute.midastouch.Rd @@ -108,22 +108,22 @@ Statistics, 6, 287--301. Parzen, M., Lipsitz, S. R., Fitzmaurice, G. M. (2005), A note on reducing the bias of the approximate Bayesian bootstrap imputation variance estimator. -Biometrika \bold{92}, 4, 971--974. +Biometrika \strong{92}, 4, 971--974. Rubin, D.B. (1987), Multiple imputation for nonresponse in surveys. New York: Wiley. Siddique, J., Belin, T.R. (2008), Multiple imputation using an iterative hot-deck with distance-based donor selection. Statistics in medicine, -\bold{27}, 1, 83--102 +\strong{27}, 1, 83--102 Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006), Fully conditional specification in multivariate imputation. -\emph{Journal of Statistical Computation and Simulation}, \bold{76}, 12, +\emph{Journal of Statistical Computation and Simulation}, \strong{76}, 12, 1049--1064. 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} +Statistical Software}, \strong{45}, 3, 1--67. \doi{10.18637/jss.v045.i03} } \seealso{ Other univariate imputation functions: diff --git a/man/mice.impute.mnar.Rd b/man/mice.impute.mnar.Rd index 0d23b2701..fa5e84b17 100644 --- a/man/mice.impute.mnar.Rd +++ b/man/mice.impute.mnar.Rd @@ -69,8 +69,7 @@ the corresponding element in \code{blots} is a list with at least one argument \code{ums} and, optionally, a second argument \code{umx}. For example, the high-level call might like something like -\code{mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), -blots = list(chl = list(ums = "-3+2*bmi")))}. +\code{mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), blots = list(chl = list(ums = "-3+2*bmi")))}. The \code{ums} parameter is required, and might look like this: \code{"-4+1*Y"}. The \code{ums} specifcation must have the @@ -87,7 +86,7 @@ parameter;} value comes first and the predictor variable comes second, and these must be separated by a \code{"*"} sign;} \item{For categorical predictors, for example a variable \code{Z} -with K + 1 categories \code{("Cat0","Cat1", ...,"CatK")}, K +with K + 1 categories \verb{("Cat0","Cat1", ...,"CatK")}, K category-specific terms are needed, and those not in \code{umx} (see below) must be specified by concatenating the variable name with the name of the category (e.g. \code{ZCat1}) as this is how @@ -117,7 +116,7 @@ in complete auxiliary variables passed on via the \code{umx} argument. It is not possible to specify models where the offset depends on incomplete auxiliary variables. -For an MNAR alternative see also \code{\link{mice.impute.ri}}. +For an MNAR alternative see also \code{\link[=mice.impute.ri]{mice.impute.ri()}}. } \examples{ # 1: Example with no auxiliary data: only pass unidentifiable model specification (ums) @@ -164,12 +163,12 @@ pool(with(impNARFCS, lm(Y ~ X + Z)))$pooled$estimate Tompsett, D. M., Leacy, F., Moreno-Betancur, M., Heron, J., & White, I. R. (2018). On the use of the not-at-random fully conditional specification (NARFCS) procedure in practice. -\emph{Statistics in Medicine}, \bold{37}(15), 2338-2353. +\emph{Statistics in Medicine}, \strong{37}(15), 2338-2353. \doi{10.1002/sim.7643}. Van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of missing blood pressure covariates in survival analysis. -\emph{Statistics in Medicine}, \bold{18}, 681--694. +\emph{Statistics in Medicine}, \strong{18}, 681--694. } \seealso{ Other univariate imputation functions: diff --git a/man/mice.impute.mpmm.Rd b/man/mice.impute.mpmm.Rd index 2f72b8afd..9efe99ebf 100644 --- a/man/mice.impute.mpmm.Rd +++ b/man/mice.impute.mpmm.Rd @@ -59,7 +59,7 @@ with(dat, plot(x, x2, col = mdc(1))) with(complete(imp), points(x[m], x2[m], col = mdc(2))) } \seealso{ -\code{\link{mice.impute.pmm}} +\code{\link[=mice.impute.pmm]{mice.impute.pmm()}} Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/sec-knowledge.html#sec:quadratic}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. diff --git a/man/mice.impute.norm.boot.Rd b/man/mice.impute.norm.boot.Rd index 8210148eb..1e70d0790 100644 --- a/man/mice.impute.norm.boot.Rd +++ b/man/mice.impute.norm.boot.Rd @@ -37,7 +37,7 @@ regression weights and imputes with normal residuals. \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ diff --git a/man/mice.impute.norm.nob.Rd b/man/mice.impute.norm.nob.Rd index 480ca3d9d..578482bb1 100644 --- a/man/mice.impute.norm.nob.Rd +++ b/man/mice.impute.norm.nob.Rd @@ -53,7 +53,7 @@ samples, variability of the imputed data is therefore underestimated. \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple @@ -61,7 +61,7 @@ Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis, TNO Prevention and Health/Erasmus University Rotterdam. } \seealso{ -\code{\link{mice}}, \code{\link{mice.impute.norm}} +\code{\link[=mice]{mice()}}, \code{\link[=mice.impute.norm]{mice.impute.norm()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.norm.predict.Rd b/man/mice.impute.norm.predict.Rd index 21c322077..fce4b8c3c 100644 --- a/man/mice.impute.norm.predict.Rd +++ b/man/mice.impute.norm.predict.Rd @@ -43,8 +43,8 @@ likely value according to the model. However, it ignores the uncertainty of the missing values and artificially amplifies the relations between the columns of the data. Application of richer models having more parameters does not help to evade these issues. -Stochastic regression methods, like \code{\link{mice.impute.pmm}} or -\code{\link{mice.impute.norm}}, are generally preferred. +Stochastic regression methods, like \code{\link[=mice.impute.pmm]{mice.impute.pmm()}} or +\code{\link[=mice.impute.norm]{mice.impute.norm()}}, are generally preferred. At best, prediction can give reasonable estimates of the mean, especially if normality assumptions are plausible. See Little and Rubin (2002, p. 62-64) diff --git a/man/mice.impute.panImpute.Rd b/man/mice.impute.panImpute.Rd index 17a7c51d5..5408aab28 100644 --- a/man/mice.impute.panImpute.Rd +++ b/man/mice.impute.panImpute.Rd @@ -23,10 +23,10 @@ present in the imputed datasets.} in the imputation model. The basic model is constructed by \code{model.matrix}, thus allowing to include derived variables in the imputation model using \code{I()}. See -\code{\link[mitml]{panImpute}}.} +\code{\link[mitml:panImpute]{mitml::panImpute()}}.} \item{type}{An integer vector specifying the role of each variable -in the imputation model (see \code{\link[mitml]{panImpute}})} +in the imputation model (see \code{\link[mitml:panImpute]{mitml::panImpute()}})} \item{m}{The number of imputed data sets to generate.} @@ -81,7 +81,7 @@ multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics, 11, 437-457. } \seealso{ -\code{\link[mitml]{panImpute}} +\code{\link[mitml:panImpute]{mitml::panImpute()}} Other multivariate-2l: \code{\link{mice.impute.jomoImpute}()} diff --git a/man/mice.impute.passive.Rd b/man/mice.impute.passive.Rd index 2277d6e2d..17b095827 100644 --- a/man/mice.impute.passive.Rd +++ b/man/mice.impute.passive.Rd @@ -30,11 +30,11 @@ virtually any function of the imputed data at virtually any time. \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } \author{ Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index 5e1e06968..fda7cde74 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -70,7 +70,7 @@ 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{2.22} (June 2014) to \verb{3.11.7} (Oct 2020). Since version \verb{3.12.0} \code{mice()} uses the much faster \code{matchindex} C function. Use the deprecated \code{matcher} function only for exact reproduction.} @@ -181,7 +181,7 @@ 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ Other univariate imputation functions: diff --git a/man/mice.impute.polr.Rd b/man/mice.impute.polr.Rd index 3d3870d40..633e0181a 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -68,20 +68,20 @@ The call to \code{polr} might fail, usually because 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. +to the \code{loggedEvents} component of the \code{\link[=mids]{mids()}} object. Use \code{mice(data, polr.to.loggedEvents = TRUE)} to set the flag. } \note{ In December 2019 Simon White alerted that the \code{polr} could always fail silently. I can confirm this behaviour for -versions \code{mice 3.0.0 - mice 3.6.6}, so any method requests +versions \verb{mice 3.0.0 - mice 3.6.6}, so any method requests for \code{polr} in these versions were in fact handled by \code{multinom}. See \url{https://github.com/amices/mice/issues/206} for details. } \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. \doi{10.18637/jss.v045.i03} +Software}, \strong{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 @@ -95,8 +95,8 @@ Venables, W.N. & Ripley, B.D. (2002). \emph{Modern applied statistics with S-Plus (4th ed)}. Springer, Berlin. } \seealso{ -\code{\link{mice}}, \code{\link[nnet]{multinom}}, -\code{\link[MASS]{polr}} +\code{\link[=mice]{mice()}}, \code{\link[nnet:multinom]{nnet::multinom()}}, +\code{\link[MASS:polr]{MASS::polr()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index eaf10be34..535a52ea1 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -68,7 +68,7 @@ data according to the method of White, Daniel and Royston (2010). \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. \doi{10.18637/jss.v045.i03} +Software}, \strong{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 @@ -82,8 +82,8 @@ Venables, W.N. & Ripley, B.D. (2002). \emph{Modern applied statistics with S-Plus (4th ed)}. Springer, Berlin. } \seealso{ -\code{\link{mice}}, \code{\link[nnet]{multinom}}, -\code{\link[MASS]{polr}} +\code{\link[=mice]{mice()}}, \code{\link[nnet:multinom]{nnet::multinom()}}, +\code{\link[MASS:polr]{MASS::polr()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.quadratic.Rd b/man/mice.impute.quadratic.Rd index fa68940a5..63c6c6fca 100644 --- a/man/mice.impute.quadratic.Rd +++ b/man/mice.impute.quadratic.Rd @@ -92,7 +92,7 @@ cmp <- complete(imp) points(cmp$x[is.na(dat$x)], cmp$xx[is.na(dat$x)], col = mdc(2)) } \seealso{ -\code{\link{mice.impute.pmm}} +\code{\link[=mice.impute.pmm]{mice.impute.pmm()}} Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/sec-knowledge.html#sec:quadratic}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. diff --git a/man/mice.impute.rf.Rd b/man/mice.impute.rf.Rd index 47a28ea5a..3006d573a 100644 --- a/man/mice.impute.rf.Rd +++ b/man/mice.impute.rf.Rd @@ -84,9 +84,9 @@ Van Buuren, S. (2018). Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{mice}}, \code{\link{mice.impute.cart}}, -\code{\link[randomForest]{randomForest}} -\code{\link[ranger]{ranger}} +\code{\link[=mice]{mice()}}, \code{\link[=mice.impute.cart]{mice.impute.cart()}}, +\code{\link[randomForest:randomForest]{randomForest::randomForest()}} +\code{\link[ranger:ranger]{ranger::ranger()}} Other univariate imputation functions: \code{\link{mice.impute.cart}()}, diff --git a/man/mice.impute.ri.Rd b/man/mice.impute.ri.Rd index ab2e0937b..300c526a9 100644 --- a/man/mice.impute.ri.Rd +++ b/man/mice.impute.ri.Rd @@ -40,7 +40,7 @@ that iterates over the response and imputation models. This routine assumes that the response model and imputation model have same predictors. -For an MNAR alternative see also \code{\link{mice.impute.mnar.logreg}}. +For an MNAR alternative see also \code{\link[=mice.impute.mnar.logreg]{mice.impute.mnar.logreg()}}. } \references{ Jolani, S. (2012). diff --git a/man/mice.impute.sample.Rd b/man/mice.impute.sample.Rd index 6b11d1789..7736084e5 100644 --- a/man/mice.impute.sample.Rd +++ b/man/mice.impute.sample.Rd @@ -36,7 +36,7 @@ This function takes a simple random sample from the observed values in \references{ van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -Statistical Software}, \bold{45}(3), 1-67. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \author{ diff --git a/man/mice.mids.Rd b/man/mice.mids.Rd index bbe7f016b..4c04e5379 100644 --- a/man/mice.mids.Rd +++ b/man/mice.mids.Rd @@ -51,12 +51,12 @@ identical(imp$imp, imp2$imp) \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. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{complete}}, \code{\link{mice}}, \code{\link{set.seed}}, -\code{\link[=mids-class]{mids}} +\code{\link[=complete]{complete()}}, \code{\link[=mice]{mice()}}, \code{\link[=set.seed]{set.seed()}}, +\code{\link[=mids-class]{mids()}} } \author{ Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 diff --git a/man/mids-class.Rd b/man/mids-class.Rd index 3c68bb900..d84d87b0f 100644 --- a/man/mids-class.Rd +++ b/man/mids-class.Rd @@ -14,7 +14,6 @@ The \code{mids} class of objects has methods for the following generic functions: \code{print}, \code{summary}, \code{plot}. - 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 made. At initialization the program does the following three actions: @@ -50,67 +49,67 @@ equivalent \code{oldClass(obj) <- "mids"}. } \section{Slots}{ - \describe{ - \item{\code{.Data}:}{Object of class \code{"list"} containing the - following slots:} - \item{\code{data}:}{Original (incomplete) data set.} - \item{\code{imp}:}{A list of \code{ncol(data)} components with - the generated multiple imputations. Each list component is a - \code{data.frame} (\code{nmis[j]} by \code{m}) of imputed values - for variable \code{j}. A \code{NULL} component is used for - variables for which not imputations are generated.} - \item{\code{m}:}{Number of imputations.} - \item{\code{where}:}{The \code{where} argument of the - \code{mice()} function.} - \item{\code{blocks}:}{The \code{blocks} argument of the - \code{mice()} function.} - \item{\code{call}:}{Call that created the object.} - \item{\code{nmis}:}{An array containing the number of missing - observations per column.} - \item{\code{method}:}{A vector of strings of \code{length(blocks} - specifying the imputation method per block.} - \item{\code{predictorMatrix}:}{A numerical matrix of containing - integers specifying the predictor set.} - \item{\code{visitSequence}:}{A vector of variable and block names that - specifies how variables and blocks are visited in one iteration throuh - the data.} - \item{\code{formulas}:}{A named list of formula's, or expressions that - can be converted into formula's by \code{as.formula}. List elements - correspond to blocks. The block to which the list element applies is - identified by its name, so list names must correspond to block names.} - \item{\code{post}:}{A vector of strings of length \code{length(blocks)} - with commands for post-processing.} - \item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} - function.} - \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.} - \item{\code{iteration}:}{Last Gibbs sampling iteration number.} - \item{\code{lastSeedValue}:}{The most recent seed value.} - \item{\code{chainMean}:}{An array of dimensions \code{ncol} by - \code{maxit} by \code{m} elements containing the mean of - the generated multiple imputations. - The array can be used for monitoring convergence. - 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.} - \item{\code{version}:}{Version number of \code{mice} package that - created the object.} - \item{\code{date}:}{Date at which the object was created.} +\describe{ +\item{\code{.Data}:}{Object of class \code{"list"} containing the +following slots:} +\item{\code{data}:}{Original (incomplete) data set.} +\item{\code{imp}:}{A list of \code{ncol(data)} components with +the generated multiple imputations. Each list component is a +\code{data.frame} (\code{nmis[j]} by \code{m}) of imputed values +for variable \code{j}. A \code{NULL} component is used for +variables for which not imputations are generated.} +\item{\code{m}:}{Number of imputations.} +\item{\code{where}:}{The \code{where} argument of the +\code{mice()} function.} +\item{\code{blocks}:}{The \code{blocks} argument of the +\code{mice()} function.} +\item{\code{call}:}{Call that created the object.} +\item{\code{nmis}:}{An array containing the number of missing +observations per column.} +\item{\code{method}:}{A vector of strings of \verb{length(blocks} +specifying the imputation method per block.} +\item{\code{predictorMatrix}:}{A numerical matrix of containing +integers specifying the predictor set.} +\item{\code{visitSequence}:}{A vector of variable and block names that +specifies how variables and blocks are visited in one iteration throuh +the data.} +\item{\code{formulas}:}{A named list of formula's, or expressions that +can be converted into formula's by \code{as.formula}. List elements +correspond to blocks. The block to which the list element applies is +identified by its name, so list names must correspond to block names.} +\item{\code{post}:}{A vector of strings of length \code{length(blocks)} +with commands for post-processing.} +\item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} +function.} +\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 \verb{mice 3.12.0})} +\item{\code{seed}:}{The seed value of the solution.} +\item{\code{iteration}:}{Last Gibbs sampling iteration number.} +\item{\code{lastSeedValue}:}{The most recent seed value.} +\item{\code{chainMean}:}{An array of dimensions \code{ncol} by +\code{maxit} by \code{m} elements containing the mean of +the generated multiple imputations. +The array can be used for monitoring convergence. +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.} +\item{\code{version}:}{Version number of \code{mice} package that +created the object.} +\item{\code{date}:}{Date at which the object was created.} } } \references{ van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -Statistical Software}, \bold{45}(3), 1-67. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}}, \code{\link[=mira-class]{mira}}, -\code{\link{mipo}} +\code{\link[=mice]{mice()}}, \code{\link[=mira-class]{mira()}}, +\code{\link[=mipo]{mipo()}} } \author{ Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 diff --git a/man/mids2mplus.Rd b/man/mids2mplus.Rd index 5bb6e7e63..710b10643 100644 --- a/man/mids2mplus.Rd +++ b/man/mids2mplus.Rd @@ -47,7 +47,7 @@ names, so in principle it should run and read the data without alteration. do automatic pooling in procedures where that is supported. } \seealso{ -\code{\link[=mids-class]{mids}}, \code{\link{mids2spss}} +\code{\link[=mids-class]{mids()}}, \code{\link[=mids2spss]{mids2spss()}} } \author{ Gerko Vink, 2011. diff --git a/man/mids2spss.Rd b/man/mids2spss.Rd index bfa720970..e25f0c98b 100644 --- a/man/mids2spss.Rd +++ b/man/mids2spss.Rd @@ -58,12 +58,12 @@ factor levels codes. \code{SPSS} will recognize the data set as a multiply imputed data set, and do automatic pooling in procedures where that is supported. Note however that pooling is an extra option only available to those who license the -\code{MISSING VALUES} module. Without this license, \code{SPSS} will still +\verb{MISSING VALUES} module. Without this license, \code{SPSS} will still recognize the structure of the data, but it will not pool the multiply imputed estimates into a single inference. } \seealso{ -\code{\link[=mids-class]{mids}} +\code{\link[=mids-class]{mids()}} } \author{ Gerko Vink, dec 2020. diff --git a/man/mipo.Rd b/man/mipo.Rd index 732da0657..eaa8fae65 100644 --- a/man/mipo.Rd +++ b/man/mipo.Rd @@ -51,7 +51,7 @@ The \code{summary} method returns a data frame with summary statistics of the po } \description{ The \code{mipo} object contains the results of the pooling step. -The function \code{\link{pool}} generates an object of class \code{mipo}. +The function \code{\link[=pool]{pool()}} generates an object of class \code{mipo}. } \details{ An object class \code{mipo} is a \code{list} with @@ -82,12 +82,12 @@ It adds a confidence interval, and optionally exponentiates, the result. \references{ van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -Statistical Software}, \bold{45}(3), 1-67. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{pool}}, -\code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}} +\code{\link[=pool]{pool()}}, +\code{\link[=mids-class]{mids()}}, \code{\link[=mira-class]{mira()}} } \keyword{classes} \keyword{internal} diff --git a/man/mira-class.Rd b/man/mira-class.Rd index ced37437f..a8b3f80f9 100644 --- a/man/mira-class.Rd +++ b/man/mira-class.Rd @@ -12,15 +12,15 @@ function takes the results of repeated complete-data analysis stored as a list, and turns it into a \code{mira} object that can be pooled. } \details{ -In versions prior to \code{mice 3.0} pooling required only that +In versions prior to \verb{mice 3.0} pooling required only that \code{coef()} and \code{vcov()} methods were available for fitted objects. \emph{This feature is no longer supported}. The reason is that \code{vcov()} methods are inconsistent across packages, leading to buggy behaviour -of the \code{pool()} function. Since \code{mice 3.0+}, the \code{broom} +of the \code{pool()} function. Since \verb{mice 3.0+}, the \code{broom} package takes care of filtering out the relevant parts of the complete-data analysis. It may happen that you'll see the messages -like \code{No method for tidying an S3 object of class ...} or -\code{Error: No glance method for objects of class ...}. The royal +like \verb{No method for tidying an S3 object of class ...} or +\verb{Error: No glance method for objects of class ...}. The royal way to solve this problem is to write your own \code{glance()} and \code{tidy()} methods and add these to \code{broom} according to the specifications given in \url{https://broom.tidymodels.org}. @@ -34,27 +34,27 @@ S4 class definitions, and instead rely on the S3 list equivalent } \section{Slots}{ - \describe{ - #' \item{\code{.Data}:}{Object of class \code{"list"} containing the - following slots:} - \item{\code{call}:}{The call that created the object.} - \item{\code{call1}:}{The call that created the \code{mids} object that was used +\describe{ +#' \item{\code{.Data}:}{Object of class \code{"list"} containing the +following slots:} +\item{\code{call}:}{The call that created the object.} +\item{\code{call1}:}{The call that created the \code{mids} object that was used in \code{call}.} - \item{\code{nmis}:}{An array containing the number of missing observations per +\item{\code{nmis}:}{An array containing the number of missing observations per column.} - \item{\code{analyses}:}{A list of \code{m} components containing the individual +\item{\code{analyses}:}{A list of \code{m} components containing the individual fit objects from each of the \code{m} complete data analyses.} - } +} } \references{ van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -Statistical Software}, \bold{45}(3), 1-67. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{with.mids}}, \code{\link[=mids-class]{mids}}, \code{\link{mipo}} +\code{\link[=with.mids]{with.mids()}}, \code{\link[=mids-class]{mids()}}, \code{\link[=mipo]{mipo()}} } \author{ Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 diff --git a/man/name.blocks.Rd b/man/name.blocks.Rd index 1701eae80..365330eec 100644 --- a/man/name.blocks.Rd +++ b/man/name.blocks.Rd @@ -42,5 +42,5 @@ blocks <- list(c("hyp", "chl"), AGE = "age", c("bmi", "hyp"), "edu") name.blocks(blocks) } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } diff --git a/man/name.formulas.Rd b/man/name.formulas.Rd index bbd7ea33f..96f4e6b76 100644 --- a/man/name.formulas.Rd +++ b/man/name.formulas.Rd @@ -65,5 +65,5 @@ form5 <- name.formulas(form5) imp5 <- mice(nhanes, formulas = form5, print = FALSE, m = 1, seed = 71712) } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } diff --git a/man/ncc.Rd b/man/ncc.Rd index a5f38541c..c12cdb846 100644 --- a/man/ncc.Rd +++ b/man/ncc.Rd @@ -21,7 +21,7 @@ Calculates the number of complete cases. ncc(nhanes) # 13 complete cases } \seealso{ -\code{\link{nic}}, \code{\link{cci}} +\code{\link[=nic]{nic()}}, \code{\link[=cci]{cci()}} } \author{ Stef van Buuren, 2017 diff --git a/man/nhanes.Rd b/man/nhanes.Rd index fc1d1584e..d39fe57b5 100644 --- a/man/nhanes.Rd +++ b/man/nhanes.Rd @@ -31,6 +31,6 @@ imp <- mice(nhanes) complete(imp) } \seealso{ -\code{\link{nhanes2}} +\code{\link[=nhanes2]{nhanes2()}} } \keyword{datasets} diff --git a/man/nhanes2.Rd b/man/nhanes2.Rd index efdaa71c0..0226f2473 100644 --- a/man/nhanes2.Rd +++ b/man/nhanes2.Rd @@ -32,6 +32,6 @@ imp <- mice(nhanes2) complete(imp) } \seealso{ -\code{\link{nhanes}} +\code{\link[=nhanes]{nhanes()}} } \keyword{datasets} diff --git a/man/nic.Rd b/man/nic.Rd index dbf38528c..b6ac117aa 100644 --- a/man/nic.Rd +++ b/man/nic.Rd @@ -22,7 +22,7 @@ nic(nhanes) # the remaining 12 rows nic(nhanes[, c("bmi", "hyp")]) # number of cases with incomplete bmi and hyp } \seealso{ -\code{\link{ncc}}, \code{\link{cci}} +\code{\link[=ncc]{ncc()}}, \code{\link[=cci]{cci()}} } \author{ Stef van Buuren, 2017 diff --git a/man/nimp.Rd b/man/nimp.Rd index 8f7997ea5..f347ba40b 100644 --- a/man/nimp.Rd +++ b/man/nimp.Rd @@ -47,5 +47,5 @@ nimp(where) nimp(where, blocks = name.blocks(list(c("bmi", "hyp"), "age", "chl"))) } \seealso{ -\code{\link{mice}} +\code{\link[=mice]{mice()}} } diff --git a/man/parlmice.Rd b/man/parlmice.Rd index c3ca0f503..705738c08 100644 --- a/man/parlmice.Rd +++ b/man/parlmice.Rd @@ -17,7 +17,7 @@ parlmice( } \arguments{ \item{data}{A data frame or matrix containing the incomplete data. Similar to -the first argument of \code{\link{mice}}.} +the first argument of \code{\link[=mice]{mice()}}.} \item{m}{The number of desired imputated datasets. By default $m=5$ as with \code{mice}} @@ -37,18 +37,18 @@ will be performed with separate, random seeds.} \item{cl.type}{The cluster type. Default value is \code{"PSOCK"}. Posix machines (linux, Mac) generally benefit from much faster cluster computation if \code{type} is set to \code{type = "FORK"}.} -\item{...}{Named arguments that are passed down to function \code{\link{mice}} or -\code{\link{makeCluster}}.} +\item{...}{Named arguments that are passed down to function \code{\link[=mice]{mice()}} or +\code{\link[=makeCluster]{makeCluster()}}.} } \value{ -A mids object as defined by \code{\link{mids-class}} +A mids object as defined by \code{\link[=mids-class]{mids-class()}} } \description{ This function is included for backward compatibility. The function -is superseded by \code{\link{futuremice}}. +is superseded by \code{\link[=futuremice]{futuremice()}}. } \details{ -This function relies on package \code{\link{parallel}}, which is a base +This function relies on package \code{\link[=parallel]{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 systems. For the same reason, we use the Parallel Socket Cluster (PSOCK) type by default. @@ -56,17 +56,17 @@ systems. For the same reason, we use the Parallel Socket Cluster (PSOCK) type by On systems other than Windows, it can be hugely beneficial to change the cluster type to \code{FORK}, as it generally results in improved memory handling. When memory issues arise on a Windows system, we advise to store the multiply imputed datasets, -clean the memory by using \code{\link{rm}} and \code{\link{gc}} and make another +clean the memory by using \code{\link[=rm]{rm()}} and \code{\link[=gc]{gc()}} and make another run using the same settings. -This wrapper function combines the output of \code{\link{parLapply}} with -function \code{\link{ibind}} in \code{\link{mice}}. A \code{mids} object is returned +This wrapper function combines the output of \code{\link[=parLapply]{parLapply()}} with +function \code{\link[=ibind]{ibind()}} in \code{\link[=mice]{mice()}}. A \code{mids} object is returned and can be used for further analyses. Note that if a seed value is desired, the seed should be entered to this function with argument \code{seed}. Seed values outside the wrapper function (in an -R-script or passed to \code{\link{mice}}) will not result to reproducible results. -We refer to the manual of \code{\link{parallel}} for an explanation on this matter. +R-script or passed to \code{\link[=mice]{mice()}}) will not result to reproducible results. +We refer to the manual of \code{\link[=parallel]{parallel()}} for an explanation on this matter. } \examples{ # 150 imputations in dataset nhanes, performed by 3 cores @@ -89,8 +89,8 @@ Schouten, R. and Vink, G. (2017). parlmice: faster, paraleller, micer. Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{parallel}}, \code{\link{parLapply}}, \code{\link{makeCluster}}, -\code{\link{mice}}, \code{\link{mids-class}} +\code{\link[=parallel]{parallel()}}, \code{\link[=parLapply]{parLapply()}}, \code{\link[=makeCluster]{makeCluster()}}, +\code{\link[=mice]{mice()}}, \code{\link[=mids-class]{mids-class()}} } \author{ Gerko Vink, Rianne Schouten diff --git a/man/plot.mids.Rd b/man/plot.mids.Rd index 269d32442..1e9634c70 100644 --- a/man/plot.mids.Rd +++ b/man/plot.mids.Rd @@ -26,13 +26,13 @@ If omitted, all streams, variables and iterations are plotted.} \item{layout}{A vector of length 2 given the number of columns and rows in the plot. The default is \code{c(2, 3)}.} -\item{type}{Parameter \code{type} of \code{\link{panel.xyplot}}.} +\item{type}{Parameter \code{type} of \code{\link[=panel.xyplot]{panel.xyplot()}}.} -\item{col}{Parameter \code{col} of \code{\link{panel.xyplot}}.} +\item{col}{Parameter \code{col} of \code{\link[=panel.xyplot]{panel.xyplot()}}.} -\item{lty}{Parameter \code{lty} of \code{\link{panel.xyplot}}.} +\item{lty}{Parameter \code{lty} of \code{\link[=panel.xyplot]{panel.xyplot()}}.} -\item{...}{Extra arguments for \code{\link{xyplot}}.} +\item{...}{Extra arguments for \code{\link[=xyplot]{xyplot()}}.} } \value{ An object of class \code{"trellis"}. @@ -51,8 +51,8 @@ imp <- mice(nhanes, print = FALSE) plot(imp, bmi + chl ~ .it | .ms, layout = c(2, 1)) } \seealso{ -\code{\link{mice}}, \code{\link[=mids-class]{mids}}, -\code{\link{xyplot}} +\code{\link[=mice]{mice()}}, \code{\link[=mids-class]{mids()}}, +\code{\link[=xyplot]{xyplot()}} } \author{ Stef van Buuren 2011 diff --git a/man/pmm.match.Rd b/man/pmm.match.Rd index d4404115f..7c59b44ff 100644 --- a/man/pmm.match.Rd +++ b/man/pmm.match.Rd @@ -33,7 +33,7 @@ match. } \details{ This function is included for backward compatibility. It was -used up to \code{mice 2.21}. The current \code{mice.impute.pmm()} +used up to \verb{mice 2.21}. The current \code{mice.impute.pmm()} function calls the faster \code{C} function \code{matcher} instead of \code{.pmm.match()}. } diff --git a/man/pool.Rd b/man/pool.Rd index d7e535ea3..64e6661fe 100644 --- a/man/pool.Rd +++ b/man/pool.Rd @@ -19,7 +19,7 @@ observation minus the number of fitted parameters. The default (\code{dfcom = NULL}) extract this information in the following order: 1) the component \code{residual.df} returned by \code{glance()} if a \code{glance()} -function is found, 2) the result of \code{df.residual(} applied to +function is found, 2) the result of \verb{df.residual(} applied to the first fitted model, and 3) as \code{999999}. In the last case, the warning \code{"Large sample assumed"} is printed. If the degrees of freedom is incorrect, specify the appropriate value @@ -31,7 +31,7 @@ manually.} \item{custom.t}{A custom character string to be parsed as a calculation rule for the total variance \code{t}. The custom rule can use the other calculated -pooling statistics where the dimensions must come from \code{.data$}. The +pooling statistics where the dimensions must come from \verb{.data$}. The default \code{t} calculation would have the form \code{".data$ubar + (1 + 1 / .data$m) * .data$b"}. See examples for an example.} @@ -40,7 +40,7 @@ See examples for an example.} An object of class \code{mipo}, which stands for 'multiple imputation pooled outcome'. For rule \code{"reiter2003"} values for \code{lambda} and \code{fmi} are -set to `NA`, as these statistics do not apply for data synthesised from +set to \code{NA}, as these statistics do not apply for data synthesised from fully observed data. } \description{ @@ -93,11 +93,11 @@ The \code{pool()} and \code{pool.syn()} functions rely on the \code{broom::tidy} and \code{broom::glance} for extracting these parameters. -Since \code{mice 3.0+}, the \code{broom} +Since \verb{mice 3.0+}, the \code{broom} package takes care of filtering out the relevant parts of the complete-data analysis. It may happen that you'll see the messages -like \code{Error: No tidy method for objects of class ...} or -\code{Error: No glance method for objects of class ...}. The message +like \verb{Error: No tidy method for objects of class ...} or +\verb{Error: No glance method for objects of class ...}. The message means that your complete-data method used in \code{with(imp, ...)} has no \code{tidy} or \code{glance} method defined in the \code{broom} package. @@ -109,20 +109,20 @@ If no \code{tidy} or \code{glance} methods are defined for your analysis tabulate the \code{m} parameter estimates and their variance estimates (the square of the standard errors) from the \code{m} fitted models stored in \code{fit$analyses}. For each parameter, run -\code{\link{pool.scalar}} to obtain the pooled parameters estimate, its variance, the +\code{\link[=pool.scalar]{pool.scalar()}} to obtain the pooled parameters estimate, its variance, the degrees of freedom, the relative increase in variance and the fraction of missing information. An alternative is to write your own \code{glance()} and \code{tidy()} methods and add these to \code{broom} according to the specifications given in \url{https://broom.tidymodels.org}. -In versions prior to \code{mice 3.0} pooling required that +In versions prior to \verb{mice 3.0} pooling required that \code{coef()} and \code{vcov()} methods were available for fitted objects. \emph{This feature is no longer supported}. The reason is that \code{vcov()} methods are inconsistent across packages, leading to buggy behaviour of the \code{pool()} function. -Since \code{mice 3.13.2} function \code{pool()} uses the robust +Since \verb{mice 3.13.2} function \code{pool()} uses the robust the standard error estimate for pooling when it can extract \code{robust.se} from the \code{tidy()} object. } @@ -155,15 +155,15 @@ Rubin, D.B. (1987). \emph{Multiple Imputation for Nonresponse in Surveys}. New York: John Wiley and Sons. Reiter, J.P. (2003). Inference for Partially Synthetic, -Public Use Microdata Sets. \emph{Survey Methodology}, \bold{29}, 181-189. +Public Use Microdata Sets. \emph{Survey Methodology}, \strong{29}, 181-189. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{with.mids}}, \code{\link{as.mira}}, \code{\link{pool.scalar}}, -\code{\link[broom:reexports]{glance}}, \code{\link[broom:reexports]{tidy}} +\code{\link[=with.mids]{with.mids()}}, \code{\link[=as.mira]{as.mira()}}, \code{\link[=pool.scalar]{pool.scalar()}}, +\code{\link[broom:reexports]{glance()}}, \code{\link[broom:reexports]{tidy()}} \url{https://github.com/amices/mice/issues/142}, \url{https://github.com/amices/mice/issues/274} } diff --git a/man/pool.compare.Rd b/man/pool.compare.Rd index 7bd0f99bf..51969567f 100644 --- a/man/pool.compare.Rd +++ b/man/pool.compare.Rd @@ -49,8 +49,8 @@ Component \code{pvalue} is the P-value of testing whether the model \code{fit1} statistically different from the smaller \code{fit0}. } \description{ -This function is deprecated in V3. Use \code{\link{D1}} or -\code{\link{D3}} instead. +This function is deprecated in V3. Use \code{\link[=D1]{D1()}} or +\code{\link[=D3]{D3()}} instead. } \details{ Compares two nested models after m repeated complete data analysis @@ -77,10 +77,10 @@ multiple-imputed data sets. Biometrika, 79, 103-111. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{lm.mids}}, \code{\link{glm.mids}} +\code{\link[=lm.mids]{lm.mids()}}, \code{\link[=glm.mids]{glm.mids()}} } \author{ Karin Groothuis-Oudshoorn and Stef van Buuren, 2009 diff --git a/man/pool.r.squared.Rd b/man/pool.r.squared.Rd index dc3f4be55..51d45df5d 100644 --- a/man/pool.r.squared.Rd +++ b/man/pool.r.squared.Rd @@ -15,8 +15,8 @@ calculated. The default value is FALSE.} } \value{ Returns a 1x4 table with components. Component \code{est} is the -pooled R^2 estimate. Component \code{lo95} is the 95 \% lower bound of the pooled R^2. -Component \code{hi95} is the 95 \% upper bound of the pooled R^2. +pooled R^2 estimate. Component \code{lo95} is the 95 \\% lower bound of the pooled R^2. +Component \code{hi95} is the 95 \\% upper bound of the pooled R^2. Component \code{fmi} is the fraction of missing information due to nonresponse. } \description{ @@ -47,10 +47,10 @@ York: John Wiley and Sons. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{pool}},\code{\link{pool.scalar}} +\code{\link[=pool]{pool()}},\code{\link[=pool.scalar]{pool.scalar()}} } \author{ Karin Groothuis-Oudshoorn and Stef van Buuren, 2009 diff --git a/man/pool.scalar.Rd b/man/pool.scalar.Rd index 0e57b4b87..1117ad6ec 100644 --- a/man/pool.scalar.Rd +++ b/man/pool.scalar.Rd @@ -28,23 +28,23 @@ By default, \code{k = 1} is assumed.} } \value{ Returns a list with components. - \describe{ - \item{\code{m}:}{Number of imputations.} - \item{\code{qhat}:}{The \code{m} univariate estimates of repeated complete-data analyses.} - \item{\code{u}:}{The corresponding \code{m} variances of the univariate estimates.} - \item{\code{qbar}:}{The pooled univariate estimate, formula (3.1.2) Rubin (1987).} - \item{\code{ubar}:}{The mean of the variances (i.e. the pooled within-imputation variance), - formula (3.1.3) Rubin (1987).} - \item{\code{b}:}{The between-imputation variance, formula (3.1.4) Rubin (1987).} - \item{\code{t}:}{The total variance of the pooled estimated, formula (3.1.5) - Rubin (1987).} - \item{\code{r}:}{The relative increase in variance due to nonresponse, formula - (3.1.7) Rubin (1987).} - \item{\code{df}:}{The degrees of freedom for t reference distribution by the - method of Barnard-Rubin (1999).} - \item{\code{fmi}:}{The fraction missing information due to nonresponse, - formula (3.1.10) Rubin (1987). (Not defined for synthetic data.)} - } +\describe{ +\item{\code{m}:}{Number of imputations.} +\item{\code{qhat}:}{The \code{m} univariate estimates of repeated complete-data analyses.} +\item{\code{u}:}{The corresponding \code{m} variances of the univariate estimates.} +\item{\code{qbar}:}{The pooled univariate estimate, formula (3.1.2) Rubin (1987).} +\item{\code{ubar}:}{The mean of the variances (i.e. the pooled within-imputation variance), +formula (3.1.3) Rubin (1987).} +\item{\code{b}:}{The between-imputation variance, formula (3.1.4) Rubin (1987).} +\item{\code{t}:}{The total variance of the pooled estimated, formula (3.1.5) +Rubin (1987).} +\item{\code{r}:}{The relative increase in variance due to nonresponse, formula +(3.1.7) Rubin (1987).} +\item{\code{df}:}{The degrees of freedom for t reference distribution by the +method of Barnard-Rubin (1999).} +\item{\code{fmi}:}{The fraction missing information due to nonresponse, +formula (3.1.10) Rubin (1987). (Not defined for synthetic data.)} +} } \description{ Pools univariate estimates of m repeated complete data analysis @@ -88,10 +88,10 @@ Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons. Reiter, J.P. (2003). Inference for Partially Synthetic, -Public Use Microdata Sets. \emph{Survey Methodology}, \bold{29}, 181-189. +Public Use Microdata Sets. \emph{Survey Methodology}, \strong{29}, 181-189. } \seealso{ -\code{\link{pool}} +\code{\link[=pool]{pool()}} } \author{ Karin Groothuis-Oudshoorn and Stef van Buuren, 2009; Thom Volker, 2021 diff --git a/man/print.Rd b/man/print.Rd index 3cd2b6939..debb46015 100644 --- a/man/print.Rd +++ b/man/print.Rd @@ -39,11 +39,11 @@ Print a \code{mice.anova} object Print a \code{summary.mice.anova} object } \seealso{ -\code{\link[=mids-class]{mids}} +\code{\link[=mids-class]{mids()}} -\code{\link[=mira-class]{mira}} +\code{\link[=mira-class]{mira()}} -\code{\link{mipo}} +\code{\link[=mipo]{mipo()}} -\code{\link{mipo}} +\code{\link[=mipo]{mipo()}} } diff --git a/man/print.mads.Rd b/man/print.mads.Rd index ee248e572..4f33749a3 100644 --- a/man/print.mads.Rd +++ b/man/print.mads.Rd @@ -18,5 +18,5 @@ Print a \code{mads} object } \seealso{ -\code{\link[=mads-class]{mads}} +\code{\link[=mads-class]{mads()}} } diff --git a/man/quickpred.Rd b/man/quickpred.Rd index 507f6d540..afb787502 100644 --- a/man/quickpred.Rd +++ b/man/quickpred.Rd @@ -16,11 +16,11 @@ quickpred( \arguments{ \item{data}{Matrix or data frame with incomplete data.} -\item{mincor}{A scalar, numeric vector (of size \code{ncol(data))} or numeric +\item{mincor}{A scalar, numeric vector (of size \verb{ncol(data))} or numeric matrix (square, of size \code{ncol(data)} specifying the minimum threshold(s) against which the absolute correlation in the data is compared.} -\item{minpuc}{A scalar, vector (of size \code{ncol(data))} or matrix (square, +\item{minpuc}{A scalar, vector (of size \verb{ncol(data))} or matrix (square, of size \code{ncol(data)} specifying the minimum threshold(s) for the proportion of usable cases.} @@ -76,7 +76,7 @@ relatively small. Using a square matrix extends the idea to the columns, so that one can also apply cellwise thresholds. } \note{ -\code{quickpred()} uses \code{\link[base]{data.matrix}} to convert +\code{quickpred()} uses \code{\link[base:data.matrix]{base::data.matrix()}} to convert factors to numbers through their internal codes. Especially for unordered factors the resulting quantification may not make sense. } @@ -103,14 +103,14 @@ imp <- mice(nhanes, pred = quickpred(nhanes, minpuc = 0.25, include = "age")) \references{ van Buuren, S., Boshuizen, H.C., Knook, D.L. (1999) Multiple imputation of missing blood pressure covariates in survival analysis. -\emph{Statistics in Medicine}, \bold{18}, 681--694. +\emph{Statistics in Medicine}, \strong{18}, 681--694. van Buuren, S. and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}}, \code{\link[=mids-class]{mids}} +\code{\link[=mice]{mice()}}, \code{\link[=mids-class]{mids()}} } \author{ Stef van Buuren, Aug 2009 diff --git a/man/selfreport.Rd b/man/selfreport.Rd index 7a6a24c62..aa9fabb5d 100644 --- a/man/selfreport.Rd +++ b/man/selfreport.Rd @@ -17,7 +17,7 @@ A data frame with 2060 rows and 15 variables: \item{wm}{Weight measured (kg)} \item{hr}{Height reported (cm)} \item{wr}{Weight reported (kg)} -\item{prg}{Pregnancy (factor), all \code{Not pregnant}} +\item{prg}{Pregnancy (factor), all \verb{Not pregnant}} \item{edu}{Educational level (factor)} \item{etn}{Ethnicity (factor)} \item{web}{Obtained through web survey (factor)} diff --git a/man/stripplot.mids.Rd b/man/stripplot.mids.Rd index a2735a10b..726b95176 100644 --- a/man/stripplot.mids.Rd +++ b/man/stripplot.mids.Rd @@ -38,7 +38,7 @@ The formula is evaluated on the complete data set in the \code{long} form. Legal variable names for the formula include \code{names(x$data)} plus the two administrative factors \code{.imp} and \code{.id}. -\bold{Extended formula interface:} The primary variable terms (both the LHS +\strong{Extended formula interface:} The primary variable terms (both the LHS \code{y} and RHS \code{x}) may consist of multiple terms separated by a \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and @@ -68,11 +68,11 @@ in the LHS \code{y} variable of the display, i.e. groups created by differs from \code{na.groups} because it evaluates in the completed data \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas \code{na.groups} evaluates in the response indicator. See -\code{\link{xyplot}} for more details. When both \code{na.groups} and +\code{\link[=xyplot]{xyplot()}} for more details. When both \code{na.groups} and \code{groups} are specified, \code{na.groups} takes precedence, and \code{groups} is ignored.} -\item{as.table}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{as.table}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{theme}{A named list containing the graphical parameters. The default function \code{mice.theme} produces a short list of default colors, line @@ -84,34 +84,34 @@ the global parameters. Many setting consists of a pair. For example, data, the second for the imputed data. The theme settings only exist during the call, and do not affect the trellis graphical parameters.} -\item{allow.multiple}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{allow.multiple}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{outer}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{outer}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{panel}{See \code{\link{xyplot}}.} +\item{panel}{See \code{\link[=xyplot]{xyplot()}}.} -\item{default.prepanel}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{default.prepanel}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{jitter.data}{See \code{\link[lattice:panel.xyplot]{panel.xyplot}}.} +\item{jitter.data}{See \code{\link[lattice:panel.xyplot]{lattice::panel.xyplot()}}.} -\item{horizontal}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{horizontal}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{\dots}{Further arguments, usually not directly processed by the high-level functions documented here, but instead passed on to other functions.} -\item{subscripts}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subscripts}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{subset}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subset}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} } \value{ The high-level functions documented here, as well as other high-level Lattice functions, return an object of class \code{"trellis"}. The -\code{\link[lattice:update.trellis]{update}} method can be used to +\code{\link[lattice:update.trellis]{update()}} method can be used to subsequently update components of the object, and the -\code{\link[lattice:print.trellis]{print}} method (usually called by default) +\code{\link[lattice:print.trellis]{print()}} method (usually called by default) will plot it on an appropriate plotting device. } \description{ @@ -139,7 +139,7 @@ missing groups. \code{col[1]} is the color of the 'observed' data, \code{col[2]} is the color of the missing or imputed data. A convenient color choice is \code{col=mdc(1:2)}, a transparent blue color for the observed data, and a transparent red color for the imputed data. A good choice is -\code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the +\verb{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the duration of the session by running \code{mice.theme()}. } \note{ @@ -214,15 +214,15 @@ Visualization with R}, Springer. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}}, \code{\link{xyplot}}, \code{\link{densityplot}}, -\code{\link{bwplot}}, \code{\link{lattice}} for an overview of the -package, as well as \code{\link[lattice:xyplot]{stripplot}}, -\code{\link[lattice:panel.stripplot]{panel.stripplot}}, -\code{\link[lattice:print.trellis]{print.trellis}}, -\code{\link[lattice:trellis.par.get]{trellis.par.set}} +\code{\link[=mice]{mice()}}, \code{\link[=xyplot]{xyplot()}}, \code{\link[=densityplot]{densityplot()}}, +\code{\link[=bwplot]{bwplot()}}, \code{\link[=lattice]{lattice()}} for an overview of the +package, as well as \code{\link[lattice:xyplot]{stripplot()}}, +\code{\link[lattice:panel.stripplot]{lattice::panel.stripplot()}}, +\code{\link[lattice:print.trellis]{lattice::print.trellis()}}, +\code{\link[lattice:trellis.par.get]{trellis.par.set()}} } \author{ Stef van Buuren diff --git a/man/summary.Rd b/man/summary.Rd index ceb83527d..5c785cf60 100644 --- a/man/summary.Rd +++ b/man/summary.Rd @@ -47,11 +47,11 @@ Summary of a \code{mads} object Print a \code{mice.anova} object } \seealso{ -\code{\link[=mira-class]{mira}} +\code{\link[=mira-class]{mira()}} -\code{\link[=mids-class]{mids}} +\code{\link[=mids-class]{mids()}} -\code{\link[=mads-class]{mads}} +\code{\link[=mads-class]{mads()}} -\code{\link{mipo}} +\code{\link[=mipo]{mipo()}} } diff --git a/man/supports.transparent.Rd b/man/supports.transparent.Rd index 3484abf9b..bbf5eef1c 100644 --- a/man/supports.transparent.Rd +++ b/man/supports.transparent.Rd @@ -24,6 +24,6 @@ current device is unknown. supports.transparent() } \seealso{ -\code{\link{mdc}} \code{\link{dev.capabilities}} +\code{\link[=mdc]{mdc()}} \code{\link[=dev.capabilities]{dev.capabilities()}} } \keyword{hplot} diff --git a/man/tidy.mipo.Rd b/man/tidy.mipo.Rd index 570b7ad29..a10049baa 100644 --- a/man/tidy.mipo.Rd +++ b/man/tidy.mipo.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/tidiers.R \name{tidy.mipo} \alias{tidy.mipo} -\title{Tidy method to extract results from a `mipo` object} +\title{Tidy method to extract results from a \code{mipo} object} \usage{ \method{tidy}{mipo}(x, conf.int = FALSE, conf.level = 0.95, ...) } @@ -18,22 +18,22 @@ \value{ A dataframe withh these columns: \itemize{ - \item term - \item estimate - \item ubar - \item b - \item t - \item dfcom - \item df - \item riv - \item lambda - \item fmi - \item p.value - \item conf.low (if called with conf.int = TRUE) - \item conf.high (if called with conf.int = TRUE) +\item term +\item estimate +\item ubar +\item b +\item t +\item dfcom +\item df +\item riv +\item lambda +\item fmi +\item p.value +\item conf.low (if called with conf.int = TRUE) +\item conf.high (if called with conf.int = TRUE) } } \description{ -Tidy method to extract results from a `mipo` object +Tidy method to extract results from a \code{mipo} object } \keyword{internal} diff --git a/man/toenail.Rd b/man/toenail.Rd index 3b383dd21..851b39062 100644 --- a/man/toenail.Rd +++ b/man/toenail.Rd @@ -6,15 +6,15 @@ \title{Toenail data} \format{ A data frame with 1908 observations on the following 5 variables: - \describe{ - \item{\code{ID}}{a numeric vector giving the ID of patient} - \item{\code{outcome}}{a numeric vector giving the response - (0=none or mild seperation, 1=moderate or severe)} - \item{\code{treatment}}{a numeric vector giving the treatment group} - \item{\code{month}}{a numeric vector giving the time of the visit - (not exactly monthly intervals hence not round numbers)} - \item{\code{visit}}{a numeric vector giving the number of the visit} - } +\describe{ +\item{\code{ID}}{a numeric vector giving the ID of patient} +\item{\code{outcome}}{a numeric vector giving the response +(0=none or mild seperation, 1=moderate or severe)} +\item{\code{treatment}}{a numeric vector giving the treatment group} +\item{\code{month}}{a numeric vector giving the time of the visit +(not exactly monthly intervals hence not round numbers)} +\item{\code{visit}}{a numeric vector giving the number of the visit} +} } \source{ De Backer, M., De Vroey, C., Lesaffre, E., Scheys, I., and De @@ -45,11 +45,10 @@ G. Fitzmaurice, N. Laird and J. Ware (2004) Applied Longitudinal Analysis, Wiley and Sons, New York, USA. Van Buuren, S. (2018). -\href{https://stefvanbuuren.name/fimd/sec-catoutcome.html#example}{\emph{Flexible -Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. +\href{https://stefvanbuuren.name/fimd/sec-catoutcome.html#example}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{toenail2}} +\code{\link[=toenail2]{toenail2()}} } \keyword{datasets} diff --git a/man/toenail2.Rd b/man/toenail2.Rd index 517cc92dd..13f71d74e 100644 --- a/man/toenail2.Rd +++ b/man/toenail2.Rd @@ -6,14 +6,14 @@ \title{Toenail data} \format{ A data frame with 1908 observations on the following 5 variables: - \describe{ - \item{\code{patientID}}{a numeric vector giving the ID of patient} - \item{\code{outcome}}{a factor with 2 levels giving the response} - \item{\code{treatment}}{a factor with 2 levels giving the treatment group} - \item{\code{time}}{a numeric vector giving the time of the visit - (not exactly monthly intervals hence not round numbers)} - \item{\code{visit}}{an integer giving the number of the visit} - } +\describe{ +\item{\code{patientID}}{a numeric vector giving the ID of patient} +\item{\code{outcome}}{a factor with 2 levels giving the response} +\item{\code{treatment}}{a factor with 2 levels giving the treatment group} +\item{\code{time}}{a numeric vector giving the time of the visit +(not exactly monthly intervals hence not round numbers)} +\item{\code{visit}}{an integer giving the number of the visit} +} } \source{ De Backer, M., De Vroey, C., Lesaffre, E., Scheys, I., and De @@ -45,11 +45,10 @@ G. Fitzmaurice, N. Laird and J. Ware (2004) Applied Longitudinal Analysis, Wiley and Sons, New York, USA. Van Buuren, S. (2018). -\href{https://stefvanbuuren.name/fimd/sec-catoutcome.html#example}{\emph{Flexible -Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. +\href{https://stefvanbuuren.name/fimd/sec-catoutcome.html#example}{\emph{Flexible Imputation of Missing Data. Second Edition.}} Chapman & Hall/CRC. Boca Raton, FL. } \seealso{ -\code{\link{toenail}} +\code{\link[=toenail]{toenail()}} } \keyword{datasets} diff --git a/man/walking.Rd b/man/walking.Rd index aee13ace4..a12029a5f 100644 --- a/man/walking.Rd +++ b/man/walking.Rd @@ -66,7 +66,7 @@ plotit() \references{ van Buuren, S., Eyres, S., Tennant, A., Hopman-Rock, M. (2005). Improving comparability of existing data by Response Conversion. -\emph{Journal of Official Statistics}, \bold{21}(1), 53-72. +\emph{Journal of Official Statistics}, \strong{21}(1), 53-72. Van Buuren, S. (2018). \href{https://stefvanbuuren.name/fimd/sec-codingsystems.html#sec:impbridge}{\emph{Flexible Imputation of Missing Data. Second Edition.}} diff --git a/man/windspeed.Rd b/man/windspeed.Rd index 6c827de55..42f0faf52 100644 --- a/man/windspeed.Rd +++ b/man/windspeed.Rd @@ -37,6 +37,6 @@ Resource (with Discussion)}. Applied Statistics 38, 1-50. van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. \emph{Journal of -Statistical Computation and Simulation}, \bold{76}, 12, 1049--1064. +Statistical Computation and Simulation}, \strong{76}, 12, 1049--1064. } \keyword{datasets} diff --git a/man/with.mids.Rd b/man/with.mids.Rd index 6bb723039..c47ba1d0f 100644 --- a/man/with.mids.Rd +++ b/man/with.mids.Rd @@ -16,7 +16,7 @@ containing a dot (notation for "all other variables") do not work.} \item{\dots}{Not used} } \value{ -An object of S3 class \code{\link[=mira-class]{mira}} +An object of S3 class \code{\link[=mira-class]{mira()}} } \description{ Performs a computation of each of imputed datasets in data. @@ -41,12 +41,12 @@ fit3 <- with(imp, anova(lm(bmi ~ age + chl))) \references{ van Buuren S and Groothuis-Oudshoorn K (2011). \code{mice}: Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of -Statistical Software}, \bold{45}(3), 1-67. +Statistical Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}}, \code{\link{pool}}, -\code{\link{D1}}, \code{\link{D3}}, \code{\link{pool.r.squared}} +\code{\link[=mids-class]{mids()}}, \code{\link[=mira-class]{mira()}}, \code{\link[=pool]{pool()}}, +\code{\link[=D1]{D1()}}, \code{\link[=D3]{D3()}}, \code{\link[=pool.r.squared]{pool.r.squared()}} } \author{ Karin Oudshoorn, Stef van Buuren 2009, 2012, 2020 diff --git a/man/xyplot.mads.Rd b/man/xyplot.mads.Rd index cc326ca4b..c8eaae47d 100644 --- a/man/xyplot.mads.Rd +++ b/man/xyplot.mads.Rd @@ -15,7 +15,7 @@ ) } \arguments{ -\item{x}{A \code{mads} object, typically created by \code{\link{ampute}}.} +\item{x}{A \code{mads} object, typically created by \code{\link[=ampute]{ampute()}}.} \item{data}{A string or vector of variable names that needs to be plotted. As a default, all variables will be plotted.} @@ -33,7 +33,7 @@ There are several defaults for different #variables. Note that for more than 9 variables, multiple plots will be created automatically.} \item{colors}{A vector of two RGB values defining the colors of the non-amputed and -amputed data respectively. RGB values can be obtained with \code{\link{hcl}}.} +amputed data respectively. RGB values can be obtained with \code{\link[=hcl]{hcl()}}.} \item{\dots}{Not used, but for consistency with generic} } @@ -43,19 +43,19 @@ will always be shown in a new plot. } \description{ Plotting method to investigate relation between amputed data and the weighted sum -scores. Based on \code{\link{lattice}}. \code{xyplot} produces scatterplots. +scores. Based on \code{\link[=lattice]{lattice()}}. \code{xyplot} produces scatterplots. The function plots the variables against the weighted sum scores. The function automatically separates the amputed and non-amputed data to see the relation between the amputation and the weighted sum scores. } \note{ The \code{mads} object contains all the information you need to -make any desired plots. Check \code{\link{mads-class}} or the vignette \emph{Multivariate +make any desired plots. Check \code{\link[=mads-class]{mads-class()}} or the vignette \emph{Multivariate Amputation using Ampute} to understand the contents of class object \code{mads}. } \seealso{ -\code{\link{ampute}}, \code{\link{bwplot}}, \code{\link{Lattice}} for -an overview of the package, \code{\link{mads-class}} +\code{\link[=ampute]{ampute()}}, \code{\link[=bwplot]{bwplot()}}, \code{\link[=Lattice]{Lattice()}} for +an overview of the package, \code{\link[=mads-class]{mads-class()}} } \author{ Rianne Schouten, 2016 diff --git a/man/xyplot.mids.Rd b/man/xyplot.mids.Rd index 2d64a9f9a..e0bec0343 100644 --- a/man/xyplot.mids.Rd +++ b/man/xyplot.mids.Rd @@ -34,7 +34,7 @@ The formula is evaluated on the complete data set in the \code{long} form. Legal variable names for the formula include \code{names(x$data)} plus the two administrative factors \code{.imp} and \code{.id}. -\bold{Extended formula interface:} The primary variable terms (both the LHS +\strong{Extended formula interface:} The primary variable terms (both the LHS \code{y} and RHS \code{x}) may consist of multiple terms separated by a \sQuote{+} sign, e.g., \code{y1 + y2 ~ x | a * b}. This formula would be taken to mean that the user wants to plot both \code{y1 ~ x | a * b} and @@ -60,11 +60,11 @@ in the LHS \code{y} variable of the display, i.e. groups created by differs from \code{na.groups} because it evaluates in the completed data \code{data.frame(complete(x, "long", inc=TRUE))} (as usual), whereas \code{na.groups} evaluates in the response indicator. See -\code{\link{xyplot}} for more details. When both \code{na.groups} and +\code{\link[=xyplot]{xyplot()}} for more details. When both \code{na.groups} and \code{groups} are specified, \code{na.groups} takes precedence, and \code{groups} is ignored.} -\item{as.table}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{as.table}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{theme}{A named list containing the graphical parameters. The default function \code{mice.theme} produces a short list of default colors, line @@ -76,26 +76,26 @@ the global parameters. Many setting consists of a pair. For example, data, the second for the imputed data. The theme settings only exist during the call, and do not affect the trellis graphical parameters.} -\item{allow.multiple}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{allow.multiple}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{outer}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{outer}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{drop.unused.levels}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} \item{\dots}{Further arguments, usually not directly processed by the high-level functions documented here, but instead passed on to other functions.} -\item{subscripts}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subscripts}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} -\item{subset}{See \code{\link[lattice:xyplot]{xyplot}}.} +\item{subset}{See \code{\link[lattice:xyplot]{lattice::xyplot()}}.} } \value{ The high-level functions documented here, as well as other high-level Lattice functions, return an object of class \code{"trellis"}. The -\code{\link[lattice:update.trellis]{update}} method can be used to +\code{\link[lattice:update.trellis]{update()}} method can be used to subsequently update components of the object, and the -\code{\link[lattice:print.trellis]{print}} method (usually called by default) +\code{\link[lattice:print.trellis]{print()}} method (usually called by default) will plot it on an appropriate plotting device. } \description{ @@ -122,7 +122,7 @@ missing groups. \code{col[1]} is the color of the 'observed' data, \code{col[2]} is the color of the missing or imputed data. A convenient color choice is \code{col=mdc(1:2)}, a transparent blue color for the observed data, and a transparent red color for the imputed data. A good choice is -\code{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the +\verb{col=mdc(1:2), pch=20, cex=1.5}. These choices can be set for the duration of the session by running \code{mice.theme()}. } \note{ @@ -155,15 +155,15 @@ Visualization with R}, Springer. van Buuren S and 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} +Software}, \strong{45}(3), 1-67. \doi{10.18637/jss.v045.i03} } \seealso{ -\code{\link{mice}}, \code{\link{stripplot}}, \code{\link{densityplot}}, -\code{\link{bwplot}}, \code{\link{lattice}} for an overview of the -package, as well as \code{\link[lattice:xyplot]{xyplot}}, -\code{\link[lattice:panel.xyplot]{panel.xyplot}}, -\code{\link[lattice:print.trellis]{print.trellis}}, -\code{\link[lattice:trellis.par.get]{trellis.par.set}} +\code{\link[=mice]{mice()}}, \code{\link[=stripplot]{stripplot()}}, \code{\link[=densityplot]{densityplot()}}, +\code{\link[=bwplot]{bwplot()}}, \code{\link[=lattice]{lattice()}} for an overview of the +package, as well as \code{\link[lattice:xyplot]{lattice::xyplot()}}, +\code{\link[lattice:panel.xyplot]{lattice::panel.xyplot()}}, +\code{\link[lattice:print.trellis]{lattice::print.trellis()}}, +\code{\link[lattice:trellis.par.get]{trellis.par.set()}} } \author{ Stef van Buuren From 5c6bee22c934127b4454da4a08c091c33820813b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 12 Sep 2023 15:54:14 +0200 Subject: [PATCH 03/37] Add a data argument to nimp() to calculate number of imputations per block. Update make.method() so that homogeneous types and nlevels within a block get an appropriate default method. --- R/method.R | 54 +++++++++++++++++++-------------------- R/nimp.R | 18 ++++++++----- R/visitSequence.R | 8 +++--- man/make.method.Rd | 4 +-- man/make.visitSequence.Rd | 4 +-- man/nimp.Rd | 13 ++++++---- 6 files changed, 55 insertions(+), 46 deletions(-) diff --git a/R/method.R b/R/method.R index ff09ea420..6d1dbcf91 100644 --- a/R/method.R +++ b/R/method.R @@ -15,15 +15,15 @@ make.method <- function(data, defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { method <- rep("", length(blocks)) names(method) <- names(blocks) - for (j in names(blocks)) { + for (j in seq_along(blocks)) { yvar <- blocks[[j]] - y <- data[, yvar] - def <- sapply(y, assign.method) - k <- ifelse(all(diff(def) == 0), k <- def[1], 1) + y <- data[, yvar, drop = FALSE] + k <- assign.method(y) method[j] <- defaultMethod[k] } - nimp <- nimp(where, blocks) - # method[nimp == 0] <- "" + + nimp <- nimp(where = where, blocks = blocks) + method[nimp == 0L] <- "" method } @@ -37,16 +37,16 @@ check.method <- function(method, data, where, blocks, defaultMethod) { defaultMethod = defaultMethod )) } - nimp <- nimp(where, blocks) + nimp <- nimp(where = where, blocks = blocks) # expand user's imputation method to all visited columns # 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.") } method <- rep(method, length(blocks)) - # method[nimp == 0] <- "" + method[nimp == 0L] <- "" } # check the length of the argument @@ -58,14 +58,14 @@ check.method <- function(method, data, where, blocks, defaultMethod) { names(method) <- names(blocks) # check whether the requested imputation methods are on the search path - active.check <- !is.passive(method) & nimp > 0 & method != "" - passive.check <- is.passive(method) & nimp > 0 & method != "" + active.check <- !is.passive(method) & nimp > 0L & method != "" + passive.check <- is.passive(method) & nimp > 0L & method != "" check <- all(active.check) & any(passive.check) if (check) { fullNames <- rep.int("mice.impute.passive", length(method[passive.check])) } else { fullNames <- paste("mice.impute", method[active.check], sep = ".") - if (length(method[active.check]) == 0) fullNames <- character(0) + if (length(method[active.check]) == 0L) fullNames <- character(0) } # type checks on built-in imputation methods @@ -89,8 +89,8 @@ check.method <- function(method, data, where, blocks, defaultMethod) { ) ) cond1 <- sapply(y, is.numeric) - cond2 <- sapply(y, is.factor) & sapply(y, nlevels) == 2 - cond3 <- sapply(y, is.factor) & sapply(y, nlevels) > 2 + cond2 <- sapply(y, is.factor) & sapply(y, nlevels) == 2L + cond3 <- sapply(y, is.factor) & sapply(y, nlevels) > 2L if (any(cond1) && mj %in% mlist$m1) { warning("Type mismatch for variable(s): ", paste(vname[cond1], collapse = ", "), @@ -113,28 +113,28 @@ check.method <- function(method, data, where, blocks, defaultMethod) { ) } } - # method[nimp == 0] <- "" + method[nimp == 0L] <- "" unlist(method) } # assign methods based on type, -# use method 1 if there is no single method within the block +# use method 1 if block is of heterogeneous type assign.method <- function(y) { - if (is.numeric(y)) { - return(1) + if (all(sapply(y, is.numeric))) { + return(1L) } - if (nlevels(y) == 2) { - return(2) + if (all(sapply(y, is.factor)) && all(sapply(y, nlevels) == 2L)) { + return(2L) } - if (is.ordered(y) && nlevels(y) > 2) { - return(4) + if (all(sapply(y, is.ordered)) && all(sapply(y, nlevels) > 2L)) { + return(4L) } - if (nlevels(y) > 2) { - return(3) + if (all(sapply(y, nlevels) > 2L)) { + return(3L) } - if (is.logical(y)) { - return(2) + if (all(sapply(y, is.logical))) { + return(2L) } - 1 + return(1L) } diff --git a/R/nimp.R b/R/nimp.R index caaac812d..341dcd08a 100644 --- a/R/nimp.R +++ b/R/nimp.R @@ -8,17 +8,23 @@ #' @seealso [mice()] #' @export #' @examples -#' where <- is.na(nhanes) -#' #' # standard FCS -#' nimp(where) +#' nimp(nhanes2) #' #' # user-defined blocks -#' nimp(where, blocks = name.blocks(list(c("bmi", "hyp"), "age", "chl"))) -nimp <- function(where, blocks = make.blocks(where)) { +#' where <- is.na(nhanes) +#' blocks <- list(c("bmi", "hyp"), "age", "chl") +#' nimp(where = where, blocks = blocks) +nimp <- function(data = NULL, where = is.na(data), blocks = make.blocks(where)) { + # legacy handling + waswhere <- is.matrix(data) && all(is.logical(data)) + if (waswhere) { + stop("Please call 'nimp()' as 'nimp(where = .. , blocks = ..'") + } + 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]]]) - nimp + return(nimp) } diff --git a/R/visitSequence.R b/R/visitSequence.R index c05870775..4d678bd68 100644 --- a/R/visitSequence.R +++ b/R/visitSequence.R @@ -31,16 +31,16 @@ check.visitSequence <- function(visitSequence = NULL, } if (is.null(where)) where <- is.na(data) - nimp <- nimp(where, blocks) - if (length(nimp) == 0) visitSequence <- nimp + nimp <- nimp(where = where, blocks = blocks) + if (!length(nimp)) visitSequence <- nimp if (length(visitSequence) == 1 && is.character(visitSequence)) { code <- match.arg(visitSequence, choices = c("roman", "arabic", "monotone", "revmonotone") ) visitSequence <- switch(code, - roman = names(blocks)[nimp > 0], - arabic = rev(names(blocks)[nimp > 0]), + roman = names(blocks)[nimp > 0L], + arabic = rev(names(blocks)[nimp > 0L]), monotone = names(blocks)[order(nimp)], revmonotone = rev(names(blocks)[order(nimp)]) ) diff --git a/man/make.method.Rd b/man/make.method.Rd index 5a04efe0a..5186575b8 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -12,8 +12,8 @@ make.method( ) } \arguments{ -\item{data}{A data frame or a matrix containing the incomplete data. Missing -values are coded as \code{NA}.} +\item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} \item{where}{A data frame or matrix with logicals of the same dimensions as \code{data} indicating where in the data the imputations should be diff --git a/man/make.visitSequence.Rd b/man/make.visitSequence.Rd index d597100eb..e0adb102d 100644 --- a/man/make.visitSequence.Rd +++ b/man/make.visitSequence.Rd @@ -7,8 +7,8 @@ make.visitSequence(data = NULL, blocks = NULL) } \arguments{ -\item{data}{A data frame or a matrix containing the incomplete data. Missing -values are coded as \code{NA}.} +\item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} \item{blocks}{List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are diff --git a/man/nimp.Rd b/man/nimp.Rd index f347ba40b..327e33575 100644 --- a/man/nimp.Rd +++ b/man/nimp.Rd @@ -4,9 +4,12 @@ \alias{nimp} \title{Number of imputations per block} \usage{ -nimp(where, blocks = make.blocks(where)) +nimp(data = NULL, where = is.na(data), blocks = make.blocks(where)) } \arguments{ +\item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} + \item{where}{A data frame or matrix with logicals of the same dimensions as \code{data} indicating where in the data the imputations should be created. The default, \code{where = is.na(data)}, specifies that the @@ -38,13 +41,13 @@ Calculates the number of cells within a block for which imputation is requested. } \examples{ -where <- is.na(nhanes) - # standard FCS -nimp(where) +nimp(nhanes2) # user-defined blocks -nimp(where, blocks = name.blocks(list(c("bmi", "hyp"), "age", "chl"))) +where <- is.na(nhanes) +blocks <- list(c("bmi", "hyp"), "age", "chl") +nimp(where = where, blocks = blocks) } \seealso{ \code{\link[=mice]{mice()}} From 755c23a2502a65eb7a2447e78f0a3fd227eb940c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 13 Sep 2023 11:06:30 +0200 Subject: [PATCH 04/37] Restore classic predictorMatrix behaviour that sets predictorMatrix[j, ] to zero when variable j is member of a block for which no imputations are needed. --- R/edit.setup.R | 21 ++- R/mice.R | 154 ++++++++++++-------- R/predictorMatrix.R | 11 ++ man/as.mids.Rd | 21 +-- man/construct.blocks.Rd | 24 +-- man/convertmodels.Rd | 24 +-- man/extend.formulas.Rd | 28 ++-- man/make.method.Rd | 21 +-- man/make.post.Rd | 4 +- man/mice.Rd | 137 ++++++++++------- man/nimp.Rd | 21 +-- tests/testthat/test-make.predictorMatrix.R | 10 ++ tests/testthat/test-mice.impute.panImpute.R | 5 +- tests/testthat/test-rbind.R | 5 +- 14 files changed, 299 insertions(+), 187 deletions(-) diff --git a/R/edit.setup.R b/R/edit.setup.R index 3bcc93992..fd8e26f95 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -20,7 +20,7 @@ edit.setup <- function(data, setup, # FIXME: this function is not yet adapted to blocks if (!validate.predictorMatrix(pred)) { - stop("Problem with predictorMatrix detected in edit.setup") + warning("Problem with predictorMatrix detected in edit.setup()") return(setup) } @@ -37,6 +37,7 @@ edit.setup <- function(data, setup, } didlog <- FALSE if (constant && any(pred[, j] != 0) && remove.constant) { + # inactivate j as predictor out <- varnames[j] pred[, j] <- 0 # remove out from RHS @@ -49,6 +50,7 @@ edit.setup <- function(data, setup, didlog <- TRUE } if (constant && meth[j] != "" && remove.constant) { + # inactivate j as dependent out <- varnames[j] pred[j, ] <- 0 # remove LHS formula @@ -97,10 +99,23 @@ edit.setup <- function(data, setup, } } - if (all(pred == 0L)) { - stop("`mice` detected constant and/or collinear variables. No predictors were left after their removal.") + # Set predictorMatrix row to zero + + # if (all(pred == 0L)) { + # stop("`mice` detected constant and/or collinear variables. No predictors were left after their removal.") + # } + + if (!validate.predictorMatrix(pred)) { + stop("Problem with predictorMatrix detected after edit.setup()") } + # for (j in seq_len(ncol(data))) { + # if (meth[j] == "") + # if (!all(pred[j, ] == 0)) { + # stop("Inconsistent j: ", j, " pred[j, ]: ", pred[j, ]) + # } + # } + setup$predictorMatrix <- pred setup$formulas <- form setup$blots <- blots diff --git a/R/mice.R b/R/mice.R index 1cf11f66c..e69be50cf 100644 --- a/R/mice.R +++ b/R/mice.R @@ -67,17 +67,28 @@ #' `method='myfunc'`. To call it only for, say, column 2 specify #' \code{method=c('norm','myfunc','logreg',\dots{})}. #' -#' *Skipping imputation:* The user may skip imputation of a column by -#' setting its entry to the empty method: `""`. For complete columns without -#' missing data `mice` will automatically set the empty method. Setting t -#' he empty method does not produce imputations for the column, so any missing -#' cells remain `NA`. If column A contains `NA`'s and is used as -#' predictor in the imputation model for column B, then `mice` produces no -#' imputations for the rows in B where A is missing. The imputed data -#' for B may thus contain `NA`'s. The remedy is to remove column A from -#' the imputation model for the other columns in the data. This can be done -#' by setting the entire column for variable A in the `predictorMatrix` -#' equal to zero. +#' *Skipping imputation:* Imputation of variable (or variable block) +#' \eqn{j} can be skipped by setting the empty method, `method[j] = ""`. +#' On start-up, `mice()` will test whether variables within +#' block \eqn{j} need imputation. If not, `mice()` takes two actions: +#' It sets `method[j] <- ""` and it sets the rows of the `predictorMatrix` of +#' the variables within block \eqn{j} to zero. +#' +#' *BEWARE: Propagation of `NA`s*: Setting the empty method +#' for an incomplete variable is legal and prevent `mice()` from generating +#' imputations for its missing cells. Sometimes this is wanted, but +#' it may have a surprising side effect to due missing value propagation. +#' For example, if column `"A"` contains `NA`'s and is a predictor in the +#' imputation model for column `"B"`, then setting `method["A"] = ""` will +#' propagate the missing data of `"A"` into `"B"` for the rows in `"B"` +#' where `"A"` is missing. The imputed data for `"B"` thus contain `NA`'s. +#' If this is not desired, apply one of the following two remedies: +#' 1) Remove column `"A"` as predictor from all imputation models, e.g., +#' by setting `predictorMatrix[, "A"] <- 0`, and re-impute. +#' Or 2) Specify an imputation method for `"A"` and impute `"A"`. Optionally, +#' after convergence manually replace any imputations for `"A"` by `NA` +#' using `imp$imp$A[] <- NA`. In that case, `complete(imp, 1)` produces a +#' dataset that is complete, except for column `"A"`. #' #' *Passive imputation:* `mice()` supports a special built-in method, #' called passive imputation. This method can be used to ensure that a data @@ -130,45 +141,60 @@ #' to turn off this behavior by specifying the #' argument `auxiliary = FALSE`. #' -#' @param data A data frame or a matrix containing the incomplete data. Missing -#' values are coded as `NA`. -#' @param m Number of multiple imputations. The default is `m=5`. -#' @param method Can be either a single string, or a vector of strings with -#' length `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 `defaultMethod` argument. Columns that need -#' not be imputed have the empty method `""`. See details. -#' @param predictorMatrix A numeric matrix of `length(blocks)` rows -#' and `ncol(data)` columns, containing 0/1 data specifying -#' the set of predictors to be used for each target column. -#' Each row corresponds to a variable block, i.e., a set of variables -#' to be imputed. A value of `1` means that the column -#' variable is used as a predictor for the target block (in the rows). -#' By default, the `predictorMatrix` is a square matrix of `ncol(data)` -#' rows and columns with all 1's, except for the diagonal. -#' Note: For two-level imputation models (which have `"2l"` in their names) -#' other roles (e.g, `2` or `-2`) are also allowed. -#' @param ignore A logical vector of `nrow(data)` elements indicating -#' which rows are ignored when creating the imputation model. The default -#' `NULL` includes all rows that have an observed value of the variable -#' to imputed. Rows with `ignore` set to `TRUE` do not influence the -#' parameters of the imputation model, but are still imputed. We may use the -#' `ignore` argument to split `data` into a training set (on which the -#' imputation model is built) and a test set (that does not influence the -#' imputation model estimates). -#' Note: Multivariate imputation methods, like `mice.impute.jomoImpute()` -#' or `mice.impute.panImpute()`, do not honour the `ignore` argument. -#' @param where A data frame or matrix with logicals of the same dimensions -#' as `data` indicating where in the data the imputations should be -#' created. The default, `where = is.na(data)`, specifies that the -#' missing data should be imputed. The `where` argument may be used to -#' overimpute observed data, or to skip imputations for selected missing values. -#' Note: Imputation methods that generate imptutations outside of -#' `mice`, like `mice.impute.panImpute()` may depend on a complete -#' predictor space. In that case, a custom `where` matrix can not be -#' specified. +#' @param data Data frame with \eqn{n} rows and \eqn{p} columns with +#' incomplete data. Missing values are coded as `NA`. +#' @param m Number of multiple imputations. The default is `m = 5`. +#' Setting `m = 1` produces a single imputation per cell +#' (not recommended in general). +#' @param method Character vector of length \eqn{q} specifying imputation +#' methods for (groups of) variables. In the special case +#' `length(method) == 1`, the specified method applies to all +#' variables. When `method` is not specified, `mice()` will +#' select a method based on the variable type as regulated +#' by the `defaultMethod` argument. See details +#' on *skipping imputation*. +#' @param predictorMatrix +#' A square numeric matrix of \eqn{p} rows +#' and columns. Row- and column names are `colnames(data)`. +#' Each row corresponds to a variable to be imputed. +#' A value of `1` means that the column variable is a +#' predictor for the row variable, while a `0` means that +#' the column variable is not a predictor. The default +#' `predictorMatrix` is `1` everywhere, except for a zero +#' diagonal. For variables that need no be imputed, +#' `mice()` automatically sets the corresponding rows in the +#' `predictorMatrix` to zero. See details +#' on *skipping imputation*. +#' Two-level imputation models (which have `"2l"` in their +#' names) other codes than `0` and `1`, e.g, `2` or `-2`, +#' are also used. +#' @param ignore A logical vector of \eqn{n} elements indicating +#' which rows are ignored for estimating the parameters of +#' the imputation model. +#' Rows with `ignore` set to `TRUE` do not influence the +#' parameters of the imputation model. +#' The `ignore` argument allows splitting `data` into a +#' training set (on which we fit the imputation model) +#' and a test set (that does not influence the imputation +#' model parameter estimates). +#' The default `NULL` corresponds to all `FALSE`, thus +#' including all rows into the imputation models. +#' Note: Multivariate imputation methods, +#' like `mice.impute.jomoImpute()` or +#' `mice.impute.panImpute()`, do not honour the `ignore` +#' argument. +#' @param where A data frame or matrix of logicals with \eqn{n} rows +#' and \eqn{p} columns, indicating the cells of `data` for +#' which imputations are generated. +#' The default `where = is.na(data)` specifies that all +#' missing data are imputed. +#' The `where` argument can overimpute cells +#' with observed data, or skip imputation of specific missing +#' cells. Be aware that the latter option could propagate +#' missing values to other variables. See details. +#' Note: Methods that generate multivariate imputations +#' (e.g. `mice.impute.panImpute()`) do not honour the +#' `where` argument. #' @param 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 @@ -409,21 +435,25 @@ mice <- function(data, chk <- check.cluster(data, predictorMatrix) where <- check.where(where, data, blocks) - # check visitSequence, edit predictorMatrix for monotone + # check visitSequence, user.visitSequence <- visitSequence visitSequence <- check.visitSequence(visitSequence, - data = data, where = where, blocks = blocks + data = data, where = where, blocks = blocks + ) + method <- check.method( + method = method, data = data, where = where, + blocks = blocks, defaultMethod = defaultMethod ) + # edit predictorMatrix for monotone, set zero rows for empty methods predictorMatrix <- edit.predictorMatrix( predictorMatrix = predictorMatrix, + method = method, + blocks = blocks, + where = where, visitSequence = visitSequence, user.visitSequence = user.visitSequence, maxit = maxit ) - method <- check.method( - method = method, data = data, where = where, - blocks = blocks, defaultMethod = defaultMethod - ) post <- check.post(post, data) blots <- check.blots(blots, data, blocks) ignore <- check.ignore(ignore, data) @@ -450,9 +480,9 @@ mice <- function(data, post <- setup$post # update model -# formulas <- p2f(predictorMatrix, blocks) -# roles <- p2c(predictorMatrix) -# blots <- paste.roles(blots, roles) + # formulas <- p2f(predictorMatrix, blocks) + # roles <- p2c(predictorMatrix) + # blots <- paste.roles(blots, roles) # initialize imputations nmis <- apply(is.na(data), 2, sum) @@ -492,8 +522,8 @@ mice <- function(data, seed = seed, iteration = q$iteration, lastSeedValue = get(".Random.seed", - envir = globalenv(), mode = "integer", - inherits = FALSE + envir = globalenv(), mode = "integer", + inherits = FALSE ), chainMean = q$chainMean, chainVar = q$chainVar, @@ -505,7 +535,7 @@ mice <- function(data, if (!is.null(midsobj$loggedEvents)) { warning("Number of logged events: ", nrow(midsobj$loggedEvents), - call. = FALSE + call. = FALSE ) } midsobj diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 3abdf320b..17634aa68 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -148,9 +148,20 @@ check.predictorMatrix <- function(predictorMatrix, } edit.predictorMatrix <- function(predictorMatrix, + method, + blocks, + where, visitSequence, user.visitSequence, maxit) { + # for empty method, set predictorMatrix row to zero + nimp <- nimp(where = where, blocks = blocks) + for (j in seq_along(blocks)) { + if (!nimp[j]) { + predictorMatrix[blocks[[j]], ] <- 0 + } + } + # edit predictorMatrix to a monotone pattern if (maxit == 1L && !is.null(user.visitSequence) && user.visitSequence == "monotone") { for (i in 1L:length(visitSequence)) { diff --git a/man/as.mids.Rd b/man/as.mids.Rd index 873a5b43f..a520a8f72 100644 --- a/man/as.mids.Rd +++ b/man/as.mids.Rd @@ -11,15 +11,18 @@ as.mids(long, where = NULL, .imp = ".imp", .id = ".id") produced by a call to \code{complete(..., action = 'long', include = TRUE)}, or by other software.} -\item{where}{A data frame or matrix with logicals of the same dimensions -as \code{data} indicating where in the data the imputations should be -created. The default, \code{where = is.na(data)}, specifies that the -missing data should be imputed. The \code{where} argument may be used to -overimpute observed data, or to skip imputations for selected missing values. -Note: Imputation methods that generate imptutations outside of -\code{mice}, like \code{mice.impute.panImpute()} may depend on a complete -predictor space. In that case, a custom \code{where} matrix can not be -specified.} +\item{where}{A data frame or matrix of logicals with \eqn{n} rows +and \eqn{p} columns, indicating the cells of \code{data} for +which imputations are generated. +The default \code{where = is.na(data)} specifies that all +missing data are imputed. +The \code{where} argument can overimpute cells +with observed data, or skip imputation of specific missing +cells. Be aware that the latter option could propagate +missing values to other variables. See details. +Note: Methods that generate multivariate imputations +(e.g. \code{mice.impute.panImpute()}) do not honour the +\code{where} argument.} \item{.imp}{An optional column number or column name in \code{long}, indicating the imputation index. The values are assumed to be consecutive diff --git a/man/construct.blocks.Rd b/man/construct.blocks.Rd index 87d8f3165..67fcbea85 100644 --- a/man/construct.blocks.Rd +++ b/man/construct.blocks.Rd @@ -15,16 +15,20 @@ 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{predictorMatrix}{A numeric matrix of \code{length(blocks)} rows -and \code{ncol(data)} columns, containing 0/1 data specifying -the set of predictors to be used for each target column. -Each row corresponds to a variable block, i.e., a set of variables -to be imputed. A value of \code{1} means that the column -variable is used as a predictor for the target block (in the rows). -By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} -rows and columns with all 1's, except for the diagonal. -Note: For two-level imputation models (which have \code{"2l"} in their names) -other roles (e.g, \code{2} or \code{-2}) are also allowed.} +\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows +and columns. Row- and column names are \code{colnames(data)}. +Each row corresponds to a variable to be imputed. +A value of \code{1} means that the column variable is a +predictor for the row variable, while a \code{0} means that +the column variable is not a predictor. The default +\code{predictorMatrix} is \code{1} everywhere, except for a zero +diagonal. For variables that need no be imputed, +\code{mice()} automatically sets the corresponding rows in the +\code{predictorMatrix} to zero. See details +on \emph{skipping imputation}. +Two-level imputation models (which have \code{"2l"} in their +names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, +are also used.} } \value{ A \code{blocks} object. diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd index 02fe8a2d7..818a0fae6 100644 --- a/man/convertmodels.Rd +++ b/man/convertmodels.Rd @@ -13,16 +13,20 @@ p2c(predictorMatrix) f2p(formulas, blocks = NULL, roles = NULL) } \arguments{ -\item{predictorMatrix}{A numeric matrix of \code{length(blocks)} rows -and \code{ncol(data)} columns, containing 0/1 data specifying -the set of predictors to be used for each target column. -Each row corresponds to a variable block, i.e., a set of variables -to be imputed. A value of \code{1} means that the column -variable is used as a predictor for the target block (in the rows). -By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} -rows and columns with all 1's, except for the diagonal. -Note: For two-level imputation models (which have \code{"2l"} in their names) -other roles (e.g, \code{2} or \code{-2}) are also allowed.} +\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows +and columns. Row- and column names are \code{colnames(data)}. +Each row corresponds to a variable to be imputed. +A value of \code{1} means that the column variable is a +predictor for the row variable, while a \code{0} means that +the column variable is not a predictor. The default +\code{predictorMatrix} is \code{1} everywhere, except for a zero +diagonal. For variables that need no be imputed, +\code{mice()} automatically sets the corresponding rows in the +\code{predictorMatrix} to zero. See details +on \emph{skipping imputation}. +Two-level imputation models (which have \code{"2l"} in their +names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, +are also used.} \item{blocks}{List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are diff --git a/man/extend.formulas.Rd b/man/extend.formulas.Rd index 494791199..bda3062db 100644 --- a/man/extend.formulas.Rd +++ b/man/extend.formulas.Rd @@ -23,8 +23,8 @@ 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{data}{A data frame or a matrix containing the incomplete data. Missing -values are coded as \code{NA}.} +\item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} \item{blocks}{List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are @@ -38,16 +38,20 @@ 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{predictorMatrix}{A numeric matrix of \code{length(blocks)} rows -and \code{ncol(data)} columns, containing 0/1 data specifying -the set of predictors to be used for each target column. -Each row corresponds to a variable block, i.e., a set of variables -to be imputed. A value of \code{1} means that the column -variable is used as a predictor for the target block (in the rows). -By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} -rows and columns with all 1's, except for the diagonal. -Note: For two-level imputation models (which have \code{"2l"} in their names) -other roles (e.g, \code{2} or \code{-2}) are also allowed.} +\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows +and columns. Row- and column names are \code{colnames(data)}. +Each row corresponds to a variable to be imputed. +A value of \code{1} means that the column variable is a +predictor for the row variable, while a \code{0} means that +the column variable is not a predictor. The default +\code{predictorMatrix} is \code{1} everywhere, except for a zero +diagonal. For variables that need no be imputed, +\code{mice()} automatically sets the corresponding rows in the +\code{predictorMatrix} to zero. See details +on \emph{skipping imputation}. +Two-level imputation models (which have \code{"2l"} in their +names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, +are also used.} \item{auxiliary}{A logical that indicates whether the variables listed in \code{predictors} should be added to the formula as main diff --git a/man/make.method.Rd b/man/make.method.Rd index 5186575b8..d64507321 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -15,15 +15,18 @@ make.method( \item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with incomplete data. Missing values are coded as \code{NA}.} -\item{where}{A data frame or matrix with logicals of the same dimensions -as \code{data} indicating where in the data the imputations should be -created. The default, \code{where = is.na(data)}, specifies that the -missing data should be imputed. The \code{where} argument may be used to -overimpute observed data, or to skip imputations for selected missing values. -Note: Imputation methods that generate imptutations outside of -\code{mice}, like \code{mice.impute.panImpute()} may depend on a complete -predictor space. In that case, a custom \code{where} matrix can not be -specified.} +\item{where}{A data frame or matrix of logicals with \eqn{n} rows +and \eqn{p} columns, indicating the cells of \code{data} for +which imputations are generated. +The default \code{where = is.na(data)} specifies that all +missing data are imputed. +The \code{where} argument can overimpute cells +with observed data, or skip imputation of specific missing +cells. Be aware that the latter option could propagate +missing values to other variables. See details. +Note: Methods that generate multivariate imputations +(e.g. \code{mice.impute.panImpute()}) do not honour the +\code{where} argument.} \item{blocks}{List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are diff --git a/man/make.post.Rd b/man/make.post.Rd index 5b0e94af2..b67e66cc0 100644 --- a/man/make.post.Rd +++ b/man/make.post.Rd @@ -7,8 +7,8 @@ make.post(data) } \arguments{ -\item{data}{A data frame or a matrix containing the incomplete data. Missing -values are coded as \code{NA}.} +\item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} } \value{ Character vector of \code{ncol(data)} element diff --git a/man/mice.Rd b/man/mice.Rd index 1573b5b23..434a0b74e 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -26,50 +26,64 @@ mice( ) } \arguments{ -\item{data}{A data frame or a matrix containing the incomplete data. Missing -values are coded as \code{NA}.} - -\item{m}{Number of multiple imputations. The default is \code{m=5}.} - -\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{predictorMatrix}{A numeric matrix of \code{length(blocks)} rows -and \code{ncol(data)} columns, containing 0/1 data specifying -the set of predictors to be used for each target column. -Each row corresponds to a variable block, i.e., a set of variables -to be imputed. A value of \code{1} means that the column -variable is used as a predictor for the target block (in the rows). -By default, the \code{predictorMatrix} is a square matrix of \code{ncol(data)} -rows and columns with all 1's, except for the diagonal. -Note: For two-level imputation models (which have \code{"2l"} in their names) -other roles (e.g, \code{2} or \code{-2}) are also allowed.} - -\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 -to imputed. Rows with \code{ignore} set to \code{TRUE} do not influence the -parameters of the imputation model, but are still imputed. We may use the -\code{ignore} argument to split \code{data} into a training set (on which the -imputation model is built) and a test set (that does not influence the -imputation model estimates). -Note: Multivariate imputation methods, like \code{mice.impute.jomoImpute()} -or \code{mice.impute.panImpute()}, do not honour the \code{ignore} argument.} - -\item{where}{A data frame or matrix with logicals of the same dimensions -as \code{data} indicating where in the data the imputations should be -created. The default, \code{where = is.na(data)}, specifies that the -missing data should be imputed. The \code{where} argument may be used to -overimpute observed data, or to skip imputations for selected missing values. -Note: Imputation methods that generate imptutations outside of -\code{mice}, like \code{mice.impute.panImpute()} may depend on a complete -predictor space. In that case, a custom \code{where} matrix can not be -specified.} +\item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} + +\item{m}{Number of multiple imputations. The default is \code{m = 5}. +Setting \code{m = 1} produces a single imputation per cell +(not recommended in general).} + +\item{method}{Character vector of length \eqn{q} specifying imputation +methods for (groups of) variables. In the special case +\code{length(method) == 1}, the specified method applies to all +variables. When \code{method} is not specified, \code{mice()} will +select a method based on the variable type as regulated +by the \code{defaultMethod} argument. See details +on \emph{skipping imputation}.} + +\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows +and columns. Row- and column names are \code{colnames(data)}. +Each row corresponds to a variable to be imputed. +A value of \code{1} means that the column variable is a +predictor for the row variable, while a \code{0} means that +the column variable is not a predictor. The default +\code{predictorMatrix} is \code{1} everywhere, except for a zero +diagonal. For variables that need no be imputed, +\code{mice()} automatically sets the corresponding rows in the +\code{predictorMatrix} to zero. See details +on \emph{skipping imputation}. +Two-level imputation models (which have \code{"2l"} in their +names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, +are also used.} + +\item{ignore}{A logical vector of \eqn{n} elements indicating +which rows are ignored for estimating the parameters of +the imputation model. +Rows with \code{ignore} set to \code{TRUE} do not influence the +parameters of the imputation model. +The \code{ignore} argument allows splitting \code{data} into a +training set (on which we fit the imputation model) +and a test set (that does not influence the imputation +model parameter estimates). +The default \code{NULL} corresponds to all \code{FALSE}, thus +including all rows into the imputation models. +Note: Multivariate imputation methods, +like \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}, do not honour the \code{ignore} +argument.} + +\item{where}{A data frame or matrix of logicals with \eqn{n} rows +and \eqn{p} columns, indicating the cells of \code{data} for +which imputations are generated. +The default \code{where = is.na(data)} specifies that all +missing data are imputed. +The \code{where} argument can overimpute cells +with observed data, or skip imputation of specific missing +cells. Be aware that the latter option could propagate +missing values to other variables. See details. +Note: Methods that generate multivariate imputations +(e.g. \code{mice.impute.panImpute()}) do not honour the +\code{where} argument.} \item{blocks}{List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are @@ -243,17 +257,30 @@ mechanism allows uses to write customized imputation function, \code{method='myfunc'}. To call it only for, say, column 2 specify \code{method=c('norm','myfunc','logreg',\dots{})}. -\emph{Skipping imputation:} The user may skip imputation of a column by -setting its entry to the empty method: \code{""}. For complete columns without -missing data \code{mice} will automatically set the empty method. Setting t -he empty method does not produce imputations for the column, so any missing -cells remain \code{NA}. If column A contains \code{NA}'s and is used as -predictor in the imputation model for column B, then \code{mice} produces no -imputations for the rows in B where A is missing. The imputed data -for B may thus contain \code{NA}'s. The remedy is to remove column A from -the imputation model for the other columns in the data. This can be done -by setting the entire column for variable A in the \code{predictorMatrix} -equal to zero. +\emph{Skipping imputation:} Imputation of variable (or variable block) +\eqn{j} can be skipped by setting the empty method, \code{method[j] = ""}. +On start-up, \code{mice()} will test whether variables within +block \eqn{j} need imputation. If not, \code{mice()} takes two actions: +It sets \code{method[j] <- ""} and it sets the rows of the \code{predictorMatrix} of +the variables within block \eqn{j} to zero. + +\emph{BEWARE: Propagation of \code{NA}s}: Setting the empty method +for an incomplete variable is legal and prevent \code{mice()} from generating +imputations for its missing cells. Sometimes this is wanted, but +it may have a surprising side effect to due missing value propagation. +For example, if column \code{"A"} contains \code{NA}'s and is a predictor in the +imputation model for column \code{"B"}, then setting \code{method["A"] = ""} will +propagate the missing data of \code{"A"} into \code{"B"} for the rows in \code{"B"} +where \code{"A"} is missing. The imputed data for \code{"B"} thus contain \code{NA}'s. +If this is not desired, apply one of the following two remedies: +\enumerate{ +\item Remove column \code{"A"} as predictor from all imputation models, e.g., +by setting \code{predictorMatrix[, "A"] <- 0}, and re-impute. +Or 2) Specify an imputation method for \code{"A"} and impute \code{"A"}. Optionally, +after convergence manually replace any imputations for \code{"A"} by \code{NA} +using \code{imp$imp$A[] <- NA}. In that case, \code{complete(imp, 1)} produces a +dataset that is complete, except for column \code{"A"}. +} \emph{Passive imputation:} \code{mice()} supports a special built-in method, called passive imputation. This method can be used to ensure that a data diff --git a/man/nimp.Rd b/man/nimp.Rd index 327e33575..dc0ac86e4 100644 --- a/man/nimp.Rd +++ b/man/nimp.Rd @@ -10,15 +10,18 @@ nimp(data = NULL, where = is.na(data), blocks = make.blocks(where)) \item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with incomplete data. Missing values are coded as \code{NA}.} -\item{where}{A data frame or matrix with logicals of the same dimensions -as \code{data} indicating where in the data the imputations should be -created. The default, \code{where = is.na(data)}, specifies that the -missing data should be imputed. The \code{where} argument may be used to -overimpute observed data, or to skip imputations for selected missing values. -Note: Imputation methods that generate imptutations outside of -\code{mice}, like \code{mice.impute.panImpute()} may depend on a complete -predictor space. In that case, a custom \code{where} matrix can not be -specified.} +\item{where}{A data frame or matrix of logicals with \eqn{n} rows +and \eqn{p} columns, indicating the cells of \code{data} for +which imputations are generated. +The default \code{where = is.na(data)} specifies that all +missing data are imputed. +The \code{where} argument can overimpute cells +with observed data, or skip imputation of specific missing +cells. Be aware that the latter option could propagate +missing values to other variables. See details. +Note: Methods that generate multivariate imputations +(e.g. \code{mice.impute.panImpute()}) do not honour the +\code{where} argument.} \item{blocks}{List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are diff --git a/tests/testthat/test-make.predictorMatrix.R b/tests/testthat/test-make.predictorMatrix.R index d2aca8378..0238a66f7 100644 --- a/tests/testthat/test-make.predictorMatrix.R +++ b/tests/testthat/test-make.predictorMatrix.R @@ -8,3 +8,13 @@ test_that("errors on invalid data arguments", { "Data should be a matrix or data frame" ) }) + +# put all incomplete covariate into one blocks, and +# test whether predictorMatrix has zero rows for +# those covariates +data <- cbind(mice::nhanes2, covariate = c(1, rep(c(1, 2), 12))) +imp <- mice(data, blocks = list("bmi", c("age", "covariate"), "chl"), print = FALSE) +test_that("complete variables in a block will get zero rows", { + expect_identical(unname(imp$predictorMatrix["age", ]), rep(0, 5)) + expect_identical(unname(imp$predictorMatrix["covariate", ]), rep(0, 5)) +}) diff --git a/tests/testthat/test-mice.impute.panImpute.R b/tests/testthat/test-mice.impute.panImpute.R index 746301584..5c78c143c 100644 --- a/tests/testthat/test-mice.impute.panImpute.R +++ b/tests/testthat/test-mice.impute.panImpute.R @@ -16,8 +16,7 @@ pred <- make.predictorMatrix(nhanes, blocks) pred[c("bmi", "chl", "hyp"), "hyp"] <- -2 imp1 <- mice(nhanes, blocks = blocks, method = method, pred = pred, - maxit = 1, seed = 1, print = TRUE -) + maxit = 1, seed = 1, print = FALSE) z <- complete(imp1) test_that("mice can call panImpute with type argument", { @@ -29,7 +28,7 @@ method <- c("panImpute", "pmm") formulas <- list(bmi + chl + hyp ~ 1 | age, age ~ bmi + chl + hyp) formulas <- name.formulas(formulas) -imp2 <- mice(nhanes, formulas = formulas, method = method, maxit = 1, seed = 1, print = TRUE) +imp2 <- mice(nhanes, formulas = formulas, method = method, maxit = 1, seed = 1, print = FALSE) z <- complete(imp2) test_that("mice can call panImpute with formula argument", { diff --git a/tests/testthat/test-rbind.R b/tests/testthat/test-rbind.R index ba4b04bd5..38c3970c0 100644 --- a/tests/testthat/test-rbind.R +++ b/tests/testthat/test-rbind.R @@ -14,7 +14,7 @@ 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_warning(imp6 <<- mice(nhanes[1:13, 2:3], m = 2, maxit = 2, print = FALSE)) nh3 <- nhanes colnames(nh3) <- c("AGE", "bmi", "hyp", "chl") imp7 <- mice(nh3[14:25, ], m = 2, maxit = 2, print = FALSE) @@ -82,8 +82,7 @@ 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)) expect_warning(D_mids <<- mice(D[1:5, ], print = FALSE, remove.collinear = FALSE)) D_rbind <- mice:::rbind.mids(D_mids, D[6:10, ]) From c2da03c57d173b7e55cb1e0b500674a753e8af3c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 13 Sep 2023 11:52:48 +0200 Subject: [PATCH 05/37] Clean up source, identicate that there is still a problem with edit.setup() --- R/edit.setup.R | 41 +++++++++++++---------------------------- 1 file changed, 13 insertions(+), 28 deletions(-) diff --git a/R/edit.setup.R b/R/edit.setup.R index fd8e26f95..b49945d32 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -7,18 +7,25 @@ edit.setup <- function(data, setup, # 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 + # Procedure to detect constant or collinear variables + # + # If found: + # - writes to loggedEvents + # - edits predictorMatrix, method, formulas, visitSequence and post + # - continues with reduced imputation model + # + # Specify remove.constant = FALSE and remove.collinear = FALSE to bypass + # these checks and edits pred <- setup$predictorMatrix meth <- setup$method form <- setup$formulas - blots <- setup$blots + blots <- setup$blots # not used vis <- setup$visitSequence post <- setup$post - # FIXME: this function is not yet adapted to blocks + # FIXME: need to generalise indexing and updating of meth, vis and post to blocks + if (!validate.predictorMatrix(pred)) { warning("Problem with predictorMatrix detected in edit.setup()") return(setup) @@ -40,12 +47,6 @@ edit.setup <- function(data, setup, # inactivate j as predictor out <- varnames[j] pred[, j] <- 0 - # remove out from RHS - #for (fn in names(form)) { - # tt <- terms(form[[fn]]) - # ff <- drop.terms(tt, which(labels(tt) %in% out)) - # form[[fn]] <- ff - #} updateLog(out = out, meth = "constant") didlog <- TRUE } @@ -53,14 +54,11 @@ edit.setup <- function(data, setup, # inactivate j as dependent out <- varnames[j] pred[j, ] <- 0 - # remove LHS formula - #if (hasName(form, out)) { - # form[out] <- NULL - #} if (!didlog) { updateLog(out = out, meth = "constant") } form <- p2f(pred, blocks = construct.blocks(form, pred)) + # this following three statements do not work for blocks meth[j] <- "" vis <- vis[vis != j] post[j] <- "" @@ -99,23 +97,10 @@ edit.setup <- function(data, setup, } } - # Set predictorMatrix row to zero - - # if (all(pred == 0L)) { - # stop("`mice` detected constant and/or collinear variables. No predictors were left after their removal.") - # } - if (!validate.predictorMatrix(pred)) { stop("Problem with predictorMatrix detected after edit.setup()") } - # for (j in seq_len(ncol(data))) { - # if (meth[j] == "") - # if (!all(pred[j, ] == 0)) { - # stop("Inconsistent j: ", j, " pred[j, ]: ", pred[j, ]) - # } - # } - setup$predictorMatrix <- pred setup$formulas <- form setup$blots <- blots From 28821a6b528c16024e6273ee65cc14599391dcb6 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 13 Sep 2023 22:23:29 +0200 Subject: [PATCH 06/37] Create a make.nest(), n2b() and b2n() function for working with nest argument --- NAMESPACE | 2 + R/blocks.R | 31 +++-- R/convert.R | 21 ++- R/method.R | 6 +- R/mice.R | 25 +++- R/nest.R | 178 ++++++++++++++++++++++++++ R/where.R | 2 + man/construct.nest.Rd | 55 ++++++++ man/make.blocks.Rd | 13 +- man/make.nest.Rd | 58 +++++++++ man/mice.Rd | 3 + tests/testthat/test-blocks.R | 80 +++++++++++- tests/testthat/test-mice-initialize.R | 20 +-- 13 files changed, 451 insertions(+), 43 deletions(-) create mode 100644 R/nest.R create mode 100644 man/construct.nest.Rd create mode 100644 man/make.nest.Rd diff --git a/NAMESPACE b/NAMESPACE index 41ab79344..5aa524994 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -65,6 +65,7 @@ export(cc) export(cci) export(complete) export(construct.blocks) +export(construct.nest) export(convergence) export(densityplot) export(estimice) @@ -93,6 +94,7 @@ export(make.blocks) export(make.blots) export(make.formulas) export(make.method) +export(make.nest) export(make.post) export(make.predictorMatrix) export(make.visitSequence) diff --git a/R/blocks.R b/R/blocks.R index 161bf6828..d6ba7f296 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -2,11 +2,10 @@ #' #' This helper function generates a list of the type needed for #' `blocks` argument in the [mice()] function. -#' @param data A `data.frame`, character vector with +#' @param x A `data.frame`, character vector with #' variable names, or `list` with variable names. -#' @param partition A character vector of length 1 used to assign -#' variables to blocks when `data` is a `data.frame`. Value -#' `"scatter"` (default) will assign each column to it own +#' @param partition Only relevant when `x` is a `data.frame`. Value +#' `"scatter"` (default) will assign each column to a separate #' block. Value `"collect"` assigns all variables to one block, #' whereas `"void"` produces an empty list. #' @param calltype A character vector of `length(block)` elements @@ -50,19 +49,19 @@ #' make.blocks(nhanes) #' make.blocks(c("age", "sex", "edu")) #' @export -make.blocks <- function(data, +make.blocks <- function(x, partition = c("scatter", "collect", "void"), calltype = "pred") { - if (is.vector(data) && !is.list(data)) { - v <- as.list(as.character(data)) - names(v) <- as.character(data) + if (is.vector(x) && !is.list(x)) { + v <- as.list(as.character(x)) + names(v) <- as.character(x) ct <- rep(calltype, length(v)) names(ct) <- names(v) attr(v, "calltype") <- ct return(v) } - if (is.list(data) && !is.data.frame(data)) { - v <- name.blocks(data) + if (is.list(x) && !is.data.frame(x)) { + v <- name.blocks(x) if (length(calltype) == 1L) { ct <- rep(calltype, length(v)) names(ct) <- names(v) @@ -74,23 +73,23 @@ make.blocks <- function(data, } return(v) } - data <- as.data.frame(data) + x <- as.data.frame(x) partition <- match.arg(partition) switch(partition, scatter = { - v <- as.list(names(data)) - names(v) <- names(data) + v <- as.list(names(x)) + names(v) <- names(x) }, collect = { - v <- list(names(data)) + v <- list(names(x)) names(v) <- "collect" }, void = { v <- list() }, { - v <- as.list(names(data)) - names(v) <- names(data) + v <- as.list(names(x)) + names(v) <- names(x) } ) if (length(calltype) == 1L) { diff --git a/R/convert.R b/R/convert.R index 911668ea0..ccf1faec5 100644 --- a/R/convert.R +++ b/R/convert.R @@ -93,7 +93,13 @@ f2p <- function(formulas, blocks = NULL, roles = NULL) { n2b <- function(nest, silent = FALSE) { # nest to block stopifnot(validate.nest(nest, silent = silent)) - nf <- factor(nest) + if (all(nest == "")) { + nest[1L:length(nest)] <- names(nest) + } + if (any(nest == "")) { + stop("Cannot convert a partially named nest to blocks") + } + nf <- factor(nest, levels = unique(nest)) blocknames <- levels(nf) blocks <- vector("list", length = length(blocknames)) names(blocks) <- blocknames @@ -108,8 +114,13 @@ b2n <- function(blocks, silent = FALSE) { stopifnot(validate.blocks(blocks, silent = silent)) vars <- unlist(blocks) nest <- rep(names(blocks), sapply(blocks, length)) + if (any(duplicated(vars))) { + warning("Duplicated name(s) removed: ", + paste(vars[duplicated(vars)], collapse = ", ")) + } names(nest) <- vars nest <- nest[!duplicated(names(nest))] + stopifnot(validate.nest(nest)) return(nest) } @@ -137,6 +148,14 @@ validate.nest <- function(nest, silent = FALSE) { if (!silent) warning("nest has no names", call. = FALSE) return(FALSE) } + if (any(duplicated(names(nest)))) { + if (!silent) warning( + "duplicated names in nest: ", + paste({names(nest)}[duplicated(names(nest))], collapse = ", "), + call. = FALSE) + return(FALSE) + } + return(TRUE) } diff --git a/R/method.R b/R/method.R index 6d1dbcf91..897bc0a0d 100644 --- a/R/method.R +++ b/R/method.R @@ -30,12 +30,12 @@ make.method <- function(data, check.method <- function(method, data, where, blocks, defaultMethod) { if (is.null(method)) { - return(make.method( + method <- make.method( data = data, where = where, blocks = blocks, - defaultMethod = defaultMethod - )) + defaultMethod = defaultMethod) + return(method) } nimp <- nimp(where = where, blocks = blocks) diff --git a/R/mice.R b/R/mice.R index e69be50cf..a698a342f 100644 --- a/R/mice.R +++ b/R/mice.R @@ -261,6 +261,7 @@ #' imputation. #' @param \dots Named arguments that are passed down to the univariate imputation #' functions. +#' @param nest experimental variable grouping input #' #' @return Returns an S3 object of class [`mids()`][mids-class] #' (multiply imputed data set) @@ -336,6 +337,7 @@ mice <- function(data, m = 5, method = NULL, predictorMatrix, + nest = NULL, ignore = NULL, where = NULL, blocks, @@ -358,6 +360,11 @@ mice <- function(data, data <- check.dataform(data) m <- check.m(m) + # add support nest + if (!is.null(nest)) { + blocks <- n2b(nest, silent = FALSE) + } + # determine input combination: predictorMatrix, blocks, formulas mp <- missing(predictorMatrix) mb <- missing(blocks) @@ -373,7 +380,8 @@ mice <- function(data, # case B if (!mp & mb & mf) { # predictorMatrix leads - predictorMatrix <- check.predictorMatrix(predictorMatrix, data) + # predictorMatrix <- check.predictorMatrix(predictorMatrix, data) + predictorMatrix <- make.predictorMatrix(data, predictorMatrix = predictorMatrix) blocks <- make.blocks(colnames(predictorMatrix), partition = "scatter") formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) } @@ -444,6 +452,7 @@ mice <- function(data, method = method, data = data, where = where, blocks = blocks, defaultMethod = defaultMethod ) + # edit predictorMatrix for monotone, set zero rows for empty methods predictorMatrix <- edit.predictorMatrix( predictorMatrix = predictorMatrix, @@ -454,6 +463,20 @@ mice <- function(data, user.visitSequence = user.visitSequence, maxit = maxit ) + + # for variables not in model, set predictorMatrix column to zero + # and update formulas (#583) + # nomissings <- colnames(data)[!apply(is.na(data), 2, sum)] + # notinmodel <- setdiff(colnames(data), unlist(blocks)) + # setrowzero <- intersect(nomissings, notinmodel) + # setcolzero <- setdiff(notinmodel, nomissings) + # predictorMatrix[, setcolzero] <- 0 + # predictorMatrix[setrowzero, ] <- 0 + # formulas <- p2f(predictorMatrix, + # blocks = construct.blocks(formulas, predictorMatrix)) + # formulas[notinmodel] <- NULL + + # other checks post <- check.post(post, data) blots <- check.blots(blots, data, blocks) ignore <- check.ignore(ignore, data) diff --git a/R/nest.R b/R/nest.R new file mode 100644 index 000000000..67aa3fdf8 --- /dev/null +++ b/R/nest.R @@ -0,0 +1,178 @@ +#' Creates a `nest` argument +#' +#' This helper function generates a character vector for the +#' `nest` argument in the [mice()] function. +#' +#' @param x A `data.frame`, an unnamed character vector, a named +#' character vector or a `list`. +#' @param partition Only relevant if `x` is a `data.frame`. Value +#' `"scatter"` (default) will assign each variable to a separate +#' nest. Value `"collect"` assigns all variables to one nest, +#' whereas `"void"` does not assign any variable to a nest. +#' @param prefix A character vector of length 1 with the prefix to +#' be using for naming any unnamed blocks with two or more variables. +#' @return A character vector of length `ncol(data)` that specifies +#' the nest name per variable +#' +#' @details Choices `"scatter"` and `"collect"` represent to two +#' extreme scenarios for assigning variables to imputation nests. +#' Use `"scatter"` to create an imputation model based on +#' *fully conditionally specification* (FCS). Use `"collect"` to +#' gather all variables to be imputed by a *joint model* (JM). +#' +#' Any variable not listed in the result will not be imputed. +#' Specification `"void"` represents the extreme scenario where +#' nothing is imputed. +#' +#' Unlike blocks, a variable cannot be allocated to multiple nests. +#' @examples +#' +#' # default nest creation (scatter) +#' make.nest(nhanes) +#' +#' # make nest from variable names +#' make.nest(c("age", "sex", "edu")) +#' +#' # put hgt, wgt and bmi into one nest, automatic naming +#' make.nest(list("age", "sex", c("hgt", "wgt", "bmi"))) +#' +#' # same, but with custom nest names +#' make.nest(list("age", "sex", anthro = c("hgt", "wgt", "bmi"))) +#' +#' # all variables into one nest +#' make.nest(nhanes, partition = "collect", prefix = "myblock") +#' @export +make.nest <- function(x, + partition = c("scatter", "collect", "void"), + prefix = "A") { + + # unnamed vector + if (is.vector(x) && is.null(names(x)) && !is.list(x)) { + nest <- as.character(x) + names(nest) <- as.character(x) + return(nest) + } + + # named vector, preserve name order + if (is.vector(x) && !is.null(names(x)) && !is.list(x)) { + nest <- as.character(x) + names(nest) <- names(x) + return(nest) + } + + # unnamed list + if (is.list(x) && is.null(names(x)) && !is.data.frame(x)) { + nest <- b2n(name.blocks(x, prefix = prefix)) + return(nest) + } + + # named list + if (is.list(x) && !is.null(names(x)) && !is.data.frame(x)) { + nest <- b2n(x) + return(nest) + } + + x <- as.data.frame(x) + partition <- match.arg(partition) + switch(partition, + scatter = { + nest <- colnames(x) + names(nest) <- names(x) + }, + collect = { + nest <- rep(prefix, ncol(x)) + names(nest) <- names(x) + }, + void = { + nest <- rep("", ncol(x)) + names(nest) <- names(x) + }, + { + nest <- names(x) + names(nest) <- names(x) + } + ) + return(nest) +} + +name.nest <- function(x) x + +check.nest <- function(nest, data) { + data <- check.dataform(data) + nest <- name.nest(nest) + + # check that all variable names exists in data + nv <- names(nest) + notFound <- !nv %in% colnames(data) + if (any(notFound)) { + stop(paste( + "The following names were not found in `data`:", + paste(nv[notFound], collapse = ", ") + )) + } + + nest +} + +#' Construct blocks from `formulas` and `predictorMatrix` +#' +#' This helper function attempts to find blocks of variables in the +#' specification of the `formulas` and/or `predictorMatrix` +#' objects. Blocks specified by `formulas` may consist of +#' multiple variables. Blocks specified by `predictorMatrix` are +#' assumed to consist of single variables. Any duplicates in names are +#' removed, and the formula specification is preferred. +#' `predictorMatrix` and `formulas`. When both arguments +#' specify models for the same block, the model for the +#' `predictMatrix` is removed, and priority is given to the +#' specification given in `formulas`. +#' @inheritParams mice +#' @return A `blocks` object. +#' @seealso [make.blocks()], [name.blocks()] +#' @examples +#' form <- list(bmi + hyp ~ chl + age, chl ~ bmi) +#' pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) +#' construct.blocks(formulas = form, pred = pred) +#' @export +construct.nest <- function(formulas = NULL, predictorMatrix = NULL) { + blocks.f <- blocks.p <- NULL + if (!is.null(formulas)) { + if (!all(sapply(formulas, is.formula))) { + return(NULL) + } + blocks.f <- name.blocks(lapply(name.formulas(formulas), lhs)) + ct <- rep("formula", length(blocks.f)) + names(ct) <- names(blocks.f) + attr(blocks.f, "calltype") <- ct + if (is.null(predictorMatrix)) { + return(blocks.f) + } + } + + if (!is.null(predictorMatrix)) { + if (is.null(row.names(predictorMatrix))) { + stop("No row names in predictorMatrix", call. = FALSE) + } + blocks.p <- name.blocks(row.names(predictorMatrix)) + ct <- rep("pred", length(blocks.p)) + names(ct) <- names(blocks.p) + attr(blocks.p, "calltype") <- ct + if (is.null(formulas)) { + return(blocks.p) + } + } + + # combine into unique blocks + blocknames <- unique(c(names(blocks.f), names(blocks.p))) + vars.f <- unlist(lapply(formulas, lhs)) + keep <- setdiff(blocknames, vars.f) + add.p <- blocks.p[names(blocks.p) %in% keep] + blocks <- c(blocks.f, add.p) + ct <- c( + rep("formula", length(formulas)), + rep("pred", length(add.p)) + ) + names(ct) <- names(blocks) + attr(blocks, "calltype") <- ct + blocks +} diff --git a/R/where.R b/R/where.R index 2e77d2ff2..01e86de76 100644 --- a/R/where.R +++ b/R/where.R @@ -63,6 +63,8 @@ check.where <- function(where, data, blocks) { where <- matrix(where, nrow = nrow(data), ncol = ncol(data)) dimnames(where) <- dimnames(data) + # #583 + # where[, !colnames(where) %in% unlist(blocks)] <- FALSE where[, !colnames(where) %in% unlist(blocks)] <- FALSE where } diff --git a/man/construct.nest.Rd b/man/construct.nest.Rd new file mode 100644 index 000000000..bf2d480bf --- /dev/null +++ b/man/construct.nest.Rd @@ -0,0 +1,55 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/nest.R +\name{construct.nest} +\alias{construct.nest} +\title{Construct blocks from \code{formulas} and \code{predictorMatrix}} +\usage{ +construct.nest(formulas = NULL, predictorMatrix = NULL) +} +\arguments{ +\item{formulas}{A named list of formula's, or expressions that +can be converted into formula's by \code{as.formula}. List elements +correspond to blocks. The block to which the list element applies is +identified by its name, so list names must correspond to block names. +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{predictorMatrix}{A square numeric matrix of \eqn{p} rows +and columns. Row- and column names are \code{colnames(data)}. +Each row corresponds to a variable to be imputed. +A value of \code{1} means that the column variable is a +predictor for the row variable, while a \code{0} means that +the column variable is not a predictor. The default +\code{predictorMatrix} is \code{1} everywhere, except for a zero +diagonal. For variables that need no be imputed, +\code{mice()} automatically sets the corresponding rows in the +\code{predictorMatrix} to zero. See details +on \emph{skipping imputation}. +Two-level imputation models (which have \code{"2l"} in their +names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, +are also used.} +} +\value{ +A \code{blocks} object. +} +\description{ +This helper function attempts to find blocks of variables in the +specification of the \code{formulas} and/or \code{predictorMatrix} +objects. Blocks specified by \code{formulas} may consist of +multiple variables. Blocks specified by \code{predictorMatrix} are +assumed to consist of single variables. Any duplicates in names are +removed, and the formula specification is preferred. +\code{predictorMatrix} and \code{formulas}. When both arguments +specify models for the same block, the model for the +\code{predictMatrix} is removed, and priority is given to the +specification given in \code{formulas}. +} +\examples{ +form <- list(bmi + hyp ~ chl + age, chl ~ bmi) +pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) +construct.blocks(formulas = form, pred = pred) +} +\seealso{ +\code{\link[=make.blocks]{make.blocks()}}, \code{\link[=name.blocks]{name.blocks()}} +} diff --git a/man/make.blocks.Rd b/man/make.blocks.Rd index 4becda4fe..beb74b404 100644 --- a/man/make.blocks.Rd +++ b/man/make.blocks.Rd @@ -4,19 +4,14 @@ \alias{make.blocks} \title{Creates a \code{blocks} argument} \usage{ -make.blocks( - data, - partition = c("scatter", "collect", "void"), - calltype = "pred" -) +make.blocks(x, partition = c("scatter", "collect", "void"), calltype = "pred") } \arguments{ -\item{data}{A \code{data.frame}, character vector with +\item{x}{A \code{data.frame}, character vector with variable names, or \code{list} with variable names.} -\item{partition}{A character vector of length 1 used to assign -variables to blocks when \code{data} is a \code{data.frame}. Value -\code{"scatter"} (default) will assign each column to it own +\item{partition}{Only relevant when \code{x} is a \code{data.frame}. Value +\code{"scatter"} (default) will assign each column to a separate block. Value \code{"collect"} assigns all variables to one block, whereas \code{"void"} produces an empty list.} diff --git a/man/make.nest.Rd b/man/make.nest.Rd new file mode 100644 index 000000000..f998cf7e7 --- /dev/null +++ b/man/make.nest.Rd @@ -0,0 +1,58 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/nest.R +\name{make.nest} +\alias{make.nest} +\title{Creates a \code{nest} argument} +\usage{ +make.nest(x, partition = c("scatter", "collect", "void"), prefix = "A") +} +\arguments{ +\item{x}{A \code{data.frame}, an unnamed character vector, a named +character vector or a \code{list}.} + +\item{partition}{Only relevant if \code{x} is a \code{data.frame}. Value +\code{"scatter"} (default) will assign each variable to a separate +nest. Value \code{"collect"} assigns all variables to one nest, +whereas \code{"void"} does not assign any variable to a nest.} + +\item{prefix}{A character vector of length 1 with the prefix to +be using for naming any unnamed blocks with two or more variables.} +} +\value{ +A character vector of length \code{ncol(data)} that specifies +the nest name per variable +} +\description{ +This helper function generates a character vector for the +\code{nest} argument in the \code{\link[=mice]{mice()}} function. +} +\details{ +Choices \code{"scatter"} and \code{"collect"} represent to two +extreme scenarios for assigning variables to imputation nests. +Use \code{"scatter"} to create an imputation model based on +\emph{fully conditionally specification} (FCS). Use \code{"collect"} to +gather all variables to be imputed by a \emph{joint model} (JM). + +Any variable not listed in the result will not be imputed. +Specification \code{"void"} represents the extreme scenario where +nothing is imputed. + +Unlike blocks, a variable cannot be allocated to multiple nests. +} +\examples{ + +# default nest creation (scatter) +make.nest(nhanes) + +# make nest from variable names +make.nest(c("age", "sex", "edu")) + +# put hgt, wgt and bmi into one nest, automatic naming +make.nest(list("age", "sex", c("hgt", "wgt", "bmi"))) + +# same, but with custom nest names +make.nest(list("age", "sex", anthro = c("hgt", "wgt", "bmi"))) + +# all variables into one nest +make.nest(nhanes, partition = "collect", prefix = "myblock") +} diff --git a/man/mice.Rd b/man/mice.Rd index 434a0b74e..b35e2a2bb 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -10,6 +10,7 @@ mice( m = 5, method = NULL, predictorMatrix, + nest = NULL, ignore = NULL, where = NULL, blocks, @@ -56,6 +57,8 @@ Two-level imputation models (which have \code{"2l"} in their names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, are also used.} +\item{nest}{experimental variable grouping input} + \item{ignore}{A logical vector of \eqn{n} elements indicating which rows are ignored for estimating the parameters of the imputation model. diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 8b57529a3..4e8e688a5 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -1,14 +1,38 @@ context("blocks") +# case with two non-standard problems +# 1) a duplicate bmi is acceptable through blocks +# 2) hyp not specified, +# +# The current policy is not satisfying: +# Currently, where[, "hyp"] is set to FALSE, so hyp is not imputed. +# However, it is still is predictor for block B1, bmi and age, thus +# leading to missing data propagation +# -imp <- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 10, print = FALSE) -# plot(imp) +library(mice) # branch support_blocks +imp <- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 1, print = FALSE) + +head(complete(imp)) +imp$blocks +imp$formulas +head(imp$where) +imp$method +imp$predictorMatrix + +# A better policy might be inactivating any unmentioned variable j by +# 1) set method[j] to "", +# 2) set predictorMatrix[, j] to 0 (take j out as predictor) +# 3) leave predictorMatrix[j, ] untouched +# 4) leave where[, j] untouched +# As a result, j is not imputed and is not a predictor anywhere test_that("removes variables from 'where'", { expect_identical(sum(imp$where[, "hyp"]), 0L) }) + # 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"), m = 1, maxit = 2, seed = 1, print = FALSE) @@ -19,5 +43,55 @@ test_that("expands a univariate method to all variables in the block", { 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)) + expect_identical(complete(imp3, 1), complete(imp4, 1)) }) + + +context("nest") + +# model with duplicate bmi cannot be specified with nest + +# EXPECT WARNING: In b2n(name.blocks(x, prefix = prefix)) : Duplicated name(s) removed: bmi +expect_warning( + nest1a <<- make.nest(list(c("bmi", "chl"), "bmi", "age"))) +nest1b <- setNames( c("A", "A", "bmi", "age"), + nm = c("bmi", "chl", "bmi", "age")) + +expect_silent(imp1a <- mice(nhanes, nest = nest1a, m = 10, print = FALSE)) +# EXPECT ERROR: validate.nest(nest, silent = silent) is not TRUE +expect_error(suppressWarnings(imp1b <<- mice(nhanes, nest = nest1b, m = 10, print = FALSE))) + +# Getting around the error by the visitSequence +# test_that("nest formulation is equivalent to blocks", { +# expect_identical(complete(imp1, 1), complete(imp1a, 1)) +# expect_identical(complete(imp1, 1), complete(imp1b, 1)) +# }) +# + + +# reprex https://github.com/amices/mice/issues/326 +imp1 <- mice(nhanes, seed = 1, m = 1, maxit = 2, print = FALSE) +imp2 <- mice(nhanes, nest = make.nest(list(c("bmi", "hyp"), "chl")), 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)) +}) + +# neat nest formulation +nest2 <- setNames(c("A", "A", "chl"), + nm = c("bmi", "hyp", "chl")) +imp2a <- mice(nhanes, nest = nest2, m = 1, maxit = 2, seed = 1, print = FALSE) +test_that("setNames nest formulation yields same solution", { + expect_identical(complete(imp2, 1), complete(imp2a, 1)) +}) + +# different order +nest3 <- setNames(c("A", "A", "chl"), + nm = c("hyp", "bmi", "chl")) +imp3 <- mice(nhanes, nest = nest3, 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("nests alter the visit sequence", { + expect_identical(complete(imp3, 1), complete(imp4, 1)) +}) + +complete(imp3, 1) + diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index b7ce800f2..38a7d3409 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -38,23 +38,23 @@ test_that("Case B tests the predictorMatrix", { predictorMatrix = pred2, "Missing row/column names in `predictorMatrix`." )) - expect_equal(nrow(imp3$predictorMatrix), 2L) + expect_equal(nrow(imp3$predictorMatrix), 4L) expect_error(mice(data, predictorMatrix = pred4)) }) pred <- imp3$predictorMatrix blocks <- imp3$blocks -test_that("Case B finds blocks", { - expect_identical(names(blocks), c("bmi", "hyp")) -}) +# test_that("Case B finds blocks", { +# expect_identical(names(blocks), c("bmi", "hyp")) +# }) form <- imp3$formulas -test_that("Case B finds formulas", { - expect_identical( - attr(terms(form[["bmi"]]), "term.labels"), - names(pred["bmi", ])[pred["bmi", ] == 1] - ) -}) +# test_that("Case B finds formulas", { +# expect_identical( +# attr(terms(form[["bmi"]]), "term.labels"), +# names(pred["bmi", ])[pred["bmi", ] == 1] +# ) +# }) # Case C: Only blocks argument From 731bf25276780dddf136e364ff92c20ce03d7695 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 13 Sep 2023 22:38:43 +0200 Subject: [PATCH 07/37] Insist that predictorMatrix has a zero diagonal --- R/predictorMatrix.R | 5 +++++ tests/testthat/test-mice-initialize.R | 18 +++++++++--------- tests/testthat/test-mice.impute.jomoImpute.R | 1 + tests/testthat/test-mice.impute.panImpute.R | 2 ++ 4 files changed, 17 insertions(+), 9 deletions(-) diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 17634aa68..56c4a7584 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -34,6 +34,8 @@ make.predictorMatrix <- function(data, blocks = make.blocks(data), } } } + # but insist on zero diagonal + diag(predictorMatrix) <- 0 valid <- validate.predictorMatrix(predictorMatrix) if (!valid) { warning("Malformed predictorMatrix. See ?make.predictorMatrix") @@ -193,6 +195,9 @@ validate.predictorMatrix <- function(predictorMatrix, silent = FALSE) { if (!silent) warning("predictorMatrix has no row/column names") return(FALSE) } + if (any(diag(predictorMatrix) != 0)) { + if (!silent) warning("predictorMatrix has no zero diagonal") + } return(TRUE) } diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index 38a7d3409..a42b16542 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -44,17 +44,17 @@ test_that("Case B tests the predictorMatrix", { pred <- imp3$predictorMatrix blocks <- imp3$blocks -# test_that("Case B finds blocks", { -# expect_identical(names(blocks), c("bmi", "hyp")) -# }) +test_that("Case B finds blocks", { + expect_identical(names(blocks), c("age", "bmi", "hyp", "chl")) +}) form <- imp3$formulas -# test_that("Case B finds formulas", { -# expect_identical( -# attr(terms(form[["bmi"]]), "term.labels"), -# names(pred["bmi", ])[pred["bmi", ] == 1] -# ) -# }) +test_that("Case B finds formulas", { + expect_identical( + attr(terms(form[["bmi"]]), "term.labels"), + names(pred["bmi", ])[pred["bmi", ] == 1] + ) +}) # Case C: Only blocks argument diff --git a/tests/testthat/test-mice.impute.jomoImpute.R b/tests/testthat/test-mice.impute.jomoImpute.R index 2a690c533..b8f206233 100644 --- a/tests/testthat/test-mice.impute.jomoImpute.R +++ b/tests/testthat/test-mice.impute.jomoImpute.R @@ -14,6 +14,7 @@ blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("jomoImpute", "pmm") pred <- make.predictorMatrix(nhanes, blocks) pred[c("bmi", "chl", "hyp"), "hyp"] <- -2 +diag(pred) <- 0 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 5c78c143c..df078ee31 100644 --- a/tests/testthat/test-mice.impute.panImpute.R +++ b/tests/testthat/test-mice.impute.panImpute.R @@ -14,6 +14,8 @@ blocks <- make.blocks(list(c("bmi", "chl", "hyp"), "age")) method <- c("panImpute", "pmm") pred <- make.predictorMatrix(nhanes, blocks) pred[c("bmi", "chl", "hyp"), "hyp"] <- -2 +diag(pred) <- 0 + imp1 <- mice(nhanes, blocks = blocks, method = method, pred = pred, maxit = 1, seed = 1, print = FALSE) From 8f92307353b9b4beaec9ba117373d9c1c15f7525 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 10:24:18 +0200 Subject: [PATCH 08/37] - Prevention of NA propagation - Stricter controls on input predictorMatrix - Output test of mids object --- NAMESPACE | 2 + NEWS.md | 18 ++++ R/convert.R | 6 +- R/formula.R | 54 +++++++++++- R/imports.R | 2 +- R/method.R | 40 ++++++--- R/mice.R | 53 +++++++----- R/mids.R | 12 +++ R/predictorMatrix.R | 116 +++++++++----------------- R/where.R | 1 - man/convertmodels.Rd | 5 +- man/make.method.Rd | 6 +- man/remove.rhs.variables.Rd | 27 ++++++ tests/testthat/test-blocks.R | 2 +- tests/testthat/test-convert.R | 22 +++++ tests/testthat/test-mice-initialize.R | 35 ++++---- 16 files changed, 262 insertions(+), 139 deletions(-) create mode 100644 man/remove.rhs.variables.Rd create mode 100644 tests/testthat/test-convert.R diff --git a/NAMESPACE b/NAMESPACE index 5aa524994..7c75fc436 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -162,6 +162,7 @@ export(pool.syn) export(pool.table) export(quickpred) export(rbind) +export(remove.rhs.variables) export(squeeze) export(stripplot) export(supports.transparent) @@ -260,6 +261,7 @@ importFrom(stats,spline) importFrom(stats,summary.glm) importFrom(stats,terms) importFrom(stats,update) +importFrom(stats,update.formula) importFrom(stats,var) importFrom(stats,vcov) importFrom(tidyr,complete) diff --git a/NEWS.md b/NEWS.md index e4afbe154..d0b3a03be 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,21 @@ +## New behaviours + +1. Prevention of `NA` propagation by removing incomplete predictors. This version detects when a predictor contains missing values that are not imputed. In order to prevent NA propagation, `mice()` does the following actions: 1) removes incomplete predictor(s) from the RHS, 2) adds incomplete predictor(s) to formulas `(var ~ 1)` and block components, sets `method[var] = ""`, and sets the `predictorMatrix` column and row to zero + +2. The `predictorMatrix` input can be a square submatrix of the full `predictorMatrix`. `mice()` will augment `predictorMatrix` to the full matrix and always return a p * p named matrix corresponding to the p columns in the data. The inactive variables will have zero columns and rows. + +3. The `predictorMatrix` input may be unnamed if its size is p * p. For other than p * p, an unnamed matrix generated an error. + + +## Changes + +- Adds supports a tiny predictorMatrix +- Solves bug in f2p() +- Adds new function `remove.rhs.variables()` +- Adds a `validate.mids()` check at exit that errors if `rownames(predictorMatrix)` differ from `colnames(data)`. Some more output tests need to be added. +- Removes codes designed to work specifically with a non-square `predictorMatrix` +- Generates an error if `predictorMatrix` has fewer rows than length of `blocks` + * Prepares for the deprecation of the `blocks` argument at various places * Removes the need for `blocks` in `initialize_chain()` * In `rbind()`, when formulas are concatenated and duplicate names are found, also rename the duplicated variables in formulas by their new name diff --git a/R/convert.R b/R/convert.R index ccf1faec5..bd665b511 100644 --- a/R/convert.R +++ b/R/convert.R @@ -55,15 +55,16 @@ p2c <- function(predictorMatrix) { #' Convert formulas into predictorMatrix #' #' @rdname convertmodels +#' @inheritParams mice #' @param roles A list with `ncol(data)` elements, each with a row of the #' `predictorMatrix` when it contains values other than 0 or 1. #' The argument is only needed if the model contains non-standard #'values in the `predictorMatrix`. #' @export -f2p <- function(formulas, blocks = NULL, roles = NULL) { +f2p <- function(formulas, data, blocks = NULL, roles = NULL) { # converts formulas and roles into predictorMatrix blks <- names(formulas) - vars <- unique(as.vector(unlist(sapply(formulas, all.vars)))) + vars <- colnames(data) predictorMatrix <- matrix(0, nrow = length(vars), ncol = length(vars)) dimnames(predictorMatrix) <- list(vars, vars) for (b in blks) { @@ -72,7 +73,6 @@ f2p <- function(formulas, blocks = NULL, roles = NULL) { ynames <- lhs(f) for (yname in ynames) { xn <- setdiff(fv, yname) - # xn <- union(setdiff(ynames, yname), xnames) if (is.null(roles[[yname]])) { # code all variables in same block as 1 predictorMatrix[yname, xn] <- 1 diff --git a/R/formula.R b/R/formula.R index 279ab8299..2152525c9 100644 --- a/R/formula.R +++ b/R/formula.R @@ -35,8 +35,8 @@ make.formulas <- function(data, blocks = make.blocks(data), if (is.null(predictorMatrix)) { predictors <- colnames(data) } else { - type <- predictorMatrix[h, ] - predictors <- names(type)[type != 0] + type <- predictorMatrix[y, , drop = FALSE] + predictors <- colnames(type)[apply(type != 0, 2, any)] } x <- setdiff(predictors, y) if (length(x) == 0) { @@ -133,10 +133,60 @@ check.formulas <- function(formulas, data) { return(formulas) } formulas <- lapply(formulas, as.formula) + + # find dependent variables + # find variables in data that are not imputed + # add components y ~ 1 for y to formulas + ynames <- unique(as.vector(unlist(sapply(formulas, lhs)))) + notimputed <- setdiff(colnames(data), ynames) + for (y in notimputed) { + formulas[[y]] <- as.formula(paste(y, "~ 1")) + } formulas } +# remove variables for RHS + + +#' Remove RHS terms involving specified variable names +#' +#' @param ff a formula +#' @param vars a vector with varianble names to be removed from rhs +#' @details +#' If all variable are removed, the function return the intercept only model. +#' @examples +#' f1 <- y1 + y2 ~ 1 | z + x1 + x2 + x1 * x2 +#' remove.rhs.variables(f1, c("x1", "z")) +#' +#' # do not touch lhs +#' f2 <- bmi + chl + hyp ~ 1 | age +#' remove.rhs.variables(f2, "bmi") +#' @export +remove.rhs.variables <- function(ff, vars) { + stopifnot(is.formula(ff)) + pattern <- paste(vars, collapse = "|") + if (pattern == "") { + return(ff) + } + tt <- terms(ff) + rhs.old <- attr(tt, "term.labels") + xp <- strsplit(rhs.old, "[+]") |> unlist() + loc <- grep(pattern, xp) + if (length(loc)) { + xn <- xp[-loc] + } else { + xn <- xp + } + rhs.new <- paste(xn, collapse = "+") + if (rhs.new != "") { + ff.new <- reformulate(rhs.new, response = ff[[2]]) + } else { + ff.new <- update.formula(ff, . ~ 1) + } + return(ff.new) +} + #' Extends formula's with predictor matrix settings #' #' @inheritParams mice diff --git a/R/imports.R b/R/imports.R index aa8a5d8b0..e658ae873 100644 --- a/R/imports.R +++ b/R/imports.R @@ -20,7 +20,7 @@ #' 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 summary.glm terms update update.formula var vcov #' @importFrom tidyr complete #' @importFrom utils askYesNo flush.console hasName head install.packages #' methods packageDescription packageVersion diff --git a/R/method.R b/R/method.R index 897bc0a0d..d73fd2464 100644 --- a/R/method.R +++ b/R/method.R @@ -3,6 +3,8 @@ #' This helper function creates a valid `method` vector. The #' `method` vector is an argument to the `mice` function that #' specifies the method for each block. +#' @param user.predictorMatrix the unedited `predictorMatrix` specified by the +#' user in the call to `mice()` #' @inheritParams mice #' @return Vector of `length(blocks)` element with method names #' @seealso [mice()] @@ -12,14 +14,24 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), - defaultMethod = c("pmm", "logreg", "polyreg", "polr")) { + defaultMethod = c("pmm", "logreg", "polyreg", "polr"), + user.predictorMatrix = NULL) { + # support tiny predictorMatrix + if (is.null(user.predictorMatrix) || + ncol(user.predictorMatrix) == ncol(data)) { + include <- colnames(data) + } else { + include <- colnames(user.predictorMatrix) + } method <- rep("", length(blocks)) names(method) <- names(blocks) for (j in seq_along(blocks)) { yvar <- blocks[[j]] y <- data[, yvar, drop = FALSE] k <- assign.method(y) - method[j] <- defaultMethod[k] + if (all(yvar %in% include)) { + method[j] <- defaultMethod[k] + } } nimp <- nimp(where = where, blocks = blocks) @@ -28,13 +40,15 @@ make.method <- function(data, } -check.method <- function(method, data, where, blocks, defaultMethod) { +check.method <- function(method, data, where, blocks, defaultMethod, + user.predictorMatrix) { if (is.null(method)) { method <- make.method( data = data, where = where, blocks = blocks, - defaultMethod = defaultMethod) + defaultMethod = defaultMethod, + user.predictorMatrix = user.predictorMatrix) return(method) } nimp <- nimp(where = where, blocks = blocks) @@ -93,23 +107,23 @@ check.method <- function(method, data, where, blocks, defaultMethod) { cond3 <- sapply(y, is.factor) & sapply(y, nlevels) > 2L if (any(cond1) && mj %in% mlist$m1) { warning("Type mismatch for variable(s): ", - paste(vname[cond1], collapse = ", "), - "\nImputation method ", mj, " is for categorical data.", - call. = FALSE + paste(vname[cond1], collapse = ", "), + "\nImputation method ", mj, " is for categorical data.", + call. = FALSE ) } if (any(cond2) && mj %in% mlist$m2) { warning("Type mismatch for variable(s): ", - paste(vname[cond2], collapse = ", "), - "\nImputation method ", mj, " is not for factors.", - call. = FALSE + paste(vname[cond2], collapse = ", "), + "\nImputation method ", mj, " is not for factors.", + call. = FALSE ) } if (any(cond3) && mj %in% mlist$m3) { warning("Type mismatch for variable(s): ", - paste(vname[cond3], collapse = ", "), - "\nImputation method ", mj, " is not for factors with >2 levels.", - call. = FALSE + paste(vname[cond3], collapse = ", "), + "\nImputation method ", mj, " is not for factors with >2 levels.", + call. = FALSE ) } } diff --git a/R/mice.R b/R/mice.R index a698a342f..72381cccf 100644 --- a/R/mice.R +++ b/R/mice.R @@ -370,18 +370,23 @@ mice <- function(data, mb <- missing(blocks) mf <- missing(formulas) + # store unedited user predictorMatrix + user.predictorMatrix <- NULL + if (!mp) { + user.predictorMatrix <- predictorMatrix + } + # case A if (mp & mb & mf) { # formulas leads formulas <- make.formulas(data) - predictorMatrix <- f2p(formulas) + predictorMatrix <- f2p(formulas, data) blocks <- construct.blocks(formulas) } # case B if (!mp & mb & mf) { # predictorMatrix leads - # predictorMatrix <- check.predictorMatrix(predictorMatrix, data) - predictorMatrix <- make.predictorMatrix(data, predictorMatrix = predictorMatrix) + predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- make.blocks(colnames(predictorMatrix), partition = "scatter") formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) } @@ -399,17 +404,15 @@ mice <- function(data, # formulas leads formulas <- check.formulas(formulas, data) blocks <- construct.blocks(formulas) - predictorMatrix <- f2p(formulas, blocks) + predictorMatrix <- f2p(formulas, data, blocks) } # case E if (!mp & !mb & mf) { - # predictor leads + # predictor leads (use for multivariate imputation) + predictorMatrix <- check.predictorMatrix(predictorMatrix, data) blocks <- check.blocks(blocks, data, calltype = "pred") - z <- check.predictorMatrix(predictorMatrix, data, blocks) - predictorMatrix <- z$predictorMatrix - blocks <- z$blocks - formulas <- p2f(predictorMatrix, blocks) + formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) } # case F @@ -448,9 +451,12 @@ mice <- function(data, visitSequence <- check.visitSequence(visitSequence, data = data, where = where, blocks = blocks ) + + # derive method vector method <- check.method( method = method, data = data, where = where, - blocks = blocks, defaultMethod = defaultMethod + blocks = blocks, defaultMethod = defaultMethod, + user.predictorMatrix = user.predictorMatrix ) # edit predictorMatrix for monotone, set zero rows for empty methods @@ -464,17 +470,24 @@ mice <- function(data, maxit = maxit ) - # for variables not in model, set predictorMatrix column to zero - # and update formulas (#583) - # nomissings <- colnames(data)[!apply(is.na(data), 2, sum)] - # notinmodel <- setdiff(colnames(data), unlist(blocks)) - # setrowzero <- intersect(nomissings, notinmodel) - # setcolzero <- setdiff(notinmodel, nomissings) + # evasion of NA propagation by inactivating unimputed incomplete predictors + # issue #583 + # 1) find unimputed incomplete predictors + # 2) set predictorMatrix entries to zero + # 3) update formulas + + # step 1: uip = unimputed incomplete predictors + nomissings <- colnames(data)[!apply(is.na(data), 2, sum)] + uip <- setdiff(colnames(data), unlist(blocks)) + + # step 2: update predictorMatrix + # setrowzero <- intersect(nomissings, uip) + # setcolzero <- setdiff(uip, nomissings) # predictorMatrix[, setcolzero] <- 0 # predictorMatrix[setrowzero, ] <- 0 - # formulas <- p2f(predictorMatrix, - # blocks = construct.blocks(formulas, predictorMatrix)) - # formulas[notinmodel] <- NULL + + # step 3: update formulas + # formulas <- lapply(formulas, remove.rhs.variables, vars = uip) # other checks post <- check.post(post, data) @@ -556,6 +569,8 @@ mice <- function(data, ) oldClass(midsobj) <- "mids" + stopifnot(validate.mids(midsobj)) + if (!is.null(midsobj$loggedEvents)) { warning("Number of logged events: ", nrow(midsobj$loggedEvents), call. = FALSE diff --git a/R/mids.R b/R/mids.R index b8abe44d5..6cf891839 100644 --- a/R/mids.R +++ b/R/mids.R @@ -105,3 +105,15 @@ #' \doi{10.18637/jss.v045.i03} #' @keywords classes NULL + +validate.mids <- function(x, silent = FALSE) { + if (!is.mids(x)) { + if (!silent) warning("not a mids object", call. = FALSE) + return(FALSE) + } + # if (any(row.names(x$predictorMatrix) != colnames(x$data))) { + # if (!silent) warning("row names of predictorMatrix do not match colnames(data)", call. = FALSE) + # return(FALSE) + # } + return(TRUE) +} diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 56c4a7584..b3c3fc6a1 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -55,83 +55,26 @@ check.predictorMatrix <- function(predictorMatrix, stop("predictorMatrix has no rows or columns", call. = FALSE) } - # if we have no blocks, restrict to square predictorMatrix - if (is.null(blocks)) { - if (nrow(predictorMatrix) != ncol(predictorMatrix)) { - stop( - paste( - "If no blocks are specified, predictorMatrix must", - "have same number of rows and columns" - ), - call. = FALSE - ) - } - if (is.null(dimnames(predictorMatrix))) { - if (ncol(predictorMatrix) == ncol(data)) { - dimnames(predictorMatrix) <- list(colnames(data), colnames(data)) - } else { - stop("Missing row/column names in predictorMatrix", call. = FALSE) - } - } - for (i in row.names(predictorMatrix)) { - predictorMatrix[i, grep(paste0("^", i, "$"), colnames(predictorMatrix))] <- 0 - } - valid <- validate.predictorMatrix(predictorMatrix) - if (!valid) { - warning("Malformed predictorMatrix. See ?make.predictorMatrix") + # restrict to square predictorMatrix + if (nrow(predictorMatrix) != ncol(predictorMatrix)) { + stop("predictorMatrix must have same number of rows and columns", + call. = FALSE + ) + } + + if (is.null(dimnames(predictorMatrix))) { + if (ncol(predictorMatrix) == ncol(data)) { + dimnames(predictorMatrix) <- list(colnames(data), colnames(data)) + } else { + stop("Missing row/column names in predictorMatrix", call. = FALSE) } - return(predictorMatrix) } - # # check conforming arguments - # if (nrow(predictorMatrix) > length(blocks)) { - # stop( - # paste0( - # "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)) { - # rownames(predictorMatrix) <- names(blocks) - # } - # if (is.null(rownames(predictorMatrix))) { - # stop("Unable to set row names of predictorMatrix", call. = FALSE) - # } - # - # # borrow blocknames from predictorMatrix if needed - # if (is.null(names(blocks)) && - # nrow(predictorMatrix) == length(blocks)) { - # names(blocks) <- rownames(predictorMatrix) - # } - # if (is.null(names(blocks))) { - # stop("Unable to set names of blocks", call. = FALSE) - # } - # - # # 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 - # ) - # } - # - # # borrow colnames from data if needed - # if (is.null(colnames(predictorMatrix)) && - # ncol(predictorMatrix) == ncol(data)) { - # colnames(predictorMatrix) <- names(data) - # } - # if (is.null(colnames(predictorMatrix))) { - # stop("Unable to set column names of predictorMatrix", call. = FALSE) - # } + # set diagonal to zero + diag(predictorMatrix) <- 0 - # check existence of variable names on data - found <- colnames(predictorMatrix) %in% names(data) + # check existence of variable names in data + found <- colnames(predictorMatrix) %in% colnames(data) if (!all(found)) { stop("Names not found in data: ", paste(colnames(predictorMatrix)[!found], collapse = ", "), @@ -139,14 +82,33 @@ check.predictorMatrix <- function(predictorMatrix, ) } + # grow predictorMatrix to all variables in data + if (ncol(predictorMatrix) < ncol(data)) { + p <- matrix(0, nrow = ncol(data), ncol = ncol(data), + dimnames = list(colnames(data), colnames(data))) + p[row.names(predictorMatrix), colnames(predictorMatrix)] <- predictorMatrix + predictorMatrix <- p + } + + # needed for cases E and H + if (!is.null(blocks)) { + if (nrow(predictorMatrix) < length(blocks)) { + stop( + paste0( + "predictorMatrix has fewer rows (", nrow(predictorMatrix), + ") than blocks (", length(blocks), ")" + ), + call. = FALSE + ) + } + } + valid <- validate.predictorMatrix(predictorMatrix) + if (!valid) { warning("Malformed predictorMatrix. See ?make.predictorMatrix") } - list( - predictorMatrix = predictorMatrix, - blocks = blocks - ) + return(predictorMatrix) } edit.predictorMatrix <- function(predictorMatrix, diff --git a/R/where.R b/R/where.R index 01e86de76..8aea111e0 100644 --- a/R/where.R +++ b/R/where.R @@ -65,6 +65,5 @@ check.where <- function(where, data, blocks) { dimnames(where) <- dimnames(data) # #583 # where[, !colnames(where) %in% unlist(blocks)] <- FALSE - where[, !colnames(where) %in% unlist(blocks)] <- FALSE where } diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd index 818a0fae6..bc459a018 100644 --- a/man/convertmodels.Rd +++ b/man/convertmodels.Rd @@ -10,7 +10,7 @@ p2f(predictorMatrix, blocks = NULL, silent = TRUE) p2c(predictorMatrix) -f2p(formulas, blocks = NULL, roles = NULL) +f2p(formulas, data, blocks = NULL, roles = NULL) } \arguments{ \item{predictorMatrix}{A square numeric matrix of \eqn{p} rows @@ -50,6 +50,9 @@ 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{data}{Data frame with \eqn{n} rows and \eqn{p} columns with +incomplete data. Missing values are coded as \code{NA}.} + \item{roles}{A list with \code{ncol(data)} elements, each with a row of the \code{predictorMatrix} when it contains values other than 0 or 1. The argument is only needed if the model contains non-standard diff --git a/man/make.method.Rd b/man/make.method.Rd index d64507321..2fb247e80 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -8,7 +8,8 @@ make.method( data, where = make.where(data), blocks = make.blocks(data), - defaultMethod = c("pmm", "logreg", "polyreg", "polr") + defaultMethod = c("pmm", "logreg", "polyreg", "polr"), + user.predictorMatrix = NULL ) } \arguments{ @@ -48,6 +49,9 @@ ordered levels. By default, the method uses regression imputation (binary data, factor with 2 levels) \code{polyreg}, polytomous regression imputation for unordered categorical data (factor > 2 levels) \code{polr}, proportional odds model for (ordered, > 2 levels).} + +\item{user.predictorMatrix}{the unedited \code{predictorMatrix} specified by the +user in the call to \code{mice()}} } \value{ Vector of \code{length(blocks)} element with method names diff --git a/man/remove.rhs.variables.Rd b/man/remove.rhs.variables.Rd new file mode 100644 index 000000000..e9f2753f0 --- /dev/null +++ b/man/remove.rhs.variables.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/formula.R +\name{remove.rhs.variables} +\alias{remove.rhs.variables} +\title{Remove RHS terms involving specified variable names} +\usage{ +remove.rhs.variables(ff, vars) +} +\arguments{ +\item{ff}{a formula} + +\item{vars}{a vector with varianble names to be removed from rhs} +} +\description{ +Remove RHS terms involving specified variable names +} +\details{ +If all variable are removed, the function return the intercept only model. +} +\examples{ +f1 <- y1 + y2 ~ 1 | z + x1 + x2 + x1 * x2 +remove.rhs.variables(f1, c("x1", "z")) + +# do not touch lhs +f2 <- bmi + chl + hyp ~ 1 | age +remove.rhs.variables(f2, "bmi") +} diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 4e8e688a5..115037c7e 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -28,7 +28,7 @@ imp$predictorMatrix # As a result, j is not imputed and is not a predictor anywhere test_that("removes variables from 'where'", { - expect_identical(sum(imp$where[, "hyp"]), 0L) + expect_identical(sum(imp$where[, "hyp"]), 8L) }) diff --git a/tests/testthat/test-convert.R b/tests/testthat/test-convert.R new file mode 100644 index 000000000..39a96d330 --- /dev/null +++ b/tests/testthat/test-convert.R @@ -0,0 +1,22 @@ +context("p2f") + +# p2f is not required to do this + +# method <- c("panImpute", "pmm") +# formulas <- list(bmi + chl + hyp ~ 1 | age, +# age ~ bmi + chl + hyp) +# formulas <- name.formulas(formulas) +# predictorMatrix <- +# structure(c(0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0), +# dim = c(4L, 4L), +# dimnames = list(c("bmi", "chl", "hyp", "age"), +# c("bmi", "chl", "hyp", "age"))) +# form2 <- p2f(predictorMatrix, +# blocks = construct.blocks(formulas, predictorMatrix)) +# form2 +# test_that("p2f() preserves random intercept '1 | age' in formula", { +# expect_identical( +# attr(terms(formulas[["F1"]]), "term.labels"), +# attr(terms(form2[["F1"]]), "term.labels") +# ) +# }) diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index a42b16542..8bd210295 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -20,7 +20,7 @@ test_that("Case A finds formulas", { # case B: only predictorMatrix argument pred1 <- matrix(1, nrow = 4, ncol = 4) -pred2 <- matrix(1, nrow = 2, ncol = 2) +pred2 <- matrix(0, nrow = 2, ncol = 2) pred3 <- matrix(1, nrow = 2, ncol = 2, dimnames = list(c("bmi", "hyp"), c("bmi", "hyp")) @@ -29,17 +29,17 @@ 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) +expect_error(mice(data, predictorMatrix = pred2, print = FALSE, m = 1, maxit = 1), + "Missing row/column names in predictorMatrix") imp3 <- mice(data, predictorMatrix = pred3, print = FALSE, m = 1, maxit = 1) +expect_error(mice(data, predictorMatrix = pred4, print = FALSE, m = 1, maxit = 1), + "predictorMatrix must have same number of rows and columns") -test_that("Case B tests the predictorMatrix", { +test_that("Case B yields four rows of the predictorMatrix", { expect_equal(nrow(imp1$predictorMatrix), 4L) - expect_error(mice(data, - predictorMatrix = pred2, - "Missing row/column names in `predictorMatrix`." - )) expect_equal(nrow(imp3$predictorMatrix), 4L) - expect_error(mice(data, predictorMatrix = pred4)) }) pred <- imp3$predictorMatrix @@ -160,11 +160,11 @@ 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) -expect_error( - suppressWarnings(imp1a <- mice(data, blocks = blocks1, pred = matrix(1, nr = 4, nc = 4), 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) expect_error( - suppressWarnings(imp2a <- mice(data, blocks = blocks2, pred = matrix(1, nr = 2, nc = 4), m = 1, maxit = 1, print = FALSE))) + suppressWarnings(imp2a <- mice(data, blocks = blocks2, pred = matrix(1, nr = 2, nc = 4), m = 1, maxit = 1, print = FALSE)), + "predictorMatrix must have same number of rows and columns") imp3 <- mice(data, blocks = blocks3, pred = pred3, m = 1, maxit = 1, print = FALSE) expect_error( suppressWarnings(imp3a <- mice(data, blocks = blocks3, pred = matrix(1, nr = 1, nc = 4), m = 1, maxit = 1, print = FALSE))) @@ -185,15 +185,12 @@ expect_error( test_that("Case E name setting fails on incompatible sizes", { expect_error( suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 2, nc = 2))), - "Malformed predictorMatrix" - ) + "Missing row/column names in predictorMatrix") expect_error( - suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 1, nc = 4))), - "Malformed predictorMatrix" - ) - expect_error( - suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4))), - "Malformed predictorMatrix") + mice(data, blocks = blocks2, pred = matrix(1, nr = 1, nc = 4)), + regexp = "predictorMatrix must have same number of rows and columns") + expect_silent(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4), + maxit = 1, m = 1, print = FALSE)) }) colnames(pred1) <- c("A", "B", "chl", "bmi") @@ -204,8 +201,6 @@ test_that("Case E detects incompatible arguments", { mice(data, blocks = blocks1, pred = pred1), "Names not found in data: A, B") - expect_error(suppressWarnings(mice(data, blocks = blocks2, pred = matrix(1, nr = 4, nc = 4)))) - expect_error( mice(data, blocks = blocks2, pred = pred2a), "predictorMatrix has no rows or columns") From 772c876f7ca3c0a55e3c30cf837b342446269898 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 10:52:54 +0200 Subject: [PATCH 09/37] Add exit checks on mids object --- NEWS.md | 11 +++++++++++ R/mids.R | 25 +++++++++++++++++++++++-- 2 files changed, 34 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index d0b3a03be..af93aa07e 100644 --- a/NEWS.md +++ b/NEWS.md @@ -16,6 +16,17 @@ - Removes codes designed to work specifically with a non-square `predictorMatrix` - Generates an error if `predictorMatrix` has fewer rows than length of `blocks` +## New exit checks + +- `rownames(predictorMatrix)` must match `colnames(data)` +- length of `formulas` and `blocks` must be equal +- length of `formulas` and `method` must be equal +- length of `blots` and `method` must be equal +- length of `method` vector cannot exceed number of variables +- length of `imp` and number of variables must be equal + +## Other fixes + * Prepares for the deprecation of the `blocks` argument at various places * Removes the need for `blocks` in `initialize_chain()` * In `rbind()`, when formulas are concatenated and duplicate names are found, also rename the duplicated variables in formulas by their new name diff --git a/R/mids.R b/R/mids.R index 6cf891839..59c1caafc 100644 --- a/R/mids.R +++ b/R/mids.R @@ -111,9 +111,30 @@ validate.mids <- function(x, silent = FALSE) { if (!silent) warning("not a mids object", call. = FALSE) return(FALSE) } - # if (any(row.names(x$predictorMatrix) != colnames(x$data))) { - # if (!silent) warning("row names of predictorMatrix do not match colnames(data)", call. = FALSE) + if (any(row.names(x$predictorMatrix) != colnames(x$data))) { + if (!silent) warning("row names of predictorMatrix do not match colnames(data)", call. = FALSE) + return(FALSE) + } + if (length(x$formulas) != length(x$blocks)) { + if (!silent) warning("lengths of formulas and blocks differ", call. = FALSE) + return(FALSE) + } + if (length(x$formulas) != length(x$method)) { + if (!silent) warning("lengths of formulas and method differ", call. = FALSE) + return(FALSE) + } + # if (length(x$blots) != length(x$method)) { + # if (!silent) warning("lengths of blots and method differ", call. = FALSE) # return(FALSE) # } + if (length(x$method) > ncol(x$data)) { + if (!silent) warning("method vector is longer than number of variables", call. = FALSE) + return(FALSE) + } + if (length(x$imp) != ncol(x$data)) { + if (!silent) warning("length of imp differs from the number of variables", call. = FALSE) + return(FALSE) + } + return(TRUE) } From 465bd5c4f845a4f443d522027108bd2b620b3ca4 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 14:57:33 +0200 Subject: [PATCH 10/37] Add test for zero predictorMatrix row if method == "", deal with related issues in check.blocks(), make.method(), edit.predictorMatrix() --- R/blocks.R | 7 +++++++ R/method.R | 27 +++++++++++++++++++-------- R/mice.R | 18 +++++++++++++++--- R/mids.R | 9 +++++++++ R/predictorMatrix.R | 11 +++++++++++ man/make.method.Rd | 6 +++++- tests/testthat/test-mice-initialize.R | 6 ++++-- 7 files changed, 70 insertions(+), 14 deletions(-) diff --git a/R/blocks.R b/R/blocks.R index d6ba7f296..b0e4bf69f 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -156,6 +156,13 @@ check.blocks <- function(blocks, data, calltype = "formula") { )) } + # add blocks for unspecified variables + ynames <- unique(as.vector(unname(unlist(blocks)))) + notimputed <- setdiff(colnames(data), ynames) + for (y in notimputed) { + blocks[[y]] <- y + } + if (length(calltype) == 1L) { ct <- rep(calltype, length(blocks)) names(ct) <- names(blocks) diff --git a/R/method.R b/R/method.R index d73fd2464..87ee276e2 100644 --- a/R/method.R +++ b/R/method.R @@ -5,6 +5,8 @@ #' specifies the method for each block. #' @param user.predictorMatrix the unedited `predictorMatrix` specified by the #' user in the call to `mice()` +#' @param user.blocks the unedited `blocks` specified by the +#' user in the call to `mice()` #' @inheritParams mice #' @return Vector of `length(blocks)` element with method names #' @seealso [mice()] @@ -15,14 +17,22 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), defaultMethod = c("pmm", "logreg", "polyreg", "polr"), - user.predictorMatrix = NULL) { + user.predictorMatrix = NULL, + user.blocks = NULL) { # support tiny predictorMatrix - if (is.null(user.predictorMatrix) || - ncol(user.predictorMatrix) == ncol(data)) { - include <- colnames(data) - } else { - include <- colnames(user.predictorMatrix) + include <- colnames(data) + if (!is.null(user.predictorMatrix)) { + if (!is.null(dimnames(user.predictorMatrix))) { + include <- colnames(user.predictorMatrix) + } else { + include <- colnames(data) + } } + # support tiny blocks + if (!is.null(user.blocks)) { + include <- unique(as.vector(unname(unlist(blocks)))) + } + method <- rep("", length(blocks)) names(method) <- names(blocks) for (j in seq_along(blocks)) { @@ -41,14 +51,15 @@ make.method <- function(data, check.method <- function(method, data, where, blocks, defaultMethod, - user.predictorMatrix) { + user.predictorMatrix, user.blocks) { if (is.null(method)) { method <- make.method( data = data, where = where, blocks = blocks, defaultMethod = defaultMethod, - user.predictorMatrix = user.predictorMatrix) + user.predictorMatrix = user.predictorMatrix, + user.blocks) return(method) } nimp <- nimp(where = where, blocks = blocks) diff --git a/R/mice.R b/R/mice.R index 72381cccf..6111b93e6 100644 --- a/R/mice.R +++ b/R/mice.R @@ -375,6 +375,10 @@ mice <- function(data, if (!mp) { user.predictorMatrix <- predictorMatrix } + user.blocks <- NULL + if (!mb) { + user.blocks <- blocks + } # case A if (mp & mb & mf) { @@ -456,7 +460,8 @@ mice <- function(data, method <- check.method( method = method, data = data, where = where, blocks = blocks, defaultMethod = defaultMethod, - user.predictorMatrix = user.predictorMatrix + user.predictorMatrix = user.predictorMatrix, + user.blocks = user.blocks ) # edit predictorMatrix for monotone, set zero rows for empty methods @@ -470,6 +475,13 @@ mice <- function(data, maxit = maxit ) + # update formulas to ~ 1 if method = "" + for (b in names(method)) { + if (hasName(formulas, b) && method[[b]] == "") { + formulas[[b]] <- as.formula(paste(b, "~ 1")) + } + } + # evasion of NA propagation by inactivating unimputed incomplete predictors # issue #583 # 1) find unimputed incomplete predictors @@ -477,8 +489,8 @@ mice <- function(data, # 3) update formulas # step 1: uip = unimputed incomplete predictors - nomissings <- colnames(data)[!apply(is.na(data), 2, sum)] - uip <- setdiff(colnames(data), unlist(blocks)) + # nomissings <- colnames(data)[!apply(is.na(data), 2, sum)] + # uip <- setdiff(colnames(data), unlist(blocks)) # step 2: update predictorMatrix # setrowzero <- intersect(nomissings, uip) diff --git a/R/mids.R b/R/mids.R index 59c1caafc..a95512b37 100644 --- a/R/mids.R +++ b/R/mids.R @@ -135,6 +135,15 @@ validate.mids <- function(x, silent = FALSE) { if (!silent) warning("length of imp differs from the number of variables", call. = FALSE) return(FALSE) } + for (b in names(x$method)) { + ynames <- x$blocks[[b]] + for (j in ynames) { + if (x$method[b] != "") next + if (all(x$predictorMatrix[j, ] == 0)) next + warning(paste("predictorMatrix row not zero for variable", j), call. = FALSE) + return(FALSE) + } + } return(TRUE) } diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index b3c3fc6a1..ea6f3b440 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -119,6 +119,16 @@ edit.predictorMatrix <- function(predictorMatrix, user.visitSequence, maxit) { # for empty method, set predictorMatrix row to zero + for (b in names(method)) { + ynames <- blocks[[b]] + for (j in ynames) { + if (method[b] == "") { + predictorMatrix[j, ] <- 0 + } + } + } + + # for variables that will not be imputed, set predictorMatrix row to zero nimp <- nimp(where = where, blocks = blocks) for (j in seq_along(blocks)) { if (!nimp[j]) { @@ -132,6 +142,7 @@ edit.predictorMatrix <- function(predictorMatrix, predictorMatrix[visitSequence[i], visitSequence[i:length(visitSequence)]] <- 0 } } + valid <- validate.predictorMatrix(predictorMatrix) if (!valid) { warning("Malformed predictorMatrix. See ?make.predictorMatrix") diff --git a/man/make.method.Rd b/man/make.method.Rd index 2fb247e80..98c2220b0 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -9,7 +9,8 @@ make.method( where = make.where(data), blocks = make.blocks(data), defaultMethod = c("pmm", "logreg", "polyreg", "polr"), - user.predictorMatrix = NULL + user.predictorMatrix = NULL, + user.blocks = NULL ) } \arguments{ @@ -52,6 +53,9 @@ levels) \code{polr}, proportional odds model for (ordered, > 2 levels).} \item{user.predictorMatrix}{the unedited \code{predictorMatrix} specified by the user in the call to \code{mice()}} + +\item{user.blocks}{the unedited \code{blocks} specified by the +user in the call to \code{mice()}} } \value{ Vector of \code{length(blocks)} element with method names diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index 8bd210295..07a442e25 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -73,8 +73,8 @@ imp2 <- mice(data, blocks = list(c("bmi", "chl"), "hyp"), print = FALSE, m = 1, 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$blocks), c("B1", "hyp")) - expect_identical(names(imp3$blocks), c("all")) + expect_identical(names(imp2$blocks), c("B1", "hyp", "age")) + expect_identical(names(imp3$blocks), c("all", "age")) }) test_that("Case C finds predictorMatrix", { @@ -88,6 +88,8 @@ test_that("Case C finds formulas", { test_that("Case C yields same imputations for FCS and multivariate", { expect_identical(complete(imp1), complete(imp2)) + # NOTE: next comparison will not work for nhanes2, because pmm instead + # of logreg is used to impute hyp expect_identical(complete(imp1), complete(imp3)) }) From c8ed33596d5ce55aeee9b28f7891cc2d015ec813 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 14:59:09 +0200 Subject: [PATCH 11/37] Update news --- NEWS.md | 1 + 1 file changed, 1 insertion(+) diff --git a/NEWS.md b/NEWS.md index af93aa07e..d650f3e46 100644 --- a/NEWS.md +++ b/NEWS.md @@ -9,6 +9,7 @@ ## Changes +- Performs stricter checks on zero rows in predictorMatrix under empty imputation method - Adds supports a tiny predictorMatrix - Solves bug in f2p() - Adds new function `remove.rhs.variables()` From 05a020973ea1f5a832e91c51f792a18c71fe447c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 16:30:49 +0200 Subject: [PATCH 12/37] Update documentation for mice() arguments --- R/mice.R | 218 +++++++++++++++++++++-------------- man/as.mids.Rd | 6 +- man/construct.blocks.Rd | 53 ++++++--- man/construct.nest.Rd | 53 ++++++--- man/convertmodels.Rd | 80 +++++++++---- man/extend.formulas.Rd | 85 +++++++++----- man/make.method.Rd | 49 ++++---- man/make.visitSequence.Rd | 27 +++-- man/mice.Rd | 236 +++++++++++++++++++++++--------------- man/name.blocks.Rd | 27 +++-- man/name.formulas.Rd | 33 +++++- man/nimp.Rd | 33 +++--- 12 files changed, 577 insertions(+), 323 deletions(-) diff --git a/R/mice.R b/R/mice.R index 6111b93e6..a50d15dd2 100644 --- a/R/mice.R +++ b/R/mice.R @@ -154,35 +154,38 @@ #' by the `defaultMethod` argument. See details #' on *skipping imputation*. #' @param predictorMatrix -#' A square numeric matrix of \eqn{p} rows -#' and columns. Row- and column names are `colnames(data)`. +#' A square numeric matrix of maximal \eqn{p} rows and +#' maximal \eqn{p} columns. Row- and column names are +#' `colnames(data)`. #' Each row corresponds to a variable to be imputed. #' A value of `1` means that the column variable is a #' predictor for the row variable, while a `0` means that #' the column variable is not a predictor. The default #' `predictorMatrix` is `1` everywhere, except for a zero -#' diagonal. For variables that need no be imputed, -#' `mice()` automatically sets the corresponding rows in the -#' `predictorMatrix` to zero. See details -#' on *skipping imputation*. +#' diagonal. Row- and column-names are optional for the +#' maximum \eqn{p} by \eqn{p} size. The user may specify a +#' smaller `predictorMatrix`, but column and row names are +#' then mandatory and should match be part of `colnames(data)`. +#' For variables that are not imputed, `mice()` automatically +#' sets the corresponding rows in the `predictorMatrix` to +#' zero. See details on *skipping imputation*. #' Two-level imputation models (which have `"2l"` in their -#' names) other codes than `0` and `1`, e.g, `2` or `-2`, -#' are also used. +#' names) support other codes than `0` and `1`, e.g, `2` +#' or `-2` to signal variable with special roles. #' @param ignore A logical vector of \eqn{n} elements indicating #' which rows are ignored for estimating the parameters of #' the imputation model. #' Rows with `ignore` set to `TRUE` do not influence the #' parameters of the imputation model. #' The `ignore` argument allows splitting `data` into a -#' training set (on which we fit the imputation model) +#' training set (on which `mice()` fits the imputation model) #' and a test set (that does not influence the imputation #' model parameter estimates). #' The default `NULL` corresponds to all `FALSE`, thus #' including all rows into the imputation models. -#' Note: Multivariate imputation methods, -#' like `mice.impute.jomoImpute()` or -#' `mice.impute.panImpute()`, do not honour the `ignore` -#' argument. +#' Note: Not all imputation methods may support the `ignore` +#' argument (e.g., `mice.impute.jomoImpute()` or +#' `mice.impute.panImpute()`). #' @param where A data frame or matrix of logicals with \eqn{n} rows #' and \eqn{p} columns, indicating the cells of `data` for #' which imputations are generated. @@ -192,76 +195,125 @@ #' with observed data, or skip imputation of specific missing #' cells. Be aware that the latter option could propagate #' missing values to other variables. See details. -#' Note: Methods that generate multivariate imputations -#' (e.g. `mice.impute.panImpute()`) do not honour the -#' `where` argument. -#' @param 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 `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 -#' `blocks` are imputed. The relevant columns in the `where` -#' matrix are set to `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. -#' @param visitSequence A vector of block names of arbitrary length, specifying the -#' sequence of blocks that are imputed during one iteration of the Gibbs -#' sampler. A block is a collection of variables. All variables that are -#' members of the same block are imputed -#' when the block is visited. A variable that is a member of multiple blocks -#' is re-imputed within the same iteration. -#' The default `visitSequence = "roman"` visits the blocks (left to right) -#' in the order in which they appear in `blocks`. -#' One may also use one of the following keywords: `"arabic"` -#' (right to left), `"monotone"` (ordered low to high proportion -#' of missing data) and `"revmonotone"` (reverse of monotone). -#' *Special case*: If you specify both `visitSequence = "monotone"` and -#' `maxit = 1`, then the procedure will edit the `predictorMatrix` -#' to conform to the monotone pattern. Realize that convergence in one -#' iteration is only guaranteed if the missing data pattern is actually -#' monotone. The procedure does not check this. -#' @param formulas A named list of formula's, or expressions that -#' can be converted into formula's by `as.formula`. List elements -#' correspond to blocks. The block to which the list element applies is -#' identified by its name, so list names must correspond to block names. -#' The `formulas` argument is an alternative to the -#' `predictorMatrix` argument that allows for more flexibility in -#' specifying imputation models, e.g., for specifying interaction terms. -#' @param blots A named `list` of `alist`'s that can be used -#' to pass down arguments to lower level imputation function. The entries -#' of element `blots[[blockname]]` are passed down to the function -#' called for block `blockname`. -#' @param post A vector of strings with length `ncol(data)` specifying -#' expressions as strings. Each string is parsed and -#' executed within the `sampler()` function to post-process -#' imputed values during the iterations. -#' The default is a vector of empty strings, indicating no post-processing. -#' Multivariate (block) imputation methods ignore the `post` parameter. -#' @param 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 -#' ordered levels. By default, the method uses -#' `pmm`, predictive mean matching (numeric data) `logreg`, logistic -#' regression imputation (binary data, factor with 2 levels) `polyreg`, -#' polytomous regression imputation for unordered categorical data (factor > 2 -#' levels) `polr`, proportional odds model for (ordered, > 2 levels). -#' @param maxit A scalar giving the number of iterations. The default is 5. -#' @param printFlag If `TRUE`, `mice` will print history on console. -#' Use `print=FALSE` for silent computation. -#' @param seed An integer that is used as argument by the `set.seed()` for -#' offsetting the random number generator. Default is to leave the random number -#' generator alone. -#' @param data.init A data frame of the same size and type as `data`, -#' without missing data, used to initialize imputations before the start of the -#' iterative process. The default `NULL` implies that starting imputation -#' are created by a simple random draw from the data. Note that specification of -#' `data.init` will start all `m` Gibbs sampling streams from the same -#' imputation. -#' @param \dots Named arguments that are passed down to the univariate imputation -#' functions. -#' @param nest experimental variable grouping input +#' Note: Not all imputation methods may support the `where` +#' argument (e.g., `mice.impute.jomoImpute()` or +#' `mice.impute.panImpute()`). +#' @param blocks List of \eqn{q} character vectors that identifies the +#' variable names per block. The name of list elements +#' identify blocks. `mice()` will provide default names +#' (`"B1"`, `"B2"`, ...) for blocks containing multiple +#' variables. Variables within a block are imputed as a +#' block, e.g. by a multivariate imputation method, or +#' by an iterated version of the same univariate imputation +#' method. By default each variable is allocated to a +#' separate block, which is effectively fully conditional +#' specification (FCS) by univariate models +#' (variable-by-variable imputation). +#' All data variables are assigned to a block. +#' A variable can belong to only one block, so there are +#' at most \eqn{p} blocks. +#' See the `nest` argument for an easier alternative to +#' the `blocks` argument. +#' @param visitSequence +#' A vector of block names of arbitrary length, specifying +#' the sequence of blocks in which blocks are imputed. +#' The `visitSequence` defines one iteration through the +#' data. A given block may be visited multiple times +#' within one iteration. +#' Variables that are members of the same block +#' are imputed togeteher when the block is visited. +#' The default `visitSequence = "roman"` visits the blocks +#' (left to right) in the order in which they appear +#' in `blocks`. One may also use one of the following +#' keywords: `"arabic"` (right to left), `"monotone"` +#' (ordered low to high proportion of missing data) and +#' `"revmonotone"` (reverse of monotone). +#' *Special case*: If you specify both +#' `visitSequence = "monotone"` and `maxit = 1`, then the +#' procedure will edit the `predictorMatrix` to conform to +#' the monotone pattern, so convergence is then immediate. +#' Realize that convergence in one iteration is only +#' guaranteed if the missing data pattern is actually +#' monotone. `mice()` does not check for monotonicity. +#' @param formulas A named list with \eqn{q} component, each containing +#' one formula. The left hand side (LHS) specifies the +#' variables to be imputed, and the right hand side (RHS) +#' specifies the predictors used for imputation. For example, +#' model `y1 + y2 ~ x1 + x2` imputes `y1` and `y2` using `x1` +#' and `x2` as predictors. Imputation by a multivariate +#' imputation model imputes `y1` and `y2` simultaneously +#' by a joint model, whereas `mice()` can also impute +#' `y1` and `y2` by a repeated univariate model as +#' `y1 ~ y2 + x1 + x2` and `y2 ~ y1 + x1 + x2`. +#' The `formulas` argument is an alternative to the +#' combination of the `predictorMatrix` and +#' `blocks` arguments. It is more compact and allows for +#' more flexibility in specifying imputation models, +#' e.g., for adding +#' interaction terms (`y1 + y2 ~ x1 * x2` ), +#' logical variables (`y1 + y2 ~ x1 + (x2 > 20)`), +#' three-level categories (`y1 + y2 ~ x1 + cut(age, 3)`), +#' polytomous terms (`y1 + y2 ~ x1 + poly(age, 3)`, +#' smoothing terms (`y1 + y2 ~ x1 + bs(age)`), +#' sum scores (`y1 + y2 ~ I(x1 + x2)`) or +#' quotients (`y1 + y2 ~ I(x1 / x2)`) +#' on the fly. +#' Optionally, the user can name formulas. If not named, +#' `mice()` will name formulas with multiple variables +#' as `F1`, `F2`, and so on. Formulas with one +#' dependent (e.g. `ses ~ x1 + x2`) will be named +#' after the dependent variable `"ses"`. +#' @param blots A named `list` with maximally \eqn{q} `alist` used to +#' pass down optional arguments to lower level imputation +#' functions. +#' The entries of element `blots[[h]]` are passed down to +#' the method called on block `h` or formula `h`. +#' For example, `blots = list(age = alist(donor = 20))` +#' specifies that imputation of `age` should draw from +#' imputations using 20 (instead of the default five) nearest +#' neighbours. +#' @param post A vector of length \eqn{p}, each specifying an expression +#' as a string. The string is parsed and executed within +#' the `sampler()` function to post-process imputed +#' values during the iterations. The default is a vector +#' of empty strings, indicating no post-processing. +#' Multivariate imputation methods ignore the `post` +#' parameter. +#' @param defaultMethod +#' A vector of length 4 containing the default imputation +#' methods for +#' 1) numeric data (`"pmm"`) +#' 2) factor data with 2 levels, (`"logreg"`) +#' 3) factor data with > 2 unordered levels, (`"polyreg"`) and +#' 4) factor data with > 2 ordered levels (`"polr"`). +#' The `defaultMethod` can be used to alter to default mapping +#' of variable type to imputation method. +#' @param maxit A scalar giving the number of iterations. The default is 5. +#' In general, the user should study the convergence of the +#' algorithm, e.g., by `plot(imp)`. +#' @param printFlag If `printFlag = TRUE` (default) then `mice()` will +#' print iteration history on the console. This is useful for +#' checking how far the algorithm is. Use `print = FALSE` +#' for silent computation, simulations, and to suppress +#' iteration output on the console. +#' @param seed An integer that is used as argument by the `set.seed()` +#' for offsetting the random number generator. Default is +#' to leave the random number generator alone. Use `seed` to +#' be reproduce a given imputation. +#' @param data.init A data frame of the same size and type as `data`, but +#' without missing data, used to initialize imputations +#' before the start of the iterative process. +#' The default `data.init = NULL` generates starting +#' imputations by a simple random draw from marginal +#' of the observed data. +#' Note that specification of `data.init` will start all +#' `m` Gibbs sampling streams from the same imputation. +#' @param \dots Named arguments that are passed down to the univariate +#' imputation functions. Use `blots` for a more fine-grained +#' alternative. +#' @param nest A character vector with \eqn{p} elements identifying the +#' variable group (or block) to which each variable is +#' allocated. #' #' @return Returns an S3 object of class [`mids()`][mids-class] #' (multiply imputed data set) diff --git a/man/as.mids.Rd b/man/as.mids.Rd index a520a8f72..da25283bb 100644 --- a/man/as.mids.Rd +++ b/man/as.mids.Rd @@ -20,9 +20,9 @@ The \code{where} argument can overimpute cells with observed data, or skip imputation of specific missing cells. Be aware that the latter option could propagate missing values to other variables. See details. -Note: Methods that generate multivariate imputations -(e.g. \code{mice.impute.panImpute()}) do not honour the -\code{where} argument.} +Note: Not all imputation methods may support the \code{where} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} \item{.imp}{An optional column number or column name in \code{long}, indicating the imputation index. The values are assumed to be consecutive diff --git a/man/construct.blocks.Rd b/man/construct.blocks.Rd index 67fcbea85..500e60346 100644 --- a/man/construct.blocks.Rd +++ b/man/construct.blocks.Rd @@ -7,28 +7,53 @@ construct.blocks(formulas = NULL, predictorMatrix = NULL) } \arguments{ -\item{formulas}{A named list of formula's, or expressions that -can be converted into formula's by \code{as.formula}. List elements -correspond to blocks. The block to which the list element applies is -identified by its name, so list names must correspond to block names. +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. 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.} +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} -\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows -and columns. Row- and column names are \code{colnames(data)}. +\item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and +maximal \eqn{p} columns. Row- and column names are +\code{colnames(data)}. Each row corresponds to a variable to be imputed. A value of \code{1} means that the column variable is a predictor for the row variable, while a \code{0} means that the column variable is not a predictor. The default \code{predictorMatrix} is \code{1} everywhere, except for a zero -diagonal. For variables that need no be imputed, -\code{mice()} automatically sets the corresponding rows in the -\code{predictorMatrix} to zero. See details -on \emph{skipping imputation}. +diagonal. Row- and column-names are optional for the +maximum \eqn{p} by \eqn{p} size. The user may specify a +smaller \code{predictorMatrix}, but column and row names are +then mandatory and should match be part of \code{colnames(data)}. +For variables that are not imputed, \code{mice()} automatically +sets the corresponding rows in the \code{predictorMatrix} to +zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their -names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, -are also used.} +names) support other codes than \code{0} and \code{1}, e.g, \code{2} +or \code{-2} to signal variable with special roles.} } \value{ A \code{blocks} object. diff --git a/man/construct.nest.Rd b/man/construct.nest.Rd index bf2d480bf..905e95520 100644 --- a/man/construct.nest.Rd +++ b/man/construct.nest.Rd @@ -7,28 +7,53 @@ construct.nest(formulas = NULL, predictorMatrix = NULL) } \arguments{ -\item{formulas}{A named list of formula's, or expressions that -can be converted into formula's by \code{as.formula}. List elements -correspond to blocks. The block to which the list element applies is -identified by its name, so list names must correspond to block names. +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. 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.} +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} -\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows -and columns. Row- and column names are \code{colnames(data)}. +\item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and +maximal \eqn{p} columns. Row- and column names are +\code{colnames(data)}. Each row corresponds to a variable to be imputed. A value of \code{1} means that the column variable is a predictor for the row variable, while a \code{0} means that the column variable is not a predictor. The default \code{predictorMatrix} is \code{1} everywhere, except for a zero -diagonal. For variables that need no be imputed, -\code{mice()} automatically sets the corresponding rows in the -\code{predictorMatrix} to zero. See details -on \emph{skipping imputation}. +diagonal. Row- and column-names are optional for the +maximum \eqn{p} by \eqn{p} size. The user may specify a +smaller \code{predictorMatrix}, but column and row names are +then mandatory and should match be part of \code{colnames(data)}. +For variables that are not imputed, \code{mice()} automatically +sets the corresponding rows in the \code{predictorMatrix} to +zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their -names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, -are also used.} +names) support other codes than \code{0} and \code{1}, e.g, \code{2} +or \code{-2} to signal variable with special roles.} } \value{ A \code{blocks} object. diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd index bc459a018..e775a8211 100644 --- a/man/convertmodels.Rd +++ b/man/convertmodels.Rd @@ -13,42 +13,72 @@ p2c(predictorMatrix) f2p(formulas, data, blocks = NULL, roles = NULL) } \arguments{ -\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows -and columns. Row- and column names are \code{colnames(data)}. +\item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and +maximal \eqn{p} columns. Row- and column names are +\code{colnames(data)}. Each row corresponds to a variable to be imputed. A value of \code{1} means that the column variable is a predictor for the row variable, while a \code{0} means that the column variable is not a predictor. The default \code{predictorMatrix} is \code{1} everywhere, except for a zero -diagonal. For variables that need no be imputed, -\code{mice()} automatically sets the corresponding rows in the -\code{predictorMatrix} to zero. See details -on \emph{skipping imputation}. +diagonal. Row- and column-names are optional for the +maximum \eqn{p} by \eqn{p} size. The user may specify a +smaller \code{predictorMatrix}, but column and row names are +then mandatory and should match be part of \code{colnames(data)}. +For variables that are not imputed, \code{mice()} automatically +sets the corresponding rows in the \code{predictorMatrix} to +zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their -names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, -are also used.} +names) support other codes than \code{0} and \code{1}, e.g, \code{2} +or \code{-2} to signal variable with special roles.} -\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{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} \item{silent}{Logical for additional diagnostics} -\item{formulas}{A named list of formula's, or expressions that -can be converted into formula's by \code{as.formula}. List elements -correspond to blocks. The block to which the list element applies is -identified by its name, so list names must correspond to block names. +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. 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.} +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} \item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with incomplete data. Missing values are coded as \code{NA}.} diff --git a/man/extend.formulas.Rd b/man/extend.formulas.Rd index bda3062db..297bd0048 100644 --- a/man/extend.formulas.Rd +++ b/man/extend.formulas.Rd @@ -15,43 +15,73 @@ extend.formulas( ) } \arguments{ -\item{formulas}{A named list of formula's, or expressions that -can be converted into formula's by \code{as.formula}. List elements -correspond to blocks. The block to which the list element applies is -identified by its name, so list names must correspond to block names. +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. 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.} +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} \item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with incomplete data. Missing values are coded as \code{NA}.} -\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{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} -\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows -and columns. Row- and column names are \code{colnames(data)}. +\item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and +maximal \eqn{p} columns. Row- and column names are +\code{colnames(data)}. Each row corresponds to a variable to be imputed. A value of \code{1} means that the column variable is a predictor for the row variable, while a \code{0} means that the column variable is not a predictor. The default \code{predictorMatrix} is \code{1} everywhere, except for a zero -diagonal. For variables that need no be imputed, -\code{mice()} automatically sets the corresponding rows in the -\code{predictorMatrix} to zero. See details -on \emph{skipping imputation}. +diagonal. Row- and column-names are optional for the +maximum \eqn{p} by \eqn{p} size. The user may specify a +smaller \code{predictorMatrix}, but column and row names are +then mandatory and should match be part of \code{colnames(data)}. +For variables that are not imputed, \code{mice()} automatically +sets the corresponding rows in the \code{predictorMatrix} to +zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their -names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, -are also used.} +names) support other codes than \code{0} and \code{1}, e.g, \code{2} +or \code{-2} to signal variable with special roles.} \item{auxiliary}{A logical that indicates whether the variables listed in \code{predictors} should be added to the formula as main @@ -60,8 +90,9 @@ effects. The default is \code{TRUE}.} \item{include.intercept}{A logical that indicated whether the intercept should be included in the result.} -\item{...}{Named arguments that are passed down to the univariate imputation -functions.} +\item{...}{Named arguments that are passed down to the univariate +imputation functions. Use \code{blots} for a more fine-grained +alternative.} } \value{ A list of formula's diff --git a/man/make.method.Rd b/man/make.method.Rd index 98c2220b0..51abbcabb 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -26,30 +26,35 @@ The \code{where} argument can overimpute cells with observed data, or skip imputation of specific missing cells. Be aware that the latter option could propagate missing values to other variables. See details. -Note: Methods that generate multivariate imputations -(e.g. \code{mice.impute.panImpute()}) do not honour the -\code{where} argument.} +Note: Not all imputation methods may support the \code{where} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} -\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{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} -\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 -ordered levels. By default, the method uses -\code{pmm}, predictive mean matching (numeric data) \code{logreg}, logistic -regression imputation (binary data, factor with 2 levels) \code{polyreg}, -polytomous regression imputation for unordered categorical data (factor > 2 -levels) \code{polr}, proportional odds model for (ordered, > 2 levels).} +\item{defaultMethod}{A vector of length 4 containing the default imputation +methods for +1) numeric data (\code{"pmm"}) +2) factor data with 2 levels, (\code{"logreg"}) +3) factor data with > 2 unordered levels, (\code{"polyreg"}) and +4) factor data with > 2 ordered levels (\code{"polr"}). +The \code{defaultMethod} can be used to alter to default mapping +of variable type to imputation method.} \item{user.predictorMatrix}{the unedited \code{predictorMatrix} specified by the user in the call to \code{mice()}} diff --git a/man/make.visitSequence.Rd b/man/make.visitSequence.Rd index e0adb102d..983985c5d 100644 --- a/man/make.visitSequence.Rd +++ b/man/make.visitSequence.Rd @@ -10,17 +10,22 @@ make.visitSequence(data = NULL, blocks = NULL) \item{data}{Data frame with \eqn{n} rows and \eqn{p} columns with incomplete data. Missing values are coded as \code{NA}.} -\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{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} } \value{ Vector containing block names diff --git a/man/mice.Rd b/man/mice.Rd index b35e2a2bb..6855e37e9 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -42,22 +42,28 @@ select a method based on the variable type as regulated by the \code{defaultMethod} argument. See details on \emph{skipping imputation}.} -\item{predictorMatrix}{A square numeric matrix of \eqn{p} rows -and columns. Row- and column names are \code{colnames(data)}. +\item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and +maximal \eqn{p} columns. Row- and column names are +\code{colnames(data)}. Each row corresponds to a variable to be imputed. A value of \code{1} means that the column variable is a predictor for the row variable, while a \code{0} means that the column variable is not a predictor. The default \code{predictorMatrix} is \code{1} everywhere, except for a zero -diagonal. For variables that need no be imputed, -\code{mice()} automatically sets the corresponding rows in the -\code{predictorMatrix} to zero. See details -on \emph{skipping imputation}. +diagonal. Row- and column-names are optional for the +maximum \eqn{p} by \eqn{p} size. The user may specify a +smaller \code{predictorMatrix}, but column and row names are +then mandatory and should match be part of \code{colnames(data)}. +For variables that are not imputed, \code{mice()} automatically +sets the corresponding rows in the \code{predictorMatrix} to +zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their -names) other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2}, -are also used.} +names) support other codes than \code{0} and \code{1}, e.g, \code{2} +or \code{-2} to signal variable with special roles.} -\item{nest}{experimental variable grouping input} +\item{nest}{A character vector with \eqn{p} elements identifying the +variable group (or block) to which each variable is +allocated.} \item{ignore}{A logical vector of \eqn{n} elements indicating which rows are ignored for estimating the parameters of @@ -65,15 +71,14 @@ the imputation model. Rows with \code{ignore} set to \code{TRUE} do not influence the parameters of the imputation model. The \code{ignore} argument allows splitting \code{data} into a -training set (on which we fit the imputation model) +training set (on which \code{mice()} fits the imputation model) and a test set (that does not influence the imputation model parameter estimates). The default \code{NULL} corresponds to all \code{FALSE}, thus including all rows into the imputation models. -Note: Multivariate imputation methods, -like \code{mice.impute.jomoImpute()} or -\code{mice.impute.panImpute()}, do not honour the \code{ignore} -argument.} +Note: Not all imputation methods may support the \code{ignore} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} \item{where}{A data frame or matrix of logicals with \eqn{n} rows and \eqn{p} columns, indicating the cells of \code{data} for @@ -84,86 +89,131 @@ The \code{where} argument can overimpute cells with observed data, or skip imputation of specific missing cells. Be aware that the latter option could propagate missing values to other variables. See details. -Note: Methods that generate multivariate imputations -(e.g. \code{mice.impute.panImpute()}) do not honour the -\code{where} argument.} - -\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{visitSequence}{A vector of block names of arbitrary length, specifying the -sequence of blocks that are imputed during one iteration of the Gibbs -sampler. A block is a collection of variables. All variables that are -members of the same block are imputed -when the block is visited. A variable that is a member of multiple blocks -is re-imputed within the same iteration. -The default \code{visitSequence = "roman"} visits the blocks (left to right) -in the order in which they appear in \code{blocks}. -One may also use one of the following keywords: \code{"arabic"} -(right to left), \code{"monotone"} (ordered low to high proportion -of missing data) and \code{"revmonotone"} (reverse of monotone). -\emph{Special case}: If you specify both \code{visitSequence = "monotone"} and -\code{maxit = 1}, then the procedure will edit the \code{predictorMatrix} -to conform to the monotone pattern. Realize that convergence in one -iteration is only guaranteed if the missing data pattern is actually -monotone. The procedure does not check this.} - -\item{formulas}{A named list of formula's, or expressions that -can be converted into formula's by \code{as.formula}. List elements -correspond to blocks. The block to which the list element applies is -identified by its name, so list names must correspond to block names. +Note: Not all imputation methods may support the \code{where} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} + +\item{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} + +\item{visitSequence}{A vector of block names of arbitrary length, specifying +the sequence of blocks in which blocks are imputed. +The \code{visitSequence} defines one iteration through the +data. A given block may be visited multiple times +within one iteration. +Variables that are members of the same block +are imputed togeteher when the block is visited. +The default \code{visitSequence = "roman"} visits the blocks +(left to right) in the order in which they appear +in \code{blocks}. One may also use one of the following +keywords: \code{"arabic"} (right to left), \code{"monotone"} +(ordered low to high proportion of missing data) and +\code{"revmonotone"} (reverse of monotone). +\emph{Special case}: If you specify both +\code{visitSequence = "monotone"} and \code{maxit = 1}, then the +procedure will edit the \code{predictorMatrix} to conform to +the monotone pattern, so convergence is then immediate. +Realize that convergence in one iteration is only +guaranteed if the missing data pattern is actually +monotone. \code{mice()} does not check for monotonicity.} + +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. 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{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 -called for block \code{blockname}.} - -\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 -imputed values during the iterations. -The default is a vector of empty strings, indicating no post-processing. -Multivariate (block) imputation methods ignore the \code{post} parameter.} - -\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 -ordered levels. By default, the method uses -\code{pmm}, predictive mean matching (numeric data) \code{logreg}, logistic -regression imputation (binary data, factor with 2 levels) \code{polyreg}, -polytomous regression imputation for unordered categorical data (factor > 2 -levels) \code{polr}, proportional odds model for (ordered, > 2 levels).} - -\item{maxit}{A scalar giving the number of iterations. The default is 5.} - -\item{printFlag}{If \code{TRUE}, \code{mice} will print history on console. -Use \code{print=FALSE} for silent computation.} - -\item{seed}{An integer that is used as argument by the \code{set.seed()} for -offsetting the random number generator. Default is to leave the random number -generator alone.} - -\item{data.init}{A data frame of the same size and type as \code{data}, -without missing data, used to initialize imputations before the start of the -iterative process. The default \code{NULL} implies that starting imputation -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{\dots}{Named arguments that are passed down to the univariate imputation -functions.} +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} + +\item{blots}{A named \code{list} with maximally \eqn{q} \code{alist} used to +pass down optional arguments to lower level imputation +functions. +The entries of element \code{blots[[h]]} are passed down to +the method called on block \code{h} or formula \code{h}. +For example, \code{blots = list(age = alist(donor = 20))} +specifies that imputation of \code{age} should draw from +imputations using 20 (instead of the default five) nearest +neighbours.} + +\item{post}{A vector of length \eqn{p}, each specifying an expression +as a string. The string is parsed and executed within +the \code{sampler()} function to post-process imputed +values during the iterations. The default is a vector +of empty strings, indicating no post-processing. +Multivariate imputation methods ignore the \code{post} +parameter.} + +\item{defaultMethod}{A vector of length 4 containing the default imputation +methods for +1) numeric data (\code{"pmm"}) +2) factor data with 2 levels, (\code{"logreg"}) +3) factor data with > 2 unordered levels, (\code{"polyreg"}) and +4) factor data with > 2 ordered levels (\code{"polr"}). +The \code{defaultMethod} can be used to alter to default mapping +of variable type to imputation method.} + +\item{maxit}{A scalar giving the number of iterations. The default is 5. +In general, the user should study the convergence of the +algorithm, e.g., by \code{plot(imp)}.} + +\item{printFlag}{If \code{printFlag = TRUE} (default) then \code{mice()} will +print iteration history on the console. This is useful for +checking how far the algorithm is. Use \code{print = FALSE} +for silent computation, simulations, and to suppress +iteration output on the console.} + +\item{seed}{An integer that is used as argument by the \code{set.seed()} +for offsetting the random number generator. Default is +to leave the random number generator alone. Use \code{seed} to +be reproduce a given imputation.} + +\item{data.init}{A data frame of the same size and type as \code{data}, but +without missing data, used to initialize imputations +before the start of the iterative process. +The default \code{data.init = NULL} generates starting +imputations by a simple random draw from marginal +of the observed data. +Note that specification of \code{data.init} will start all +\code{m} Gibbs sampling streams from the same imputation.} + +\item{\dots}{Named arguments that are passed down to the univariate +imputation functions. Use \code{blots} for a more fine-grained +alternative.} } \value{ Returns an S3 object of class \code{\link[=mids-class]{mids()}} diff --git a/man/name.blocks.Rd b/man/name.blocks.Rd index 365330eec..c382ecfe7 100644 --- a/man/name.blocks.Rd +++ b/man/name.blocks.Rd @@ -7,17 +7,22 @@ name.blocks(blocks, prefix = "B") } \arguments{ -\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{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} \item{prefix}{A character vector of length 1 with the prefix to be using for naming any unnamed blocks with two or more variables.} diff --git a/man/name.formulas.Rd b/man/name.formulas.Rd index 96f4e6b76..6a16b7743 100644 --- a/man/name.formulas.Rd +++ b/man/name.formulas.Rd @@ -7,13 +7,34 @@ name.formulas(formulas, prefix = "F") } \arguments{ -\item{formulas}{A named list of formula's, or expressions that -can be converted into formula's by \code{as.formula}. List elements -correspond to blocks. The block to which the list element applies is -identified by its name, so list names must correspond to block names. +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. 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.} +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} \item{prefix}{A character vector of length 1 with the prefix to be using for naming any unnamed blocks with two or more variables.} diff --git a/man/nimp.Rd b/man/nimp.Rd index dc0ac86e4..16db6f6f5 100644 --- a/man/nimp.Rd +++ b/man/nimp.Rd @@ -19,21 +19,26 @@ The \code{where} argument can overimpute cells with observed data, or skip imputation of specific missing cells. Be aware that the latter option could propagate missing values to other variables. See details. -Note: Methods that generate multivariate imputations -(e.g. \code{mice.impute.panImpute()}) do not honour the -\code{where} argument.} +Note: Not all imputation methods may support the \code{where} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} -\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{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} } \value{ A numeric vector of length \code{length(blocks)} containing From 6033fc6f9fc00a86a54d72f8130c3f3157fc9717 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 16:56:05 +0200 Subject: [PATCH 13/37] Update list of builtin imputation methods --- R/mice.R | 11 ++++++++++- man/construct.blocks.Rd | 2 +- man/construct.nest.Rd | 2 +- man/convertmodels.Rd | 2 +- man/extend.formulas.Rd | 2 +- man/mice.Rd | 11 ++++++++++- 6 files changed, 24 insertions(+), 6 deletions(-) diff --git a/R/mice.R b/R/mice.R index a50d15dd2..eaefe4cf1 100644 --- a/R/mice.R +++ b/R/mice.R @@ -40,6 +40,7 @@ #' `lasso.select.norm` \tab numeric \tab Lasso select + linear regression\cr #' `quadratic` \tab numeric \tab Imputation of quadratic terms\cr #' `ri` \tab numeric \tab Random indicator for nonignorable data\cr +#' `mnar.norm` \tab numeric \tab NARFCS under user-specified MNAR\cr #' `logreg` \tab binary \tab Logistic regression\cr #' `logreg.boot` \tab binary \tab Logistic regression with bootstrap\cr #' `lasso.logreg` \tab binary \tab Lasso logistic regression\cr @@ -56,6 +57,14 @@ #' `2lonly.pmm` \tab any \tab Level-2 class predictive mean matching #' } #' +#' Built-in multivariate imputation methods are: +#' +#' \tabular{lll}{ +#' `mpmm` \tab any \tab Multivariate PMM\cr +#' `jomoImpute` \tab any \tab `jomo::jomo()` through `mitml::jomoImpute()`\cr +#' `panImpute` \tab numeric \tab `pan::pan()` through `mitml::panImpute()` +#' } +#' #' These corresponding functions are coded in the `mice` library under #' names `mice.impute.method`, where `method` is a string with the #' name of the univariate imputation method name, for example `norm`. The @@ -171,7 +180,7 @@ #' zero. See details on *skipping imputation*. #' Two-level imputation models (which have `"2l"` in their #' names) support other codes than `0` and `1`, e.g, `2` -#' or `-2` to signal variable with special roles. +#' or `-2` that assign special roles to some variables. #' @param ignore A logical vector of \eqn{n} elements indicating #' which rows are ignored for estimating the parameters of #' the imputation model. diff --git a/man/construct.blocks.Rd b/man/construct.blocks.Rd index 500e60346..0f378d9aa 100644 --- a/man/construct.blocks.Rd +++ b/man/construct.blocks.Rd @@ -53,7 +53,7 @@ sets the corresponding rows in the \code{predictorMatrix} to zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their names) support other codes than \code{0} and \code{1}, e.g, \code{2} -or \code{-2} to signal variable with special roles.} +or \code{-2} that assign special roles to some variables.} } \value{ A \code{blocks} object. diff --git a/man/construct.nest.Rd b/man/construct.nest.Rd index 905e95520..58a4754a0 100644 --- a/man/construct.nest.Rd +++ b/man/construct.nest.Rd @@ -53,7 +53,7 @@ sets the corresponding rows in the \code{predictorMatrix} to zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their names) support other codes than \code{0} and \code{1}, e.g, \code{2} -or \code{-2} to signal variable with special roles.} +or \code{-2} that assign special roles to some variables.} } \value{ A \code{blocks} object. diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd index e775a8211..975c02f35 100644 --- a/man/convertmodels.Rd +++ b/man/convertmodels.Rd @@ -30,7 +30,7 @@ sets the corresponding rows in the \code{predictorMatrix} to zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their names) support other codes than \code{0} and \code{1}, e.g, \code{2} -or \code{-2} to signal variable with special roles.} +or \code{-2} that assign special roles to some variables.} \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements diff --git a/man/extend.formulas.Rd b/man/extend.formulas.Rd index 297bd0048..e283df3f0 100644 --- a/man/extend.formulas.Rd +++ b/man/extend.formulas.Rd @@ -81,7 +81,7 @@ sets the corresponding rows in the \code{predictorMatrix} to zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their names) support other codes than \code{0} and \code{1}, e.g, \code{2} -or \code{-2} to signal variable with special roles.} +or \code{-2} that assign special roles to some variables.} \item{auxiliary}{A logical that indicates whether the variables listed in \code{predictors} should be added to the formula as main diff --git a/man/mice.Rd b/man/mice.Rd index 6855e37e9..41ba68ac4 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -59,7 +59,7 @@ sets the corresponding rows in the \code{predictorMatrix} to zero. See details on \emph{skipping imputation}. Two-level imputation models (which have \code{"2l"} in their names) support other codes than \code{0} and \code{1}, e.g, \code{2} -or \code{-2} to signal variable with special roles.} +or \code{-2} that assign special roles to some variables.} \item{nest}{A character vector with \eqn{p} elements identifying the variable group (or block) to which each variable is @@ -283,6 +283,7 @@ Built-in univariate imputation methods are: \code{lasso.select.norm} \tab numeric \tab Lasso select + linear regression\cr \code{quadratic} \tab numeric \tab Imputation of quadratic terms\cr \code{ri} \tab numeric \tab Random indicator for nonignorable data\cr +\code{mnar.norm} \tab numeric \tab NARFCS under user-specified MNAR\cr \code{logreg} \tab binary \tab Logistic regression\cr \code{logreg.boot} \tab binary \tab Logistic regression with bootstrap\cr \code{lasso.logreg} \tab binary \tab Lasso logistic regression\cr @@ -299,6 +300,14 @@ Built-in univariate imputation methods are: \verb{2lonly.pmm} \tab any \tab Level-2 class predictive mean matching } +Built-in multivariate imputation methods are: + +\tabular{lll}{ +\code{mpmm} \tab any \tab Multivariate PMM\cr +\code{jomoImpute} \tab any \tab \code{jomo::jomo()} through \code{mitml::jomoImpute()}\cr +\code{panImpute} \tab numeric \tab \code{pan::pan()} through \code{mitml::panImpute()} +} + These corresponding functions are coded in the \code{mice} library under names \code{mice.impute.method}, where \code{method} is a string with the name of the univariate imputation method name, for example \code{norm}. The From 29fee22236870e46466514124901af955262566d Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 18 Sep 2023 17:07:15 +0200 Subject: [PATCH 14/37] Reorder sequence of mice() arguments --- R/mice.R | 4 +-- man/mice.Rd | 96 ++++++++++++++++++++++++++--------------------------- 2 files changed, 50 insertions(+), 50 deletions(-) diff --git a/R/mice.R b/R/mice.R index eaefe4cf1..056bd9afd 100644 --- a/R/mice.R +++ b/R/mice.R @@ -399,11 +399,11 @@ mice <- function(data, method = NULL, predictorMatrix, nest = NULL, + blocks, + formulas, ignore = NULL, where = NULL, - blocks, visitSequence = NULL, - formulas, blots = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), diff --git a/man/mice.Rd b/man/mice.Rd index 41ba68ac4..daef5831d 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -11,11 +11,11 @@ mice( method = NULL, predictorMatrix, nest = NULL, + blocks, + formulas, ignore = NULL, where = NULL, - blocks, visitSequence = NULL, - formulas, blots = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), @@ -65,6 +65,52 @@ or \code{-2} that assign special roles to some variables.} variable group (or block) to which each variable is allocated.} +\item{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{nest} argument for an easier alternative to +the \code{blocks} argument.} + +\item{formulas}{A named list with \eqn{q} component, each containing +one formula. The left hand side (LHS) specifies the +variables to be imputed, and the right hand side (RHS) +specifies the predictors used for imputation. For example, +model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} +and \code{x2} as predictors. Imputation by a multivariate +imputation model imputes \code{y1} and \code{y2} simultaneously +by a joint model, whereas \code{mice()} can also impute +\code{y1} and \code{y2} by a repeated univariate model as +\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. +The \code{formulas} argument is an alternative to the +combination of the \code{predictorMatrix} and +\code{blocks} arguments. It is more compact and allows for +more flexibility in specifying imputation models, +e.g., for adding +interaction terms (\code{y1 + y2 ~ x1 * x2} ), +logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), +three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), +polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, +smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), +sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or +quotients (\code{y1 + y2 ~ I(x1 / x2)}) +on the fly. +Optionally, the user can name formulas. If not named, +\code{mice()} will name formulas with multiple variables +as \code{F1}, \code{F2}, and so on. Formulas with one +dependent (e.g. \code{ses ~ x1 + x2}) will be named +after the dependent variable \code{"ses"}.} + \item{ignore}{A logical vector of \eqn{n} elements indicating which rows are ignored for estimating the parameters of the imputation model. @@ -93,23 +139,6 @@ Note: Not all imputation methods may support the \code{where} argument (e.g., \code{mice.impute.jomoImpute()} or \code{mice.impute.panImpute()}).} -\item{blocks}{List of \eqn{q} character vectors that identifies the -variable names per block. The name of list elements -identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple -variables. Variables within a block are imputed as a -block, e.g. by a multivariate imputation method, or -by an iterated version of the same univariate imputation -method. By default each variable is allocated to a -separate block, which is effectively fully conditional -specification (FCS) by univariate models -(variable-by-variable imputation). -All data variables are assigned to a block. -A variable can belong to only one block, so there are -at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to -the \code{blocks} argument.} - \item{visitSequence}{A vector of block names of arbitrary length, specifying the sequence of blocks in which blocks are imputed. The \code{visitSequence} defines one iteration through the @@ -131,35 +160,6 @@ Realize that convergence in one iteration is only guaranteed if the missing data pattern is actually monotone. \code{mice()} does not check for monotonicity.} -\item{formulas}{A named list with \eqn{q} component, each containing -one formula. The left hand side (LHS) specifies the -variables to be imputed, and the right hand side (RHS) -specifies the predictors used for imputation. For example, -model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} -and \code{x2} as predictors. Imputation by a multivariate -imputation model imputes \code{y1} and \code{y2} simultaneously -by a joint model, whereas \code{mice()} can also impute -\code{y1} and \code{y2} by a repeated univariate model as -\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. -The \code{formulas} argument is an alternative to the -combination of the \code{predictorMatrix} and -\code{blocks} arguments. It is more compact and allows for -more flexibility in specifying imputation models, -e.g., for adding -interaction terms (\code{y1 + y2 ~ x1 * x2} ), -logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), -three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), -polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, -smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), -sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or -quotients (\code{y1 + y2 ~ I(x1 / x2)}) -on the fly. -Optionally, the user can name formulas. If not named, -\code{mice()} will name formulas with multiple variables -as \code{F1}, \code{F2}, and so on. Formulas with one -dependent (e.g. \code{ses ~ x1 + x2}) will be named -after the dependent variable \code{"ses"}.} - \item{blots}{A named \code{list} with maximally \eqn{q} \code{alist} used to pass down optional arguments to lower level imputation functions. From fef881b5723786196d47833b29c5bafef3f2ce4a Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 19 Sep 2023 11:40:02 +0200 Subject: [PATCH 15/37] Reorder nest in data sequence --- R/mice.R | 8 ++++---- R/mids.R | 4 ++++ R/nest.R | 8 +++++++- R/print.R | 2 ++ man/make.nest.Rd | 2 +- tests/testthat/test-blocks.R | 2 +- 6 files changed, 19 insertions(+), 7 deletions(-) diff --git a/R/mice.R b/R/mice.R index 056bd9afd..87aa44889 100644 --- a/R/mice.R +++ b/R/mice.R @@ -588,10 +588,9 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # update model - # formulas <- p2f(predictorMatrix, blocks) - # roles <- p2c(predictorMatrix) - # blots <- paste.roles(blots, roles) + # update nest + nest <- b2n(blocks) + nest <- reorder.nest(nest, data) # initialize imputations nmis <- apply(is.na(data), 2, sum) @@ -618,6 +617,7 @@ mice <- function(data, imp = q$imp, m = m, where = where, + nest = nest, blocks = blocks, call = call, nmis = nmis, diff --git a/R/mids.R b/R/mids.R index a95512b37..7a6e14c19 100644 --- a/R/mids.R +++ b/R/mids.R @@ -135,6 +135,10 @@ validate.mids <- function(x, silent = FALSE) { if (!silent) warning("length of imp differs from the number of variables", call. = FALSE) return(FALSE) } + if (length(x$nest) != ncol(x$data)) { + if (!silent) warning("length of nest differs from the number of variables", call. = FALSE) + return(FALSE) + } for (b in names(x$method)) { ynames <- x$blocks[[b]] for (j in ynames) { diff --git a/R/nest.R b/R/nest.R index 67aa3fdf8..efa9bb237 100644 --- a/R/nest.R +++ b/R/nest.R @@ -44,7 +44,7 @@ #' @export make.nest <- function(x, partition = c("scatter", "collect", "void"), - prefix = "A") { + prefix = "b") { # unnamed vector if (is.vector(x) && is.null(names(x)) && !is.list(x)) { @@ -176,3 +176,9 @@ construct.nest <- function(formulas = NULL, predictorMatrix = NULL) { attr(blocks, "calltype") <- ct blocks } + + +reorder.nest <- function(nest, data) { + idx <- colnames(data) + return(nest[idx]) +} diff --git a/R/print.R b/R/print.R index d205bacbe..a91087404 100644 --- a/R/print.R +++ b/R/print.R @@ -14,6 +14,8 @@ print.mids <- function(x, ...) { print(x$method, ...) cat("PredictorMatrix:\n") print(head(x$predictorMatrix), ...) + cat("Variable nests:\n") + print(x$nest, ...) if (!is.null(x$loggedEvents)) { cat("Number of logged events: ", nrow(x$loggedEvents), "\n") print(head(x$loggedEvents), ...) diff --git a/man/make.nest.Rd b/man/make.nest.Rd index f998cf7e7..8519755ca 100644 --- a/man/make.nest.Rd +++ b/man/make.nest.Rd @@ -4,7 +4,7 @@ \alias{make.nest} \title{Creates a \code{nest} argument} \usage{ -make.nest(x, partition = c("scatter", "collect", "void"), prefix = "A") +make.nest(x, partition = c("scatter", "collect", "void"), prefix = "b") } \arguments{ \item{x}{A \code{data.frame}, an unnamed character vector, a named diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 115037c7e..5cadee36b 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -11,7 +11,7 @@ context("blocks") # library(mice) # branch support_blocks -imp <- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 1, print = FALSE) +expect_warning(imp <<- mice(nhanes, blocks = make.blocks(list(c("bmi", "chl"), "bmi", "age")), m = 1, print = FALSE)) head(complete(imp)) imp$blocks From ba383eb29d9dd21d0837356f2e8d6042409c5f4d Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 19 Sep 2023 11:40:58 +0200 Subject: [PATCH 16/37] Use lowercase 'b' and 'f' for automatic naming of blocks and formulas --- R/blocks.R | 2 +- R/formula.R | 2 +- man/name.blocks.Rd | 2 +- man/name.formulas.Rd | 2 +- tests/testthat/test-cbind.R | 2 +- tests/testthat/test-mice-initialize.R | 4 ++-- tests/testthat/test-mice.R | 6 +++--- 7 files changed, 10 insertions(+), 10 deletions(-) diff --git a/R/blocks.R b/R/blocks.R index b0e4bf69f..05ef74acb 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -124,7 +124,7 @@ make.blocks <- function(x, #' blocks <- list(c("hyp", "chl"), AGE = "age", c("bmi", "hyp"), "edu") #' name.blocks(blocks) #' @export -name.blocks <- function(blocks, prefix = "B") { +name.blocks <- function(blocks, prefix = "b") { if (!is.list(blocks)) { return(make.blocks(blocks)) } diff --git a/R/formula.R b/R/formula.R index 2152525c9..aa183b02d 100644 --- a/R/formula.R +++ b/R/formula.R @@ -99,7 +99,7 @@ make.formulas <- function(data, blocks = make.blocks(data), #' form5 <- name.formulas(form5) #' imp5 <- mice(nhanes, formulas = form5, print = FALSE, m = 1, seed = 71712) #' @export -name.formulas <- function(formulas, prefix = "F") { +name.formulas <- function(formulas, prefix = "f") { if (!is.list(formulas)) { stop("Argument `formulas` not a list", call. = FALSE) } diff --git a/man/name.blocks.Rd b/man/name.blocks.Rd index c382ecfe7..832ac9793 100644 --- a/man/name.blocks.Rd +++ b/man/name.blocks.Rd @@ -4,7 +4,7 @@ \alias{name.blocks} \title{Name imputation blocks} \usage{ -name.blocks(blocks, prefix = "B") +name.blocks(blocks, prefix = "b") } \arguments{ \item{blocks}{List of \eqn{q} character vectors that identifies the diff --git a/man/name.formulas.Rd b/man/name.formulas.Rd index 6a16b7743..f52d6df55 100644 --- a/man/name.formulas.Rd +++ b/man/name.formulas.Rd @@ -4,7 +4,7 @@ \alias{name.formulas} \title{Name formula list elements} \usage{ -name.formulas(formulas, prefix = "F") +name.formulas(formulas, prefix = "f") } \arguments{ \item{formulas}{A named list with \eqn{q} component, each containing diff --git a/tests/testthat/test-cbind.R b/tests/testthat/test-cbind.R index 7da60d4c5..afb1efeca 100644 --- a/tests/testthat/test-cbind.R +++ b/tests/testthat/test-cbind.R @@ -48,7 +48,7 @@ imp <- cbind(imp1, imp2) impc <- mice.mids(imp, max = 2, print = FALSE) test_that("duplicate blocks names renames block", { - expect_identical(names(impc$blocks)[3], "B1.1") + expect_identical(names(impc$blocks)[3], "b1.1") }) diff --git a/tests/testthat/test-mice-initialize.R b/tests/testthat/test-mice-initialize.R index 07a442e25..757501518 100644 --- a/tests/testthat/test-mice-initialize.R +++ b/tests/testthat/test-mice-initialize.R @@ -73,7 +73,7 @@ imp2 <- mice(data, blocks = list(c("bmi", "chl"), "hyp"), print = FALSE, m = 1, 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$blocks), c("B1", "hyp", "age")) + expect_identical(names(imp2$blocks), c("b1", "hyp", "age")) expect_identical(names(imp3$blocks), c("all", "age")) }) @@ -83,7 +83,7 @@ test_that("Case C finds predictorMatrix", { }) test_that("Case C finds formulas", { - expect_identical(sort(all.vars(imp2$formulas[["B1"]])), sort(colnames(data))) + expect_identical(sort(all.vars(imp2$formulas[["b1"]])), sort(colnames(data))) }) test_that("Case C yields same imputations for FCS and multivariate", { diff --git a/tests/testthat/test-mice.R b/tests/testthat/test-mice.R index 3cbda8ddd..bb4eba2ca 100644 --- a/tests/testthat/test-mice.R +++ b/tests/testthat/test-mice.R @@ -19,7 +19,7 @@ test_that("blocks run as expected", { blocks = list(c("age", "hyp"), chl = "chl", "bmi"), print = FALSE, m = 1, maxit = 1, seed = 1 )) - expect_silent(imp2b <<- mice(nhanes2, + expect_warning(imp2b <<- mice(nhanes2, blocks = list(c("age", "hyp", "bmi"), "chl", "bmi"), print = FALSE, m = 1, maxit = 1, seed = 1 )) @@ -37,7 +37,7 @@ test_that("blocks run as expected", { }) test_that("Block names are generated automatically", { - expect_identical(names(imp1b$blocks), c("B1", "chl", "bmi")) + expect_identical(names(imp1b$blocks), c("b1", "chl", "bmi")) }) test_that("Method `pmm` is used for mixed variable types", { expect_identical(unname(imp2b$method[1]), "pmm") @@ -151,7 +151,7 @@ test_that("formulas run as expected", { }) test_that("Formula names are generated automatically", { - expect_identical(names(imp1f$blocks), c("F1", "chl", "bmi")) + expect_identical(names(imp1f$blocks), c("f1", "chl", "bmi")) }) test_that("Method `pmm` is used for mixed variable types", { expect_identical(unname(imp2f$method[1]), "pmm") From 4175534d7ad656e91969bda5cee57b2e2c071623 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 19 Sep 2023 21:31:28 +0200 Subject: [PATCH 17/37] Update error message in mpmm --- R/mice.impute.mpmm.R | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/R/mice.impute.mpmm.R b/R/mice.impute.mpmm.R index fda9c2cb8..97b99a07d 100644 --- a/R/mice.impute.mpmm.R +++ b/R/mice.impute.mpmm.R @@ -65,15 +65,20 @@ mpmm.impute <- function(data, ...) { r <- !is.na(data) mpat <- apply(r, 1, function(x) paste(as.numeric(x), collapse = "")) nmpat <- length(unique(mpat)) - if (nmpat != 2) stop("There are more than one missingness patterns") + if (nmpat != 2) { + stop("mpmm does not support more than one missing data pattern", + call. = FALSE) + } r <- unique(r) r <- r[rowSums(r) < ncol(r), ] y <- data[, which(r == FALSE), drop = FALSE] ry <- !is.na(y)[, 1] x <- data[, which(r == TRUE), drop = FALSE] wy <- !ry - ES <- eigen(solve(cov(y[ry, , drop = FALSE], y[ry, , drop = FALSE])) %*% cov(y[ry, , drop = FALSE], x[ry, , drop = FALSE]) - %*% solve(cov(x[ry, , drop = FALSE], x[ry, , drop = FALSE])) %*% cov(x[ry, , drop = FALSE], y[ry, , drop = FALSE])) + ES <- eigen(solve(cov(y[ry, , drop = FALSE], y[ry, , drop = FALSE])) %*% + cov(y[ry, , drop = FALSE], x[ry, , drop = FALSE]) + %*% solve(cov(x[ry, , drop = FALSE], x[ry, , drop = FALSE])) %*% + cov(x[ry, , drop = FALSE], y[ry, , drop = FALSE])) parm <- as.matrix(ES$vectors[, 1]) z <- as.matrix(y) %*% parm imp <- mice.impute.pmm(z, ry, x) From 016699229d67c73b79b396421a93d34bdcd7d809 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Tue, 19 Sep 2023 21:32:15 +0200 Subject: [PATCH 18/37] Sort terms both for pred and formulas --- R/sampler.R | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/R/sampler.R b/R/sampler.R index c0da4eaf1..0a683e35b 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -238,12 +238,14 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, if (length(ymove) > 0L) { formula <- update(formula, paste("~ . + ", paste(ymove, collapse = "+"))) } - s <- unlist(strsplit(format(formula), "[~]")) - xp <- sort(unlist(strsplit(s[2], "[+]"))) - xp <- sort(gsub(" ", "", xp)) - formula <- reformulate(paste(xp, collapse = "+"), j, env = environment(formula)) } + # sort terms in alphabetic order to obtain exact reproducibility + s <- unlist(strsplit(format(formula), "[~]")) + xp <- sort(unlist(strsplit(s[2], "[+]"))) + xp <- sort(gsub(" ", "", xp)) + formula <- reformulate(paste(xp, collapse = "+"), j, env = environment(formula)) + # get the model matrix x <- obtain.design(data, formula) From 35b6084cf50bb7caafb8de29effe630e79ce9ca2 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 13:48:44 +0200 Subject: [PATCH 19/37] Create a mechanism to inform check.method() of the set of variables to impute (ynames) --- R/blocks.R | 5 ++++- R/collect.ynames.R | 8 +++++++ R/formula.R | 33 +++++++++++++++++++++------ R/method.R | 54 +++++++++++++++++++++++++++------------------ R/mice.R | 21 +++++++----------- R/predictorMatrix.R | 7 ++++++ man/make.method.Rd | 9 ++------ 7 files changed, 87 insertions(+), 50 deletions(-) create mode 100644 R/collect.ynames.R diff --git a/R/blocks.R b/R/blocks.R index 05ef74acb..480841031 100644 --- a/R/blocks.R +++ b/R/blocks.R @@ -156,8 +156,11 @@ check.blocks <- function(blocks, data, calltype = "formula") { )) } - # add blocks for unspecified variables + # save ynames (variables to impute) for use in check.method() ynames <- unique(as.vector(unname(unlist(blocks)))) + attr(blocks, "ynames") <- ynames + + # add blocks for unspecified variables notimputed <- setdiff(colnames(data), ynames) for (y in notimputed) { blocks[[y]] <- y diff --git a/R/collect.ynames.R b/R/collect.ynames.R new file mode 100644 index 000000000..c6394d6cf --- /dev/null +++ b/R/collect.ynames.R @@ -0,0 +1,8 @@ +collect.ynames <- function(predictorMatrix, blocks, formulas) { + # reads and combines the ynames attributes + ynames1 <- attr(predictorMatrix, "ynames") + ynames2 <- attr(blocks, "ynames") + ynames3 <- attr(formulas, "ynames") + ynames <- unique(c(ynames1, ynames2, ynames3)) + return(ynames) +} diff --git a/R/formula.R b/R/formula.R index aa183b02d..b9af911f2 100644 --- a/R/formula.R +++ b/R/formula.R @@ -134,17 +134,28 @@ check.formulas <- function(formulas, data) { } formulas <- lapply(formulas, as.formula) - # find dependent variables - # find variables in data that are not imputed - # add components y ~ 1 for y to formulas + # NA-propagation prevention + # find all dependent (imputed) variables ynames <- unique(as.vector(unlist(sapply(formulas, lhs)))) + # find all variables in data that are not imputed notimputed <- setdiff(colnames(data), ynames) + # select uip: unimputed incomplete predictors + completevars <- colnames(data)[!apply(is.na(data), 2, sum)] + uip <- setdiff(notimputed, completevars) + # if any of these are in RHS for formulas, remove them + formulas <- lapply(formulas, remove.rhs.variables, vars = uip) + # add components y ~ 1 for y to formulas for (y in notimputed) { formulas[[y]] <- as.formula(paste(y, "~ 1")) } - formulas -} + # backdoor communication to check.method + # settoempty <- setNames(rep(FALSE, ncol(data)), colnames(data)) + # settoempty[notimputed] <- TRUE + attr(formulas, "ynames") <- ynames + + return(formulas) +} # remove variables for RHS @@ -296,8 +307,16 @@ expand.dots <- function(formula, data) { return(formula) } - y <- lhs(formula) - x <- setdiff(colnames(data), y) + if (any(lhs(formula) == ".")) { + newvars <- setdiff(colnames(data), all.vars(formula)) + yold <- setdiff(lhs(formula), ".") + xold <- attr(terms(formula, data = data), "term.labels") + y <- union(yold, setdiff(newvars, xold)) + x <- ifelse(length(xold), xold, "1") + } else { + y <- lhs(formula) + x <- setdiff(colnames(data), y) + } fs <- paste(paste(y, collapse = "+"), "~", paste(x, collapse = "+")) as.formula(fs) } diff --git a/R/method.R b/R/method.R index 87ee276e2..44338b28a 100644 --- a/R/method.R +++ b/R/method.R @@ -3,10 +3,7 @@ #' This helper function creates a valid `method` vector. The #' `method` vector is an argument to the `mice` function that #' specifies the method for each block. -#' @param user.predictorMatrix the unedited `predictorMatrix` specified by the -#' user in the call to `mice()` -#' @param user.blocks the unedited `blocks` specified by the -#' user in the call to `mice()` +#' @param ynames vector of names of variables to be imputed #' @inheritParams mice #' @return Vector of `length(blocks)` element with method names #' @seealso [mice()] @@ -17,21 +14,34 @@ make.method <- function(data, where = make.where(data), blocks = make.blocks(data), defaultMethod = c("pmm", "logreg", "polyreg", "polr"), - user.predictorMatrix = NULL, - user.blocks = NULL) { - # support tiny predictorMatrix - include <- colnames(data) - if (!is.null(user.predictorMatrix)) { - if (!is.null(dimnames(user.predictorMatrix))) { - include <- colnames(user.predictorMatrix) - } else { - include <- colnames(data) - } - } - # support tiny blocks - if (!is.null(user.blocks)) { - include <- unique(as.vector(unname(unlist(blocks)))) + ynames = NULL) { + # support tiny predictorMatrix, blocks and formulas + if (is.null(ynames)) { + ynames <- colnames(data) } + # FIXME colnames(data) may be too large if user specifies blocks argument + # to make.method() + + # if (!is.null(user.predictorMatrix)) { + # if (!is.null(dimnames(user.predictorMatrix))) { + # include <- colnames(user.predictorMatrix) + # } else { + # include1 <- colnames(data) + # } + # } + # # support tiny blocks + # if (!is.null(user.blocks)) { + # include2 <- unique(as.vector(unname(unlist(user.blocks)))) + # } + # + # support tiny formulas + # if (!is.null(user.formulas)) { + # include <- unique(as.vector(sapply(user.formulas, all.vars))) + # } + # support tiny formulas + # if (!is.null(formulas)) { + # include3 <- attr(formulas, "ynames") + # } method <- rep("", length(blocks)) names(method) <- names(blocks) @@ -39,11 +49,12 @@ make.method <- function(data, yvar <- blocks[[j]] y <- data[, yvar, drop = FALSE] k <- assign.method(y) - if (all(yvar %in% include)) { + if (all(yvar %in% ynames)) { method[j] <- defaultMethod[k] } } + # FIXME do we really need this here? nimp <- nimp(where = where, blocks = blocks) method[nimp == 0L] <- "" method @@ -51,15 +62,14 @@ make.method <- function(data, check.method <- function(method, data, where, blocks, defaultMethod, - user.predictorMatrix, user.blocks) { + ynames) { if (is.null(method)) { method <- make.method( data = data, where = where, blocks = blocks, defaultMethod = defaultMethod, - user.predictorMatrix = user.predictorMatrix, - user.blocks) + ynames = ynames) return(method) } nimp <- nimp(where = where, blocks = blocks) diff --git a/R/mice.R b/R/mice.R index 87aa44889..55198d536 100644 --- a/R/mice.R +++ b/R/mice.R @@ -431,20 +431,11 @@ mice <- function(data, mb <- missing(blocks) mf <- missing(formulas) - # store unedited user predictorMatrix - user.predictorMatrix <- NULL - if (!mp) { - user.predictorMatrix <- predictorMatrix - } - user.blocks <- NULL - if (!mb) { - user.blocks <- blocks - } - # case A if (mp & mb & mf) { # formulas leads formulas <- make.formulas(data) + attr(formulas, "ynames") <- colnames(data) predictorMatrix <- f2p(formulas, data) blocks <- construct.blocks(formulas) } @@ -517,13 +508,17 @@ mice <- function(data, data = data, where = where, blocks = blocks ) + # collect the ynames (variables to impute) from the model and clean + ynames <- collect.ynames(predictorMatrix, blocks, formulas) + attr(predictorMatrix, "ynames") <- NULL + attr(blocks, "ynames") <- NULL + attr(formulas, "ynames") <- NULL + # derive method vector method <- check.method( method = method, data = data, where = where, blocks = blocks, defaultMethod = defaultMethod, - user.predictorMatrix = user.predictorMatrix, - user.blocks = user.blocks - ) + ynames) # edit predictorMatrix for monotone, set zero rows for empty methods predictorMatrix <- edit.predictorMatrix( diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index ea6f3b440..3668e29cb 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -82,6 +82,10 @@ check.predictorMatrix <- function(predictorMatrix, ) } + # calculate ynames (variables to impute) for use in check.method() + hit <- apply(predictorMatrix, 1, function(x) any(x != 0)) + ynames <- row.names(predictorMatrix)[hit] + # grow predictorMatrix to all variables in data if (ncol(predictorMatrix) < ncol(data)) { p <- matrix(0, nrow = ncol(data), ncol = ncol(data), @@ -90,6 +94,9 @@ check.predictorMatrix <- function(predictorMatrix, predictorMatrix <- p } + # save calculated ynames + attr(predictorMatrix, "ynames") <- ynames + # needed for cases E and H if (!is.null(blocks)) { if (nrow(predictorMatrix) < length(blocks)) { diff --git a/man/make.method.Rd b/man/make.method.Rd index 51abbcabb..10849c34b 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -9,8 +9,7 @@ make.method( where = make.where(data), blocks = make.blocks(data), defaultMethod = c("pmm", "logreg", "polyreg", "polr"), - user.predictorMatrix = NULL, - user.blocks = NULL + ynames = NULL ) } \arguments{ @@ -56,11 +55,7 @@ methods for The \code{defaultMethod} can be used to alter to default mapping of variable type to imputation method.} -\item{user.predictorMatrix}{the unedited \code{predictorMatrix} specified by the -user in the call to \code{mice()}} - -\item{user.blocks}{the unedited \code{blocks} specified by the -user in the call to \code{mice()}} +\item{ynames}{vector of names of variables to be imputed} } \value{ Vector of \code{length(blocks)} element with method names From 65f544f1f7fee817fefa807baeeaacbf173d69d9 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 13:49:44 +0200 Subject: [PATCH 20/37] Introduce NA types in initialize.imp() --- R/initialize.imp.R | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/R/initialize.imp.R b/R/initialize.imp.R index 9efb8f4d1..8ab349990 100644 --- a/R/initialize.imp.R +++ b/R/initialize.imp.R @@ -8,7 +8,13 @@ initialize.imp <- function(data, m, ignore, where, blocks, visitSequence, y <- data[, j] ry <- r[, j] & !ignore wy <- where[, j] - imp[[j]] <- as.data.frame(matrix(NA, nrow = sum(wy), ncol = m)) + type <- typeof(y) + na.type <- switch(type, + double = NA_real_, + integer = NA_integer_, + character = NA_character_, + NA) + imp[[j]] <- as.data.frame(matrix(na.type, nrow = sum(wy), ncol = m)) dimnames(imp[[j]]) <- list(row.names(data)[wy], 1:m) if (method[h] != "") { for (i in seq_len(m)) { From d9c6fa67425d8162fd4b2fcc850d85f62ab7f4b4 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 13:50:14 +0200 Subject: [PATCH 21/37] Update nest printing in print.mids() --- R/print.R | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/R/print.R b/R/print.R index a91087404..9c2385482 100644 --- a/R/print.R +++ b/R/print.R @@ -12,10 +12,12 @@ print.mids <- function(x, ...) { cat("Number of multiple imputations: ", x$m, "\n") cat("Imputation methods:\n") print(x$method, ...) - cat("PredictorMatrix:\n") + cat("predictorMatrix:\n") print(head(x$predictorMatrix), ...) - cat("Variable nests:\n") - print(x$nest, ...) + if (any(x$nest != colnames(x$data))) { + cat("Variable nest:\n") + print(x$nest, ...) + } if (!is.null(x$loggedEvents)) { cat("Number of logged events: ", nrow(x$loggedEvents), "\n") print(head(x$loggedEvents), ...) From b9e398ede7bfbee71d4650621ddbeb5d1205fd5b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 16:02:53 +0200 Subject: [PATCH 22/37] Add support for blots to multivariate imputation models --- R/sampler.R | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/R/sampler.R b/R/sampler.R index 0a683e35b..873c5f704 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -103,10 +103,8 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, fm <- paste("mice.impute", theMethod, sep = ".") if (calltype == "formula") { - imputes <- do.call(fm, args = list( - data = data, - formula = ff, ... - )) + args <- c(list(data = data, formula = ff), user, list(...)) + imputes <- do.call(fm, args = args) } else if (calltype == "pred") { typecodes <- function(x) { # jomoImpute type codes @@ -134,9 +132,8 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, return(as.vector(type)) } type <- typecodes(predictorMatrix[blocks[[h]], ]) - imputes <- do.call(fm, args = list( - data = data, - type = type, ...)) + args <- c(list(data = data, type = type), user, list(...)) + imputes <- do.call(fm, args = args) } else { stop("Cannot call function of type ", calltype, call. = FALSE @@ -204,7 +201,8 @@ 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, + sort.terms = TRUE, ...) { j <- yname[1L] if (calltype == "pred") { @@ -241,10 +239,13 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, } # sort terms in alphabetic order to obtain exact reproducibility - s <- unlist(strsplit(format(formula), "[~]")) - xp <- sort(unlist(strsplit(s[2], "[+]"))) - xp <- sort(gsub(" ", "", xp)) - formula <- reformulate(paste(xp, collapse = "+"), j, env = environment(formula)) + # FIXME Is this sort really needed? It can crash with more complex formulas + if (sort.terms) { + s <- unlist(strsplit(format(formula), "[~]")) + xp <- sort(unlist(strsplit(s[2], "[+]"))) + xp <- sort(gsub(" ", "", xp)) + formula <- reformulate(paste(xp, collapse = "+"), j, env = environment(formula)) + } # get the model matrix x <- obtain.design(data, formula) From 0345ec3061d47ad7ae9be169410e715ac848c1e8 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 16:12:20 +0200 Subject: [PATCH 23/37] Rename `nest` to `parcel` --- NAMESPACE | 4 +- R/convert.R | 54 +++++------ R/mice.R | 20 ++--- R/mids.R | 4 +- R/nest.R | 90 +++++++++---------- R/print.R | 6 +- ...{construct.nest.Rd => construct.parcel.Rd} | 6 +- man/convertmodels.Rd | 2 +- man/extend.formulas.Rd | 2 +- man/make.method.Rd | 2 +- man/{make.nest.Rd => make.parcel.Rd} | 40 ++++----- man/make.visitSequence.Rd | 2 +- man/mice.Rd | 6 +- man/name.blocks.Rd | 2 +- man/nimp.Rd | 2 +- tests/testthat/test-blocks.R | 32 +++---- 16 files changed, 137 insertions(+), 137 deletions(-) rename man/{construct.nest.Rd => construct.parcel.Rd} (97%) rename man/{make.nest.Rd => make.parcel.Rd} (52%) diff --git a/NAMESPACE b/NAMESPACE index 7c75fc436..ead38383a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -65,7 +65,7 @@ export(cc) export(cci) export(complete) export(construct.blocks) -export(construct.nest) +export(construct.parcel) export(convergence) export(densityplot) export(estimice) @@ -94,7 +94,7 @@ export(make.blocks) export(make.blots) export(make.formulas) export(make.method) -export(make.nest) +export(make.parcel) export(make.post) export(make.predictorMatrix) export(make.visitSequence) diff --git a/R/convert.R b/R/convert.R index bd665b511..6580de669 100644 --- a/R/convert.R +++ b/R/convert.R @@ -90,38 +90,38 @@ f2p <- function(formulas, data, blocks = NULL, roles = NULL) { return(predictorMatrix) } -n2b <- function(nest, silent = FALSE) { - # nest to block - stopifnot(validate.nest(nest, silent = silent)) - if (all(nest == "")) { - nest[1L:length(nest)] <- names(nest) +n2b <- function(parcel, silent = FALSE) { + # parcel to block + stopifnot(validate.parcel(parcel, silent = silent)) + if (all(parcel == "")) { + parcel[1L:length(parcel)] <- names(parcel) } - if (any(nest == "")) { - stop("Cannot convert a partially named nest to blocks") + if (any(parcel == "")) { + stop("Cannot convert a partially named parcel to blocks") } - nf <- factor(nest, levels = unique(nest)) + nf <- factor(parcel, levels = unique(parcel)) blocknames <- levels(nf) blocks <- vector("list", length = length(blocknames)) names(blocks) <- blocknames for (b in names(blocks)) { - blocks[[b]] <- names(nest)[nest == b] + blocks[[b]] <- names(parcel)[parcel == b] } return(blocks) } b2n <- function(blocks, silent = FALSE) { - # block to nest + # block to parcel stopifnot(validate.blocks(blocks, silent = silent)) vars <- unlist(blocks) - nest <- rep(names(blocks), sapply(blocks, length)) + parcel <- rep(names(blocks), sapply(blocks, length)) if (any(duplicated(vars))) { warning("Duplicated name(s) removed: ", paste(vars[duplicated(vars)], collapse = ", ")) } - names(nest) <- vars - nest <- nest[!duplicated(names(nest))] - stopifnot(validate.nest(nest)) - return(nest) + names(parcel) <- vars + parcel <- parcel[!duplicated(names(parcel))] + stopifnot(validate.parcel(parcel)) + return(parcel) } paste.roles <- function(blots, roles, blocks) { @@ -131,27 +131,27 @@ paste.roles <- function(blots, roles, blocks) { return(blots) } -validate.nest <- function(nest, silent = FALSE) { - if (!is.vector(nest)) { - if (!silent) warning("nest is not a vector", call. = FALSE) +validate.parcel <- function(parcel, silent = FALSE) { + if (!is.vector(parcel)) { + if (!silent) warning("parcel is not a vector", call. = FALSE) return(FALSE) } - if (!is.character(nest)) { - if (!silent) warning("nest is not of type character", call. = FALSE) + if (!is.character(parcel)) { + if (!silent) warning("parcel is not of type character", call. = FALSE) return(FALSE) } - if (!length(nest)) { - if (!silent) warning("nest has length zero", call. = FALSE) + if (!length(parcel)) { + if (!silent) warning("parcel has length zero", call. = FALSE) return(FALSE) } - if (is.null(names(nest))) { - if (!silent) warning("nest has no names", call. = FALSE) + if (is.null(names(parcel))) { + if (!silent) warning("parcel has no names", call. = FALSE) return(FALSE) } - if (any(duplicated(names(nest)))) { + if (any(duplicated(names(parcel)))) { if (!silent) warning( - "duplicated names in nest: ", - paste({names(nest)}[duplicated(names(nest))], collapse = ", "), + "duplicated names in parcel: ", + paste({names(parcel)}[duplicated(names(parcel))], collapse = ", "), call. = FALSE) return(FALSE) } diff --git a/R/mice.R b/R/mice.R index 55198d536..3976b4fc4 100644 --- a/R/mice.R +++ b/R/mice.R @@ -221,7 +221,7 @@ #' All data variables are assigned to a block. #' A variable can belong to only one block, so there are #' at most \eqn{p} blocks. -#' See the `nest` argument for an easier alternative to +#' See the `parcel` argument for an easier alternative to #' the `blocks` argument. #' @param visitSequence #' A vector of block names of arbitrary length, specifying @@ -320,7 +320,7 @@ #' @param \dots Named arguments that are passed down to the univariate #' imputation functions. Use `blots` for a more fine-grained #' alternative. -#' @param nest A character vector with \eqn{p} elements identifying the +#' @param parcel A character vector with \eqn{p} elements identifying the #' variable group (or block) to which each variable is #' allocated. #' @@ -398,7 +398,7 @@ mice <- function(data, m = 5, method = NULL, predictorMatrix, - nest = NULL, + parcel = NULL, blocks, formulas, ignore = NULL, @@ -421,9 +421,9 @@ mice <- function(data, data <- check.dataform(data) m <- check.m(m) - # add support nest - if (!is.null(nest)) { - blocks <- n2b(nest, silent = FALSE) + # add support parcel + if (!is.null(parcel)) { + blocks <- n2b(parcel, silent = FALSE) } # determine input combination: predictorMatrix, blocks, formulas @@ -583,9 +583,9 @@ mice <- function(data, visitSequence <- setup$visitSequence post <- setup$post - # update nest - nest <- b2n(blocks) - nest <- reorder.nest(nest, data) + # update parcel + parcel <- b2n(blocks) + parcel <- reorder.parcel(parcel, data) # initialize imputations nmis <- apply(is.na(data), 2, sum) @@ -612,7 +612,7 @@ mice <- function(data, imp = q$imp, m = m, where = where, - nest = nest, + parcel = parcel, blocks = blocks, call = call, nmis = nmis, diff --git a/R/mids.R b/R/mids.R index 7a6e14c19..6eb28cc02 100644 --- a/R/mids.R +++ b/R/mids.R @@ -135,8 +135,8 @@ validate.mids <- function(x, silent = FALSE) { if (!silent) warning("length of imp differs from the number of variables", call. = FALSE) return(FALSE) } - if (length(x$nest) != ncol(x$data)) { - if (!silent) warning("length of nest differs from the number of variables", call. = FALSE) + if (length(x$parcel) != ncol(x$data)) { + if (!silent) warning("length of parcel differs from the number of variables", call. = FALSE) return(FALSE) } for (b in names(x$method)) { diff --git a/R/nest.R b/R/nest.R index efa9bb237..3157fa91f 100644 --- a/R/nest.R +++ b/R/nest.R @@ -1,21 +1,21 @@ -#' Creates a `nest` argument +#' Creates a `parcel` argument #' #' This helper function generates a character vector for the -#' `nest` argument in the [mice()] function. +#' `parcel` argument in the [mice()] function. #' #' @param x A `data.frame`, an unnamed character vector, a named #' character vector or a `list`. #' @param partition Only relevant if `x` is a `data.frame`. Value #' `"scatter"` (default) will assign each variable to a separate -#' nest. Value `"collect"` assigns all variables to one nest, -#' whereas `"void"` does not assign any variable to a nest. +#' parcel. Value `"collect"` assigns all variables to one parcel, +#' whereas `"void"` does not assign any variable to a parcel. #' @param prefix A character vector of length 1 with the prefix to #' be using for naming any unnamed blocks with two or more variables. #' @return A character vector of length `ncol(data)` that specifies -#' the nest name per variable +#' the parcel name per variable #' #' @details Choices `"scatter"` and `"collect"` represent to two -#' extreme scenarios for assigning variables to imputation nests. +#' extreme scenarios for assigning variables to imputation parcels. #' Use `"scatter"` to create an imputation model based on #' *fully conditionally specification* (FCS). Use `"collect"` to #' gather all variables to be imputed by a *joint model* (JM). @@ -24,85 +24,85 @@ #' Specification `"void"` represents the extreme scenario where #' nothing is imputed. #' -#' Unlike blocks, a variable cannot be allocated to multiple nests. +#' Unlike blocks, a variable cannot be allocated to multiple parcels. #' @examples #' -#' # default nest creation (scatter) -#' make.nest(nhanes) +#' # default parcel creation (scatter) +#' make.parcel(nhanes) #' -#' # make nest from variable names -#' make.nest(c("age", "sex", "edu")) +#' # make parcel from variable names +#' make.parcel(c("age", "sex", "edu")) #' -#' # put hgt, wgt and bmi into one nest, automatic naming -#' make.nest(list("age", "sex", c("hgt", "wgt", "bmi"))) +#' # put hgt, wgt and bmi into one parcel, automatic naming +#' make.parcel(list("age", "sex", c("hgt", "wgt", "bmi"))) #' -#' # same, but with custom nest names -#' make.nest(list("age", "sex", anthro = c("hgt", "wgt", "bmi"))) +#' # same, but with custom parcel names +#' make.parcel(list("age", "sex", anthro = c("hgt", "wgt", "bmi"))) #' -#' # all variables into one nest -#' make.nest(nhanes, partition = "collect", prefix = "myblock") +#' # all variables into one parcel +#' make.parcel(nhanes, partition = "collect", prefix = "myblock") #' @export -make.nest <- function(x, +make.parcel <- function(x, partition = c("scatter", "collect", "void"), prefix = "b") { # unnamed vector if (is.vector(x) && is.null(names(x)) && !is.list(x)) { - nest <- as.character(x) - names(nest) <- as.character(x) - return(nest) + parcel <- as.character(x) + names(parcel) <- as.character(x) + return(parcel) } # named vector, preserve name order if (is.vector(x) && !is.null(names(x)) && !is.list(x)) { - nest <- as.character(x) - names(nest) <- names(x) - return(nest) + parcel <- as.character(x) + names(parcel) <- names(x) + return(parcel) } # unnamed list if (is.list(x) && is.null(names(x)) && !is.data.frame(x)) { - nest <- b2n(name.blocks(x, prefix = prefix)) - return(nest) + parcel <- b2n(name.blocks(x, prefix = prefix)) + return(parcel) } # named list if (is.list(x) && !is.null(names(x)) && !is.data.frame(x)) { - nest <- b2n(x) - return(nest) + parcel <- b2n(x) + return(parcel) } x <- as.data.frame(x) partition <- match.arg(partition) switch(partition, scatter = { - nest <- colnames(x) - names(nest) <- names(x) + parcel <- colnames(x) + names(parcel) <- names(x) }, collect = { - nest <- rep(prefix, ncol(x)) - names(nest) <- names(x) + parcel <- rep(prefix, ncol(x)) + names(parcel) <- names(x) }, void = { - nest <- rep("", ncol(x)) - names(nest) <- names(x) + parcel <- rep("", ncol(x)) + names(parcel) <- names(x) }, { - nest <- names(x) - names(nest) <- names(x) + parcel <- names(x) + names(parcel) <- names(x) } ) - return(nest) + return(parcel) } -name.nest <- function(x) x +name.parcel <- function(x) x -check.nest <- function(nest, data) { +check.parcel <- function(parcel, data) { data <- check.dataform(data) - nest <- name.nest(nest) + parcel <- name.parcel(parcel) # check that all variable names exists in data - nv <- names(nest) + nv <- names(parcel) notFound <- !nv %in% colnames(data) if (any(notFound)) { stop(paste( @@ -111,7 +111,7 @@ check.nest <- function(nest, data) { )) } - nest + parcel } #' Construct blocks from `formulas` and `predictorMatrix` @@ -134,7 +134,7 @@ check.nest <- function(nest, data) { #' pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) #' construct.blocks(formulas = form, pred = pred) #' @export -construct.nest <- function(formulas = NULL, predictorMatrix = NULL) { +construct.parcel <- function(formulas = NULL, predictorMatrix = NULL) { blocks.f <- blocks.p <- NULL if (!is.null(formulas)) { if (!all(sapply(formulas, is.formula))) { @@ -178,7 +178,7 @@ construct.nest <- function(formulas = NULL, predictorMatrix = NULL) { } -reorder.nest <- function(nest, data) { +reorder.parcel <- function(parcel, data) { idx <- colnames(data) - return(nest[idx]) + return(parcel[idx]) } diff --git a/R/print.R b/R/print.R index 9c2385482..689f95070 100644 --- a/R/print.R +++ b/R/print.R @@ -14,9 +14,9 @@ print.mids <- function(x, ...) { print(x$method, ...) cat("predictorMatrix:\n") print(head(x$predictorMatrix), ...) - if (any(x$nest != colnames(x$data))) { - cat("Variable nest:\n") - print(x$nest, ...) + if (any(x$parcel != colnames(x$data))) { + cat("parcel:\n") + print(x$parcel, ...) } if (!is.null(x$loggedEvents)) { cat("Number of logged events: ", nrow(x$loggedEvents), "\n") diff --git a/man/construct.nest.Rd b/man/construct.parcel.Rd similarity index 97% rename from man/construct.nest.Rd rename to man/construct.parcel.Rd index 58a4754a0..03399d6b4 100644 --- a/man/construct.nest.Rd +++ b/man/construct.parcel.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/nest.R -\name{construct.nest} -\alias{construct.nest} +\name{construct.parcel} +\alias{construct.parcel} \title{Construct blocks from \code{formulas} and \code{predictorMatrix}} \usage{ -construct.nest(formulas = NULL, predictorMatrix = NULL) +construct.parcel(formulas = NULL, predictorMatrix = NULL) } \arguments{ \item{formulas}{A named list with \eqn{q} component, each containing diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd index 975c02f35..644b23b99 100644 --- a/man/convertmodels.Rd +++ b/man/convertmodels.Rd @@ -46,7 +46,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} \item{silent}{Logical for additional diagnostics} diff --git a/man/extend.formulas.Rd b/man/extend.formulas.Rd index e283df3f0..ca0073c9e 100644 --- a/man/extend.formulas.Rd +++ b/man/extend.formulas.Rd @@ -61,7 +61,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} \item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and diff --git a/man/make.method.Rd b/man/make.method.Rd index 10849c34b..b2e918cf6 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -43,7 +43,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} \item{defaultMethod}{A vector of length 4 containing the default imputation diff --git a/man/make.nest.Rd b/man/make.parcel.Rd similarity index 52% rename from man/make.nest.Rd rename to man/make.parcel.Rd index 8519755ca..7b071d046 100644 --- a/man/make.nest.Rd +++ b/man/make.parcel.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/nest.R -\name{make.nest} -\alias{make.nest} -\title{Creates a \code{nest} argument} +\name{make.parcel} +\alias{make.parcel} +\title{Creates a \code{parcel} argument} \usage{ -make.nest(x, partition = c("scatter", "collect", "void"), prefix = "b") +make.parcel(x, partition = c("scatter", "collect", "void"), prefix = "b") } \arguments{ \item{x}{A \code{data.frame}, an unnamed character vector, a named @@ -12,23 +12,23 @@ character vector or a \code{list}.} \item{partition}{Only relevant if \code{x} is a \code{data.frame}. Value \code{"scatter"} (default) will assign each variable to a separate -nest. Value \code{"collect"} assigns all variables to one nest, -whereas \code{"void"} does not assign any variable to a nest.} +parcel. Value \code{"collect"} assigns all variables to one parcel, +whereas \code{"void"} does not assign any variable to a parcel.} \item{prefix}{A character vector of length 1 with the prefix to be using for naming any unnamed blocks with two or more variables.} } \value{ A character vector of length \code{ncol(data)} that specifies -the nest name per variable +the parcel name per variable } \description{ This helper function generates a character vector for the -\code{nest} argument in the \code{\link[=mice]{mice()}} function. +\code{parcel} argument in the \code{\link[=mice]{mice()}} function. } \details{ Choices \code{"scatter"} and \code{"collect"} represent to two -extreme scenarios for assigning variables to imputation nests. +extreme scenarios for assigning variables to imputation parcels. Use \code{"scatter"} to create an imputation model based on \emph{fully conditionally specification} (FCS). Use \code{"collect"} to gather all variables to be imputed by a \emph{joint model} (JM). @@ -37,22 +37,22 @@ Any variable not listed in the result will not be imputed. Specification \code{"void"} represents the extreme scenario where nothing is imputed. -Unlike blocks, a variable cannot be allocated to multiple nests. +Unlike blocks, a variable cannot be allocated to multiple parcels. } \examples{ -# default nest creation (scatter) -make.nest(nhanes) +# default parcel creation (scatter) +make.parcel(nhanes) -# make nest from variable names -make.nest(c("age", "sex", "edu")) +# make parcel from variable names +make.parcel(c("age", "sex", "edu")) -# put hgt, wgt and bmi into one nest, automatic naming -make.nest(list("age", "sex", c("hgt", "wgt", "bmi"))) +# put hgt, wgt and bmi into one parcel, automatic naming +make.parcel(list("age", "sex", c("hgt", "wgt", "bmi"))) -# same, but with custom nest names -make.nest(list("age", "sex", anthro = c("hgt", "wgt", "bmi"))) +# same, but with custom parcel names +make.parcel(list("age", "sex", anthro = c("hgt", "wgt", "bmi"))) -# all variables into one nest -make.nest(nhanes, partition = "collect", prefix = "myblock") +# all variables into one parcel +make.parcel(nhanes, partition = "collect", prefix = "myblock") } diff --git a/man/make.visitSequence.Rd b/man/make.visitSequence.Rd index 983985c5d..7d9e5dc3f 100644 --- a/man/make.visitSequence.Rd +++ b/man/make.visitSequence.Rd @@ -24,7 +24,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} } \value{ diff --git a/man/mice.Rd b/man/mice.Rd index daef5831d..a35dcd31f 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -10,7 +10,7 @@ mice( m = 5, method = NULL, predictorMatrix, - nest = NULL, + parcel = NULL, blocks, formulas, ignore = NULL, @@ -61,7 +61,7 @@ Two-level imputation models (which have \code{"2l"} in their names) support other codes than \code{0} and \code{1}, e.g, \code{2} or \code{-2} that assign special roles to some variables.} -\item{nest}{A character vector with \eqn{p} elements identifying the +\item{parcel}{A character vector with \eqn{p} elements identifying the variable group (or block) to which each variable is allocated.} @@ -79,7 +79,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} \item{formulas}{A named list with \eqn{q} component, each containing diff --git a/man/name.blocks.Rd b/man/name.blocks.Rd index 832ac9793..88e4095b3 100644 --- a/man/name.blocks.Rd +++ b/man/name.blocks.Rd @@ -21,7 +21,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} \item{prefix}{A character vector of length 1 with the prefix to diff --git a/man/nimp.Rd b/man/nimp.Rd index 16db6f6f5..c50a18964 100644 --- a/man/nimp.Rd +++ b/man/nimp.Rd @@ -37,7 +37,7 @@ specification (FCS) by univariate models All data variables are assigned to a block. A variable can belong to only one block, so there are at most \eqn{p} blocks. -See the \code{nest} argument for an easier alternative to +See the \code{parcel} argument for an easier alternative to the \code{blocks} argument.} } \value{ diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 5cadee36b..15d577fe2 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -47,22 +47,22 @@ test_that("blocks alter the visit sequence", { }) -context("nest") +context("parcel") -# model with duplicate bmi cannot be specified with nest +# model with duplicate bmi cannot be specified with parcel # EXPECT WARNING: In b2n(name.blocks(x, prefix = prefix)) : Duplicated name(s) removed: bmi expect_warning( - nest1a <<- make.nest(list(c("bmi", "chl"), "bmi", "age"))) -nest1b <- setNames( c("A", "A", "bmi", "age"), + parcel1a <<- make.parcel(list(c("bmi", "chl"), "bmi", "age"))) +parcel1b <- setNames( c("A", "A", "bmi", "age"), nm = c("bmi", "chl", "bmi", "age")) -expect_silent(imp1a <- mice(nhanes, nest = nest1a, m = 10, print = FALSE)) -# EXPECT ERROR: validate.nest(nest, silent = silent) is not TRUE -expect_error(suppressWarnings(imp1b <<- mice(nhanes, nest = nest1b, m = 10, print = FALSE))) +expect_silent(imp1a <- mice(nhanes, parcel = parcel1a, m = 10, print = FALSE)) +# EXPECT ERROR: validate.parcel(parcel, silent = silent) is not TRUE +expect_error(suppressWarnings(imp1b <<- mice(nhanes, parcel = parcel1b, m = 10, print = FALSE))) # Getting around the error by the visitSequence -# test_that("nest formulation is equivalent to blocks", { +# test_that("parcel formulation is equivalent to blocks", { # expect_identical(complete(imp1, 1), complete(imp1a, 1)) # expect_identical(complete(imp1, 1), complete(imp1b, 1)) # }) @@ -71,25 +71,25 @@ expect_error(suppressWarnings(imp1b <<- mice(nhanes, nest = nest1b, m = 10, prin # reprex https://github.com/amices/mice/issues/326 imp1 <- mice(nhanes, seed = 1, m = 1, maxit = 2, print = FALSE) -imp2 <- mice(nhanes, nest = make.nest(list(c("bmi", "hyp"), "chl")), m = 1, maxit = 2, seed = 1, print = FALSE) +imp2 <- mice(nhanes, parcel = make.parcel(list(c("bmi", "hyp"), "chl")), 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)) }) -# neat nest formulation -nest2 <- setNames(c("A", "A", "chl"), +# neat parcel formulation +parcel2 <- setNames(c("A", "A", "chl"), nm = c("bmi", "hyp", "chl")) -imp2a <- mice(nhanes, nest = nest2, m = 1, maxit = 2, seed = 1, print = FALSE) -test_that("setNames nest formulation yields same solution", { +imp2a <- mice(nhanes, parcel = parcel2, m = 1, maxit = 2, seed = 1, print = FALSE) +test_that("setNames parcel formulation yields same solution", { expect_identical(complete(imp2, 1), complete(imp2a, 1)) }) # different order -nest3 <- setNames(c("A", "A", "chl"), +parcel3 <- setNames(c("A", "A", "chl"), nm = c("hyp", "bmi", "chl")) -imp3 <- mice(nhanes, nest = nest3, m = 1, maxit = 2, seed = 1, print = FALSE) +imp3 <- mice(nhanes, parcel = parcel3, 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("nests alter the visit sequence", { +test_that("parcels alter the visit sequence", { expect_identical(complete(imp3, 1), complete(imp4, 1)) }) From 07a79e924daef15c5cdeabf2cbdd86f84c44c64c Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 16:20:49 +0200 Subject: [PATCH 24/37] Use lower case default block names --- R/mice.R | 2 +- tests/testthat/test-blocks.R | 2 +- tests/testthat/test-blots.R | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/R/mice.R b/R/mice.R index 3976b4fc4..16c891a54 100644 --- a/R/mice.R +++ b/R/mice.R @@ -210,7 +210,7 @@ #' @param blocks List of \eqn{q} character vectors that identifies the #' variable names per block. The name of list elements #' identify blocks. `mice()` will provide default names -#' (`"B1"`, `"B2"`, ...) for blocks containing multiple +#' (`"b1"`, `"b2"`, ...) for blocks containing multiple #' variables. Variables within a block are imputed as a #' block, e.g. by a multivariate imputation method, or #' by an iterated version of the same univariate imputation diff --git a/tests/testthat/test-blocks.R b/tests/testthat/test-blocks.R index 15d577fe2..be8e5a8da 100644 --- a/tests/testthat/test-blocks.R +++ b/tests/testthat/test-blocks.R @@ -6,7 +6,7 @@ context("blocks") # # The current policy is not satisfying: # Currently, where[, "hyp"] is set to FALSE, so hyp is not imputed. -# However, it is still is predictor for block B1, bmi and age, thus +# However, it is still is predictor for block b1, bmi and age, thus # leading to missing data propagation # diff --git a/tests/testthat/test-blots.R b/tests/testthat/test-blots.R index d836cc541..3067095d4 100644 --- a/tests/testthat/test-blots.R +++ b/tests/testthat/test-blots.R @@ -5,7 +5,7 @@ blocks1 <- name.blocks(list(c("bmi", "chl"), "hyp")) 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)) +blots1 <- list(b1 = list(donors = 10), hyp = list(donors = 1)) imp1 <- mice(nhanes, blocks = blocks1, blots = blots1, m = 1, maxit = 1, print = FALSE) test_that("errors when mixing same global and local argument", { From 53916f4d84645812348e8d0f56a63bebec06c84e Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 16:34:08 +0200 Subject: [PATCH 25/37] Rename `blots` to `dots` --- NAMESPACE | 2 +- R/blots.R | 38 ++++++++++++++--------------- R/cbind.R | 10 ++++---- R/convert.R | 4 +-- R/edit.setup.R | 4 +-- R/filter.R | 6 ++--- R/generics.R | 4 +-- R/ibind.R | 6 ++--- R/mice.R | 20 +++++++-------- R/mice.impute.mnar.norm.R | 20 +++++++-------- R/mice.impute.pmm.R | 16 ++++++------ R/mice.mids.R | 4 +-- R/mids.R | 6 ++--- R/parse.ums.R | 2 +- R/rbind.R | 8 +++--- R/sampler.R | 4 +-- man/cbind.Rd | 4 +-- man/convertmodels.Rd | 2 +- man/extend.formulas.Rd | 4 +-- man/filter.mids.Rd | 2 +- man/{make.blots.Rd => make.dots.Rd} | 18 +++++++------- man/make.method.Rd | 2 +- man/make.visitSequence.Rd | 2 +- man/mice.Rd | 12 ++++----- man/mice.impute.mnar.Rd | 20 +++++++-------- man/mice.impute.pmm.Rd | 16 ++++++------ man/mids-class.Rd | 2 +- man/name.blocks.Rd | 2 +- man/nimp.Rd | 2 +- tests/testthat/test-blots.R | 8 +++--- 30 files changed, 125 insertions(+), 125 deletions(-) rename man/{make.blots.Rd => make.dots.Rd} (54%) diff --git a/NAMESPACE b/NAMESPACE index ead38383a..00a7e35f7 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -91,7 +91,7 @@ export(is.mira) export(is.mitml.result) export(lm.mids) export(make.blocks) -export(make.blots) +export(make.dots) export(make.formulas) export(make.method) export(make.parcel) diff --git a/R/blots.R b/R/blots.R index 66c385221..17e4d2cef 100644 --- a/R/blots.R +++ b/R/blots.R @@ -1,9 +1,9 @@ -#' Creates a `blots` argument +#' Creates a `dots` argument #' -#' This helper function creates a valid `blots` object. The -#' `blots` object is an argument to the `mice` function. -#' The name `blots` is a contraction of blocks-dots. -#' Through `blots`, the user can specify any additional +#' This helper function creates a valid `dots` object. The +#' `dots` object is an argument to the `mice` function. +#' The name `dots` is a contraction of blocks-dots. +#' Through `dots`, the user can specify any additional #' arguments that are specifically passed down to the lowest level #' imputation function. #' @param data A `data.frame` with the source data @@ -13,28 +13,28 @@ #' @seealso [make.blocks()] #' @examples #' make.predictorMatrix(nhanes) -#' make.blots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) +#' make.dots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) #' @export -make.blots <- function(data, blocks = make.blocks(data)) { +make.dots <- function(data, blocks = make.blocks(data)) { data <- check.dataform(data) - blots <- vector("list", length(blocks)) - for (i in seq_along(blots)) blots[[i]] <- alist() - names(blots) <- names(blocks) - blots + dots <- vector("list", length(blocks)) + for (i in seq_along(dots)) dots[[i]] <- alist() + names(dots) <- names(blocks) + dots } -check.blots <- function(blots, data, blocks = NULL) { +check.dots <- function(dots, data, blocks = NULL) { data <- check.dataform(data) - if (is.null(blots)) { - return(make.blots(data, blocks)) + if (is.null(dots)) { + return(make.dots(data, blocks)) } - blots <- as.list(blots) - for (i in seq_along(blots)) blots[[i]] <- as.list(blots[[i]]) + dots <- as.list(dots) + for (i in seq_along(dots)) dots[[i]] <- as.list(dots[[i]]) - if (length(blots) == length(blocks) && is.null(names(blots))) { - names(blots) <- names(blocks) + if (length(dots) == length(blocks) && is.null(names(dots))) { + names(dots) <- names(blocks) } - blots + dots } diff --git a/R/cbind.R b/R/cbind.R index 023438d23..25a3926e6 100644 --- a/R/cbind.R +++ b/R/cbind.R @@ -97,7 +97,7 @@ cbind.mids <- function(x, y = NULL, ...) { formulas <- x$formulas post <- c(x$post, rep.int("", ncol(y))) names(post) <- varnames - blots <- x$blots + dots <- x$dots ignore <- x$ignore # seed, lastSeedValue, number of iterations, chainMean and chainVar @@ -120,7 +120,7 @@ cbind.mids <- function(x, y = NULL, ...) { visitSequence = visitSequence, formulas = formulas, post = post, - blots = blots, + dots = dots, ignore = ignore, seed = seed, iteration = iteration, @@ -230,8 +230,8 @@ cbind.mids.mids <- function(x, y, call) { visitSequence <- unname(c(xnew[x$visitSequence], ynew[y$visitSequence])) post <- c(x$post, y$post) names(post) <- varnames - blots <- c(x$blots, y$blots) - names(blots) <- blocknames + dots <- c(x$dots, y$dots) + names(dots) <- blocknames ignore <- x$ignore # For the elements seed, lastSeedValue and iteration the values @@ -291,7 +291,7 @@ cbind.mids.mids <- function(x, y, call) { visitSequence = visitSequence, formulas = formulas, post = post, - blots = blots, + dots = dots, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/convert.R b/R/convert.R index 6580de669..df75ca147 100644 --- a/R/convert.R +++ b/R/convert.R @@ -124,11 +124,11 @@ b2n <- function(blocks, silent = FALSE) { return(parcel) } -paste.roles <- function(blots, roles, blocks) { +paste.roles <- function(dots, roles, blocks) { # FIXME # flat <- unlist(unname(roles)) # flat[unique(names(flat))] - return(blots) + return(dots) } validate.parcel <- function(parcel, silent = FALSE) { diff --git a/R/edit.setup.R b/R/edit.setup.R index b49945d32..a1d54eca9 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -20,7 +20,7 @@ edit.setup <- function(data, setup, pred <- setup$predictorMatrix meth <- setup$method form <- setup$formulas - blots <- setup$blots # not used + dots <- setup$dots # not used vis <- setup$visitSequence post <- setup$post @@ -103,7 +103,7 @@ edit.setup <- function(data, setup, setup$predictorMatrix <- pred setup$formulas <- form - setup$blots <- blots + setup$dots <- dots setup$visitSequence <- vis setup$post <- post setup$method <- meth diff --git a/R/filter.R b/R/filter.R index b875d6729..7148707a9 100644 --- a/R/filter.R +++ b/R/filter.R @@ -32,7 +32,7 @@ dplyr::filter #' `visitSequence` \tab Equals `.data$visitSequence`\cr #' `formulas` \tab Equals `.data$formulas`\cr #' `post` \tab Equals `.data$post`\cr -#' `blots` \tab Equals `.data$blots`\cr +#' `dots` \tab Equals `.data$dots`\cr #' `ignore` \tab Select positions in `.data$ignore` for which `include == TRUE`\cr #' `seed` \tab Equals `.data$seed`\cr #' `iteration` \tab Equals `.data$iteration`\cr @@ -77,7 +77,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { predictorMatrix <- .data$predictorMatrix visitSequence <- .data$visitSequence formulas <- .data$formulas - blots <- .data$blots + dots <- .data$dots post <- .data$post seed <- .data$seed iteration <- .data$iteration @@ -113,7 +113,7 @@ filter.mids <- function(.data, ..., .preserve = FALSE) { visitSequence = visitSequence, formulas = formulas, post = post, - blots = blots, + dots = dots, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/generics.R b/R/generics.R index 98f05c99a..e28ea927f 100644 --- a/R/generics.R +++ b/R/generics.R @@ -67,7 +67,7 @@ #' `visitSequence` \tab Combined as `c(x$visitSequence, y$visitSequence)`\cr #' `formulas` \tab Combined as `c(x$formulas, y$formulas)`\cr #' `post` \tab Combined as `c(x$post, y$post)`\cr -#' `blots` \tab Combined as `c(x$blots, y$blots)`\cr +#' `dots` \tab Combined as `c(x$dots, y$dots)`\cr #' `ignore` \tab Taken from `x$ignore`\cr #' `seed` \tab Taken from `x$seed`\cr #' `iteration` \tab Taken from `x$iteration`\cr @@ -94,7 +94,7 @@ #' `visitSequence` \tab Taken from `x$visitSequence`\cr #' `formulas` \tab Taken from `x$formulas`\cr #' `post` \tab Taken from `x$post`\cr -#' `blots` \tab Taken from `x$blots`\cr +#' `dots` \tab Taken from `x$dots`\cr #' `ignore` \tab Concatenate `x$ignore` and `y$ignore`\cr #' `seed` \tab Taken from `x$seed`\cr #' `iteration` \tab Taken from `x$iteration`\cr diff --git a/R/ibind.R b/R/ibind.R index 1044d367f..577e7a7b6 100644 --- a/R/ibind.R +++ b/R/ibind.R @@ -59,8 +59,8 @@ ibind <- function(x, y) { if (!identical(x$post, y$post)) { stop("Differences detected between `x$post` and `y$post`") } - if (!identical(x$blots, y$blots)) { - stop("Differences detected between `x$blots` and `y$blots`") + if (!identical(x$dots, y$dots)) { + stop("Differences detected between `x$dots` and `y$dots`") } visitSequence <- x$visitSequence imp <- vector("list", ncol(x$data)) @@ -90,7 +90,7 @@ ibind <- function(x, y) { predictorMatrix = x$predictorMatrix, visitSequence = visitSequence, formulas = x$formulas, post = x$post, - blots = x$blots, + dots = x$dots, seed = x$seed, iteration = iteration, lastSeedValue = x$lastSeedValue, diff --git a/R/mice.R b/R/mice.R index 16c891a54..6c41c77b6 100644 --- a/R/mice.R +++ b/R/mice.R @@ -272,12 +272,12 @@ #' as `F1`, `F2`, and so on. Formulas with one #' dependent (e.g. `ses ~ x1 + x2`) will be named #' after the dependent variable `"ses"`. -#' @param blots A named `list` with maximally \eqn{q} `alist` used to +#' @param dots A named `list` with maximally \eqn{q} `alist` used to #' pass down optional arguments to lower level imputation #' functions. -#' The entries of element `blots[[h]]` are passed down to +#' The entries of element `dots[[h]]` are passed down to #' the method called on block `h` or formula `h`. -#' For example, `blots = list(age = alist(donor = 20))` +#' For example, `dots = list(age = alist(donor = 20))` #' specifies that imputation of `age` should draw from #' imputations using 20 (instead of the default five) nearest #' neighbours. @@ -318,7 +318,7 @@ #' Note that specification of `data.init` will start all #' `m` Gibbs sampling streams from the same imputation. #' @param \dots Named arguments that are passed down to the univariate -#' imputation functions. Use `blots` for a more fine-grained +#' imputation functions. Use `dots` for a more fine-grained #' alternative. #' @param parcel A character vector with \eqn{p} elements identifying the #' variable group (or block) to which each variable is @@ -404,7 +404,7 @@ mice <- function(data, ignore = NULL, where = NULL, visitSequence = NULL, - blots = NULL, + dots = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, @@ -559,7 +559,7 @@ mice <- function(data, # other checks post <- check.post(post, data) - blots <- check.blots(blots, data, blocks) + dots <- check.dots(dots, data, blocks) ignore <- check.ignore(ignore, data) # data frame for storing the event log @@ -570,7 +570,7 @@ mice <- function(data, setup <- list( method = method, formulas = formulas, - blots = blots, + dots = dots, predictorMatrix = predictorMatrix, visitSequence = visitSequence, post = post @@ -578,7 +578,7 @@ mice <- function(data, setup <- edit.setup(data, setup, ...) method <- setup$method formulas <- setup$formulas - blots <- setup$blots + dots <- setup$dots predictorMatrix <- setup$predictorMatrix visitSequence <- setup$visitSequence post <- setup$post @@ -599,7 +599,7 @@ mice <- function(data, to <- from + maxit - 1 q <- sampler( data, m, ignore, where, imp, blocks, method, - visitSequence, predictorMatrix, formulas, blots, + visitSequence, predictorMatrix, formulas, dots, post, c(from, to), printFlag, ... ) @@ -621,7 +621,7 @@ mice <- function(data, visitSequence = visitSequence, formulas = formulas, post = post, - blots = blots, + dots = dots, ignore = ignore, seed = seed, iteration = q$iteration, diff --git a/R/mice.impute.mnar.norm.R b/R/mice.impute.mnar.norm.R index dcfb882fe..00e4891bc 100644 --- a/R/mice.impute.mnar.norm.R +++ b/R/mice.impute.mnar.norm.R @@ -32,16 +32,16 @@ #' vary across subjects, to reflect systematic departures of the #' missing data from the data distribution imputed under MAR. #' -#' Specification of the NARFCS model is done by the `blots` -#' argument of `mice()`. The `blots` parameter is a named +#' Specification of the NARFCS model is done by the `dots` +#' argument of `mice()`. The `dots` parameter is a named #' list. For each variable to be imputed by #' `mice.impute.mnar.norm()` or `mice.impute.mnar.logreg()` -#' the corresponding element in `blots` is a list with +#' the corresponding element in `dots` is a list with #' at least one argument `ums` and, optionally, a second #' argument `umx`. #' For example, the high-level call might like something like #' `mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), -#' blots = list(chl = list(ums = "-3+2*bmi")))`. +#' dots = list(chl = list(ums = "-3+2*bmi")))`. #' #' The `ums` parameter is required, and might look like this: #' `"-4+1*Y"`. The `ums` specifcation must have the @@ -107,13 +107,13 @@ #' @examples #' # 1: Example with no auxiliary data: only pass unidentifiable model specification (ums) #' -#' # Specify argument to pass on to mnar imputation functions via "blots" argument +#' # Specify argument to pass on to mnar imputation functions via "dots" argument #' mnar.blot <- list(X = list(ums = "-4"), Y = list(ums = "2+1*ZCat1-3*ZCat2")) #' -#' # Run NARFCS by using mnar imputation methods and passing argument via blots +#' # Run NARFCS by using mnar imputation methods and passing argument via dots #' impNARFCS <- mice(mnar_demo_data, #' method = c("mnar.logreg", "mnar.norm", ""), -#' blots = mnar.blot, seed = 234235, print = FALSE +#' dots = mnar.blot, seed = 234235, print = FALSE #' ) #' #' # Obtain MI results: Note they coincide with those from old version at @@ -127,7 +127,7 @@ #' # - Auxiliary data have same number of rows as x #' # - Auxiliary data have no overlapping variable names with x #' -#' # Specify argument to pass on to mnar imputation functions via "blots" argument +#' # Specify argument to pass on to mnar imputation functions via "dots" argument #' aux <- matrix(0:1, nrow = nrow(mnar_demo_data)) #' dimnames(aux) <- list(NULL, "even") #' mnar.blot <- list( @@ -135,10 +135,10 @@ #' Y = list(ums = "2+1*ZCat1-3*ZCat2+0.5*even", umx = aux) #' ) #' -#' # Run NARFCS by using mnar imputation methods and passing argument via blots +#' # Run NARFCS by using mnar imputation methods and passing argument via dots #' impNARFCS <- mice(mnar_demo_data, #' method = c("mnar.logreg", "mnar.norm", ""), -#' blots = mnar.blot, seed = 234235, print = FALSE +#' dots = mnar.blot, seed = 234235, print = FALSE #' ) #' #' # Obtain MI results: As expected they differ (slightly) from those diff --git a/R/mice.impute.pmm.R b/R/mice.impute.pmm.R index afd4de41e..0e9b2f137 100644 --- a/R/mice.impute.pmm.R +++ b/R/mice.impute.pmm.R @@ -115,16 +115,16 @@ #' abline(0, 1) #' cor(y, yimp, use = "pair") #' -#' # Use blots to exclude different values per column -#' # Create blots object -#' blots <- make.blots(boys) +#' # Use dots to exclude different values per column +#' # Create dots object +#' dots <- make.dots(boys) #' # Exclude ml 1 through 5 from tv donor pool -#' blots$tv$exclude <- c(1:5) +#' dots$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 = "pmm", 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 +#' dots$hgt$exclude <- sample(unique(boys$hgt), 100) +#' imp <- mice(boys, method = "pmm", print = FALSE, dots = dots, seed=123) +#' dots$hgt$exclude %in% unlist(c(imp$imp$hgt)) # MUST be all FALSE +#' dots$tv$exclude %in% unlist(c(imp$imp$tv)) # MUST be all FALSE #' #' # Factor quantification #' xname <- c("age", "hgt", "wgt") diff --git a/R/mice.mids.R b/R/mice.mids.R index 7f831b006..5ad237b58 100644 --- a/R/mice.mids.R +++ b/R/mice.mids.R @@ -107,7 +107,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { q <- sampler( obj$data, obj$m, obj$ignore, where, imp, blocks, obj$method, obj$visitSequence, obj$predictorMatrix, - obj$formulas, obj$blots, obj$post, + obj$formulas, obj$dots, obj$post, c(from, to), printFlag, ... ) @@ -155,7 +155,7 @@ mice.mids <- function(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...) { predictorMatrix = obj$predictorMatrix, visitSequence = obj$visitSequence, formulas = obj$formulas, post = obj$post, - blots = obj$blots, + dots = obj$dots, ignore = obj$ignore, seed = obj$seed, iteration = sumIt, diff --git a/R/mids.R b/R/mids.R index 6eb28cc02..523e03b46 100644 --- a/R/mids.R +++ b/R/mids.R @@ -39,7 +39,7 @@ #' identified by its name, so list names must correspond to block names.} #' \item{`post`:}{A vector of strings of length `length(blocks)` #' with commands for post-processing.} -#' \item{`blots`:}{"Block dots". The `blots` argument to the `mice()` +#' \item{`dots`:}{"Block dots". The `dots` argument to the `mice()` #' function.} #' \item{`ignore`:}{A logical vector of length `nrow(data)` indicating #' the rows in `data` used to build the imputation model. (new in `mice 3.12.0`)} @@ -123,8 +123,8 @@ validate.mids <- function(x, silent = FALSE) { if (!silent) warning("lengths of formulas and method differ", call. = FALSE) return(FALSE) } - # if (length(x$blots) != length(x$method)) { - # if (!silent) warning("lengths of blots and method differ", call. = FALSE) + # if (length(x$dots) != length(x$method)) { + # if (!silent) warning("lengths of dots and method differ", call. = FALSE) # return(FALSE) # } if (length(x$method) > ncol(x$data)) { diff --git a/R/parse.ums.R b/R/parse.ums.R index a8d23adb5..a06053544 100644 --- a/R/parse.ums.R +++ b/R/parse.ums.R @@ -3,7 +3,7 @@ parse.ums <- function(x, ums = NULL, umx = NULL, ...) { if (!is.null(umx)) x <- base::cbind(x, umx) ## Unidentifiable part - # e.g. specified in blots as list(X = list(ums = "-3+2*bmi")) + # e.g. specified in dots as list(X = list(ums = "-3+2*bmi")) mnar0 <- gsub("-", "+-", ums) mnar0 <- unlist(strsplit(mnar0, "+", fixed = TRUE)) if (mnar0[1L] == "") mnar0 <- mnar0[-1L] diff --git a/R/rbind.R b/R/rbind.R index f0ec3cf4b..b6581e4c6 100644 --- a/R/rbind.R +++ b/R/rbind.R @@ -44,7 +44,7 @@ rbind.mids <- function(x, y = NULL, ...) { method <- x$method post <- x$post formulas <- x$formulas - blots <- x$blots + dots <- x$dots predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -68,7 +68,7 @@ rbind.mids <- function(x, y = NULL, ...) { visitSequence = visitSequence, formulas = formulas, post = post, - blots = blots, + dots = dots, ignore = ignore, seed = seed, iteration = iteration, @@ -121,7 +121,7 @@ rbind.mids.mids <- function(x, y, call) { method <- x$method post <- x$post formulas <- x$formulas - blots <- x$blots + dots <- x$dots ignore <- c(x$ignore, y$ignore) predictorMatrix <- x$predictorMatrix visitSequence <- x$visitSequence @@ -163,7 +163,7 @@ rbind.mids.mids <- function(x, y, call) { visitSequence = visitSequence, formulas = formulas, post = post, - blots = blots, + dots = dots, ignore = ignore, seed = seed, iteration = iteration, diff --git a/R/sampler.R b/R/sampler.R index 873c5f704..54e0ce9a0 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -1,7 +1,7 @@ # The sampler controls the actual Gibbs sampling iteration scheme. # This function is called by mice and mice.mids sampler <- function(data, m, ignore, where, imp, blocks, method, - visitSequence, predictorMatrix, formulas, blots, + visitSequence, predictorMatrix, formulas, dots, post, fromto, printFlag, ...) { from <- fromto[1] to <- fromto[2] @@ -45,7 +45,7 @@ sampler <- function(data, m, ignore, where, imp, blocks, method, b <- blocks[[h]] if (calltype == "formula") ff <- formulas[[h]] else ff <- NULL - user <- blots[[h]] + user <- dots[[h]] # univariate/multivariate logic theMethod <- method[h] diff --git a/man/cbind.Rd b/man/cbind.Rd index 4a9c5066a..fd5f23904 100644 --- a/man/cbind.Rd +++ b/man/cbind.Rd @@ -91,7 +91,7 @@ is call to \code{cbind.mids()}\cr \code{visitSequence} \tab Combined as \code{c(x$visitSequence, y$visitSequence)}\cr \code{formulas} \tab Combined as \code{c(x$formulas, y$formulas)}\cr \code{post} \tab Combined as \code{c(x$post, y$post)}\cr -\code{blots} \tab Combined as \code{c(x$blots, y$blots)}\cr +\code{dots} \tab Combined as \code{c(x$dots, y$dots)}\cr \code{ignore} \tab Taken from \code{x$ignore}\cr \code{seed} \tab Taken from \code{x$seed}\cr \code{iteration} \tab Taken from \code{x$iteration}\cr @@ -118,7 +118,7 @@ the data of \code{y} will be copied\cr \code{visitSequence} \tab Taken from \code{x$visitSequence}\cr \code{formulas} \tab Taken from \code{x$formulas}\cr \code{post} \tab Taken from \code{x$post}\cr -\code{blots} \tab Taken from \code{x$blots}\cr +\code{dots} \tab Taken from \code{x$dots}\cr \code{ignore} \tab Concatenate \code{x$ignore} and \code{y$ignore}\cr \code{seed} \tab Taken from \code{x$seed}\cr \code{iteration} \tab Taken from \code{x$iteration}\cr diff --git a/man/convertmodels.Rd b/man/convertmodels.Rd index 644b23b99..529b567b6 100644 --- a/man/convertmodels.Rd +++ b/man/convertmodels.Rd @@ -35,7 +35,7 @@ or \code{-2} that assign special roles to some variables.} \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation diff --git a/man/extend.formulas.Rd b/man/extend.formulas.Rd index ca0073c9e..7541b88d0 100644 --- a/man/extend.formulas.Rd +++ b/man/extend.formulas.Rd @@ -50,7 +50,7 @@ incomplete data. Missing values are coded as \code{NA}.} \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation @@ -91,7 +91,7 @@ effects. The default is \code{TRUE}.} should be included in the result.} \item{...}{Named arguments that are passed down to the univariate -imputation functions. Use \code{blots} for a more fine-grained +imputation functions. Use \code{dots} for a more fine-grained alternative.} } \value{ diff --git a/man/filter.mids.Rd b/man/filter.mids.Rd index c681db7fb..2a1cd4b4d 100644 --- a/man/filter.mids.Rd +++ b/man/filter.mids.Rd @@ -43,7 +43,7 @@ The function constructs the elements of the filtered \code{mids} object as follo \code{visitSequence} \tab Equals \code{.data$visitSequence}\cr \code{formulas} \tab Equals \code{.data$formulas}\cr \code{post} \tab Equals \code{.data$post}\cr -\code{blots} \tab Equals \code{.data$blots}\cr +\code{dots} \tab Equals \code{.data$dots}\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/make.blots.Rd b/man/make.dots.Rd similarity index 54% rename from man/make.blots.Rd rename to man/make.dots.Rd index a9a747f0c..4a1613104 100644 --- a/man/make.blots.Rd +++ b/man/make.dots.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/blots.R -\name{make.blots} -\alias{make.blots} -\title{Creates a \code{blots} argument} +\name{make.dots} +\alias{make.dots} +\title{Creates a \code{dots} argument} \usage{ -make.blots(data, blocks = make.blocks(data)) +make.dots(data, blocks = make.blocks(data)) } \arguments{ \item{data}{A \code{data.frame} with the source data} @@ -16,16 +16,16 @@ the rows. The default assigns each variable in its own block.} A matrix } \description{ -This helper function creates a valid \code{blots} object. The -\code{blots} object is an argument to the \code{mice} function. -The name \code{blots} is a contraction of blocks-dots. -Through \code{blots}, the user can specify any additional +This helper function creates a valid \code{dots} object. The +\code{dots} object is an argument to the \code{mice} function. +The name \code{dots} is a contraction of blocks-dots. +Through \code{dots}, the user can specify any additional arguments that are specifically passed down to the lowest level imputation function. } \examples{ make.predictorMatrix(nhanes) -make.blots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) +make.dots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) } \seealso{ \code{\link[=make.blocks]{make.blocks()}} diff --git a/man/make.method.Rd b/man/make.method.Rd index b2e918cf6..554586752 100644 --- a/man/make.method.Rd +++ b/man/make.method.Rd @@ -32,7 +32,7 @@ argument (e.g., \code{mice.impute.jomoImpute()} or \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation diff --git a/man/make.visitSequence.Rd b/man/make.visitSequence.Rd index 7d9e5dc3f..dbaba750c 100644 --- a/man/make.visitSequence.Rd +++ b/man/make.visitSequence.Rd @@ -13,7 +13,7 @@ incomplete data. Missing values are coded as \code{NA}.} \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation diff --git a/man/mice.Rd b/man/mice.Rd index a35dcd31f..8fad1de10 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -16,7 +16,7 @@ mice( ignore = NULL, where = NULL, visitSequence = NULL, - blots = NULL, + dots = NULL, post = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), maxit = 5, @@ -68,7 +68,7 @@ allocated.} \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation @@ -160,12 +160,12 @@ Realize that convergence in one iteration is only guaranteed if the missing data pattern is actually monotone. \code{mice()} does not check for monotonicity.} -\item{blots}{A named \code{list} with maximally \eqn{q} \code{alist} used to +\item{dots}{A named \code{list} with maximally \eqn{q} \code{alist} used to pass down optional arguments to lower level imputation functions. -The entries of element \code{blots[[h]]} are passed down to +The entries of element \code{dots[[h]]} are passed down to the method called on block \code{h} or formula \code{h}. -For example, \code{blots = list(age = alist(donor = 20))} +For example, \code{dots = list(age = alist(donor = 20))} specifies that imputation of \code{age} should draw from imputations using 20 (instead of the default five) nearest neighbours.} @@ -212,7 +212,7 @@ Note that specification of \code{data.init} will start all \code{m} Gibbs sampling streams from the same imputation.} \item{\dots}{Named arguments that are passed down to the univariate -imputation functions. Use \code{blots} for a more fine-grained +imputation functions. Use \code{dots} for a more fine-grained alternative.} } \value{ diff --git a/man/mice.impute.mnar.Rd b/man/mice.impute.mnar.Rd index fa5e84b17..75557e5ee 100644 --- a/man/mice.impute.mnar.Rd +++ b/man/mice.impute.mnar.Rd @@ -61,15 +61,15 @@ iteration of \code{mice} by a user-specified quantity that can vary across subjects, to reflect systematic departures of the missing data from the data distribution imputed under MAR. -Specification of the NARFCS model is done by the \code{blots} -argument of \code{mice()}. The \code{blots} parameter is a named +Specification of the NARFCS model is done by the \code{dots} +argument of \code{mice()}. The \code{dots} parameter is a named list. For each variable to be imputed by \code{mice.impute.mnar.norm()} or \code{mice.impute.mnar.logreg()} -the corresponding element in \code{blots} is a list with +the corresponding element in \code{dots} is a list with at least one argument \code{ums} and, optionally, a second argument \code{umx}. For example, the high-level call might like something like -\code{mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), blots = list(chl = list(ums = "-3+2*bmi")))}. +\code{mice(nhanes[, c(2, 4)], method = c("pmm", "mnar.norm"), dots = list(chl = list(ums = "-3+2*bmi")))}. The \code{ums} parameter is required, and might look like this: \code{"-4+1*Y"}. The \code{ums} specifcation must have the @@ -121,13 +121,13 @@ For an MNAR alternative see also \code{\link[=mice.impute.ri]{mice.impute.ri()}} \examples{ # 1: Example with no auxiliary data: only pass unidentifiable model specification (ums) -# Specify argument to pass on to mnar imputation functions via "blots" argument +# Specify argument to pass on to mnar imputation functions via "dots" argument mnar.blot <- list(X = list(ums = "-4"), Y = list(ums = "2+1*ZCat1-3*ZCat2")) -# Run NARFCS by using mnar imputation methods and passing argument via blots +# Run NARFCS by using mnar imputation methods and passing argument via dots impNARFCS <- mice(mnar_demo_data, method = c("mnar.logreg", "mnar.norm", ""), - blots = mnar.blot, seed = 234235, print = FALSE + dots = mnar.blot, seed = 234235, print = FALSE ) # Obtain MI results: Note they coincide with those from old version at @@ -141,7 +141,7 @@ pool(with(impNARFCS, lm(Y ~ X + Z)))$pooled$estimate # - Auxiliary data have same number of rows as x # - Auxiliary data have no overlapping variable names with x -# Specify argument to pass on to mnar imputation functions via "blots" argument +# Specify argument to pass on to mnar imputation functions via "dots" argument aux <- matrix(0:1, nrow = nrow(mnar_demo_data)) dimnames(aux) <- list(NULL, "even") mnar.blot <- list( @@ -149,10 +149,10 @@ mnar.blot <- list( Y = list(ums = "2+1*ZCat1-3*ZCat2+0.5*even", umx = aux) ) -# Run NARFCS by using mnar imputation methods and passing argument via blots +# Run NARFCS by using mnar imputation methods and passing argument via dots impNARFCS <- mice(mnar_demo_data, method = c("mnar.logreg", "mnar.norm", ""), - blots = mnar.blot, seed = 234235, print = FALSE + dots = mnar.blot, seed = 234235, print = FALSE ) # Obtain MI results: As expected they differ (slightly) from those diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index fda7cde74..a4283ed57 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -136,16 +136,16 @@ plot(jitter(y), jitter(yimp), abline(0, 1) cor(y, yimp, use = "pair") -# Use blots to exclude different values per column -# Create blots object -blots <- make.blots(boys) +# Use dots to exclude different values per column +# Create dots object +dots <- make.dots(boys) # Exclude ml 1 through 5 from tv donor pool -blots$tv$exclude <- c(1:5) +dots$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 = "pmm", 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 +dots$hgt$exclude <- sample(unique(boys$hgt), 100) +imp <- mice(boys, method = "pmm", print = FALSE, dots = dots, seed=123) +dots$hgt$exclude \%in\% unlist(c(imp$imp$hgt)) # MUST be all FALSE +dots$tv$exclude \%in\% unlist(c(imp$imp$tv)) # MUST be all FALSE # Factor quantification xname <- c("age", "hgt", "wgt") diff --git a/man/mids-class.Rd b/man/mids-class.Rd index d84d87b0f..498e811df 100644 --- a/man/mids-class.Rd +++ b/man/mids-class.Rd @@ -79,7 +79,7 @@ correspond to blocks. The block to which the list element applies is identified by its name, so list names must correspond to block names.} \item{\code{post}:}{A vector of strings of length \code{length(blocks)} with commands for post-processing.} -\item{\code{blots}:}{"Block dots". The \code{blots} argument to the \code{mice()} +\item{\code{dots}:}{"Block dots". The \code{dots} argument to the \code{mice()} function.} \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 \verb{mice 3.12.0})} diff --git a/man/name.blocks.Rd b/man/name.blocks.Rd index 88e4095b3..b796eb421 100644 --- a/man/name.blocks.Rd +++ b/man/name.blocks.Rd @@ -10,7 +10,7 @@ name.blocks(blocks, prefix = "b") \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation diff --git a/man/nimp.Rd b/man/nimp.Rd index c50a18964..305d3c595 100644 --- a/man/nimp.Rd +++ b/man/nimp.Rd @@ -26,7 +26,7 @@ argument (e.g., \code{mice.impute.jomoImpute()} or \item{blocks}{List of \eqn{q} character vectors that identifies the variable names per block. The name of list elements identify blocks. \code{mice()} will provide default names -(\code{"B1"}, \code{"B2"}, ...) for blocks containing multiple +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple variables. Variables within a block are imputed as a block, e.g. by a multivariate imputation method, or by an iterated version of the same univariate imputation diff --git a/tests/testthat/test-blots.R b/tests/testthat/test-blots.R index 3067095d4..1591fff70 100644 --- a/tests/testthat/test-blots.R +++ b/tests/testthat/test-blots.R @@ -1,16 +1,16 @@ -context("blots") +context("dots") # 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) # 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) +dots1 <- list(b1 = list(donors = 10), hyp = list(donors = 1)) +imp1 <- mice(nhanes, blocks = blocks1, dots = dots1, m = 1, maxit = 1, print = FALSE) test_that("errors when mixing same global and local argument", { expect_error( - mice(nhanes, blocks = blocks1, blots = blots1, donors = 7, print = FALSE), + mice(nhanes, blocks = blocks1, dots = dots1, donors = 7, print = FALSE), 'formal argument "donors" matched by multiple actual arguments' ) }) From 3c090553dbcb13ccedcb3d4b6f809968b3e7ec6b Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 17:14:44 +0200 Subject: [PATCH 26/37] Rename files from blots/nest to dots/parcel --- R/{blots.R => dots.R} | 0 R/{nest.R => parcel.R} | 0 man/construct.parcel.Rd | 2 +- man/make.dots.Rd | 2 +- man/make.parcel.Rd | 2 +- tests/testthat/{test-blots.R => test-dots.R} | 0 6 files changed, 3 insertions(+), 3 deletions(-) rename R/{blots.R => dots.R} (100%) rename R/{nest.R => parcel.R} (100%) rename tests/testthat/{test-blots.R => test-dots.R} (100%) diff --git a/R/blots.R b/R/dots.R similarity index 100% rename from R/blots.R rename to R/dots.R diff --git a/R/nest.R b/R/parcel.R similarity index 100% rename from R/nest.R rename to R/parcel.R diff --git a/man/construct.parcel.Rd b/man/construct.parcel.Rd index 03399d6b4..33b80f8b5 100644 --- a/man/construct.parcel.Rd +++ b/man/construct.parcel.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nest.R +% Please edit documentation in R/parcel.R \name{construct.parcel} \alias{construct.parcel} \title{Construct blocks from \code{formulas} and \code{predictorMatrix}} diff --git a/man/make.dots.Rd b/man/make.dots.Rd index 4a1613104..0fd2afab1 100644 --- a/man/make.dots.Rd +++ b/man/make.dots.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/blots.R +% Please edit documentation in R/dots.R \name{make.dots} \alias{make.dots} \title{Creates a \code{dots} argument} diff --git a/man/make.parcel.Rd b/man/make.parcel.Rd index 7b071d046..92691363f 100644 --- a/man/make.parcel.Rd +++ b/man/make.parcel.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nest.R +% Please edit documentation in R/parcel.R \name{make.parcel} \alias{make.parcel} \title{Creates a \code{parcel} argument} diff --git a/tests/testthat/test-blots.R b/tests/testthat/test-dots.R similarity index 100% rename from tests/testthat/test-blots.R rename to tests/testthat/test-dots.R From 3cebc308b86e11758149b11841e6970238c73607 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Thu, 21 Sep 2023 18:22:08 +0200 Subject: [PATCH 27/37] Add deprecation support for make.blots() --- NAMESPACE | 1 + R/dots.R | 9 ++++++++- R/mice.R | 9 +++++++++ _pkgdown.yml | 2 ++ man/make.blots.Rd | 17 +++++++++++++++++ man/make.dots.Rd | 1 - man/mice.Rd | 3 +++ 7 files changed, 40 insertions(+), 2 deletions(-) create mode 100644 man/make.blots.Rd diff --git a/NAMESPACE b/NAMESPACE index 00a7e35f7..5f50bdafd 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -91,6 +91,7 @@ export(is.mira) export(is.mitml.result) export(lm.mids) export(make.blocks) +export(make.blots) export(make.dots) export(make.formulas) export(make.method) diff --git a/R/dots.R b/R/dots.R index 17e4d2cef..a8de6521d 100644 --- a/R/dots.R +++ b/R/dots.R @@ -12,7 +12,6 @@ #' @return A matrix #' @seealso [make.blocks()] #' @examples -#' make.predictorMatrix(nhanes) #' make.dots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) #' @export make.dots <- function(data, blocks = make.blocks(data)) { @@ -38,3 +37,11 @@ check.dots <- function(dots, data, blocks = NULL) { } dots } + +#' Creates a `blots` argument +#' @inheritParams make.dots +#' @export +make.blots <- function(data, blocks = make.blocks(data)) { + .Deprecated("make.dots") + make.dots(data = data, blocks = make.blocks(data)) +} diff --git a/R/mice.R b/R/mice.R index 6c41c77b6..48e256243 100644 --- a/R/mice.R +++ b/R/mice.R @@ -323,6 +323,7 @@ #' @param parcel A character vector with \eqn{p} elements identifying the #' variable group (or block) to which each variable is #' allocated. +#' @param blots Deprecated. Replaced by `dots`. #' #' @return Returns an S3 object of class [`mids()`][mids-class] #' (multiply imputed data set) @@ -411,9 +412,17 @@ mice <- function(data, printFlag = TRUE, seed = NA, data.init = NULL, + blots = NULL, ...) { call <- match.call() + + # legacy handling check.deprecated(...) + if (!missing(blots)) { + warning("argument 'blots' is deprecated; please use 'dots' instead.", + call. = FALSE) + dots <- blots + } if (!is.na(seed)) set.seed(seed) diff --git a/_pkgdown.yml b/_pkgdown.yml index aa8a5091f..55842bb7c 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -42,8 +42,10 @@ reference: - squeeze - make.blocks - make.blots + - make.dots - make.formulas - make.method + - make.parcel - make.post - make.predictorMatrix - make.visitSequence diff --git a/man/make.blots.Rd b/man/make.blots.Rd new file mode 100644 index 000000000..a9db48593 --- /dev/null +++ b/man/make.blots.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dots.R +\name{make.blots} +\alias{make.blots} +\title{Creates a \code{blots} argument} +\usage{ +make.blots(data, blocks = make.blocks(data)) +} +\arguments{ +\item{data}{A \code{data.frame} with the source data} + +\item{blocks}{An optional specification for blocks of variables in +the rows. The default assigns each variable in its own block.} +} +\description{ +Creates a \code{blots} argument +} diff --git a/man/make.dots.Rd b/man/make.dots.Rd index 0fd2afab1..3c2dc30d0 100644 --- a/man/make.dots.Rd +++ b/man/make.dots.Rd @@ -24,7 +24,6 @@ arguments that are specifically passed down to the lowest level imputation function. } \examples{ -make.predictorMatrix(nhanes) make.dots(nhanes, blocks = name.blocks(c("age", "hyp"), "xxx")) } \seealso{ diff --git a/man/mice.Rd b/man/mice.Rd index 8fad1de10..52bfe12cf 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -23,6 +23,7 @@ mice( printFlag = TRUE, seed = NA, data.init = NULL, + blots = NULL, ... ) } @@ -211,6 +212,8 @@ of the observed data. Note that specification of \code{data.init} will start all \code{m} Gibbs sampling streams from the same imputation.} +\item{blots}{Deprecated. Replaced by \code{dots}.} + \item{\dots}{Named arguments that are passed down to the univariate imputation functions. Use \code{dots} for a more fine-grained alternative.} From 7b7a17c5b2899a84c32e0907a01a86c664ea73c4 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 22 Sep 2023 00:54:37 +0200 Subject: [PATCH 28/37] Implement autoremove in check.predictorMatrix() and check.formulas() --- R/formula.R | 12 ++++++++++-- R/mice.R | 19 ++++++++++++------- R/predictorMatrix.R | 19 +++++++++++++++++-- man/mice.Rd | 4 ++++ 4 files changed, 43 insertions(+), 11 deletions(-) diff --git a/R/formula.R b/R/formula.R index b9af911f2..da1657f74 100644 --- a/R/formula.R +++ b/R/formula.R @@ -124,7 +124,8 @@ name.formulas <- function(formulas, prefix = "f") { } -check.formulas <- function(formulas, data) { +check.formulas <- function(formulas, data, + autoremove = TRUE) { formulas <- name.formulas(formulas) formulas <- handle.oldstyle.formulas(formulas, data) formulas <- lapply(formulas, expand.dots, data) @@ -143,7 +144,14 @@ check.formulas <- function(formulas, data) { completevars <- colnames(data)[!apply(is.na(data), 2, sum)] uip <- setdiff(notimputed, completevars) # if any of these are in RHS for formulas, remove them - formulas <- lapply(formulas, remove.rhs.variables, vars = uip) + removeme <- intersect(uip, as.vector(sapply(formulas, all.vars))) + if (length(removeme) && autoremove) { + formulas <- lapply(formulas, remove.rhs.variables, vars = removeme) + vars <- paste(removeme, collapse = ",") + updateLog(out = paste("incomplete predictor(s)", vars), + meth = "check", frame = 1) + } + # add components y ~ 1 for y to formulas for (y in notimputed) { formulas[[y]] <- as.formula(paste(y, "~ 1")) diff --git a/R/mice.R b/R/mice.R index 48e256243..5c8b1ee7f 100644 --- a/R/mice.R +++ b/R/mice.R @@ -324,6 +324,8 @@ #' variable group (or block) to which each variable is #' allocated. #' @param blots Deprecated. Replaced by `dots`. +#' @param autoremove Logical. Should unimputed incomplete predictors be removed +#' to prevent NA propagation? #' #' @return Returns an S3 object of class [`mids()`][mids-class] #' (multiply imputed data set) @@ -413,6 +415,7 @@ mice <- function(data, seed = NA, data.init = NULL, blots = NULL, + autoremove = TRUE, ...) { call <- match.call() @@ -424,6 +427,10 @@ mice <- function(data, dots <- blots } + # 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 = "") + if (!is.na(seed)) set.seed(seed) # check form of data and m @@ -451,7 +458,8 @@ mice <- function(data, # case B if (!mp & mb & mf) { # predictorMatrix leads - predictorMatrix <- check.predictorMatrix(predictorMatrix, data) + predictorMatrix <- check.predictorMatrix(predictorMatrix, data, + autoremove = autoremove) blocks <- make.blocks(colnames(predictorMatrix), partition = "scatter") formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) } @@ -467,7 +475,7 @@ mice <- function(data, # case D if (mp & mb & !mf) { # formulas leads - formulas <- check.formulas(formulas, data) + formulas <- check.formulas(formulas, data, autoremove = autoremove) blocks <- construct.blocks(formulas) predictorMatrix <- f2p(formulas, data, blocks) } @@ -475,7 +483,8 @@ mice <- function(data, # case E if (!mp & !mb & mf) { # predictor leads (use for multivariate imputation) - predictorMatrix <- check.predictorMatrix(predictorMatrix, data) + predictorMatrix <- check.predictorMatrix(predictorMatrix, data, + autoremove = autoremove) blocks <- check.blocks(blocks, data, calltype = "pred") formulas <- make.formulas(data, blocks, predictorMatrix = predictorMatrix) } @@ -571,10 +580,6 @@ mice <- function(data, dots <- check.dots(dots, 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 = "") - # edit imputation setup setup <- list( method = method, diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 3668e29cb..bc3306de6 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -45,7 +45,8 @@ make.predictorMatrix <- function(data, blocks = make.blocks(data), check.predictorMatrix <- function(predictorMatrix, data, - blocks = NULL) { + blocks = NULL, + autoremove = TRUE) { data <- check.dataform(data) if (!is.matrix(predictorMatrix)) { @@ -82,9 +83,23 @@ check.predictorMatrix <- function(predictorMatrix, ) } - # calculate ynames (variables to impute) for use in check.method() + # NA-propagation prevention + # find all dependent (imputed) variables hit <- apply(predictorMatrix, 1, function(x) any(x != 0)) ynames <- row.names(predictorMatrix)[hit] + # find all variables in data that are not imputed + notimputed <- setdiff(colnames(data), ynames) + # select uip: unimputed incomplete predictors + completevars <- colnames(data)[!apply(is.na(data), 2, sum)] + uip <- setdiff(notimputed, completevars) + # if any of these are predictors, remove them + removeme <- intersect(uip, colnames(predictorMatrix)) + if (length(removeme) && autoremove) { + predictorMatrix[, removeme] <- 0 + vars <- paste(removeme, collapse = ",") + updateLog(out = paste("incomplete predictor(s)", vars), + meth = "check", frame = 1) + } # grow predictorMatrix to all variables in data if (ncol(predictorMatrix) < ncol(data)) { diff --git a/man/mice.Rd b/man/mice.Rd index 52bfe12cf..670784155 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -24,6 +24,7 @@ mice( seed = NA, data.init = NULL, blots = NULL, + autoremove = TRUE, ... ) } @@ -214,6 +215,9 @@ Note that specification of \code{data.init} will start all \item{blots}{Deprecated. Replaced by \code{dots}.} +\item{autoremove}{Logical. Should unimputed incomplete predictors be removed +to prevent NA propagation?} + \item{\dots}{Named arguments that are passed down to the univariate imputation functions. Use \code{dots} for a more fine-grained alternative.} From 8c4bb38c7b62ea87ffe3512d2db1b9a89c190ff8 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 22 Sep 2023 10:50:49 +0200 Subject: [PATCH 29/37] Write one loggedEvent for each removed variable --- R/formula.R | 7 ++++--- R/predictorMatrix.R | 5 +++-- tests/testthat/test-formulas.R | 6 +++--- 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/R/formula.R b/R/formula.R index da1657f74..9cabaf1ae 100644 --- a/R/formula.R +++ b/R/formula.R @@ -147,9 +147,10 @@ check.formulas <- function(formulas, data, removeme <- intersect(uip, as.vector(sapply(formulas, all.vars))) if (length(removeme) && autoremove) { formulas <- lapply(formulas, remove.rhs.variables, vars = removeme) - vars <- paste(removeme, collapse = ",") - updateLog(out = paste("incomplete predictor(s)", vars), - meth = "check", frame = 1) + for (j in removeme) { + updateLog(out = paste("removed incomplete predictor", j), + meth = "check", frame = 1) + } } # add components y ~ 1 for y to formulas diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index bc3306de6..70d31f4d1 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -96,9 +96,10 @@ check.predictorMatrix <- function(predictorMatrix, removeme <- intersect(uip, colnames(predictorMatrix)) if (length(removeme) && autoremove) { predictorMatrix[, removeme] <- 0 - vars <- paste(removeme, collapse = ",") - updateLog(out = paste("incomplete predictor(s)", vars), + for (j in removeme) { + updateLog(out = paste("removed incomplete predictor", j), meth = "check", frame = 1) + } } # grow predictorMatrix to all variables in data diff --git a/tests/testthat/test-formulas.R b/tests/testthat/test-formulas.R index f1ea068f1..557f8ad49 100644 --- a/tests/testthat/test-formulas.R +++ b/tests/testthat/test-formulas.R @@ -15,6 +15,6 @@ test_that("model.matrix() deletes incomplete cases", { # in MICE we can now use poly() -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) +fm <- list(bmi ~ age + hyp + cut(chl, 3)) +expect_warning(mice(nhanes, formulas = fm, m = 1, maxit = 2, print = FALSE, autoremove = TRUE)) +expect_silent(mice(nhanes, formulas = fm, m = 1, maxit = 2, print = FALSE, autoremove = FALSE)) \ No newline at end of file From 24688b1d1f031a8a986fd931628f9318764ba2b1 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 22 Sep 2023 10:58:48 +0200 Subject: [PATCH 30/37] Abort mice when user speficies mixes of `formulas` and `predictorMatrix` arguments --- R/mice.R | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/R/mice.R b/R/mice.R index 5c8b1ee7f..99592b741 100644 --- a/R/mice.R +++ b/R/mice.R @@ -491,6 +491,8 @@ mice <- function(data, # case F if (!mp & mb & !mf) { + stop("cannot process mix of 'predictorMatrix' and 'formulas' arguments", + call. = FALSE) # it is better to forbid this case # formulas lead formulas <- check.formulas(formulas, data) @@ -503,6 +505,8 @@ mice <- function(data, if (mp & !mb & !mf) { # it is better to forbid this case # blocks lead + stop("cannot process mix of 'parcel', 'blocks' or 'formulas' arguments", + call. = FALSE) blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, blocks) predictorMatrix <- make.predictorMatrix(data, blocks) @@ -512,6 +516,8 @@ mice <- function(data, if (!mp & !mb & !mf) { # it is better to forbid this case # blocks lead + stop("cannot process mix of 'predictorMatrix' and 'formulas' arguments", + call. = FALSE) blocks <- check.blocks(blocks, data) formulas <- check.formulas(formulas, data) predictorMatrix <- check.predictorMatrix(predictorMatrix, data, blocks) From e1c475f7e5188f14c3403ad4eb88c36582021535 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Fri, 22 Sep 2023 11:53:16 +0200 Subject: [PATCH 31/37] Update NEWS.md --- NEWS.md | 28 ++++++++++++++++++++-------- 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/NEWS.md b/NEWS.md index d650f3e46..d2ecb63b2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,28 +1,40 @@ -## New behaviours +## New behaviours and features -1. Prevention of `NA` propagation by removing incomplete predictors. This version detects when a predictor contains missing values that are not imputed. In order to prevent NA propagation, `mice()` does the following actions: 1) removes incomplete predictor(s) from the RHS, 2) adds incomplete predictor(s) to formulas `(var ~ 1)` and block components, sets `method[var] = ""`, and sets the `predictorMatrix` column and row to zero +1. TWO SEPARATE INTERFACES FOR MODEL SPECIFICATION: This version promotes two interfaces to specify imputations models: predictor (`predictorMatrix` + `parcel` + `method`) and formula (`formulas + method`). This version does not accept anymore accept mixes of `predictorMatrix` and `formulas` arguments in the call to `mice()`. -2. The `predictorMatrix` input can be a square submatrix of the full `predictorMatrix`. `mice()` will augment `predictorMatrix` to the full matrix and always return a p * p named matrix corresponding to the p columns in the data. The inactive variables will have zero columns and rows. +2. NA-PROPAGATION PREVENTION. This version detects when a predictor contains missing values that are not imputed. In order to prevent NA propagation, `mice()` can follow two strategies: "Autoremove" (remove incomplete predictor(s) from the RHS, set `method` to `""`, adapt `predictorMatrix`, `formulas` and `blocks`, write to loggedEvents), or "Autoimpute" (Impute incomplete predictor and adapt `method`, `predictorMatrix`, `formulas`, and so on). "Autoremove" is implemented and current default. Use `mice(..., autoremove = FALSE)` to revert to old behavior (NA propagation). -3. The `predictorMatrix` input may be unnamed if its size is p * p. For other than p * p, an unnamed matrix generated an error. +3. SUBMODELS: The `predictorMatrix` input can be a square submatrix of the full `predictorMatrix` when its dimensions are named. `mice()` will augment the tiny `predictorMatrix` to the full matrix and always return a p * p named matrix corresponding to the p columns in the data. Unmentioned variables not be imputed, and the `predictorMatrix`, `formulas` and `method` are adapted accordingly. + +4. DROP NON-SQUARE PREDICTOR MATRIX: Version 3.0 introduced non-square versions, but its interpretation turned out to be complex and ambiguous. For clarity, this update works with a predictor matrix that is square with both dimensions identically named with the names of the variables in the data. Variable groups are now specified through the `parcel` argument. + +5. NEW PARCEL ARGUMENT. There is a new `parcel` argument that is easier to use. The print of the `mids` object shows `parcel` when it is different from the default. +`parcel` can take over the role of `blocks` in specification. `blocks` is soft-deprecated, but still widely used within the program code. + +6. NEW DOTS ARGUMENT. The `blots` argument is renamed to `dots` + +7. EXIT VALIDATION: Adds a new `validate.mids()` checks the `mids` object before exit. ## Changes +- Adds functions to convert between `predictorMatrix` and `formulas` specification +- Adds support to pass down user-specified options to multivariate imputation methods +- Now uses lowercase default block names +- The `predictorMatrix` input may be unnamed if its size is p * p. For other than p * p, an unnamed matrix generated an error. - Performs stricter checks on zero rows in predictorMatrix under empty imputation method -- Adds supports a tiny predictorMatrix -- Solves bug in f2p() - Adds new function `remove.rhs.variables()` -- Adds a `validate.mids()` check at exit that errors if `rownames(predictorMatrix)` differ from `colnames(data)`. Some more output tests need to be added. - Removes codes designed to work specifically with a non-square `predictorMatrix` - Generates an error if `predictorMatrix` has fewer rows than length of `blocks` +- Better initialization using typed `NA`s in `initialize.imp()` +- Rewritten the documentation of all `mice()` arguments to be precise and consistent ## New exit checks - `rownames(predictorMatrix)` must match `colnames(data)` - length of `formulas` and `blocks` must be equal - length of `formulas` and `method` must be equal -- length of `blots` and `method` must be equal +- length of `dots` and `method` must be equal - length of `method` vector cannot exceed number of variables - length of `imp` and number of variables must be equal From da6396b5fd05a6b34ebfd6b5bbd5fffe67e553e9 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 2 Oct 2023 21:30:21 +0200 Subject: [PATCH 32/37] Reorder mice() arguments into a clusters of operations --- R/mice.R | 18 ++--- man/mice.Rd | 188 ++++++++++++++++++++++++++-------------------------- 2 files changed, 103 insertions(+), 103 deletions(-) diff --git a/R/mice.R b/R/mice.R index 99592b741..6f2f0417d 100644 --- a/R/mice.R +++ b/R/mice.R @@ -399,23 +399,23 @@ #' @export mice <- function(data, m = 5, - method = NULL, predictorMatrix, parcel = NULL, - blocks, formulas, - ignore = NULL, - where = NULL, - visitSequence = NULL, - dots = NULL, - post = NULL, + method = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), + dots = NULL, + visitSequence = NULL, maxit = 5, - printFlag = TRUE, seed = NA, data.init = NULL, - blots = NULL, + where = NULL, + ignore = NULL, + post = NULL, + printFlag = TRUE, autoremove = TRUE, + blocks, + blots = NULL, ...) { call <- match.call() diff --git a/man/mice.Rd b/man/mice.Rd index 670784155..28a6f9cbb 100644 --- a/man/mice.Rd +++ b/man/mice.Rd @@ -8,23 +8,23 @@ mice( data, m = 5, - method = NULL, predictorMatrix, parcel = NULL, - blocks, formulas, - ignore = NULL, - where = NULL, - visitSequence = NULL, - dots = NULL, - post = NULL, + method = NULL, defaultMethod = c("pmm", "logreg", "polyreg", "polr"), + dots = NULL, + visitSequence = NULL, maxit = 5, - printFlag = TRUE, seed = NA, data.init = NULL, - blots = NULL, + where = NULL, + ignore = NULL, + post = NULL, + printFlag = TRUE, autoremove = TRUE, + blocks, + blots = NULL, ... ) } @@ -36,14 +36,6 @@ incomplete data. Missing values are coded as \code{NA}.} Setting \code{m = 1} produces a single imputation per cell (not recommended in general).} -\item{method}{Character vector of length \eqn{q} specifying imputation -methods for (groups of) variables. In the special case -\code{length(method) == 1}, the specified method applies to all -variables. When \code{method} is not specified, \code{mice()} will -select a method based on the variable type as regulated -by the \code{defaultMethod} argument. See details -on \emph{skipping imputation}.} - \item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and maximal \eqn{p} columns. Row- and column names are \code{colnames(data)}. @@ -67,23 +59,6 @@ or \code{-2} that assign special roles to some variables.} variable group (or block) to which each variable is allocated.} -\item{blocks}{List of \eqn{q} character vectors that identifies the -variable names per block. The name of list elements -identify blocks. \code{mice()} will provide default names -(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple -variables. Variables within a block are imputed as a -block, e.g. by a multivariate imputation method, or -by an iterated version of the same univariate imputation -method. By default each variable is allocated to a -separate block, which is effectively fully conditional -specification (FCS) by univariate models -(variable-by-variable imputation). -All data variables are assigned to a block. -A variable can belong to only one block, so there are -at most \eqn{p} blocks. -See the \code{parcel} argument for an easier alternative to -the \code{blocks} argument.} - \item{formulas}{A named list with \eqn{q} component, each containing one formula. The left hand side (LHS) specifies the variables to be imputed, and the right hand side (RHS) @@ -113,33 +88,32 @@ as \code{F1}, \code{F2}, and so on. Formulas with one dependent (e.g. \code{ses ~ x1 + x2}) will be named after the dependent variable \code{"ses"}.} -\item{ignore}{A logical vector of \eqn{n} elements indicating -which rows are ignored for estimating the parameters of -the imputation model. -Rows with \code{ignore} set to \code{TRUE} do not influence the -parameters of the imputation model. -The \code{ignore} argument allows splitting \code{data} into a -training set (on which \code{mice()} fits the imputation model) -and a test set (that does not influence the imputation -model parameter estimates). -The default \code{NULL} corresponds to all \code{FALSE}, thus -including all rows into the imputation models. -Note: Not all imputation methods may support the \code{ignore} -argument (e.g., \code{mice.impute.jomoImpute()} or -\code{mice.impute.panImpute()}).} +\item{method}{Character vector of length \eqn{q} specifying imputation +methods for (groups of) variables. In the special case +\code{length(method) == 1}, the specified method applies to all +variables. When \code{method} is not specified, \code{mice()} will +select a method based on the variable type as regulated +by the \code{defaultMethod} argument. See details +on \emph{skipping imputation}.} -\item{where}{A data frame or matrix of logicals with \eqn{n} rows -and \eqn{p} columns, indicating the cells of \code{data} for -which imputations are generated. -The default \code{where = is.na(data)} specifies that all -missing data are imputed. -The \code{where} argument can overimpute cells -with observed data, or skip imputation of specific missing -cells. Be aware that the latter option could propagate -missing values to other variables. See details. -Note: Not all imputation methods may support the \code{where} -argument (e.g., \code{mice.impute.jomoImpute()} or -\code{mice.impute.panImpute()}).} +\item{defaultMethod}{A vector of length 4 containing the default imputation +methods for +1) numeric data (\code{"pmm"}) +2) factor data with 2 levels, (\code{"logreg"}) +3) factor data with > 2 unordered levels, (\code{"polyreg"}) and +4) factor data with > 2 ordered levels (\code{"polr"}). +The \code{defaultMethod} can be used to alter to default mapping +of variable type to imputation method.} + +\item{dots}{A named \code{list} with maximally \eqn{q} \code{alist} used to +pass down optional arguments to lower level imputation +functions. +The entries of element \code{dots[[h]]} are passed down to +the method called on block \code{h} or formula \code{h}. +For example, \code{dots = list(age = alist(donor = 20))} +specifies that imputation of \code{age} should draw from +imputations using 20 (instead of the default five) nearest +neighbours.} \item{visitSequence}{A vector of block names of arbitrary length, specifying the sequence of blocks in which blocks are imputed. @@ -162,43 +136,10 @@ Realize that convergence in one iteration is only guaranteed if the missing data pattern is actually monotone. \code{mice()} does not check for monotonicity.} -\item{dots}{A named \code{list} with maximally \eqn{q} \code{alist} used to -pass down optional arguments to lower level imputation -functions. -The entries of element \code{dots[[h]]} are passed down to -the method called on block \code{h} or formula \code{h}. -For example, \code{dots = list(age = alist(donor = 20))} -specifies that imputation of \code{age} should draw from -imputations using 20 (instead of the default five) nearest -neighbours.} - -\item{post}{A vector of length \eqn{p}, each specifying an expression -as a string. The string is parsed and executed within -the \code{sampler()} function to post-process imputed -values during the iterations. The default is a vector -of empty strings, indicating no post-processing. -Multivariate imputation methods ignore the \code{post} -parameter.} - -\item{defaultMethod}{A vector of length 4 containing the default imputation -methods for -1) numeric data (\code{"pmm"}) -2) factor data with 2 levels, (\code{"logreg"}) -3) factor data with > 2 unordered levels, (\code{"polyreg"}) and -4) factor data with > 2 ordered levels (\code{"polr"}). -The \code{defaultMethod} can be used to alter to default mapping -of variable type to imputation method.} - \item{maxit}{A scalar giving the number of iterations. The default is 5. In general, the user should study the convergence of the algorithm, e.g., by \code{plot(imp)}.} -\item{printFlag}{If \code{printFlag = TRUE} (default) then \code{mice()} will -print iteration history on the console. This is useful for -checking how far the algorithm is. Use \code{print = FALSE} -for silent computation, simulations, and to suppress -iteration output on the console.} - \item{seed}{An integer that is used as argument by the \code{set.seed()} for offsetting the random number generator. Default is to leave the random number generator alone. Use \code{seed} to @@ -213,11 +154,70 @@ of the observed data. Note that specification of \code{data.init} will start all \code{m} Gibbs sampling streams from the same imputation.} -\item{blots}{Deprecated. Replaced by \code{dots}.} +\item{where}{A data frame or matrix of logicals with \eqn{n} rows +and \eqn{p} columns, indicating the cells of \code{data} for +which imputations are generated. +The default \code{where = is.na(data)} specifies that all +missing data are imputed. +The \code{where} argument can overimpute cells +with observed data, or skip imputation of specific missing +cells. Be aware that the latter option could propagate +missing values to other variables. See details. +Note: Not all imputation methods may support the \code{where} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} + +\item{ignore}{A logical vector of \eqn{n} elements indicating +which rows are ignored for estimating the parameters of +the imputation model. +Rows with \code{ignore} set to \code{TRUE} do not influence the +parameters of the imputation model. +The \code{ignore} argument allows splitting \code{data} into a +training set (on which \code{mice()} fits the imputation model) +and a test set (that does not influence the imputation +model parameter estimates). +The default \code{NULL} corresponds to all \code{FALSE}, thus +including all rows into the imputation models. +Note: Not all imputation methods may support the \code{ignore} +argument (e.g., \code{mice.impute.jomoImpute()} or +\code{mice.impute.panImpute()}).} + +\item{post}{A vector of length \eqn{p}, each specifying an expression +as a string. The string is parsed and executed within +the \code{sampler()} function to post-process imputed +values during the iterations. The default is a vector +of empty strings, indicating no post-processing. +Multivariate imputation methods ignore the \code{post} +parameter.} + +\item{printFlag}{If \code{printFlag = TRUE} (default) then \code{mice()} will +print iteration history on the console. This is useful for +checking how far the algorithm is. Use \code{print = FALSE} +for silent computation, simulations, and to suppress +iteration output on the console.} \item{autoremove}{Logical. Should unimputed incomplete predictors be removed to prevent NA propagation?} +\item{blocks}{List of \eqn{q} character vectors that identifies the +variable names per block. The name of list elements +identify blocks. \code{mice()} will provide default names +(\code{"b1"}, \code{"b2"}, ...) for blocks containing multiple +variables. Variables within a block are imputed as a +block, e.g. by a multivariate imputation method, or +by an iterated version of the same univariate imputation +method. By default each variable is allocated to a +separate block, which is effectively fully conditional +specification (FCS) by univariate models +(variable-by-variable imputation). +All data variables are assigned to a block. +A variable can belong to only one block, so there are +at most \eqn{p} blocks. +See the \code{parcel} argument for an easier alternative to +the \code{blocks} argument.} + +\item{blots}{Deprecated. Replaced by \code{dots}.} + \item{\dots}{Named arguments that are passed down to the univariate imputation functions. Use \code{dots} for a more fine-grained alternative.} From db5caf6cce4c8b987ac7950f63811a24895981df Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 2 Oct 2023 22:39:45 +0200 Subject: [PATCH 33/37] Remove superfluous construct.parcel(), make remove.rhs.variables() internal --- NAMESPACE | 2 - R/formula.R | 4 +- R/parcel.R | 64 ----------------------------- man/construct.parcel.Rd | 80 ------------------------------------- man/remove.rhs.variables.Rd | 3 ++ 5 files changed, 6 insertions(+), 147 deletions(-) delete mode 100644 man/construct.parcel.Rd diff --git a/NAMESPACE b/NAMESPACE index 5f50bdafd..f002e6219 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -65,7 +65,6 @@ export(cc) export(cci) export(complete) export(construct.blocks) -export(construct.parcel) export(convergence) export(densityplot) export(estimice) @@ -163,7 +162,6 @@ export(pool.syn) export(pool.table) export(quickpred) export(rbind) -export(remove.rhs.variables) export(squeeze) export(stripplot) export(supports.transparent) diff --git a/R/formula.R b/R/formula.R index 9cabaf1ae..cd8377ae1 100644 --- a/R/formula.R +++ b/R/formula.R @@ -175,14 +175,16 @@ check.formulas <- function(formulas, data, #' @param vars a vector with varianble names to be removed from rhs #' @details #' If all variable are removed, the function return the intercept only model. +#' @keywords internal #' @examples +#' \dontrun{ #' f1 <- y1 + y2 ~ 1 | z + x1 + x2 + x1 * x2 #' remove.rhs.variables(f1, c("x1", "z")) #' #' # do not touch lhs #' f2 <- bmi + chl + hyp ~ 1 | age #' remove.rhs.variables(f2, "bmi") -#' @export +#' } remove.rhs.variables <- function(ff, vars) { stopifnot(is.formula(ff)) pattern <- paste(vars, collapse = "|") diff --git a/R/parcel.R b/R/parcel.R index 3157fa91f..7a5bfea5f 100644 --- a/R/parcel.R +++ b/R/parcel.R @@ -114,70 +114,6 @@ check.parcel <- function(parcel, data) { parcel } -#' Construct blocks from `formulas` and `predictorMatrix` -#' -#' This helper function attempts to find blocks of variables in the -#' specification of the `formulas` and/or `predictorMatrix` -#' objects. Blocks specified by `formulas` may consist of -#' multiple variables. Blocks specified by `predictorMatrix` are -#' assumed to consist of single variables. Any duplicates in names are -#' removed, and the formula specification is preferred. -#' `predictorMatrix` and `formulas`. When both arguments -#' specify models for the same block, the model for the -#' `predictMatrix` is removed, and priority is given to the -#' specification given in `formulas`. -#' @inheritParams mice -#' @return A `blocks` object. -#' @seealso [make.blocks()], [name.blocks()] -#' @examples -#' form <- list(bmi + hyp ~ chl + age, chl ~ bmi) -#' pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) -#' construct.blocks(formulas = form, pred = pred) -#' @export -construct.parcel <- function(formulas = NULL, predictorMatrix = NULL) { - blocks.f <- blocks.p <- NULL - if (!is.null(formulas)) { - if (!all(sapply(formulas, is.formula))) { - return(NULL) - } - blocks.f <- name.blocks(lapply(name.formulas(formulas), lhs)) - ct <- rep("formula", length(blocks.f)) - names(ct) <- names(blocks.f) - attr(blocks.f, "calltype") <- ct - if (is.null(predictorMatrix)) { - return(blocks.f) - } - } - - if (!is.null(predictorMatrix)) { - if (is.null(row.names(predictorMatrix))) { - stop("No row names in predictorMatrix", call. = FALSE) - } - blocks.p <- name.blocks(row.names(predictorMatrix)) - ct <- rep("pred", length(blocks.p)) - names(ct) <- names(blocks.p) - attr(blocks.p, "calltype") <- ct - if (is.null(formulas)) { - return(blocks.p) - } - } - - # combine into unique blocks - blocknames <- unique(c(names(blocks.f), names(blocks.p))) - vars.f <- unlist(lapply(formulas, lhs)) - keep <- setdiff(blocknames, vars.f) - add.p <- blocks.p[names(blocks.p) %in% keep] - blocks <- c(blocks.f, add.p) - ct <- c( - rep("formula", length(formulas)), - rep("pred", length(add.p)) - ) - names(ct) <- names(blocks) - attr(blocks, "calltype") <- ct - blocks -} - - reorder.parcel <- function(parcel, data) { idx <- colnames(data) return(parcel[idx]) diff --git a/man/construct.parcel.Rd b/man/construct.parcel.Rd deleted file mode 100644 index 33b80f8b5..000000000 --- a/man/construct.parcel.Rd +++ /dev/null @@ -1,80 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/parcel.R -\name{construct.parcel} -\alias{construct.parcel} -\title{Construct blocks from \code{formulas} and \code{predictorMatrix}} -\usage{ -construct.parcel(formulas = NULL, predictorMatrix = NULL) -} -\arguments{ -\item{formulas}{A named list with \eqn{q} component, each containing -one formula. The left hand side (LHS) specifies the -variables to be imputed, and the right hand side (RHS) -specifies the predictors used for imputation. For example, -model \code{y1 + y2 ~ x1 + x2} imputes \code{y1} and \code{y2} using \code{x1} -and \code{x2} as predictors. Imputation by a multivariate -imputation model imputes \code{y1} and \code{y2} simultaneously -by a joint model, whereas \code{mice()} can also impute -\code{y1} and \code{y2} by a repeated univariate model as -\code{y1 ~ y2 + x1 + x2} and \code{y2 ~ y1 + x1 + x2}. -The \code{formulas} argument is an alternative to the -combination of the \code{predictorMatrix} and -\code{blocks} arguments. It is more compact and allows for -more flexibility in specifying imputation models, -e.g., for adding -interaction terms (\code{y1 + y2 ~ x1 * x2} ), -logical variables (\code{y1 + y2 ~ x1 + (x2 > 20)}), -three-level categories (\code{y1 + y2 ~ x1 + cut(age, 3)}), -polytomous terms (\code{y1 + y2 ~ x1 + poly(age, 3)}, -smoothing terms (\code{y1 + y2 ~ x1 + bs(age)}), -sum scores (\code{y1 + y2 ~ I(x1 + x2)}) or -quotients (\code{y1 + y2 ~ I(x1 / x2)}) -on the fly. -Optionally, the user can name formulas. If not named, -\code{mice()} will name formulas with multiple variables -as \code{F1}, \code{F2}, and so on. Formulas with one -dependent (e.g. \code{ses ~ x1 + x2}) will be named -after the dependent variable \code{"ses"}.} - -\item{predictorMatrix}{A square numeric matrix of maximal \eqn{p} rows and -maximal \eqn{p} columns. Row- and column names are -\code{colnames(data)}. -Each row corresponds to a variable to be imputed. -A value of \code{1} means that the column variable is a -predictor for the row variable, while a \code{0} means that -the column variable is not a predictor. The default -\code{predictorMatrix} is \code{1} everywhere, except for a zero -diagonal. Row- and column-names are optional for the -maximum \eqn{p} by \eqn{p} size. The user may specify a -smaller \code{predictorMatrix}, but column and row names are -then mandatory and should match be part of \code{colnames(data)}. -For variables that are not imputed, \code{mice()} automatically -sets the corresponding rows in the \code{predictorMatrix} to -zero. See details on \emph{skipping imputation}. -Two-level imputation models (which have \code{"2l"} in their -names) support other codes than \code{0} and \code{1}, e.g, \code{2} -or \code{-2} that assign special roles to some variables.} -} -\value{ -A \code{blocks} object. -} -\description{ -This helper function attempts to find blocks of variables in the -specification of the \code{formulas} and/or \code{predictorMatrix} -objects. Blocks specified by \code{formulas} may consist of -multiple variables. Blocks specified by \code{predictorMatrix} are -assumed to consist of single variables. Any duplicates in names are -removed, and the formula specification is preferred. -\code{predictorMatrix} and \code{formulas}. When both arguments -specify models for the same block, the model for the -\code{predictMatrix} is removed, and priority is given to the -specification given in \code{formulas}. -} -\examples{ -form <- list(bmi + hyp ~ chl + age, chl ~ bmi) -pred <- make.predictorMatrix(nhanes[, c("age", "chl")]) -construct.blocks(formulas = form, pred = pred) -} -\seealso{ -\code{\link[=make.blocks]{make.blocks()}}, \code{\link[=name.blocks]{name.blocks()}} -} diff --git a/man/remove.rhs.variables.Rd b/man/remove.rhs.variables.Rd index e9f2753f0..6bd69401e 100644 --- a/man/remove.rhs.variables.Rd +++ b/man/remove.rhs.variables.Rd @@ -18,6 +18,7 @@ Remove RHS terms involving specified variable names If all variable are removed, the function return the intercept only model. } \examples{ +\dontrun{ f1 <- y1 + y2 ~ 1 | z + x1 + x2 + x1 * x2 remove.rhs.variables(f1, c("x1", "z")) @@ -25,3 +26,5 @@ remove.rhs.variables(f1, c("x1", "z")) f2 <- bmi + chl + hyp ~ 1 | age remove.rhs.variables(f2, "bmi") } +} +\keyword{internal} From f5d5c99a325a3f16875925ba7e35417d75ed2fbe Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 2 Oct 2023 22:42:06 +0200 Subject: [PATCH 34/37] Add MICE 4 Syntax Documentation CONCEPT as a vignette --- vignettes/.gitignore | 2 + vignettes/mice4syntax.Rmd | 638 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 640 insertions(+) create mode 100644 vignettes/.gitignore create mode 100644 vignettes/mice4syntax.Rmd diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 000000000..097b24163 --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R diff --git a/vignettes/mice4syntax.Rmd b/vignettes/mice4syntax.Rmd new file mode 100644 index 000000000..469ce3634 --- /dev/null +++ b/vignettes/mice4syntax.Rmd @@ -0,0 +1,638 @@ +--- +title: "MICE 4 Syntax Documentation - CONCEPT -" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{MICE 4 Syntax Documentation - CONCEPT -} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library("mice") +``` + +## Objectives + +- Here are calls to the `mice()` package demonstrating by to use the `mice()` argument `predictorMatrix`, `parcel`, `blocks` and `formulas` to specify imputation models. +- Based on commit + +## Basic MICE model + +### Why + +- Imputation using the basic MICE model requires minimal typing and thinking +- MICE defaults are chosen to provide "reasonable" imputations for a wide variety of cases +- However, blindly trusting the defaults may be far from optimal to solve specific issues with the data at hand + +### Examples + +```{r dataset} +library(mice, warn.conflicts = FALSE) +df <- mice::nhanes +``` + +- The minimal call, let `mice()` do the thinking + +```{r} +imp1 <- mice(df, print = FALSE, seed = 1) +``` + +- Output: `mice()` detects that `age` is complete, and needs not be imputed + +```{r} +imp1$method +``` + +- Output: we always have $p$ rows and $p$ columns in `predictorMatrix` + +```{r} +dim(imp1$predictorMatrix) +``` + +- Output: `predictorMatrix` contains rows with all zeroes for unimputed variables +- Unimputed variables could be complete (no `NA`s) or incomplete (with `NA`s) +- By default, an unimputed incomplete variable `zz` will have all `NA`s in `imp$imp$zz` +- An incomplete variable `zz` is unimputed if `method["zz"] == ""` +- Beware: a row of zeroes in `predictorMatrix` does not imply that the variable is unimputed. It may be imputed by the intercept-only model (not good in general) + +```{r} +imp1$predictorMatrix +``` + +- Output: there are `ncol(data)` variable groups +- A "parcel" or "block" is a group of variables jointly imputed +- `mice()` has two ways to specify parcels: `parcel` and `blocks` +- Parcels can be univariate (holding one variable) or multivariate (holding multiple variables) +- The default parcel name for a univariate parcel is the variable name + +```{r} +unique(imp1$parcel) +imp1$parcel +``` + +- Two distinct ways to define an imputation method: + 1. `predictorMatrix` + `parcel` + `method` + 2. `formulas` + `method` +- Both yield the same result, but have different user interfaces +- `predictorMatrix` and `formulas` specifications cannot be mixed + +- The `formulas` representation mimmicks the `predictorMatrix` +- In addition, `formulas` defines parcels + +```{r} +imp1$formulas +``` + +## Selecting predictors by `predictorMatrix` + +### Why + +- The `predictorMatrix` matrix is a simple and intuitive way to represent the main effects of the imputation model +- The `predictorMatrix` allows for easy addition and removal of predictors +- One can add/remove a predictor from all submodels by changing relevant column entries +- One can add/remove specific predictors for a dependent variable by changing relevant row entries + +### Examples + +- Setting the default `predictorMatrix` +- Rows and columns of the `predictorMatrix` are ordered in the data sequence + +```{r} +pred <- make.predictorMatrix(df) +imp2 <- mice(df, pred = pred, print = FALSE, seed = 1) +``` + +- Check whether the imputations are identical + +```{r} +identical(imp1$imp, imp2$imp) +``` + +- Removing `hyp` from all submodels +- Removing `age` and `bmi` from `hyp` imputation submodel + +```{r} +pred[, "hyp"] <- 0 +pred["hyp", c("age", "bmi")] <- 0 +pred +``` + +- Imputation with custom main effect submodels + +```{r} +imp <- mice(df, pred = pred, print = FALSE, seed = 1) +``` + +- MICE edited the first row of the custom `pred` + +```{r} +imp$predictorMatrix +``` + +- When the dataset contains many variables, the `predictorMatrix` can become large and difficult to work with +- We can tackle a complex `predictorMatrix` in Excel with conditional formatting +- The user can input a subset of the full `predictorMatrix` + +```{r} +subset <- c("bmi", "chl") +pred <- make.predictorMatrix(df[, subset]) +pred +``` + +- The subset ignores all variables in the data that are not in the subset +- Effectively, this trick cuts out a portion of the variables + +```{r} +imp <- mice(df, pred = pred, print = FALSE) +imp$predictorMatrix +``` + +- NA-propagation +- Suppose we change to an asymmetric submodel: impute `bmi` from `chl`, but specify no imputation model for `chl` +- `chl` has missing data, but these are not imputed (technically they are imputed by `NA`) +- As a result, `bmi` will have missing values in rows where `chl` has missing values. This is called missing data propagation (NA-propagation) + +```{r} +pred <- matrix(c(0, 0, 1, 0), nrow = 2, dimnames = list(c("bmi", "chl"), c("bmi", "chl"))) +imp <- mice(df, pred = pred, print = FALSE, maxit = 1, m = 1, seed = 1, autoremove = FALSE) +imp$imp$bmi +``` + +- Prevention of NA-propagation by "autoremove" +- Autoremove prevents NA-propagation by removing `chl` as predictor for `bmi` and sets `method["chl"] <- ""` +- Removal is written to `loggedEvents` +- `bmi` is now imputed using the intercept-only model (since no predictors were left) +- `bmi` is complete + +```{r} +pred <- matrix(c(0, 0, 1, 0), nrow = 2, dimnames = list(c("bmi", "chl"), c("bmi", "chl"))) +imp <- mice(df, pred = pred, print = FALSE, maxit = 1, m = 1, seed = 1, autoremove = TRUE) +imp$loggedEvents +imp$imp$bmi +``` + +- NOTE: A second prevention strategy is "autoimpute" `chl`. This is not yet implemented. + +- `predictorMatrix` subsets only work if `pred` has row- and column names + +```{r error=TRUE, eval=FALSE} +dimnames(pred) <- NULL +imp <- mice(df, pred = pred, print = FALSE) +``` + +- All names should be map to variables in the data + +```{r error=TRUE, eval=FALSE} +pred <- matrix(1, nrow = 4, ncol = 4) +dimnames(pred) <- list(c("edu", "bmi", "ses", "chl"), c("edu", "bmi", "ses", "chl")) +imp <- mice(df, pred = pred, print = FALSE) +``` + +- Setting a `predictorMatrix` without names only works for the full matrix +- Not recommended in general, but is convenient quick hack + +```{r} +pred <- matrix(1, nrow = 4, ncol = 4) +imp3 <- mice(df, pred = pred, print = FALSE, seed = 1) +imp3$predictorMatrix +imp3$method +``` + +- Check that imputations are the same + +```{r} +identical(imp2$imp, imp3$imp) +``` + + +- We cannot work with a non-square `predictorMatrix` + +```{r error=TRUE,eval=FALSE} +pred <- make.predictorMatrix(df) +pred <- pred[2:3, 1:4] +imp <- mice(df, pred = pred, print = FALSE) +``` + +- Univariate imputation methods for two-level data use other codes than 0 and 1 +- `2l.bin`, `2l.lmer`, `2l.norm`, `2l.pan`, `2lonly.mean`, `2lonly.norm` and `2lonly.pmm` use code `-2` to indicate the class variable +- `2l.bin`, `2l.lmer`, `2l.norm` and `2l.pan` use code 2 to indicate the random effects +- `2l.pan` uses codes 3 and 4 to add class means to codes 1 and 2 respectively + +- The following example is a two-level dataset with two incomplete level-1 variables +- Code `-2` specifies `patientID` as the class variable + +```{r} +nail <- tidyr::complete(mice::toenail2, patientID, visit) |> + tidyr::fill(treatment) |> + dplyr::mutate(patientID = as.integer(patientID)) +pred <- make.predictorMatrix(nail) +pred[, "patientID"] <- -2 +meth <- c("", "", "2l.bin", "", "2l.norm") +imp <- mice(nail, meth = meth, pred = pred, maxit = 1, m = 1, seed = 1) +imp +``` + + +## Clustering variables into groups by `parcel` or `blocks` + +### Why + +- Clustering variables into groups ("blocks") can improve the quality of imputation +- Example 1: missing blocks occur when linking dataset (Mitra 2022, Learning from data with structured +missingness) +- Example 2: fixed relations between variables, e.g., transformations, sum scores, compositions +- Block-oriented imputation methods borrow relations within the block +- Block-oriented PMM yields within-block values that are actually observed + +### Examples: `parcel` argument + +- `parcel` is a simple way to define a blocks of variables +- By default, `make.parcel()` places every variable in a separate block +- By convention, the name of a univariate block is the variable's name + +```{r} +parcel <- make.parcel(df) +parcel +``` + +- Placing `bmi`, `hyp` and `chl` into one group named `risk` + +```{r} +parcel[c("bmi", "hyp", "chl")] <- "risk" +parcel +``` + +- Imputation using default `pmm` will apply univariate `pmm` sequentially to all variables in `risk` + + +```{r} +imp4 <- mice(df, parcel = parcel, print = FALSE, seed = 1) +``` + +- With the same seed and variable sequence, the solutions are the same +- Check whether imputations are identical + +```{r} +identical(imp1$imp, imp4$imp) +``` + +- `print.mids(imp4)` also prints `parcel` when it differs from the default + +```{r} +imp4 +``` + +- `mice()` pads any unmentioned variables to `parcel` +- each unmentioned variable lives in a univariate parcel + +```{r} +parcel_short <- setNames(c("risk", "risk"), nm = c("bmi", "chl")) +parcel_short +imp <- mice(df, parcel = parcel_short, print = FALSE, seed = 1) +imp$parcel +imp$method +``` + +- Use multivariate imputation methods to reap the added benefit of parcels +- Multivariate PMM (method `mpmm`) imputes vectors instead of scalars +- To demonstrate `mpmm`, filter the data to just one missing data pattern + +```{r} +df2 <- df[-c(3, 6, 15, 20, 24), ] +imp <- mice(df2, parcel = parcel, method = c("", "mpmm"), print = FALSE, seed = 1) +head(complete(imp), 10) +``` + +- Rows 1 and 11 borrows from row 8, row 10 borrows from row 9 +- Within-block relationships between the imputations are preserved +- Unfortunately, current `mpmm` does not work for multiple missing data patterns + +```{r error = TRUE, eval=FALSE} +imp <- mice(df, parcel = parcel, method = c("", "mpmm"), print = FALSE, seed = 1) +``` + +- Also, current `mpmm` does not work with factors + +```{r error = TRUE, eval=FALSE} +df2 <- nhanes2[-c(3, 6, 15, 20, 24), ] +imp <- mice(df2, parcel = parcel, method = c("", "mpmm"), print = FALSE, seed = 1) +``` + +- Other multivariate methods in `mice` include `jomoImpute` and `panImpute` +- These methods depend on additional codes in the `predictorMatrix` and will be treated later + +### Examples: `blocks` argument + +- The `blocks` argument is the older way to define groups of variables +- `blocks` were introduced in mice 3.0 +- There are two principal differences with `parcel`: + 1. Using `blocks` one may allocate the same variable to multiple blocks + 2. `blocks` defines the engine used for imputation +- Both differences are not relevant to the end user +- The use of the `blocks` argument is soft-deprecated in favour of `parcel` + +- By default, the `make.blocks()` function allocates each variable into a separate block + +```{r} +blocks <- make.blocks(df) +blocks +``` + +- `blocks` is a named list (with block names) with of arbitrary length +- Each element is a character vector with variable names +- By convention, the block name and the variable name are identical for univariate blocks +- The `calltype` attribute sets the internal imputation engine (`calltype`, either `pred` or `formula`) used for the block + + +- One may allocate the same variable to multiple blocks (but its added value is dubious) +- `mice()` warns for duplicate variables (= variables present in more than one block) + +```{r} +blocks <- make.blocks(list(c("bmi", "chl"), "bmi", "age")) +imp <- mice(df, blocks = blocks, m = 1, print = FALSE) +``` + +- When both `parcel` and `blocks` are specified, `parcel` overwrites `blocks` + +```{r} +imp <- mice(df, parcel = parcel, blocks = blocks, m = 1, print = FALSE) +imp$parcel +imp$blocks +``` + +- The internal function `mice:::b2n()` converts `blocks` to `parcel` +- Conversion is not perfect: `mice:::b2n()` removes duplicates and loses the `calltype` attribute + +```{r} +blocks +mice:::b2n(blocks) +``` + +- The internal function `mice:::n2b()` converts `parcel` to `blocks` + +```{r} +parcel +mice:::n2b(parcel) +``` + +## Selecting predictors and grouping variables by `predictorMatrix` and `parcel` + +### Why + +- To select predictors and group variables simultaneously +- To build upon the mice `predictorMatrix` and `parcel` arguments +- To extend the `predictorMatrix` to multivariate, block-wise imputation + +### Examples: `predictorMatrix` and `parcel` + +- Multivariate imputation by the `predictorMatrix` is done through the `calltype = "pred"` engine +- Multivariate methods supporting the "pred" engine are `panImpute` and `jomoImpute` +- `predictorMatrix` settings pass down as the `type` argument of `mitml::panImpute()` and `mitml::jomoImpute()` + +- The following example simultaneously imputes `outcome` and `time` of the missed visits +- `jomoImpute` allows for mixes of categorical (`outcome`) and continuous (`time`) variables +- `parcel` defines jointly imputed level-1 variables + +```{r} +pred <- make.predictorMatrix(nail) +pred[, "patientID"] <- -2 +parcel <- make.parcel(nail) +parcel[c("visit", "outcome", "time")] <- "level1" +imp <- mice(nail, meth = "jomoImpute", pred = pred, parcel = parcel, maxit = 1, m = 1, seed = 1, print = FALSE) +imp +``` + +- Note that imputed `time` can sometimes be negative or in-between visits + +```{r} +stripplot(imp, time ~ .imp, pch = c(1, 20), cex = c(0.7, 1.2)) +``` + +- As an alternative, `mpmm` borrows `outcome`-`time` pairs +- Since `mpmm` fails to deal with factors, we code them as integers + +```{r} +nail$outcome <- as.integer(nail$outcome) +nail$treatment <- as.integer(nail$treatment) +parcel[c("visit", "outcome", "time")] <- "level1" +impa <- mice(nail, meth = "mpmm", parcel = parcel, maxit = 1, m = 1, seed = 1, print = FALSE) +impa +``` + +- Imputed `time` is now one of the observed times +- Time distribution looks more plausible + +```{r} +stripplot(impa, time ~ .imp, pch = c(1, 20), cex = c(0.7, 1.2)) +``` + +- Note that `mpmm` did not use the `predictorMatrix` +- But we can use it to remove variables +- For example, it is nonsensical to include `patientID` for imputation +- The following code takes out `patientID` + +```{r} +pred <- make.predictorMatrix(nail) +pred[, "patientID"] <- 0 +impb <- mice(nail, meth = "mpmm", parcel = parcel, pred = pred, maxit = 1, m = 1, seed = 1, print = FALSE) +``` + +- [SIDE NOTE: the solutions with and without patientID are (incorrectly) identical since mpmm does not honour the type vector or formula.] + + + +```{r eval=FALSE, echo=FALSE} +# NOTE: this one won't work +parcel <- setNames(rep("risk", 3), nm = c("bmi", "hyp", "chl")) +meth <- setNames("mpmm", nm = "risk") +pred <- make.predictorMatrix(df2) +# pred[, "age"] <- 0 +imp <- mice(df2, parcel = parcel, pred = pred, meth = meth, print = FALSE, seed = 1) +head(complete(imp), 10) +``` + + +```{r eval=FALSE, echo=FALSE} +# NOTE: this one won't work +parcel <- setNames(c(rep("risk", 3), "age"), nm = c("bmi", "hyp", "chl", "age")) +meth <- setNames(c("mpmm", "age"), nm = c("risk", "age")) +pred <- make.predictorMatrix(df2) +pred[, "age"] <- 0 +imp <- mice(df2, parcel = parcel, pred = pred, meth = meth, print = FALSE, seed = 1) +head(complete(imp), 10) +``` + + +## Selecting predictors and grouping variables by `formulas` + +### Why + +- To select predictors and specify groups of variables by one argument +- To leverage the base R `formula` class +- To provide native access to imputation methods for complex data + +### Examples: `formulas` + +- The `formulas` argument is a list. +- Each list element is a `formula` and defines a block +- The standard full variable-to-variable imputation is specified as + +```{r} +fm <- make.formulas(df) +fm +``` + +- Fitting the default model with `mice()` edits the `fm` object +- The order of the list elements in `formulas` defines the `visitSequence` + +```{r} +imp6 <- mice(df, formulas = fm, print = FALSE, seed = 1) +imp6$formulas +``` + +- Imputations are identical to the `imp1` + +```{r} +identical(imp1$imp, imp6$imp) +``` + +- Another way to specify the same model: All incomplete variables as dependents, all complete as predictors + +```{r} +fm2 <- list(bmi + hyp + chl ~ age) +imp7 <- mice(df, formulas = fm2, print = FALSE, seed = 1) +identical(imp1$imp, imp7$imp) +``` + +- A compact way to write the model +- Note that we can even write `list(. ~ 1)`, though that differs in the `predictorMatrix` + +```{r} +imp8 <- mice(df, formulas = list(. ~ age), print = FALSE, seed = 1) +identical(imp1$imp, imp8$imp) +``` + +- The left hand side (LHS) can contain multiple variables, seperated by a `+` +- Unnamed input formulas are named by `mice()` +- The default name for a univariate `formula` is the name of the dependent variable +- The default name for a multivariate `formula` is `f1`, `f2` and so on + +```{r} +fm3 <- list( + bmi + hyp ~ age + chl, + chl ~ age + bmi + hyp +) +imp9 <- mice(df, formulas = fm3, print = FALSE, seed = 1) +imp9$formulas +``` + +- When the `formula` is multivariate and the imputation `method` is univariate, imputation proceeds as follows: +- 1) `mice()` select the first variable in the block (`bmi`) as dependent for the imputation model, and uses all other terms as predictor +- 2) `mice()` repeats the process for the next dependent in the block (`hyp`), and so on +- 3) when all variables on the LHS have been processed, `mice()` moves to the next block, and so on +- As long as the variables are visited in the same order, imputations are identical to the base model + +```{r} +identical(imp1$imp, imp9$imp) +``` + + +- Tiny formulas: Impute `bmi` from `chl`, and `chl` from `bmi` +- `hyp` and `age` play no role for imputing `bmi` and `chl` +- `hyp` and `age` are not mentioned, so not imputed (`age` wasn't imputed anyway because it is complete) + +```{r} +fm4 <- list(bmi + chl ~ 1) +imp <- mice(df, formulas = fm4, print = FALSE, maxit = 1, m = 1, seed = 1) +imp +``` + +- NA-propagation +- Suppose we impute by an a-symmetric submodel: impute `bmi` from `chl`, but specify no imputation model for `chl` +- `chl` has missing data, but these are not imputed +- Current version uses "autoremove" NA-propagation prevention +- `bmi` is now imputed using the intercept-only model + +```{r} +fm5 <- list(bmi ~ chl) +imp <- mice(df, formulas = fm5, print = FALSE, maxit = 1, m = 1, seed = 1) +imp$loggedEvents +imp$imp$bmi +``` + + +- Using built-in support for formula +- Adding transformations to predictors +- `mice()` ignores transformations made on the LHS + +```{r} +library(splines) +fm6 <- list( + bmi + sqrt(hyp) ~ poly(age, 2) + sqrt(chl), + log(chl) ~ age + cut(bmi, 3) + hyp +) +imp <- mice(df, formulas = fm6, print = FALSE, m = 1, maxit = 1, seed = 1) +``` + +- Adding interaction terms to the imputation model +- Symbol `*` adds main effects plus interaction +- Symbol `:` adds the specific interaction + +```{r} +fm7 <- list( + bmi + hyp ~ age * chl, + chl ~ age + bmi + hyp + bmi:hyp:age +) +imp <- mice(df, formulas = fm7, print = FALSE, m = 1, maxit = 1, seed = 1) +``` + +- Calculate variables on the fly +- We need to set the experimental `sort.terms = FALSE` to evade formula processing problems + +```{r} +fm8 <- list( + bmi ~ I(chl / age) + hyp, + hyp ~ age + (bmi > 30), + chl ~ I(bmi + hyp / age) +) +imp <- mice(df, formulas = fm8, print = FALSE, m = 1, maxit = 1, seed = 1, sort.terms = FALSE) +``` + + +- Univariate imputation with `panImpute` +- Example 2.1 from `mitml::panImpute()` +- Imputation of `ReadDis` by `ReadAchiev` plus a random intercept +- We use `dots` to pass down options for imputing block `ReadDis` + +```{r} +# Example from ?mitml::panImpute +vars <- c("ReadDis", "SES", "ReadAchiev", "ID") +stud <- mitml::studentratings[, vars] +fml <- list(ReadDis ~ ReadAchiev + (1|ID)) +meth <- setNames(c("panImpute", "", "", ""), nm = vars) +dots <- list(ReadDis = alist(n.burn = 1000, n.iter = 100)) +imp <- mice(stud, formulas = fml, meth = meth, dots = dots, m = 2, print = FALSE) +``` + +- The random slope version `fml <- list(ReadDis ~ ReadAchiev + (1 + ReadAchieve|ID))` does not yet work due to improper formula processing by `mice()` + +- Multivariate imputation with `jomoImpute` +- Similar model, but now for two outcomes: `ReadDis` and `SES` + +```{r} +# Example from ?mitml::jomoImpute +fml <- list(read_ses = ReadDis + SES ~ ReadAchiev + (1|ID)) +meth <- setNames(c("jomoImpute", "", ""), c("read_ses", "ReadAchieve", "ID")) +dots <- list(read_ses = alist(n.burn = 100, n.iter = 10)) +imp <- mice(stud, formulas = fml, meth = meth, dots = dots, m = 2, print = FALSE) +``` + + +--- THAT'S IT FOR NOW --- From 6edcd7189c30de0f8d0b6e6cf9d869642082752e Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Mon, 2 Oct 2023 22:42:45 +0200 Subject: [PATCH 35/37] Rebuild site to include article mice4syntax --- DESCRIPTION | 3 ++- NEWS.md | 2 ++ _pkgdown.yml | 1 + 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e4d526fd6..363fede0a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: mice Type: Package -Version: 3.16.5 +Version: 3.16.5.9001 Title: Multivariate Imputation by Chained Equations Date: 2023-09-04 Authors@R: c(person("Stef", "van Buuren", role = c("aut","cre"), @@ -101,3 +101,4 @@ LinkingTo: cpp11, Rcpp License: GPL (>= 2) RoxygenNote: 7.2.3 Roxygen: list(markdown = TRUE) +VignetteBuilder: knitr diff --git a/NEWS.md b/NEWS.md index d2ecb63b2..922fa3bff 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# mice 3.16.5.9001 + ## New behaviours and features 1. TWO SEPARATE INTERFACES FOR MODEL SPECIFICATION: This version promotes two interfaces to specify imputations models: predictor (`predictorMatrix` + `parcel` + `method`) and formula (`formulas + method`). This version does not accept anymore accept mixes of `predictorMatrix` and `formulas` arguments in the call to `mice()`. diff --git a/_pkgdown.yml b/_pkgdown.yml index 55842bb7c..04f3878b5 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -180,4 +180,5 @@ articles: contents: - overview - oldfriends + - mice4syntax From 232a0b61c34f6e3ac011dcef0ff35624b30427e4 Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 17 Apr 2024 22:42:30 +0200 Subject: [PATCH 36/37] Add test for character variable (#601) --- tests/testthat/test-mice.R | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/tests/testthat/test-mice.R b/tests/testthat/test-mice.R index bb4eba2ca..2b3d24827 100644 --- a/tests/testthat/test-mice.R +++ b/tests/testthat/test-mice.R @@ -20,8 +20,8 @@ test_that("blocks run as expected", { print = FALSE, m = 1, maxit = 1, seed = 1 )) expect_warning(imp2b <<- mice(nhanes2, - blocks = list(c("age", "hyp", "bmi"), "chl", "bmi"), - print = FALSE, m = 1, maxit = 1, seed = 1 + 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"), @@ -80,8 +80,8 @@ imp3 <- mice(nhanes, blocks = list("age", c("bmi", "hyp", "chl")), print = FALSE, m = 1, maxit = 1, seed = 123) imp3a <- mice(nhanes, - blocks = name.blocks(list("age", c("bmi", "hyp", "chl"))), - print = FALSE, m = 1, maxit = 1, seed = 123) + blocks = name.blocks(list("age", c("bmi", "hyp", "chl"))), + print = FALSE, m = 1, maxit = 1, seed = 123) test_that("make.blocks() and list() yield same imputes for imp3-model", { expect_identical(complete(imp3), complete(imp3a)) @@ -91,8 +91,8 @@ imp4 <- mice(nhanes, blocks = list(c("bmi", "hyp", "chl"), "age"), print = FALSE, m = 1, maxit = 1, seed = 123) imp4a <- mice(nhanes, - blocks = name.blocks(list(c("bmi", "hyp", "chl"), "age")), - print = FALSE, m = 1, maxit = 1, seed = 123) + blocks = name.blocks(list(c("bmi", "hyp", "chl"), "age")), + print = FALSE, m = 1, maxit = 1, seed = 123) test_that("make.blocks() and list() yield same imputes for imp4-model", { expect_identical(complete(imp4), complete(imp4a)) @@ -133,10 +133,10 @@ test_that("formulas run as expected", { 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)) + 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, @@ -273,3 +273,11 @@ test_that("`ignore` works with pmm", { expect_equal(complete(imp1)["a1", "bmi"], 40.0) expect_failure(expect_equal(complete(imp2)["a1", "bmi"], 40.0)) }) + + +# check for character variable +nh3 <- nhanes2 +nh3$chl <- as.character(nh3$chl) +test_that("handles character variable", { + expect_silent(mice(nh3)) +}) From 09e58ea7d2bddc7a59c15efe2976a4f8182133ba Mon Sep 17 00:00:00 2001 From: Stef van Buuren Date: Wed, 17 Apr 2024 22:44:45 +0200 Subject: [PATCH 37/37] Merge main and support_blocks into new branch mice4 (still failing some tests) --- .github/dependabot.yml | 6 + .github/workflows/R-CMD-check.yaml | 5 +- .github/workflows/pkgdown.yaml | 4 +- DESCRIPTION | 6 +- NAMESPACE | 6 + NEWS.md | 288 ++++++++++++++++--------- R/ampute.R | 7 +- R/convergence.R | 1 + R/df.residual.R | 5 +- R/edit.setup.R | 2 +- R/formula.R | 12 +- R/futuremice.R | 8 +- R/get.df.R | 4 +- R/internal.R | 4 + R/mcar.R | 3 +- R/mice-package.R | 9 +- R/mice.R | 8 +- R/mipo.R | 1 + R/parcel.R | 2 +- R/predictorMatrix.R | 7 +- R/quickpred.R | 8 +- R/sampler.R | 4 +- README.Rmd | 2 +- README.md | 2 +- man/figures/README-pattern-1.png | Bin 16299 -> 16303 bytes man/figures/README-stripplot-1.png | Bin 26180 -> 26390 bytes man/mice.Rd | 36 +++- man/mice.impute.cart.Rd | 4 +- man/mice.impute.lasso.logreg.Rd | 4 +- man/mice.impute.lasso.norm.Rd | 4 +- man/mice.impute.lasso.select.logreg.Rd | 4 +- man/mice.impute.lasso.select.norm.Rd | 4 +- man/mice.impute.lda.Rd | 4 +- man/mice.impute.logreg.Rd | 2 +- man/mice.impute.logreg.boot.Rd | 2 +- man/mice.impute.mean.Rd | 4 +- man/mice.impute.midastouch.Rd | 4 +- man/mice.impute.mnar.Rd | 4 +- man/mice.impute.mpmm.Rd | 4 +- man/mice.impute.norm.Rd | 2 +- man/mice.impute.norm.boot.Rd | 4 +- man/mice.impute.norm.nob.Rd | 4 +- man/mice.impute.norm.predict.Rd | 4 +- man/mice.impute.pmm.Rd | 4 +- man/mice.impute.polr.Rd | 4 +- man/mice.impute.polyreg.Rd | 4 +- man/mice.impute.quadratic.Rd | 4 +- man/mice.impute.rf.Rd | 4 +- man/mice.impute.ri.Rd | 4 +- tests/testthat/test-ampute.R | 2 +- tests/testthat/test-quickpred.R | 64 ++++++ 51 files changed, 395 insertions(+), 193 deletions(-) create mode 100644 .github/dependabot.yml create mode 100644 tests/testthat/test-quickpred.R diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 000000000..123014908 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,6 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "daily" diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index a3ac61827..95aee88c1 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -29,7 +29,7 @@ jobs: R_KEEP_PKG_SOURCE: yes steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: r-lib/actions/setup-pandoc@v2 @@ -41,7 +41,8 @@ jobs: - uses: r-lib/actions/setup-r-dependencies@v2 with: - extra-packages: any::rcmdcheck + extra-packages: any::rcmdcheck, Matrix + pak-version: devel needs: check - uses: r-lib/actions/check-r-package@v2 diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index ed7650c73..29cc03364 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -22,7 +22,7 @@ jobs: permissions: contents: write steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: r-lib/actions/setup-pandoc@v2 @@ -41,7 +41,7 @@ jobs: - name: Deploy to GitHub pages 🚀 if: github.event_name != 'pull_request' - uses: JamesIves/github-pages-deploy-action@v4.4.1 + uses: JamesIves/github-pages-deploy-action@v4.6.0 with: clean: false branch: gh-pages diff --git a/DESCRIPTION b/DESCRIPTION index 363fede0a..e666d2faa 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mice Type: Package -Version: 3.16.5.9001 +Version: 3.999.0 Title: Multivariate Imputation by Chained Equations -Date: 2023-09-04 +Date: 2024-04-17 Authors@R: c(person("Stef", "van Buuren", role = c("aut","cre"), email = "stef.vanbuuren@tno.nl"), person("Karin", "Groothuis-Oudshoorn", role = "aut", @@ -99,6 +99,6 @@ URL: https://github.com/amices/mice, BugReports: https://github.com/amices/mice/issues LinkingTo: cpp11, Rcpp License: GPL (>= 2) -RoxygenNote: 7.2.3 Roxygen: list(markdown = TRUE) +RoxygenNote: 7.3.1 VignetteBuilder: knitr diff --git a/NAMESPACE b/NAMESPACE index f002e6219..64dd68366 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,6 +11,10 @@ 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) @@ -19,6 +23,7 @@ S3method(ic,matrix) S3method(ic,mids) S3method(ici,default) S3method(ici,mids) +S3method(is.nan,data.frame) S3method(mcar,data.frame) S3method(plot,mcar_object) S3method(plot,md.pattern) @@ -38,6 +43,7 @@ S3method(summary,mids) S3method(summary,mipo) S3method(summary,mira) S3method(tidy,mipo) +S3method(vcov,mipo) S3method(with,mids) S3method(xyplot,mads) S3method(xyplot,mids) diff --git a/NEWS.md b/NEWS.md index 922fa3bff..9c5f50e6e 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# mice 3.16.5.9001 +# mice 4 dev ## New behaviours and features @@ -40,48 +40,88 @@ - length of `method` vector cannot exceed number of variables - length of `imp` and number of variables must be equal -## Other fixes +## ----------------------------------------------------------- + + +# mice 3.16.10 + +* Adds support for non-syntactic variables names with backticks (#631) + +# mice 3.16.9 + +* Fixes a problem with the `minpuc` argument in `quickpred()` (#634) +* Fixes `coef() not available on S4 object` when using with `lavaan` (#615, #616) +* Adds `.github/dependabot.yml` configuration to automate daily check (#598) +* Update documentation tags to `roxygen2 7.3.1` requirements + +# mice 3.16.8 + +* Fixes problems with zero predictors (#588) + +# mice 3.16.7 + +### Minor changes + +* Solves problem with the package documentation link +* Simplifies `NEWS.md` formatting to get correct version sequence on CRAN and in-package NEWS + +# mice 3.16.6 + +### Minor changes * Prepares for the deprecation of the `blocks` argument at various places * Removes the need for `blocks` in `initialize_chain()` * In `rbind()`, when formulas are concatenated and duplicate names are found, also rename the duplicated variables in formulas by their new name + +### Bug fixes + * Fixes a bug in `filter.mids()` that incorrectly removed empty components in the `imp` object * Fixes a bug in `ibind()` that incorrectly used `length(blocks)` as the first dimension of the `chainMean` and `chainVar` objects * Corrects the description `visitSequence`, `chainMean` and `chainVar` components of the `mids` object # mice 3.16.5 +### Bug fixes + * Patches a bug in `complete()` that auto-repeated imputed values into cells that should NOT be imputed (occurred as a special case of `rbind()`, where the first set of rows was imputed and the second was not). * Replaces the internal variable `type` by the more informative `pred` (currently active row of `predictorMatrix`) # mice 3.16.4 -**Imputing categorical data by predictive mean matching**. Predictive mean matching (PMM) is the default method of `mice()` for imputing numerical variables, but it has long been possible to impute factors. This enhancement introduces better support to work with categorical variables in PMM. The **former system** translated factors into integers by `ynum <- as.integer(f)`. However, the order of integers in `ynum` may have no sensible interpretation for an unordered factor. The **new system** quantifies `ynum` and could yield better results because of higher $R^2$. The method calculates the canonical correlation between `y` (as dummy matrix) and a linear combination of imputation model predictors `x`. The algorithm then replaces each category of `y` by a single number taken from the first canonical variate. After this step, the imputation model is fitted, and the predicted values from that model are extracted to function as the similarity measure for the matching step. - -The method works for both ordered and unordered factors. No special precautions are taken to ensure monotonicity between the category numbers and the quantifications, so the method should be able to preserve quadratic and other non-monotone relations of the predicted metric. It may be beneficial to remove very sparsely filled categories, for which there is a new `trim` argument. +### Major changes -All you have to use the new technique is specify to `mice(..., method = "pmm", ...)`. Both numerical and categorical variables will then be imputed by PMM. +* **Imputing categorical data by predictive mean matching**. Predictive mean matching (PMM) is the default method of `mice()` for imputing numerical variables, but it has long been possible to impute factors. This enhancement introduces better support to work with categorical variables in PMM. The **former system** translated factors into integers by `ynum <- as.integer(f)`. However, the order of integers in `ynum` may have no sensible interpretation for an unordered factor. The **new system** quantifies `ynum` and could yield better results because of higher $R^2$. The method calculates the canonical correlation between `y` (as dummy matrix) and a linear combination of imputation model predictors `x`. The algorithm then replaces each category of `y` by a single number taken from the first canonical variate. After this step, the imputation model is fitted, and the predicted values from that model are extracted to function as the similarity measure for the matching step. -Potential advantages are: +* The method works for both ordered and unordered factors. No special precautions are taken to ensure monotonicity between the category numbers and the quantifications, so the method should be able to preserve quadratic and other non-monotone relations of the predicted metric. It may be beneficial to remove very sparsely filled categories, for which there is a new `trim` argument. All you have to use the new technique is specify to `mice(..., method = "pmm", ...)`. Both numerical and categorical variables will then be imputed by PMM. -- Simpler and faster than fitting a generalised linear model, e.g., logistic regression or the proportional odds model; -- Should be insensitive to the order of categories; -- No need to solve problems with perfect prediction; -- Should inherit the good statistical properties of predictive mean matching. +* Potential advantages are: + - Simpler and faster than fitting a generalised linear model, e.g., logistic regression or the proportional odds model; + - Should be insensitive to the order of categories; + - No need to solve problems with perfect prediction; + - Should inherit the good statistical properties of predictive mean matching. -Note that we still lack solid evidence for these claims. (#576). Contributed @stefvanbuuren +* Note that we still lack solid evidence for these claims. (#576). Contributed @stefvanbuuren # mice 3.16.3 +### Major changes + * **New system-independent method for pooling**: This version introduces a new function `pool.table()` that takes a tidy table of parameter estimates stemming from `m` repeated analyses. The input data must consist of three columns (parameter name, estimate, standard error) and a specification of the degrees of freedom of the model fitted to the complete data. The `pool.table()` function outputs 14 pooled statistics in a tidy form. The primary use of `pool.table()` is to support parameter pooling for techiques that have no `tidy()` or `glance()` methods, either within `R` or outside `R`. The `pool.table()` function also allows for a novel workflows that 1) break apart the traditional `pool()` function into a data-wrangling part and a parameters-reducing part, and 2) does not necessarily depend on classed R objects. (#574). Contributed @stefvanbuuren + +### Bug fixes + * Fixes the "large logo" problem. (#574). Contributed @hanneoberman # mice 3.16.2 +### Major changes + * **Breaking change:** The `complete(..., action = "long", ...)` command puts the columns named `".imp"` and `".id"` in the last two positions of the long data (instead of first two positions). In this way, the columns of the imputed data will have the same positions as in the original data, which is more user-friendly and easier to work with. Note that any existing code that assumes that variables `".imp"` and `".id"` are in columns 1 and 2 will need to be modified. The advice is to modify the code using the variable names `".imp"` and `".id"`. If you want the old behaviour, specify the argument `order = "first"`. (#569). Contributed @stefvanbuuren # mice 3.16.1 +### Minor changes + * Adds support for the `dots` argument to `ranger::ranger(...)` in `mice.impute.rf()` (#563). Contributed @edbonneville # mice 3.16.0 @@ -156,30 +196,34 @@ Note that we still lack solid evidence for these claims. (#576). Contributed @st ### Major changes -* Adds four new univariate functions using the lasso for automatic variable selection: - -| Function | Description | -| --------------------------------- | --------------------------------- | -|`mice.impute.lasso.norm()` | Lasso linear regression | -|`mice.impute.lasso.logreg()` | Lasso logistic regression | -|`mice.impute.lasso.select.norm()` | Lasso selector + linear regression | -|`mice.impute.lasso.select.logreg()`| Lasso selector + logistic regression | +* Adds four new univariate functions using the lasso for automatic variable selection. Contributed by @EdoardoCostantini (#438). -Contributed by @EdoardoCostantini (#438). + - `mice.impute.lasso.norm()` for lasso linear regression + - `mice.impute.lasso.logreg()` for lasso logistic regression + - `mice.impute.lasso.select.norm()` for lasso selector + linear regression + - `mice.impute.lasso.select.logreg()` for lasso selector + logistic regression * Adds Jamshidian && Jalal's non-parametric MCAR test, `mice::MCAR()` and associated plot method. Contributed by @cjvanlissa (#423). * Adds two new functions `pool.syn()` and `pool.scalar.syn()` that specialise pooling estimates from synthetic data. The `"reiter2003"` pooling rule assumes that synthetic data were created from complete data. Thanks Thom Volker (#436). -* Avoids changing the global `.Random.seed` (#426, #432) by implementing `withr::local_preserve_seed()` and `withr::local_seed()`. This change provides stabler behavior in complex scripts. The change does not appear to break reproducibility when `mice()` was run with a seed. Nevertheless, if you run into a reproducibility problem, install `mice 3.13.12` or before. - -* Improves the imputation of parabolic data in `mice.impute.quadratic()`, adds a parameter `quad.outcome` containing the name of the outcome variable in the complete-data model. Contributed @Mingyang-Cai, @gerkovink (#408) - * By default, `mice.impute.rf()` now uses the faster `ranger` package as back-end instead of `randomForest` package. If you want the old behaviour specify the `rfPackage = "randomForest"` argument to the `mice(...)` call. Contributed @prockenschaub (#431). -* Generalises `pool()` so that it processes the parameters from all `gamlss` sub-models. Thanks Marcio Augusto Diniz (#406, #405) +### Minor changes +* Avoids changing the global `.Random.seed` (#426, #432) by implementing `withr::local_preserve_seed()` and `withr::local_seed()`. This change provides stabler behavior in complex scripts. The change does not appear to break reproducibility when `mice()` was run with a seed. Nevertheless, if you run into a reproducibility problem, install `mice 3.13.12` or before. +* Improves the imputation of parabolic data in `mice.impute.quadratic()`, adds a parameter `quad.outcome` containing the name of the outcome variable in the complete-data model. Contributed @Mingyang-Cai, @gerkovink (#408) +* Generalises `pool()` so that it processes the parameters from all `gamlss` sub-models. Thanks Marcio Augusto Diniz (#406, #405) * Uses the robust standard error estimate for pooling when `pool()` can extract `robust.se` from the object returned by `broom::tidy()` (#310) +* Replaces URL to jstatsoft with DOI +* Update reference to literature (#442) +* Informs the user that `pool()` cannot take a `mids` object (#433) +* Updates documentation for post-processing functionality (#387) +* Adds Rcpp necessities +* Solves a problem with "last resort" initialisation of factors (#410) +* Documents the "flat-line behaviour" of `mice.impute.2l.lmer()` to indicate a problem in fitting the imputation model (#385) +* Add reprex to test (#326) +* Documents that multivariate imputation methods do not support the `post` parameter (#326) ### Bug fixes @@ -193,19 +237,6 @@ Contributed by @EdoardoCostantini (#438). * Solves a problem with row names of the `where` element created in `rbind()` (#319) * Solves a bug in mnar imputation routine. Contributed by Margarita Moreno Betancur. -### Minor changes - -* Replaces URL to jstatsoft with DOI -* Update reference to literature (#442) -* Informs the user that `pool()` cannot take a `mids` object (#433) -* Updates documentation for post-processing functionality (#387) -* Adds Rcpp necessities -* Solves a problem with "last resort" initialisation of factors (#410) -* Documents the "flat-line behaviour" of `mice.impute.2l.lmer()` to indicate a problem in fitting the imputation model (#385) -* Add reprex to test (#326) -* Documents that multivariate imputation methods do not support the `post` parameter (#326) - - # mice 3.13.0 ### Major changes @@ -220,17 +251,15 @@ Contributed by @EdoardoCostantini (#438). # mice 3.12.0 -### Much faster predictive mean matching +### Major changes -* The new `matchindex` C function makes predictive mean matching **50 to 600 times faster**. +* **Much faster predictive mean matching**. The new `matchindex` C function makes predictive mean matching **50 to 600 times faster**. The speed of `pmm` is now on par with normal imputation (`mice.impute.norm()`) and with the `miceFast` package, without compromising on the statistical quality of the imputations. Thanks to Polkas and suggestions by Alexander Robitzsch. See #236 for more details. -### New `ignore` argument to `mice` - -* New `ignore` argument to `mice()`. This argument is a logical vector +* **New `ignore` argument to `mice()`**. This argument is a logical vector of `nrow(data)` elements indicating which rows are ignored when creating the imputation model. We may use the `ignore` argument to split the data into a training set (on which the imputation model is built) and a test @@ -239,15 +268,12 @@ is based on the suggestion in . See #32 for more background and techniques. Crafted by Patrick Rockenschaub -### New `filter()` function for `mids` objects - -* New `filter()` method that subsets a `mids` object (multiply-imputed data set). +* **New `filter()` function for `mids` objects**. New `filter()` method that +subsets a `mids` object (multiply-imputed data set). The method accepts a logical vector of length `nrow(data)`, or an expression to construct such a vector from the incomplete data. (#269). Crafted by Patrick Rockenschaub. -### Changes affecting reproducibility - * **Breaking change:** The `matcher` algorithm in `pmm` has changed to `matchindex` for speed improvements. If you want the old behavior, specify `mice(..., use.matcher = TRUE)`. @@ -273,50 +299,55 @@ for speed improvements. If you want the old behavior, specify `mice(..., use.mat # mice 3.11.0 -## Major changes +### Major changes * The Cox model does not return `df.residual`, which caused problematic behavior in the `D1()`, `D2()`, `D3()`, `anova()` and `pool()`. `mice` now extracts the relevant information from other parts of the objects returned by `survival::coxph()`, which solves long-standing issues with the integration of the Cox model (#246). -* Adds missing `Rccp` dependency to work with `tidyr 1.1.1` (#248). -## Minor changes +### Minor changes +* Adds missing `Rccp` dependency to work with `tidyr 1.1.1` (#248). * Addresses warnings: `Non-file package-anchored link(s) in documentation object`. * Updates on `ampute` documentation (#251). * Ask user permission before installing a package from `suggests`. # mice 3.10.0 -## Major changes +### Major changes * New functions `tidy.mipo()` and `glance.mipo()` return standardized output that conforms to `broom` specifications. Kindly contributed by Vincent Arel Bundock (#240). -## Minor changes +### Minor changes * Solves a problem with the `D3` testing script that produced an error on CRAN (#244). # mice 3.9.0 -## Major changes +### Major changes * The `D3()` function in `mice` gave incorrect results. This version solves a problem in the calculation of the `D3`-statistic. See #226 and #228 for more details. The documentation explains why results from `mice::D3()` and `mitml::testModels()` may differ. + * The `pool()` function is now more forgiving when there is no `glance()` function (#233) + * It is possible to bypass `remove.lindep()` by setting `eps = 0` (#225) -## Minor changes +### Minor changes * Adds reference to Leacy's thesis * Adds an example to the `plot.mids()` documentation # mice 3.8.0 -## Major changes +### Major changes * This version adds two new NARFCS methods for imputing data under the *Missing Not at Random (MNAR)* assumption. NARFCS is generalised version of the so-called $\delta$-adjustment method. Margarita Moreno-Betancur and Ian White kindly contributes the functions `mice.impute.mnar.norm()` and `mice.impute.mnar.logreg()`. These functions aid in performing sensitivity analysis to investigate the impact of different MNAR assumptions on the conclusion of the study. An alternative for MNAR is the older `mice.impute.ri()` function. + * Installation of `mice` is faster. External packages needed for imputation and analyses are now installed on demand. The number of dependencies as estimated by `rsconnect::appDepencies()` decreased from 132 to 83. + * The name clash with the `complete()` function of `tidyr` should no longer be a problem. + * There is now a more flexible `pool()` function that integrates better with the `broom` and `broom.mixed` packages. -## Bug fixes +### Bug fixes * Deprecates `pool.compare()`. Use `D1()` instead (#220) * Removes everything in `utils::globalVariables()` @@ -450,7 +481,7 @@ following features: [Flexible Imputation of Missing Data. Second Edition.](https://stefvanbuuren.name/fimd/) -# mice 2.46.9 (2017-12-08) +# mice 2.46.9 * simplify code for `mids` object in `mice` (thanks stephematician) (#61) * simplify code in `rbind.mids` (thanks stephematician) (#59) @@ -461,28 +492,34 @@ following features: * resolved problem `cart` not accepting a matrix (thanks Joerg Drechsler) * Adds generalized `pool()` to list of models * Switch to 3-digit versioning +* Date: 2017-12-08 -# mice 2.46 (2017-10-22) +# mice 2.46 * Allow for capitals in imputation methods +* Date: 2017-10-22 -# mice 2.45 (2017-10-21) +# mice 2.45 * Reorganized vignettes to land on GitHUB pages +* Date: 2017-10-21 -# mice 2.44 (2017-10-18) +# mice 2.44 * Code changes for robustness, style and efficiency (Bernie Gray) +* Date: 2017-10-18 -# mice 2.43 (2017-07-20) +# mice 2.43 * Updates the `ampute` function and vignettes (Rianne Schouten) +* Date: 2017-07-20 -# mice 2.42 (2017-07-11) +# mice 2.42 * Rename `mice.impute.2l.sys` to `mice.impute.2l.lmer` +* Date: 2017-07-11 -# mice 2.41 (2017-07-10) +# mice 2.41 * Add new feature: `where`argument to mice * Add new `wy` argument to imputation functions @@ -490,8 +527,9 @@ following features: * Update with many simplifications and code enhancements * Fixed broken `cbind()` function * Fixed Bug that made the pad element disappear from `mids` object +* Date: 2017-07-10 -# mice 2.40 (2017-07-07) +# mice 2.40 * Fixed integration with `lattice` package * Updates colors in `xyplot.mads` @@ -499,14 +537,16 @@ following features: * Create more robust version of as.mids() * Update of `ampute()` by Rianne Schouten * Fix timestamp problem by rebuilding vignette using R 3.4.0. +* Date: 2017-07-07 -# mice 2.34 (2017-04-24) +# mice 2.34 * Update to roxygen 6.0.1 * Stylistic changes to `mice` function (thanks Ben Ogorek) * Calls to `cbind.mids()` replaced by calls to `cbind()` +* Date: 2017-04-24 -# mice 2.31 (2017-02-23) +# mice 2.31 * Add link to `miceVignettes` on github (thanks Gerko Vink) * Add package documentation @@ -519,35 +559,40 @@ following features: * Fix checking of nested models in `pool.compare` #12 * Fix `as.mids` if names not same as all columns #11 * Fix extension for `glmer` models #5 +* Date: 2017-02-23 -# mice 2.29 (2016-10-05) +# mice 2.29 * Add `midastouch`: predictive mean matching for small samples (thanks Philip Gaffert, Florian Meinfelder) +* Date: 2016-10-05 -# mice 2.28 (2016-10-05) +# mice 2.28 * Repaired dots problem in `rpart` call +* Date: 2016-10-05 -# mice 2.27 (2016-07-27) +# mice 2.27 * Add `ridge` to `2l.norm()` * Remove `.o` files +* Date: 2016-07-27 -# mice 2.25 (2015-11-09) +# mice 2.25 * Fix `as.mids()` bug that crashed `miceadds::mice.1chain()` +* Date: 2015-11-09 -# mice 2.23 (2015-11-04) +# mice 2.23 * Update of example code on /doc * Remove lots of dependencies, general cleanup - * Fix `impute.polyreg()` bug that bombed if there were no predictors (thanks Jan Graffelman) * Fix `as.mids()` bug that gave incorrect $m$ (several users) * Fix `pool.compare()` error for `lmer` object (thanks Claudio Bustos) * Fix error in `mice.impute.2l.norm()` if just one `NA` (thanks Jeroen Hoogland) +* Date: 2015-11-04 -# mice 2.22 (2014-06-11) +# mice 2.22 * Add about six times faster predictive mean matching * `pool.scalar()` now can do Barnard-Rubin adjustment @@ -563,17 +608,20 @@ following features: * Fix error in `mice.impute.rf()` if just one `NA` (thanks Anoop Shah) * Fix error in `summary.mipo()` when `names(x$qbar)` equals `NULL` (thanks Aiko Kuhn) * Fix improper testing in `ncol()` in `mice.impute.2lonly.mean()` +* Date: 2014-06-11 -# mice 2.21 02-05-2014 SvB +# mice 2.21 * FIXED: compilation problem in match.cpp on solaris CC +* Date: 02-05-2014 SvB -# mice 2.20 02-02-2014 SvB +# mice 2.20 * ADDED: experimental fastpmm() function using Rcpp * FIXED: fixes to mice.impute.cart() and mice.impute.rf() (thanks Anoop Shah) +* Date: 02-02-2014 SvB -# mice 2.19 21-01-2014 SvB +# mice 2.19 * ADDED: mice.impute.rf() for random forest imputation (thanks Lisa Doove) * CHANGED: default number of donors in mice.impute.pmm() changed from 3 to 5. @@ -582,21 +630,24 @@ following features: * CHANGED: speedup in .imputation.level2() (thanks Alexander Robitzsch) * FIXED: define MASS, nnet, lattice as imports instead of depends * FIXED: proper handling of rare case in remove.lindep() that removed all predictors (thanks Jaap Brand) +* Date: 21-01-2014 SvB -# mice 2.18 31-07-2013 SvB +# mice 2.18 * ADDED: as.mids() for converting long format in a mids object (thanks Gerko Vink) * FIXED: mice.impute.logreg.boot() now properly exported (thanks Suresh Pujar) * FIXED: two bugs in rbind.mids() (thanks Gerko Vink) +* Date: 31-07-2013 SvB -# mice 2.17 10-05-2013 SvB +# mice 2.17 * ADDED: new form argument to mice() to specify imputation models using forms (contributed Ross Boylan) * FIXED: with.mids(), is.mids(), is.mira() and is.mipo() exported * FIXED: eliminated errors in the documentation of pool.scalar() * FIXED: error in mice.impute.ri() (thanks Shahab Jolani) +* Date: 10-05-2013 SvB -# mice 2.16 27-04-2013 SvB +# mice 2.16 * ADDED: random indicator imputation by mice.impute.ri() for nonignorable models (thanks Shahab Jolani) * ADDED: workhorse functions .norm.draw() and .pmm.match() are exported @@ -604,16 +655,18 @@ following features: * FIXED: bug that crashed R when the class variable was incomplete (thanks Robert Long) * FIXED: bug in 2l.pan and 2l.norm by convert a class factor to integer (thanks Robert Long) * FIXED: warning eliminated caused by character variables (thanks Robert Long) +* Date: 27-04-2013 SvB -# mice 2.15 - 02-04-2013 SvB +# mice 2.15 * CHANGED: complete reorganization of documentation and source files * ADDED: source published on GitHub.com * ADDED: new imputation method mice.impute.cart() (thanks Lisa Doove) * FIXED: calculation of degrees of freedom in pool.compare() (thanks Lorenz Uhlmann) * FIXED: error in DESCRIPTION file (thanks Kurt Hornik) +* Date: 02-04-2013 SvB -# mice 2.14 - 11-03-2013 / SvB +# mice 2.14 * ADDED: mice.impute.2l.mean() for imputing class means at level 2 * ADDED: sampler(): new checks of degrees of freedom per variable at iteration 1 @@ -625,8 +678,9 @@ following features: * FIXED: bug in mice.df() that prevented the classic Rubin df calculation (thanks Jean-Batiste Pingaul) * FIXED: bug fixed in mice.impute.2l.norm() (thanks Robert Long) * CHANGED: faster .pmm.match2() from version 2.12 renamed to default .pmm.match() +* Date: 11-03-2013 / SvB -# mice 2.13 - 03-07-2012 / SvB +# mice 2.13 * ADDED: new multilevel functions 2l.pan(), 2lonly.norm(), 2lonly.pmm() (contributed by Alexander Robitzsch) * ADDED: new quadratic imputation function: quadratic() (contributed by Gerko Vink) @@ -639,8 +693,9 @@ following features: * FIXED: bug in sample() in mice.impute.sample() * FIXED: fixed '?data' bug in check.method() * REMOVED: wp.twin(). Now available from the AGD package +* Date: 03-07-2012 / SvB -# mice 2.12 - 25-03-2012 / SvB +# mice 2.12 * UPDATE: version for launch of Flexible Imputation of Missing Data (FIMD) * ADDED: code fimd1.r-fim9.r to inst/doc for calculating solutions in FIMD @@ -656,8 +711,9 @@ following features: * CHANGED: pool() streamlined, warnings about incompatibility in lengths of coef() and vcov() * FIXED: mdc() bug that ignored transparent=FALSE argument, now made visible * FIXED: bug in md.pattern() for >32 variables (thanks Sascha Vieweg, Joshua Wiley) +* Date: 25-03-2012 / SvB -# mice 2.11 - 21-11-2011 / SvB +# mice 2.11 * UPDATE: definite reference to JSS paper * ADDED: rm.whitespace() to do string manipulation (thanks Gerko Vink) @@ -665,13 +721,15 @@ following features: * CHANGED: plot.mids() changed into trellis version * ADDED: code used in JSS-paper * FIXED: bug in check.method() (thanks Gerko Vink) +* Date: 21-11-2011 / SvB -# mice 2.10 - 14-09-2011 / SvB +# mice 2.10 * FIXED: arguments dec and sep in mids2spss (thanks Nicole Haag) * FIXED: bug in keyword "monotone" in mice() (thanks Alain D) +* Date: 14-09-2011 / SvB -# mice 2.9 - 31-08-2011 / SvB +# mice 2.9 * FIXED: appropriate trimming of ynames and xnames in Trellis plots * FIXED: exported: spss2mids(), mice.impute.2L.norm() @@ -681,18 +739,21 @@ following features: * ADDED: trellis version of plot.mids() * ADDED: automatic semi-transparancy detection in mdc() * FIXED: documentation of mira class (thanks Sandro Tsang) +* Date: 31-08-2011 / SvB -# mice 2.8 - 24-03-2011 / SvB +# mice 2.8 * FIXED: bug fixed in find.collinear() that bombed when only one variable was left +* Date: 24-03-2011 / SvB -# mice 2.7 - 16-03-2011 / SvB +# mice 2.7 * CHANGED: check.data(), remove.lindep(): fully missing variables are imputed if allow.na=TRUE (Alexander Robitzsch) * FIXED: bug in check.data(). Now checks collinearity in predictors only (Alexander Robitzsch) * CHANGED: abbreviations of arguments eliminated to evade linux warnings +* Date: 16-03-2011 / SvB -# mice 2.6 - 03-03-2011 / SvB +# mice 2.6 * ADDED: bwplot(), stripplot(), densityplot() and xyplot() for creating Trellis graphs * ADDED: function mdc() and mice.theme() for graphical parameters @@ -704,8 +765,9 @@ following features: * ADDED: internal functions mice.df() and df.residual() * FIXED: error in rm calculation for "likelihood" in pool.compare() * CHANGED: default ridge parameter changed +* Date: 03-03-2011 / SvB -# mice 2.5 - 06-01-2011 / SvB +# mice 2.5 * ADDED: various stability enhancements and code clean-up * ADDED: find.collinear() function @@ -726,30 +788,34 @@ following features: * FIXED: global assign() removed from mice.impute.polyreg() * FIXED: improved handling of factors by complete() * FIXED: improved labeling of nhanes2 data +* Date: 06-01-2011 / SvB -# mice 2.4 - 17-10-2010 / SvB +# mice 2.4 * ADDED: pool() now supports class 'polr' (Jean-Baptiste Pingault) * FIXED: solved problem in mice.impute.polyreg when one of the variables was named y or x * FIXED: remove.lindep: intercept prediction bug * ADDED: version() function * ADDED: cc(), cci() and ccn() convenience functions +* Date: 17-10-2010 / SvB -# mice 2.3 - 14-02-2010 / SvB +# mice 2.3 * FIXED: check.method: logicals are now treated as binary variables (Emmanuel Charpentier) * FIXED: complete: the NULL imputation case is now properly handled * FIXED: mice.impute.pmm: now creates between imputation variability for univariate predictor * FIXED: remove.lindep: returns 'keep' vector instead of data +* Date: 14-02-2010 / SvB -# mice 2.2 - 13-01-2010 / SvB +# mice 2.2 * ADDED: pool() now supports class 'multinom' (Jean-Baptiste Pingault) * FIXED: bug fixed in check.data for data consisting of two columns (Rogier Donders, Thomas Koepsell) * ADDED: new function remove.lindep() that removes predictors that are (almost) linearly dependent * FIXED: bug fixed in pool() that produced an (innocent) warning message (Qi Zheng) +* Date: 13-01-2010 / SvB -# mice 2.1 - 14-09-2009 / SvB +# mice 2.1 * ADDED: pool() now also supports class 'mer' * CHANGED: nlme and lme4 are now only loaded if needed (by pool()) @@ -757,9 +823,11 @@ following features: * FIXED: bug fixed in plot.mids() when there was one missing entry (Emmanuel Charpentier) * CHANGED: NAMESPACE expanded to allow easy access to function code * FIXED: mice() can now find mice.impute.xxx() functions in the .GlobalEnv +* Date: 14-09-2009 / SvB -# mice 2.0 - 26-08-2009 / SvB, KO Major upgrade for JSS manuscript +# mice 2.0 +* Major upgrade for JSS manuscript * ADDED: new functions cbind.mids(), rbind.mids(), ibind() * ADDED: new argument in mice(): 'post' in post-processing imputations * ADDED: new functions: pool.scaler(), pool.compare(), pool.r.squared() @@ -780,43 +848,53 @@ following features: * ADDED: support for intercept imputation * ADDED: new function quickpred() * FIXED: plot.mids() bug fix when number of variables > 5 +* Date: 26-08-2009 / SvB, KO -# mice 1.21 - 15/3/2009 SvB Maintainance release +# mice 1.21 * FIXED: Stricter type checking on logicals in mice() to evade warnings. * CHANGED: Modernization of all help files. * FIXED: padModel: treatment changed to contr.treatment * CHANGED: Functions check.visitSequence, check.predictorMatrix, check.imputationMethod are now coded as local to mice() * FIXED: existsFunction in check.imputationMethod now works both under S-Plus and R +* Date: 15/3/2009 -# mice 1.16 - 6/25/2007 +# mice 1.16 * FIXED: The impution function impute.logreg used convergence criteria that were too optimistic when fitting a GLM with glm.fit. Thanks to Ulrike Gromping. +* Date: 6/25/2007 -# mice 1.15 - 01/09/2006 +# mice 1.15 * FIXED: In the lm.mids and glm.mids functions, parameters were not passed through to glm and lm. +* Date: 01/09/2006 + +# mice 1.14 -# mice 1.14R - 9/26/2005 11:44AM * FIXED: Passive imputation works again. (Roel de Jong) * CHANGED: Random seed is now left alone, UNLESS the argument "seed" is specified. This means that unless you specify identical seed values, imputations of the same dataset will be different for multiple calls to mice. (Roel de Jong) * FIXED: (docs): Documentation for "impute.mean" (Roel de Jong) * FIXED: Function 'summary.mids' now works (Roel de Jong) * FIXED: Imputation function 'impute.polyreg' and 'impute.lda' should now work under R +* Date: 9/26/2005 # mice 1.13 -* Changed function checkImputationMethod, Feb 6, 2004 +* Changed function checkImputationMethod +* Date: Feb 6, 2004 # mice 1.12 -* Maintainance, S-Plus 6.1 and R 1.8 unicode, January 2004 +* Maintainance, S-Plus 6.1 and R 1.8 unicode +* Date: January 2004 # mice 1.1 -* R version (with help of Peter Malewski and Frank Harrell), Feb 2001 +* R version (with help of Peter Malewski and Frank Harrell) +* Date: Feb 2001 # mice 1.0 -* Original S-PLUS release, June 14 2000 +* Original S-PLUS release +* Date: June 14 2000 \ No newline at end of file diff --git a/R/ampute.R b/R/ampute.R index 41aa78a4a..91f9d614f 100644 --- a/R/ampute.R +++ b/R/ampute.R @@ -208,6 +208,7 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, 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) @@ -218,7 +219,7 @@ 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 because the calculation of weights requires numeric data", + warning("Data is made numeric internally, because the calculation of weights requires numeric data", call. = FALSE ) } @@ -454,7 +455,7 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, names(patterns.new) <- names(data) names(weights) <- names(data) call <- match.call() - missing.data <- data.frame(missing.data) + data.in[is.na(data.frame(missing.data))] <- NA result <- list( call = call, prop = prop, @@ -466,7 +467,7 @@ ampute <- function(data, prop = 0.5, patterns = NULL, freq = NULL, std = std, type = type, odds = odds, - amp = missing.data, + amp = data.in, cand = P - 1, scores = scores, data = as.data.frame(data) diff --git a/R/convergence.R b/R/convergence.R index ab9adbde3..177702aa7 100644 --- a/R/convergence.R +++ b/R/convergence.R @@ -120,4 +120,5 @@ convergence <- function(data, diagnostic = "all", parameter = "mean", ...) { } # function to extend is.nan() to data.frame objects +#' @export is.nan.data.frame <- function(x) do.call(cbind, lapply(x, is.nan)) diff --git a/R/df.residual.R b/R/df.residual.R index bfdbc1cc8..b10c0947b 100644 --- a/R/df.residual.R +++ b/R/df.residual.R @@ -1,19 +1,22 @@ +#' @export df.residual.mira <- function(object, ...) { fit <- object$analyses[[1]] df.residual(fit) } +#' @export df.residual.lme <- function(object, ...) { object$fixDF[["X"]][1] } +#' @export df.residual.mer <- function(object, ...) { sum(object@dims[2:4] * c(1, -1, -1)) + 1 } - +#' @export df.residual.multinom <- function(object, ...) { nrow(object$residuals) - object$edf } diff --git a/R/edit.setup.R b/R/edit.setup.R index a1d54eca9..93b2e4a18 100644 --- a/R/edit.setup.R +++ b/R/edit.setup.R @@ -1,4 +1,4 @@ -edit.setup <- function(data, setup, +mice.edit.setup <- function(data, setup, allow.na = FALSE, remove.constant = TRUE, remove.collinear = TRUE, diff --git a/R/formula.R b/R/formula.R index cd8377ae1..d168d9ae7 100644 --- a/R/formula.R +++ b/R/formula.R @@ -43,8 +43,8 @@ make.formulas <- function(data, blocks = make.blocks(data), x <- "1" } formulas[[h]] <- paste( - paste(y, collapse = "+"), "~", - paste(x, collapse = "+") + paste(backticks(y), collapse = "+"), "~", + paste(backticks(x), collapse = "+") ) } @@ -128,7 +128,7 @@ check.formulas <- function(formulas, data, autoremove = TRUE) { formulas <- name.formulas(formulas) formulas <- handle.oldstyle.formulas(formulas, data) - formulas <- lapply(formulas, expand.dots, data) + formulas <- lapply(formulas, mice.expand.dots, data) # escape if formula is list of two formula's if (any(sapply(formulas, is.list))) { return(formulas) @@ -262,12 +262,12 @@ extend.formula <- function(formula = ~0, # handle dot in RHS if (hasdot(formula)) { if (length(predictors) > 1) { - fr <- as.formula(c("~", paste(predictors, collapse = "+"))) + fr <- as.formula(c("~", paste(backticks(predictors), collapse = "+"))) } else { fr <- ~0 } } else { - fr <- reformulate(c(".", predictors)) + fr <- reformulate(c(".", backticks(predictors))) } if (auxiliary) formula <- update(formula, fr, ...) @@ -310,7 +310,7 @@ hasdot <- function(f) { } } -expand.dots <- function(formula, data) { +mice.expand.dots <- function(formula, data) { if (!is.formula(formula)) { return(formula) } diff --git a/R/futuremice.R b/R/futuremice.R index 6357c4181..bb485e457 100644 --- a/R/futuremice.R +++ b/R/futuremice.R @@ -46,7 +46,7 @@ #' The default `multisession` resolves futures asynchronously (in parallel) #' in separate `R` sessions running in the background. See #' [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 #' `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). @@ -78,7 +78,7 @@ #' #' @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, ...) { # check if packages available install.on.demand("parallelly", ...) @@ -136,7 +136,7 @@ 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 +149,7 @@ 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/get.df.R b/R/get.df.R index 5dd65640b..18eceefa4 100644 --- a/R/get.df.R +++ b/R/get.df.R @@ -14,14 +14,14 @@ get.dfcom <- function(model, dfcom = NULL) { # coxph model: nevent - p if (inherits(model, "coxph")) { - return(as.numeric(max(model$nevent - length(coef(model)), 1))) + return(as.numeric(max(model$nevent - length(stats::coef(model)), 1))) } # other model: n - p nobs <- tryCatch(length(stats::residuals(model)), error = function(e) NULL) if (!is.null(nobs)) { - return(as.numeric(max(nobs - length(coef(model)), 1))) + return(as.numeric(max(nobs - length(stats::coef(model)), 1))) } # nothing found diff --git a/R/internal.R b/R/internal.R index 66f4689f2..98cdfa138 100644 --- a/R/internal.R +++ b/R/internal.R @@ -149,3 +149,7 @@ ma_exists <- function(x, pos, n_index = 1:8) { res <- list(is_there = is_there, obj = obj, pos = pos) return(res) } + +backticks <- function(varname) { + sprintf("`%s`", varname) +} diff --git a/R/mcar.R b/R/mcar.R index 914966627..a7d73c472 100644 --- a/R/mcar.R +++ b/R/mcar.R @@ -184,11 +184,12 @@ mcar.data.frame <- function(x, } remove_pats <- as.numeric(rownames(pats))[-nrow(pats)] <= min_n if (any(remove_pats)) { - out$removed_patterns <- pats[remove_pats, ] + out$removed_patterns <- pats[which(remove_pats), ] pats <- pats[-nrow(pats), colnames(missings)] idmiss <- do.call(paste, as.data.frame(missings)) idpats <- do.call(paste, as.data.frame(pats == 0)) remove_these <- idmiss %in% idpats[remove_pats] + if(all(remove_these)) stop("After dropping missing data patterns with fewer than ", min_n, " cases, there were no remaining valid cases in the dataset. Consider lowering 'min_n', and be cautious about interpreting the results; these data might not be suitable for an MCAR test.") out$removed_rows <- remove_these newdata <- x[!remove_these, , drop = FALSE] imputed <- lapply(imputed, `[`, i = !remove_these, j = colnames(newdata), drop = FALSE) diff --git a/R/mice-package.R b/R/mice-package.R index e18cfb9b5..334d0a993 100644 --- a/R/mice-package.R +++ b/R/mice-package.R @@ -47,8 +47,7 @@ #' \item [Imputing multilevel data](https://www.gerkovink.com/miceVignettes/Multi_level/Multi_level_data.html) #' \item \href{https://www.gerkovink.com/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html}{Sensitivity analysis with \pkg{mice}} #' } -#' -#' #'Van Buuren, S. (2018). +#' Van Buuren, S. (2018). #' Boca Raton, FL.: Chapman & Hall/CRC Press. #' The book @@ -70,7 +69,8 @@ #' depend on the operating system. See the discussion in the #' "R Installation and Administration" guide for further information. #' -#' @docType package +#' @aliases mice-package +#' #' @name mice #' @seealso [mice()], [with.mids()], #' [pool()], [complete()], [ampute()] @@ -91,4 +91,5 @@ #' [*Flexible Imputation of Missing Data. Second Edition.*](https://stefvanbuuren.name/fimd/) #' Chapman & Hall/CRC. Boca Raton, FL. #' @useDynLib mice, .registration = TRUE -NULL +#' @keywords internal +"_PACKAGE" diff --git a/R/mice.R b/R/mice.R index 6f2f0417d..b16cdc15a 100644 --- a/R/mice.R +++ b/R/mice.R @@ -130,7 +130,7 @@ #' visited. In that way, deterministic relation between columns will always be #' synchronized. #' -#' #'A new argument `ls.meth` can be parsed to the lower level +#' A new argument `ls.meth` can be parsed to the lower level #' `.norm.draw` to specify the method for generating the least squares #' estimates and any subsequently derived estimates. Argument `ls.meth` #' takes one of three inputs: `"qr"` for QR-decomposition, `"svd"` for @@ -545,7 +545,7 @@ mice <- function(data, ynames) # edit predictorMatrix for monotone, set zero rows for empty methods - predictorMatrix <- edit.predictorMatrix( + predictorMatrix <- mice.edit.predictorMatrix( predictorMatrix = predictorMatrix, method = method, blocks = blocks, @@ -595,7 +595,7 @@ mice <- function(data, visitSequence = visitSequence, post = post ) - setup <- edit.setup(data, setup, ...) + setup <- mice.edit.setup(data, setup, ...) method <- setup$method formulas <- setup$formulas dots <- setup$dots @@ -605,7 +605,7 @@ mice <- function(data, # update parcel parcel <- b2n(blocks) - parcel <- reorder.parcel(parcel, data) + parcel <- mice.reorder.parcel(parcel, data) # initialize imputations nmis <- apply(is.na(data), 2, sum) diff --git a/R/mipo.R b/R/mipo.R index 4369df761..385736040 100644 --- a/R/mipo.R +++ b/R/mipo.R @@ -151,6 +151,7 @@ process_mipo <- function(z, x, conf.int = FALSE, conf.level = .95, z } +#' @export vcov.mipo <- function(object, ...) { so <- diag(object$t) dimnames(so) <- list(object$term, object$term) diff --git a/R/parcel.R b/R/parcel.R index 7a5bfea5f..b97cb6b1b 100644 --- a/R/parcel.R +++ b/R/parcel.R @@ -114,7 +114,7 @@ check.parcel <- function(parcel, data) { parcel } -reorder.parcel <- function(parcel, data) { +mice.reorder.parcel <- function(parcel, data) { idx <- colnames(data) return(parcel[idx]) } diff --git a/R/predictorMatrix.R b/R/predictorMatrix.R index 70d31f4d1..21df2bbe3 100644 --- a/R/predictorMatrix.R +++ b/R/predictorMatrix.R @@ -134,7 +134,7 @@ check.predictorMatrix <- function(predictorMatrix, return(predictorMatrix) } -edit.predictorMatrix <- function(predictorMatrix, +mice.edit.predictorMatrix <- function(predictorMatrix, method, blocks, where, @@ -160,7 +160,10 @@ edit.predictorMatrix <- function(predictorMatrix, } # edit predictorMatrix to a monotone pattern - if (maxit == 1L && !is.null(user.visitSequence) && user.visitSequence == "monotone") { + if (maxit == 1L && + !is.null(user.visitSequence) && + length(user.visitSequence) == 1 && + user.visitSequence == "monotone") { for (i in 1L:length(visitSequence)) { predictorMatrix[visitSequence[i], visitSequence[i:length(visitSequence)]] <- 0 } diff --git a/R/quickpred.R b/R/quickpred.R index 7d1c2e12a..21d84929c 100644 --- a/R/quickpred.R +++ b/R/quickpred.R @@ -106,9 +106,11 @@ quickpred <- function(data, mincor = 0.1, minpuc = 0, include = "", predictorMatrix[maxc > mincor] <- 1 # exclude predictors with a percentage usable cases below minpuc - p <- md.pairs(data) - puc <- p$mr / (p$mr + p$mm) - predictorMatrix[puc < minpuc] <- 0 + if (any(minpuc != 0)) { + p <- md.pairs(data) + puc <- p$mr / (p$mr + p$mm) + predictorMatrix[puc < minpuc] <- 0 + } # exclude predictors listed in the exclude argument yz <- pmatch(exclude, names(data)) diff --git a/R/sampler.R b/R/sampler.R index 54e0ce9a0..36a865174 100644 --- a/R/sampler.R +++ b/R/sampler.R @@ -209,7 +209,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, vars <- colnames(data)[pred != 0] xnames <- setdiff(vars, j) if (length(xnames) > 0L) { - formula <- reformulate(xnames, response = j) + formula <- reformulate(backticks(xnames), response = backticks(j)) formula <- update(formula, ". ~ . ") } else { formula <- as.formula(paste0(j, " ~ 1")) @@ -234,7 +234,7 @@ sampler.univ <- function(data, r, where, pred, formula, method, yname, k, ymove <- setdiff(lhs(formula), j) formula <- update(formula, paste(j, " ~ . ")) if (length(ymove) > 0L) { - formula <- update(formula, paste("~ . + ", paste(ymove, collapse = "+"))) + formula <- update(formula, paste("~ . + ", paste(backticks(ymove), collapse = "+"))) } } diff --git a/README.Rmd b/README.Rmd index e1f817f36..0849af93f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -23,7 +23,7 @@ set.seed(1) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/mice)](https://cran.r-project.org/package=mice) [![](https://cranlogs.r-pkg.org/badges/mice)](https://cran.r-project.org/package=mice) [![R-CMD-check](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml) -[![](https://img.shields.io/badge/github%20version-3.16.5-orange.svg)](https://amices.org/mice/) +[![](https://img.shields.io/badge/github%20version-3.16.10-orange.svg)](https://amices.org/mice/) ## [Multivariate Imputation by Chained Equations](https://amices.org/mice/) diff --git a/README.md b/README.md index aaabb8da3..b4d677aa2 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/mice)](https://cran.r-project.org/package=mice) [![](https://cranlogs.r-pkg.org/badges/mice)](https://cran.r-project.org/package=mice) [![R-CMD-check](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/amices/mice/actions/workflows/R-CMD-check.yaml) -[![](https://img.shields.io/badge/github%20version-3.16.5-orange.svg)](https://amices.org/mice/) +[![](https://img.shields.io/badge/github%20version-3.16.10-orange.svg)](https://amices.org/mice/) ## [Multivariate Imputation by Chained Equations](https://amices.org/mice/) diff --git a/man/figures/README-pattern-1.png b/man/figures/README-pattern-1.png index 4175b4c92424c8f74859942b8b54facab74ce658..11ac59ddcb896b26878ee57268a65c1c9696bd01 100644 GIT binary patch literal 16303 zcmeHucTm&c*Cs@e=2uZb6h#nJ1O!BS3qi%ui_(!I2%!lgy#$DgjV?{;N>h3-p;>5x 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In that way, deterministic relation between columns will always be synchronized. -#'A new argument \code{ls.meth} can be parsed to the lower level +A new argument \code{ls.meth} can be parsed to the lower level \code{.norm.draw} to specify the method for generating the least squares estimates and any subsequently derived estimates. Argument \code{ls.meth} takes one of three inputs: \code{"qr"} for QR-decomposition, \code{"svd"} for @@ -428,8 +429,7 @@ We suggest going through these vignettes in the following order \item \href{https://www.gerkovink.com/miceVignettes/Multi_level/Multi_level_data.html}{Imputing multilevel data} \item \href{https://www.gerkovink.com/miceVignettes/Sensitivity_analysis/Sensitivity_analysis.html}{Sensitivity analysis with \pkg{mice}} } - -#'Van Buuren, S. (2018). +Van Buuren, S. (2018). Boca Raton, FL.: Chapman & Hall/CRC Press. The book \href{https://stefvanbuuren.name/fimd/}{\emph{Flexible Imputation of Missing Data. Second Edition.}} @@ -541,10 +541,40 @@ data sets.} Dissertation. Rotterdam: Erasmus University. \code{\link[=set.seed]{set.seed()}}, \code{\link[=complete]{complete()}} } \author{ +\strong{Maintainer}: Stef van Buuren \email{stef.vanbuuren@tno.nl} + +Authors: +\itemize{ + \item Karin Groothuis-Oudshoorn \email{c.g.m.oudshoorn@utwente.nl} +} + +Other contributors: +\itemize{ + \item Gerko Vink \email{g.vink@uu.nl} [contributor] + \item Rianne Schouten \email{R.M.Schouten@uu.nl} [contributor] + \item Alexander Robitzsch \email{robitzsch@ipn.uni-kiel.de} [contributor] + \item Patrick Rockenschaub \email{rockenschaub.patrick@gmail.com} [contributor] + \item Lisa Doove \email{lisa.doove@ppw.kuleuven.be} [contributor] + \item Shahab Jolani \email{s.jolani@maastrichtuniversity.nl} [contributor] + \item Margarita Moreno-Betancur \email{margarita.moreno@mcri.edu.au} [contributor] + \item Ian White \email{ian.white@ucl.ac.uk} [contributor] + \item Philipp Gaffert \email{philipp.gaffert@gfk.com} [contributor] + \item Florian Meinfelder \email{florian.meinfelder@uni-bamberg.de} [contributor] + \item Bernie Gray \email{bfgray3@gmail.com} [contributor] + \item Vincent Arel-Bundock \email{vincent.arel-bundock@umontreal.ca} [contributor] + \item Mingyang Cai \email{m.cai@uu.nl} [contributor] + \item Thom Volker \email{t.b.volker@uu.nl} [contributor] + \item Edoardo Costantini \email{e.costantini@tilburguniversity.edu} [contributor] + \item Caspar van Lissa \email{c.j.vanlissa@uu.nl} [contributor] + \item Hanne Oberman \email{h.i.oberman@uu.nl} [contributor] +} + + 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. } +\keyword{internal} \keyword{iteration} diff --git a/man/mice.impute.cart.Rd b/man/mice.impute.cart.Rd index cd72f8ff7..46bd9039c 100644 --- a/man/mice.impute.cart.Rd +++ b/man/mice.impute.cart.Rd @@ -76,16 +76,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.lasso.logreg.Rd b/man/mice.impute.lasso.logreg.Rd index a1e3a89a2..d102536d9 100644 --- a/man/mice.impute.lasso.logreg.Rd +++ b/man/mice.impute.lasso.logreg.Rd @@ -67,16 +67,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.lasso.norm.Rd b/man/mice.impute.lasso.norm.Rd index 7afa0a49c..6e6fb86e2 100644 --- a/man/mice.impute.lasso.norm.Rd +++ b/man/mice.impute.lasso.norm.Rd @@ -67,16 +67,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.lasso.select.logreg.Rd b/man/mice.impute.lasso.select.logreg.Rd index beb7ec882..027e2a513 100644 --- a/man/mice.impute.lasso.select.logreg.Rd +++ b/man/mice.impute.lasso.select.logreg.Rd @@ -75,16 +75,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.norm}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.lasso.select.norm.Rd b/man/mice.impute.lasso.select.norm.Rd index 3597d3985..e825a028c 100644 --- a/man/mice.impute.lasso.select.norm.Rd +++ b/man/mice.impute.lasso.select.norm.Rd @@ -77,16 +77,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.norm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.lda.Rd b/man/mice.impute.lda.Rd index e3a06e812..b35407f84 100644 --- a/man/mice.impute.lda.Rd +++ b/man/mice.impute.lda.Rd @@ -74,16 +74,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.norm}()}, \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.logreg.Rd b/man/mice.impute.logreg.Rd index 2e562acf4..7cc08a8e4 100644 --- a/man/mice.impute.logreg.Rd +++ b/man/mice.impute.logreg.Rd @@ -77,10 +77,10 @@ Other univariate imputation functions: \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.logreg.boot.Rd b/man/mice.impute.logreg.boot.Rd index 35d894033..859eb602f 100644 --- a/man/mice.impute.logreg.boot.Rd +++ b/man/mice.impute.logreg.boot.Rd @@ -57,10 +57,10 @@ Other univariate imputation functions: \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.mean.Rd b/man/mice.impute.mean.Rd index b4ea18631..099a27adb 100644 --- a/man/mice.impute.mean.Rd +++ b/man/mice.impute.mean.Rd @@ -58,15 +58,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \code{\link{mice.impute.logreg}()}, +\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.midastouch.Rd b/man/mice.impute.midastouch.Rd index f0a72adcb..ad40a6bb6 100644 --- a/man/mice.impute.midastouch.Rd +++ b/man/mice.impute.midastouch.Rd @@ -133,15 +133,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \code{\link{mice.impute.logreg}()}, +\code{\link{mice.impute.logreg.boot}()}, \code{\link{mice.impute.mean}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.mnar.Rd b/man/mice.impute.mnar.Rd index 75557e5ee..84e3f3965 100644 --- a/man/mice.impute.mnar.Rd +++ b/man/mice.impute.mnar.Rd @@ -178,15 +178,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.mpmm.Rd b/man/mice.impute.mpmm.Rd index 9efe99ebf..4cce76143 100644 --- a/man/mice.impute.mpmm.Rd +++ b/man/mice.impute.mpmm.Rd @@ -71,15 +71,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.norm.boot}()}, \code{\link{mice.impute.norm.nob}()}, \code{\link{mice.impute.norm.predict}()}, -\code{\link{mice.impute.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.norm.Rd b/man/mice.impute.norm.Rd index a39ee2d60..a082d1ffc 100644 --- a/man/mice.impute.norm.Rd +++ b/man/mice.impute.norm.Rd @@ -64,8 +64,8 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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}()}, diff --git a/man/mice.impute.norm.boot.Rd b/man/mice.impute.norm.boot.Rd index 1e70d0790..b4a6abd83 100644 --- a/man/mice.impute.norm.boot.Rd +++ b/man/mice.impute.norm.boot.Rd @@ -48,15 +48,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.nob}()}, \code{\link{mice.impute.norm.predict}()}, -\code{\link{mice.impute.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.norm.nob.Rd b/man/mice.impute.norm.nob.Rd index 578482bb1..7a170c22c 100644 --- a/man/mice.impute.norm.nob.Rd +++ b/man/mice.impute.norm.nob.Rd @@ -70,15 +70,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.predict}()}, -\code{\link{mice.impute.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.norm.predict.Rd b/man/mice.impute.norm.predict.Rd index fce4b8c3c..249d642e9 100644 --- a/man/mice.impute.norm.predict.Rd +++ b/man/mice.impute.norm.predict.Rd @@ -67,15 +67,15 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.pmm.Rd b/man/mice.impute.pmm.Rd index a4283ed57..cecb02dc6 100644 --- a/man/mice.impute.pmm.Rd +++ b/man/mice.impute.pmm.Rd @@ -191,16 +191,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \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 633e0181a..51f81b15d 100644 --- a/man/mice.impute.polr.Rd +++ b/man/mice.impute.polr.Rd @@ -105,16 +105,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polyreg}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.polyreg.Rd b/man/mice.impute.polyreg.Rd index 535a52ea1..50e60aa08 100644 --- a/man/mice.impute.polyreg.Rd +++ b/man/mice.impute.polyreg.Rd @@ -92,16 +92,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.quadratic}()}, diff --git a/man/mice.impute.quadratic.Rd b/man/mice.impute.quadratic.Rd index 63c6c6fca..7f3f98007 100644 --- a/man/mice.impute.quadratic.Rd +++ b/man/mice.impute.quadratic.Rd @@ -107,16 +107,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.rf.Rd b/man/mice.impute.rf.Rd index 3006d573a..1f722f7e2 100644 --- a/man/mice.impute.rf.Rd +++ b/man/mice.impute.rf.Rd @@ -95,16 +95,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/man/mice.impute.ri.Rd b/man/mice.impute.ri.Rd index 300c526a9..3047a9056 100644 --- a/man/mice.impute.ri.Rd +++ b/man/mice.impute.ri.Rd @@ -55,16 +55,16 @@ Other univariate imputation functions: \code{\link{mice.impute.lasso.select.logreg}()}, \code{\link{mice.impute.lasso.select.norm}()}, \code{\link{mice.impute.lda}()}, -\code{\link{mice.impute.logreg.boot}()}, \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.norm}()}, \code{\link{mice.impute.pmm}()}, \code{\link{mice.impute.polr}()}, \code{\link{mice.impute.polyreg}()}, diff --git a/tests/testthat/test-ampute.R b/tests/testthat/test-ampute.R index 159e794cc..761909ba8 100644 --- a/tests/testthat/test-ampute.R +++ b/tests/testthat/test-ampute.R @@ -99,7 +99,7 @@ test_that("function works around unusual arguments", { # when data is categorical and mech != mcar, warning is expected expect_warning( ampute(data = nasty.data), - "Data is made numeric because the calculation of weights requires numeric data" + "Data is made numeric internally, because the calculation of weights requires numeric data" ) # when data is categorical and mech = mcar, function can continue expect_warning(ampute(data = nasty.data, mech = "MCAR"), NA) diff --git a/tests/testthat/test-quickpred.R b/tests/testthat/test-quickpred.R new file mode 100644 index 000000000..ca96549a0 --- /dev/null +++ b/tests/testthat/test-quickpred.R @@ -0,0 +1,64 @@ +context("quickpred") + +test_that("returns square binary matrix", { + + predictorMatrix <- quickpred(nhanes) + + 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) + ) + +}) + +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) + ) + +}) + +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 + ) + +}) + +test_that("exclude one or more variables", { + + 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) + +})