r-cran-mi-1.0/000077500000000000000000000000001275731226000131445ustar00rootroot00000000000000r-cran-mi-1.0/DESCRIPTION000066400000000000000000000034741275731226000146620ustar00rootroot00000000000000Package: mi Type: Package Title: Missing Data Imputation and Model Checking Version: 1.0 Date: 2015-04-16 Authors@R: c(person("Andrew", "Gelman", email = "gelman@stat.columbia.edu", role = "ctb"), person("Jennifer", "Hill", email = "jennifer.hill@nyu.edu", role = "ctb"), person("Yu-Sung", "Su", email = "suyusung@tsinghua.edu.cn", role = c("aut")), person("Masanao", "Yajima", email = "my2167@columbia.edu", role = "ctb"), person("Maria", "Pittau", email = "grazia@stat.columbia.edu", role = "ctb"), person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", role = c("cre", "aut")), person("Yajuan", "Si", email = "sophie2012@gmail.com", role = "ctb"), person("Jon", "Kropko", email = "jkropko@gmail.com", role = "aut")) Description: The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty. VignetteBuilder: knitr Depends: R (>= 3.0.0), methods, Matrix, stats4 Imports: arm (>= 1.4-11) Suggests: betareg, lattice, knitr, MASS, nnet, parallel, sn, survival, truncnorm, foreign URL: http://www.stat.columbia.edu/~gelman/ License: GPL (>= 2) LazyLoad: yes Packaged: 2015-04-16 14:03:10 UTC; goodrich Author: Andrew Gelman [ctb], Jennifer Hill [ctb], Yu-Sung Su [aut], Masanao Yajima [ctb], Maria Pittau [ctb], Ben Goodrich [cre, aut], Yajuan Si [ctb], Jon Kropko [aut] Maintainer: Ben Goodrich NeedsCompilation: no Repository: CRAN Date/Publication: 2015-04-16 19:53:48 r-cran-mi-1.0/MD5000066400000000000000000000067321275731226000134640ustar00rootroot000000000000004d53159f5ebb86c1335e64b13a05c5fa *DESCRIPTION 9dec5a7e584844ef9fa69fa03834be18 *NAMESPACE d40a3d06f38c44bdd479a06006eeca4d *R/AllClass.R 3ce3aa32589799fc9cc255946ced225b *R/AllGeneric.R 54582ada9780d1d0a788d3f82c90ebfb *R/change.R d98b7a3e0881312955042e7bd512a408 *R/change_family.R 18dc62aa8a9efe1667e0e4ed1f341d71 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e3ea5ea6be1b9a772bd03e679e5d4715 *man/rdata.frame.Rd d29dd537c8ef0895f39db27059264737 *man/semi-continuous.Rd 2b34d0c66bd45e044b5fe119737f7540 *tests/missing_data.frame.R 276644cf2dc960b51d26396d74b87c15 *tests/missing_variable.R 3bb603698bbb07d97015c41d35f18d4d *vignettes/mi_vignette.Rmd r-cran-mi-1.0/NAMESPACE000066400000000000000000000027741275731226000143750ustar00rootroot00000000000000#exportPattern("^[[:alpha:]]+") importFrom(arm, display, fround, pfround, traceplot, bayesglm.fit, bayespolr) importFrom(graphics, hist) importFrom(Matrix, image) importFrom(stats4, coef, plot, summary, vcov) import(methods) exportClassPattern("^[[:alpha:]]+") exportMethods(change, change_family, change_link, change_model, change_imputation_method, change_size, change_transformation, change_type, complete, fit_model, get_parameters, hist, image, mi, missing_data.frame, missing_variable, plot) export(display, mi2BUGS, mi2stata, mipply, multinomial, pool, .prune, .possible_missing_variable, Rhats, rdata.frame) S3method(as.double, missing_variable) S3method(as.double, categorical) S3method(as.double, continuous) S3method(as.double, count) S3method(as.double, irrelevant) S3method(as.double, missing_data.frame) S3method(as.data.frame, missing_data.frame) S3method(dim, missing_data.frame) S3method(dimnames, missing_data.frame) S3method(names, missing_data.frame) S3method(dim, mi) S3method(dimnames, mi) S3method(names, mi) S3method(is.na, missing_variable) S3method(is.na, missing_data.frame) S3method(is.na, mi) S3method(length, missing_variable) S3method(length, missing_data.frame) S3method(length, mi) S3method(print, mdf_list) S3method(print, mi_list) S3method("[", missing_data.frame) S3method("[<-", missing_data.frame) S3method("[[", missing_data.frame) S3method("[[<-", missing_data.frame) S3method("$", missing_data.frame) S3method("$<-", missing_data.frame) r-cran-mi-1.0/R/000077500000000000000000000000001275731226000133455ustar00rootroot00000000000000r-cran-mi-1.0/R/AllClass.R000066400000000000000000001576451275731226000152100ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # Copyright (C) 1995-2012 The R Core Team # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## NOTE: If you change something here, also update the UML graph thingie setClassUnion("MatrixTypeThing", c("matrix")) setOldClass("family") suppressWarnings(setClassUnion("WeAreFamily", c("family", "character"))) # arm + lme4 = warnings setOldClass("mi_list") setOldClass("mdf_list") .known_imputation_methods <- c("ppd", "pmm", "mean", "median", "expectation", "mode", "mcar", NA_character_) .known_families <- c("binomial", "gaussian", "Gamma", "inverse.gaussian", "poisson", "quasibinomial", "quasipoisson") # "quasi" is not supported at the moment (FIXME) .known_links <- c("logit", "probit", "cauchit", "log", "cloglog", # for binomial() "identity", "inverse", # for gaussian() plus "log", # "inverse", "identity", "log", # for Gamma() "sqrt", # for poisson() plus "log", and "identity", "1/mu^2") # for inverse.gaussian() plus "inverse", "identity" and "log" # An important class in library(mi) is the missing_variable class, which is a virtual class # for a variable that may (or may not) have missingness. The usual types of variables that # we are interested in imputing all inherit (perhaps indirectly) from the missing_variable # superclass, e.g. continuous, binary, etc. In principle, these class definitions should # provide ALL the necessary information for that variable, like the extent of its missingness # and how the missing values will be (or have been) imputed. Thus, in principle, it should # be possible to tweak the behavior of library(mi) simply by 1) creating a new class that # inherits from the relevant existing class, 2) writing methods for the mi() and fit_model() # generics and 3) perhaps a few other things that you will have to discover on your own. ## missing_variable is a virtual class for a variable that may (or may not) have missingness setClass("missing_variable", representation( variable_name = "character", # name of the variable but do not rely on for anything important raw_data = "ANY", ## DO NOT EVER CHANGE THE VALUES OF THIS SLOT data = "ANY", ## Copy the raw_data into data and modify data as necessary n_total = "integer", # total number of potential datapoints, i.e. length of raw_data all_obs = "logical", # are ALL datapoints actually observed, i.e. not missing? n_obs = "integer", # number of observed datapoints which_obs = "integer", # which datapoints are observed all_miss = "logical", # are ALL datapoints missing, only true for latent variables n_miss = "integer", # number of missing datapoints in the data slot (originally) which_miss = "integer", # which datapoints are missing in the data slot n_extra = "integer", # number of extra datapoints added (as missing) which_extra = "integer", # which datapoints are extras n_unpossible = "integer", # number of datapoints for which the variable could not be observed which_unpossible = "integer",# which datapoints could not be observed n_drawn = "integer", # number of datapoints to impute which_drawn = "integer", # which datapoints are imputed imputation_method = "character", # how to impute them family = "WeAreFamily", # see help(family) known_families = "character",# families listed on help(family) plus multinomial() known_links = "character", # see help(family) imputations = "MatrixTypeThing", # iterations x n_drawn matrix of imputation history done = "logical", # are we finished imputing? parameters = "MatrixTypeThing", # history of estimated parameters in modeling this variable model = "ANY", # last model fit fitted = "ANY", # last fitted values "VIRTUAL"), prototype( variable_name = NA_character_, imputations = matrix(NA_real_, 0, 0), parameters = matrix(NA_real_, 0, 0), imputation_method = .known_imputation_methods, family = NA_character_, known_families = .known_families, known_links = .known_links ), validity = function(object) { out <- TRUE l <- length(object@raw_data) if(l == 0) return(out) if(sum(-object@n_total, object@n_obs, object@n_miss, object@n_extra, object@n_unpossible, na.rm = TRUE)) { out <- paste(object@variable_name, ": slots 'n_obs', 'n_miss', 'n_extra', and 'n_unpossible' must sum to 'n_total'") } else if(!(length(object@which_obs) %in% c(0:1, object@n_obs))) { out <- paste(object@variable_name, ": 'n_obs' must equal the length of 'which_obs'") } else if(!(length(object@which_miss) %in% c(0:1, object@n_miss))) { out <- paste(object@variable_name, ": 'n_miss' must equal the length of 'which_miss'") } else if(!(length(object@which_extra) %in% c(0:1, object@n_extra))) { out <- paste(object@variable_name, ": 'n_extra' must equal the length of 'which_extra'") } else if(!(length(object@which_extra) %in% c(0:1, object@n_unpossible))) { out <- paste(object@variable_name, ": 'n_unpossible' must equal the length of 'which_unpossible'") } else if(sum(object@n_obs)) { temp <- sort(c(object@which_obs, object@which_miss, object@which_extra, object@which_unpossible)) names(temp) <- NULL if(!identical(1:object@n_total, temp)) { out <- paste(object@variable_name, ": ''which_*' slots must be mutually exclusive and exhaustive") } } for(i in slotNames(object)) { if(i %in% c("raw_data", "data", "which_obs", "which_miss", "which_extra", "which_unpossible", "which_drawn", "imputation_method", "known_transformations", "family", "known_families", "known_links", "levels", "cutpoints")) next if((l <- length(slot(object, i))) > 1) { out <- paste(object@variable_name, ": length of", i, "must be 0 or 1 but is", l) break } } return(out) } ) ## this initialize() method gets called for everything that inherits from missing_variable ## but can be modified by a subsequently-called initialize() method setMethod("initialize", "missing_variable", def = function(.Object, NA.strings = c("", ".", "Na", "N/a", "N / a", "NaN", "Not Applicable", "Not applicable", "Not Available", "Not available", "Not Ascertained", "Not ascertained", "Unavailable", "Unknown", "Missing", "Dk", "Don't Know", "Don't know", "Do Not Know", "Do not know"), ...) { .Object <- callNextMethod() if(length(.Object@raw_data) == 0) return(.Object) if(length(.Object@data) == 0) { # copy raw_data into data .Object@data <- .Object@raw_data names(.Object@data) <- .Object@variable_name } # bookkeeping infinites <- is.infinite(.Object@raw_data) if(any(infinites)) { warning(paste(.Object@variable_name, ": some observations are infinite, changing to NA")) .Object@data[infinites] <- NA } nans <- is.nan(.Object@raw_data) if(any(nans)) { warning(paste(.Object@variable_name, ": some observations are NaN, changing to NA")) .Object@data[nans] <- NA } NA.strings <- unique(c(NA.strings, toupper(NA.strings), tolower(NA.strings))) if(!is.numeric(.Object@raw_data)) for(i in seq_along(NA.strings)) { mark <- .Object@raw_data == NA.strings[i] if(any(mark, na.rm = TRUE)) { warning(paste(.Object@variable_name, ": some observations", NA.strings[i], "changing to NA")) .Object@data[mark] <- NA } } NAs <- which(is.na(.Object@data)) if(length(NAs)) .Object@imputation_method <- "ppd" else .Object@imputation_method <- NA_character_ .Object@n_miss <- length(NAs) .Object@which_miss <- NAs notNAs <- which(!is.na(.Object@data)) .Object@n_obs <- length(notNAs) .Object@which_obs <- notNAs .Object@n_total <- length(NAs) + length(notNAs) .Object@all_miss <- length(notNAs) == 0 .Object@all_obs <- length(NAs) == 0 if(!length(.Object@n_extra)) .Object@n_extra <- 0L if(!length(.Object@n_unpossible)) .Object@n_unpossible <- 0L .Object@n_drawn <- .Object@n_miss + .Object@n_extra .Object@which_drawn <- c(.Object@which_miss, .Object@which_extra) .Object@done <- FALSE return(.Object) }) setClass("irrelevant", representation("missing_variable"), # prototype( # imputation_method = NA_character_, # family = NA_character_) ) ## a constant variable that has no missingness (and very few methods) setClass("fixed", representation("irrelevant"), validity = function(object) { out <- TRUE vals <- unique(object@raw_data) vals <- vals[!is.na(vals)] if(sum(object@n_miss)) { out <- paste(object@variable_name, ": fixed variables cannot have missingness") } else if(length(vals) > 1) { out <- paste(object@variable_name, ": purportedly 'fixed' variables cannot have multiple unique values") } return(out) } ) setClass("group", representation("irrelevant")) ## virtual class for categorical variables, which may be unordered, ordered, binary, or interval setClass("categorical", representation( "missing_variable", levels = "character", "VIRTUAL"), prototype( known_families = c("multinomial", "binomial", "gaussian") ) ) setMethod("initialize", "categorical", def = function(.Object, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) ## FIXME: check on the unused levels thing # .Object@raw_data <- factor(.Object@raw_data) lev <- levels(factor(.Object@raw_data)) # dummies <- t(sapply(.Object@raw_data, FUN = function(x) as.integer(x == lev)))[,-1, drop = FALSE] # if(ncol(dummies) == 1) colnames(dummies) <- .Object@variable_name # else colnames(dummies) <- lev[-1] # mark <- !apply(dummies, 2, FUN = function(x) all(x == 0, na.rm = TRUE)) # dummies <- dummies[,mark, drop = FALSE] # lev <- c(lev[1], lev[-1][mark]) .Object@levels <- lev .Object@data <- as.integer(factor(.Object@raw_data)) return(.Object) }) ## this is a hacked version of binomial() multinomial <- function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) { linktemp <- deparse(linktemp) if (linktemp == "link") { warning("use of multinomial(link=link) is deprecated\n", domain = NA) linktemp <- eval(link) if (!is.character(linktemp) || length(linktemp) != 1L) stop("'link' is invalid", domain = NA) } } okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for multinomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(mu > 0) && all(mu < 1) dev.resids <- binomial()$dev.resids aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a multinomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a multinomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the multinomial family, y must be a vector of 0 and 1's\n", "or a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "multinomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") } ## unordered categorical, which corresponds to an unordered factor with more than 2 levels setClass("unordered-categorical", representation("categorical", estimator = "character", use_NA = "logical", rank = "integer"), prototype( estimator = "MNL", imputation_method = c("ppd", "pmm", "mode", "mcar", NA_character_), family = multinomial(link = "logit"), known_families = c("multinomial", "binomial"), known_links = c("logit", "probit", "cauchit", "log", "cloglog"), use_NA = FALSE, rank = NA_integer_ ), validity = function(object) { out <- TRUE values <- unique(object@raw_data) values <- values[!is.na(values)] im <- getClass(class(object))@prototype@imputation_method if(length(values) > 0 && length(values) <= 2) { out <- paste(object@variable_name, "unordered-categoricals must have more than 2 levels; otherwise use binary") } else if(!all(object@imputation_method %in% im)) { out <- paste(object@variable_name, ": 'imputation_method' must be one of:\n", paste(im, collapse = ", ")) } # else if(object@family$family != "multinomial") { # out <- "the 'family' slot of 'unordered-categorial' class must be 'multinomial(link = 'logit')'" # } # else if(object@family$link != "logit") { # out <- "the 'family' slot of 'unordered-categorial' class must be 'multinomial(link = 'logit')'" # } else if(!(object@estimator %in% c("MNL", "RNL"))) { out <- paste(object@variable_name, ": estimator not recognized") } else if(!(object@use_NA %in% c(TRUE, FALSE))) { out <- paste(object@variable_name, ": use_NA must be TRUE or FALSE") } return(out) } ) ## ordered categorical, which corresponds to an ordered factor setClass("ordered-categorical", representation("categorical", cutpoints = "numeric"), prototype( imputation_method = c("ppd", "pmm", "mode", "mcar", NA_character_), family = multinomial(link = "logit"), known_families = c("multinomial", "gaussian", "binomial", "quasibinomial"), known_links = "logit" ), validity = function(object) { out <- TRUE im <- getClass(class(object))@prototype@imputation_method if(!(object@family$family %in% getClass(class(object))@prototype@known_families)) { # interval and binary are validated separately out <- "the 'family' slot of 'ordered-categorial' class must be 'multinomial()'" } else if(object@family$family == "multinomial" && object@family$link != "logit") { out <- "the 'family' slot of 'ordered-categorial' class must be 'multinomial(link = 'logit')'" } else if(!all(object@imputation_method %in% im)) { out <- paste(object@variable_name, ": 'imputation_method' must be one of:\n", paste(im, collapse = ", ")) } return(out) } ) ## ordered categorical with known cutpoints that discretize a continuous variable (like income) setClass("interval", representation("ordered-categorical"), prototype( imputation_method = c("ppd", NA_character_), family = gaussian(), known_families = "gaussian", known_links = c("identity", "inverse", "log") ), validity = function(object) { out <- TRUE if(!(object@imputation_method[1] == "ppd")) { out <- paste(object@variable_name, ": 'imputation_method' must be 'ppd'") } else if(object@family$family != "gaussian") { out <- "the 'family' slot of 'interval' class must be 'gaussian()'" } return(out) } ) ## binary variable # binary inherits from ordered-categorical because it often makes sense to think of # those who are coded as 1 as having "more" of something than those who are coded as # zero. Also, binary logit, probit, etc. are special cases of ordinal logit, probit, # etc. with one cutpoint fixed at zero. setClass("binary", representation("ordered-categorical"), prototype( family = binomial(link = "logit"), known_families = c("binomial", "quasibinomial"), known_links = c("logit", "probit", "cauchit", "log", "cloglog"), cutpoints = 0.0), validity = function(object) { out <- TRUE if(length(object@raw_data) == 0) return(out) vals <- unique(object@raw_data) vals <- vals[!is.na(vals)] kf <- getClass(class(object))@prototype@known_families kl <- getClass(class(object))@prototype@known_links if(length(vals) != 2) { out <- paste(object@variable_name, ": binary variables must have exactly two response categories") } else if(!identical(object@cutpoints, 0.0)) { out <- paste(object@variable_name, ": 'cutpoints' must be 0.0 for a binary variable") } else if(!(object@family$family %in% kf)) { out <- paste(object@variable_name, ": the 'family' slot of a object of class 'binary' must be one of", paste(kf, collapse = ", ")) } else if(!(object@family$link %in% kl)) { out <- paste(object@variable_name, ": the 'link' slot of the 'family' slot of a object of class 'binary' must be one of", paste(kl, collapse = ", ")) } return(out) } ) setMethod("initialize", "binary", def = function(.Object, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) .Object@data <- as.integer(.Object@data == max(.Object@data, na.rm = TRUE)) + 1L return(.Object) }) setClass("grouped-binary", representation("binary", strata = "character"), prototype( imputation_method = "pmm" ), validity = function(object) { out <- TRUE if(length(object@raw_data) == 0) return(out) if(!requireNamespace("survival")) { out <- "the 'survival' package must be installed to use 'grouped-binary' variables" } else if(length(object@strata) == 0) { warning(paste("you must specify the 'strata' slot for", object@variable_name, "see help('grouped-binary-class')")) } return(out) } ) setMethod("initialize", "grouped-binary", def = function(.Object, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) .Object@imputation_method <- "pmm" return(.Object) }) ## count variables, which must be nonnegative integers setClass("count", representation("missing_variable"), prototype( imputation_method = c("ppd", "pmm", "mean", "median", "expectation", "mcar", NA_character_), family = quasipoisson(), known_families = c("quasipoisson", "poisson"), known_links = c("log", "identity", "sqrt") ), validity = function(object) { out <- TRUE l <- length(object@raw_data) if(l == 0) return(out) im <- getClass(class(object))@prototype@imputation_method if(any(object@raw_data < 0, na.rm = TRUE)) { out <- paste(object@variable_name, ": counts must be nonnegative") } else if(any(object@raw_data != as.integer(object@raw_data), na.rm = TRUE)) { out <- paste(object@variable_name, ": must contain all nonnegative integers to use the 'count' class") } else if(!all(object@imputation_method %in% im)) { out <- paste(object@variable_name, ": 'imputation_method' must be one of:\n", paste(im, collapse = ", ")) } else if(sum(object@n_unpossible)) { out <- paste(object@variable_name, ": unpossible observations not supported for count variables yet") } return(out) } ) .identity_transform <- function(y, ...) return(y) .standardize_transform <- function(y, mean = stop("must supply mean"), sd = stop("must supply sd"), inverse = FALSE) { if(inverse) return(y * 2 * sd + mean) else return( (y - mean) / (2 * sd) ) } ## continuous variables, which may have inequality restrictions or transformation functions setClass("continuous", representation( "missing_variable", transformation = "function", inverse_transformation = "function", transformed = "logical", # TRUE -> in transformed state known_transformations = "character" ), prototype( imputation_method = c("ppd", "pmm", "mean", "median", "expectation", "mcar", NA_character_), transformed = TRUE, transformation = .standardize_transform, inverse_transformation = .standardize_transform, family = gaussian(), known_families = c("gaussian", "Gamma", "inverse.gaussian", "binomial"), # binomial() is only for (SC_)proportions known_links = .known_links[.known_links != "sqrt"], known_transformations = c("standardize", "identity", "log", "logshift", "squeeze", "sqrt", "cuberoot", "qnorm") ), validity = function(object) { out <- TRUE im <- getClass(class(object))@prototype@imputation_method kf <- getClass(class(object))@prototype@known_families kl <- getClass(class(object))@prototype@known_links if(!all(object@imputation_method %in% im)) { out <- paste(object@variable_name, ": 'imputation_method' must be one of:\n", paste(im, collapse = ", ")) } else if(sum(object@n_unpossible)) { out <- paste(object@variable_name, ": unpossible observations not supported for continuous variables yet") } else if(!(object@family$family %in% kf)) { out <- paste(object@variable_name, ": the 'family' slot of a object of class 'binary' must be one of", paste(kf, collapse = ", ")) } else if(!(object@family$link %in% kl)) { out <- paste(object@variable_name, ": the 'link' slot of the 'family' slot of a object of class 'binary' must be one of", paste(kl, collapse = ", ")) } return(out) } ) setMethod("initialize", "continuous", def = function(.Object, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) if(identical(.Object@transformation, .standardize_transform)) { mean <- mean(.Object@raw_data, na.rm = TRUE) sd <- sd(.Object@raw_data, na.rm = TRUE) formals(.Object@transformation)$mean <- formals(.Object@inverse_transformation)$mean <- mean formals(.Object@transformation)$sd <- formals(.Object@inverse_transformation)$sd <- sd formals(.Object@inverse_transformation)$inverse <- TRUE } else if(identical(.Object@transformation, .logshift)) { y <- .Object@raw_data if(any(y < 0, na.rm = TRUE)) a <- - min(y, na.rm = TRUE) else a <- 0 a <- (a + min(y[y > 0], na.rm = TRUE)) / 2 formals(.Object@transformation)$a <- formals(.Object@inverse_transformation)$a <- a formals(.Object@inverse_transformation)$inverse <- TRUE } .Object@data <- .Object@transformation(.Object@raw_data) .Object@data[.Object@which_miss] <- NA_real_ return(.Object) }) setClass("bounded-continuous", representation("continuous", lower = "numeric", upper = "numeric"), prototype( imputation_method = "ppd", transformation = .identity_transform, inverse_transformation = .identity_transform ), validity = function(object) { out <- TRUE # if(any(object@raw_data <= object@lower, na.rm = TRUE)) { # out <- paste(object@variable_name, ": all observed data must be strictly greater than 'lower'") # } # else if(any(object@raw_data >= object@upper, na.rm = TRUE)) { # out <- paste(object@variable_name, ": all observed data must be strictly less than 'upper'") # } if(any(object@lower > object@upper)) { out <- paste(object@variable_name, ": lower bounds must be less than or equal to upper bounds") } else if(object@imputation_method != "ppd") { out <- paste(object@variable_name, ": 'imputation_method' must be 'ppd' for 'bounded-continuous' variables ") } else if(!requireNamespace("truncnorm")) { out <- paste(object@variable_name, ": the 'truncnorm' package must be installed to use the 'bounded-continuous' class") } return(out) } ) setMethod("initialize", "bounded-continuous", def = function(.Object, lower = -Inf, upper = Inf, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) .Object@lower <- lower .Object@upper <- upper return(.Object) }) setClass("positive-continuous", representation("continuous"), prototype( transformation = log, inverse_transformation = exp, known_transformations = c("log", "sqrt", "squeeze", "qnorm") ), validity = function(object) { out <- TRUE if(any(object@raw_data <= 0, na.rm = TRUE)) { out <- paste(object@variable_name, ": positive variables must be positive") } return(out) } ) ## must be on the (0,1) interval setClass("proportion", representation("positive-continuous", link.phi = "WeAreFamily"), prototype( transformed = FALSE, transformation = .identity_transform, inverse_transformation = .identity_transform, known_transformations = c("squeeze", "qnorm"), family = binomial(), known_families = c("binomial", "gaussian"), known_links = .known_links[.known_links != "sqrt"], link.phi = "log"), validity = function(object) { out <- TRUE kf <- getClass(class(object))@prototype@known_families kl <- getClass(class(object))@prototype@known_links if(any(object@raw_data > 1, na.rm = TRUE)) { out <- paste(object@variable_name, ": proportions must be on the unit interval") } else if(any(object@raw_data == 1, na.rm = TRUE)) { out <- paste(object@variable_name, ": some proportions are equal to 1.0 so use the SC_proportion class") } else if(!(object@family$family %in% kf)) { out <- paste(object@variable_name, ": the 'family' slot of a object of class 'proportion' must be one of", paste(kf, collapse = ", ")) } else if(!(object@family$link %in% kl)) { out <- paste(object@variable_name, ": the 'link' slot of the 'family' slot of a object of class 'proportion' must be one of", paste(kl, collapse = ", ")) } else if(object@family$family == "binomial" && !requireNamespace("betareg")) { out <- paste(object@variable_name, ": you must install the 'betareg' package to model proportions as proportions") } return(out) } ) # setClass("truncated-continuous", # representation("continuous", # lower = "ANY", # upper = "ANY", # n_lower = "integer", # which_lower = "integer", # n_upper = "integer", # which_upper = "integer", # n_both = "integer", # which_both = "integer", # n_truncated = "integer", # which_truncated = "integer", # "VIRTUAL") # ) # # setClass("NN_truncated-continuous", representation("truncated-continuous", lower = "numeric", upper = "numeric")) # # setMethod("initialize", "NN_truncated-continuous", def = # function(.Object, ...) { # .Object <- callNextMethod() # l <- length(.Object@raw_data) # if(l == 0) return(.Object) # if(identical(.Object@transformation, .standardize_transform)) { # mean <- mean(.Object@raw_data, na.rm = TRUE) # sd <- sd(.Object@raw_data, na.rm = TRUE) # formals(.Object@transformation)$mean <- formals(.Object@inverse_transformation)$mean <- mean # formals(.Object@transformation)$sd <- formals(.Object@inverse_transformation)$sd <- sd # formals(.Object@inverse_transformation)$inverse <- TRUE # } # .Object@data <- .Object@transformation(.Object@raw_data) # # if(length(.Object@lower) == 0 & length(.Object@upper) == 0) { # stop("at least one of 'lower' and 'upper' must be specified") # } # ## FIXME: Deal with interval censoring or force it to the interval class # .Object@n_both <- 0L # lowers <- .Object@raw_data <= .Object@lower # .Object@n_lower <- sum(lowers) # .Object@which_lower <- which(lowers) # uppers <- .Object@raw_data >= .Object@upper # .Object@n_uppers <- sum(uppers) # .Object@which_uppers <- which(uppers) # .Object@n_truncated <- .Object@n_lower + .Object@n_upper # .Object@which_truncated <- c(.Object@which_lower, .Object@which_upper) # return(.Object) # }) # # setClass("FN_truncated-continuous", representation("truncated-continuous", lower = "function", upper = "numeric")) # setClass("NF_truncated-continuous", representation("truncated-continuous", lower = "numeric", upper = "function")) # setClass("FF_truncated-continuous", representation("truncated-continuous", lower = "function", upper = "function")) # # setClass("censored-continuous", # representation("continuous", # lower = "ANY", # upper = "ANY", # n_lower = "integer", # which_lower = "integer", # n_upper = "integer", # which_upper = "integer", # n_both = "integer", # which_both = "integer", # n_censored = "integer", # which_censored = "integer", # lower_indicator = "binary", # upper_indicator = "binary", # "VIRTUAL") # ) # setClass("NN_censored-continuous", representation("censored-continuous", lower = "numeric", upper = "numeric")) # setMethod("initialize", "NN_censored-continuous", def = # function(.Object, ...) { # .Object <- callNextMethod() # l <- length(.Object@raw_data) # if(l == 0) return(.Object) # if(identical(.Object@transformation, .standardize_transform)) { # mean <- mean(.Object@raw_data, na.rm = TRUE) # sd <- sd(.Object@raw_data, na.rm = TRUE) # formals(.Object@transformation)$mean <- formals(.Object@inverse_transformation)$mean <- mean # formals(.Object@transformation)$sd <- formals(.Object@inverse_transformation)$sd <- sd # formals(.Object@inverse_transformation)$inverse <- TRUE # } # .Object@data <- .Object@transformation(.Object@raw_data) # # if(length(.Object@lower) == 0 & length(.Object@upper) == 0) { # stop("at least one of 'lower' and 'upper' must be specified") # } # ## FIXME: Deal with interval censoring or force it to the interval class # .Object@n_both <- 0L # lowers <- .Object@raw_data <= .Object@lower # .Object@n_lower <- sum(lowers, na.rm = TRUE) # .Object@which_lower <- which(lowers) # if(.Object@n_lower > 0) { # .Object@lower_indicator <- missing_variable(as.ordered(lowers), type = "binary", # variable_name = paste(.Object@variable_name, "lower", sep = "")) # } # uppers <- .Object@raw_data >= .Object@upper # .Object@n_upper <- sum(uppers, na.rm = TRUE) # .Object@which_upper <- which(uppers) # if(.Object@n_upper > 0) { # .Object@lower_indicator <- missing_variable(as.ordered(uppers), type = "binary", # variable_name = paste(.Object@variable_name, "upper", sep = "")) # } # .Object@n_censored <- .Object@n_lower + .Object@n_upper # .Object@which_censored <- c(.Object@which_lower, .Object@which_upper) # return(.Object) # }) # # setClass("FN_censored-continuous", representation("censored-continuous", lower = "function", upper = "numeric")) # setClass("NF_censored-continuous", representation("censored-continuous", lower = "numeric", upper = "function")) # setClass("FF_censored-continuous", representation("censored-continuous", lower = "function", upper = "function")) setClass("semi-continuous", representation("continuous", indicator = "ordered-categorical"), prototype( transformation = .identity_transform, inverse_transformation = .identity_transform) ) .logshift <- function(y, a, inverse = FALSE) { if(inverse) exp(y) - a else log(y + a) } setClass("nonnegative-continuous", representation("semi-continuous"), prototype(transformation = .logshift, inverse_transformation = .logshift, known_transformations = c("logshift", "squeeze", "identity")), validity = function(object) { out <- TRUE if(any(object@raw_data < 0, na.rm = TRUE)) { out <- paste(object@variable_name, ": nonnegative variables must be nonnegative") } return(out) } ) setMethod("initialize", "nonnegative-continuous", def = function(.Object, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) is_zero <- as.integer(.Object@raw_data == 0) if(any(is_zero, na.rm = TRUE)) { .Object@indicator <- missing_variable(is_zero, type = "binary", variable_name = paste(.Object@variable_name, ":is_zero", sep = "")) } .Object@data <- .Object@transformation(.Object@raw_data) if(!all(is.finite(.Object@data[!is.na(.Object)]))) { stop(paste(.Object@variable_name, ": some transformed values are infinite or undefined")) } return(.Object) }) .squeeze_transform <- function(y, inverse = FALSE) { n <- length(y) if(inverse) (y * n - .5) / (n - 1) else (y * (n - 1) + .5) / n } ## some values are zero and / or one setClass("SC_proportion", representation("nonnegative-continuous", link.phi = "WeAreFamily"), prototype( transformation = .squeeze_transform, inverse_transformation = .squeeze_transform, known_transformations = c("squeeze", "qnorm"), family = binomial(), known_families = "binomial", known_links = getClass("binary")@prototype@known_links, link.phi = "log" ), validity = function(object) { out <- TRUE if(any(object@data > 1, na.rm = TRUE)) { out <- paste(object@variable_name, ": proportions must be less than or equal to 1") } else if(object@family$family != "binomial") { out <- paste(object@variable_name, ": 'family' must be 'binomial'") } else if(!identical(body(object@transformation), body(.squeeze_transform))) { out <- paste(object@variable_name, ": 'transformation' must be 'squeeze'") } else if(!requireNamespace("betareg")) { out <- paste(object@variable_name, ": you must install the 'betareg' package to model proportions") } return(out) } ) setMethod("initialize", "SC_proportion", def = function(.Object, ...) { .Object <- callNextMethod() l <- length(.Object@raw_data) if(l == 0) return(.Object) if(any(.Object@raw_data == 0, na.rm = TRUE)) { if(any(.Object@raw_data == 1, na.rm = TRUE)) { is_bound <- ifelse(.Object@raw_data == 0, -1, ifelse(.Object@raw_data == 1, 1, 0)) .Object@indicator <- missing_variable(is_bound, type = "ordered-categorical", variable_name = paste(.Object@variable_name, ":is_bound", sep = "")) } else { is_zero <- as.integer(.Object@raw_data == 0) .Object@indicator <- missing_variable(is_zero, type = "binary", variable_name = paste(.Object@variable_name, ":is_zero", sep = "")) } } else { is_one <- as.integer(.Object@raw_data == 1) .Object@indicator <- missing_variable(is_one, type = "binary", variable_name = paste(.Object@variable_name, ":is_one", sep = "")) } return(.Object) }) # A missing_data.frame is a another important S4 class that is not unlike a data.frame, except # that its "columns" (actually list elements) are objects that inherit from the missing_variable # class. The missing_data.frame class should, in principle, contain ALL the necessary information # regarding how the missing_variables relate to each other. Together, the missing_variable class(es) # and the missing_data.frame class supplant the mi.info S4 class in previous versions of library(mi). .get_slot <- function(object, name, simplify = TRUE) { if(isS4(object)) return(slot(object, name)) else if(is.list(object)) sapply(object, FUN = slot, name = name, simplify = simplify) else stop("'object' not supported") } setOldClass("data.frame") setClass("missing_data.frame", representation( variables = "list", # of missing_variables no_missing = "logical", # basically a collection of the all_obs slots of the missing_variables patterns = "factor", # indicates which missingness_pattern an observation belongs to DIM = "integer", # observations x variables DIMNAMES = "list", # list of rownames and colnames postprocess = "function",# makes additional variables from existing variables (interactions, etc.) index = "list", # this indicate which variables to exclude when modeling a given variable X = "MatrixTypeThing", # ALL variables (categorical variables are in dummy-variable form) weights = "list", # this gets passed to bayesglm() and similar modeling functions priors = "list", # the elements of this get passed to bayesglm() and other modeling functions in arm correlations = "matrix", # has SMCs and Spearman correlations done = "logical", # are we done? workpath = "character"), contains = "data.frame", prototype(postprocess = function() stop("postprocess does not work yet"), X = matrix(NA_real_, 0, 0), done = FALSE), validity = function(object) { out <- TRUE l <- length(object@variables) if(l == 0) return(out) if(!all(sapply(object@variables, FUN = is, class2 = "missing_variable"))) { out <- "all of the list elements in 'variables' must inherit from the 'missing_variable' class" } else if(length(unique(.get_slot(object@variables, "n_total"))) > 1) { out <- "all missing_variables must have the same 'n_total'" } else if(!is.numeric(object@X)) { out <- "'X' must be a numeric matrix" } missingness <- .get_slot(object@variables, "which_miss", simplify = FALSE) varnames <- .get_slot(object@variables, "variable_name") names(missingness) <- varnames missingness <- missingness[sapply(missingness, length) > 0] if(length(missingness) > 1) { ## FIXME: Very slow combos <- combn(length(missingness), 2) dupes <- apply(combos, 2, FUN = function(x) { mx1 <- missingness[[x[1]]] mx2 <- missingness[[x[2]]] if(length(mx1) == length(mx2)) { if(identical(mx1, mx2)) return(1L) } else if(length(mx1) > length(mx2)) { if(all(mx2 %in% mx1)) return(2L) } else if(all(mx1 %in% mx2)) return(3L) return(0L) }) if(any(dupes == 1L)) { temp <- matrix(names(missingness)[combos[,which(dupes == 1L)]], ncol = 2, byrow = TRUE) cat("NOTE: The following pairs of variables appear to have the same missingness pattern.\n", "Please verify whether they are in fact logically distinct variables.\n") print(temp) # warning("Potentially duplicated variables detected by duplicated variable detector") } else if(any(dupes == 2L)) { temp <- matrix(names(missingness)[combos[,which(dupes == 2L)]], ncol = 2, byrow = TRUE) cat("NOTE: In the following pairs of variables, the missingness pattern of the second is a subset of the first.\n", "Please verify whether they are in fact logically distinct variables.\n") print(temp) } else if(any(dupes == 3L)) { temp <- matrix(names(missingness)[combos[,which(dupes == 3L)]], ncol = 2, byrow = TRUE) cat("NOTE: In the following pairs of variables, the missingness pattern of the first is a subset of the second.\n", "Please verify whether they are in fact logically distinct variables.\n") print(temp) } } return(out) } ) .set_priors <- function(variables, mu = 0) { ## FIXME: maybe add an option to draw from such a t distribution? foo <- function(y) { out <- list(prior.mean = mu, prior.scale = 2.5, prior.df = 1, prior.mean.for.intercept = mu, prior.scale.for.intercept = 10, prior.df.for.intercept = 1) if(is(y, "irrelevant") | y@all_obs) return(NULL) else if(is(y, "binary")) { if(y@family$link == "probit") { out[[2]] <- out[[2]] * dnorm(0) / dlogis(0) out[[4]] <- out[[4]] * dnorm(0) / dlogis(0) } } else if(is(y, "categorical")) { out <- list(prior.mean = mu, prior.scale = 2.5, prior.df = 1, prior.counts.for.bins = 1/(1 + length(y@levels))) } return(out) } out <- lapply(variables, FUN = function(y) foo(y)) for(i in seq_along(variables)) if(is(y <- variables[[i]], "semi-continuous")) out[[y@indicator@variable_name]] <- foo(y@indicator) return(out) } setMethod("initialize", "missing_data.frame", def = function(.Object, include_missingness = TRUE, skip_correlation_check = FALSE, ...) { .Object <- callNextMethod() l <- length(.Object@variables) if(l == 0) return(.Object) varnames <- names(.Object@variables) if(is.null(varnames)) { if(is.null(.Object@DIMNAMES[[2]])) names(.Object@variables) <- sapply(.Object@variables, FUN = .get_slot, name = "variable_name") else names(.Object@variables) <- .Object@DIMNAMES[[2]] } else for(i in 1:l) .Object@variables[[i]]@variable_name <- varnames[i] .Object@DIM <- c(.Object@variables[[1]]@n_total, l) .Object@no_missing <- sapply(.Object@variables, FUN = .get_slot, name = "all_obs") if(length(.Object@DIMNAMES) == 0) .Object@DIMNAMES <- list(NULL, names(.Object@variables)) Z <- lapply(.Object@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NULL) else return(is.na(y)) }) Z <- as.matrix(as.data.frame(Z[!sapply(Z, is.null)])) if(any(apply(Z, 1, all))) { warning("Some observations are missing on all included variables.\n", "Often, this indicates a more complicated model is needed for this missingness mechanism") } uZ <- unique(Z) if(nrow(uZ) == 1) { if(all(uZ[1,] == 0)) patterns <- factor(rep("nothing", nrow(Z))) else patterns <- factor(colnames(uZ)[which(uZ[1,] == 1)], nrow(Z)) } else { uZ <- uZ[order(rowSums(uZ)),,drop = FALSE] patterns <- apply(Z, 1, FUN = function(x) which(apply(uZ, 1, FUN = function(u) all(u == x)))) pattern_labels <- apply(uZ, 1, FUN = function(x) paste(names(x)[x], collapse = ", ")) if(length(pattern_labels)) { if(pattern_labels[1] == "") pattern_labels[1] <- "nothing" pattern_lables <- paste("missing:", pattern_labels) patterns <- factor(patterns, labels = pattern_labels, ordered = FALSE) } else patterns <- factor(patterns) } .Object@patterns <- patterns if(!length(.Object@workpath)) { .Object@workpath <- file.path(tempdir(), paste("mi", as.integer(Sys.time()), sep = "")) } dir.create(.Object@workpath, showWarnings = FALSE) if(is(.Object, "allcategorical_missing_data.frame")) return(.Object) Z <- Z[,!duplicated(t(Z)), drop = FALSE] Z <- Z[,apply(Z, 2, FUN = function(x) length(unique(x))) > 1, drop = FALSE] ## FIXME: What to do if two columns of Z are collinear? if(ncol(Z) > 0) colnames(Z) <- paste("missing", colnames(Z), sep = "_") else include_missingness <- FALSE X <- lapply(.Object@variables, FUN = function(x) { if(is(x, "irrelevant")) return(NULL) else if(is(x, "categorical")) return(.cat2dummies(x)) else if(is(x, "semi-continuous")) { out <- cbind(x@data, .cat2dummies(x@indicator)) colnames(out) <- c(x@variable_name, paste(x@variable_name, 2:ncol(out) - 1, sep = "_")) return(out) } else if(is(x, "censored-continuous")) { temp <- x@data if(x@n_lower) temp <- cbind(temp, lower = x@lower_indicator@data) if(x@n_upper) temp <- cbind(temp, upper = x@upper_indicator@data) if(x@n_both) stop("FIXME: censoring on both sides not supported yet") return(temp) } else if(is(x, "truncated-continuous")) { temp <- x@data n <- length(temp) if(x@n_lower) temp <- cbind(lower = x@lower_indicator@data, temp) if(x@n_upper) temp <- cbind(upper = x@upper_indicator@data, temp) if(x@n_both) stop("FIXME: censoring on both sides not supported yet") return(temp) } else return(x@data) }) ## NOTE: Might need to make this more complicated in the future X <- X[!sapply(X, is.null)] index <- vector("list", length = length(X)) names(index) <- names(X) start <- 2L end <- 0L for(i in seq_along(index)) { end <- start + NCOL(X[[i]]) - 1L index[[i]] <- start:end start <- end + 1L } if(include_missingness) for(i in seq_along(index)) { nas <- is.na(.Object@variables[[i]]) check <- apply(Z, 2, FUN = function(x) all(x == nas)) index[[i]] <- c(index[[i]], which(check) + start - 1) } else for(i in seq_along(index)) index[[i]] <- c(index[[i]], start:(start + ncol(Z) - 1)) grouped <- names(which(sapply(.Object@variables, is, class2 = "grouped-binary"))) for(i in grouped) index[[i]] <- c(index[[i]], index[[.Object@variables[[i]]@strata]], 1) .Object@index <- index .Object@X <- cbind("(Intercept)" = 1, as.matrix(as.data.frame(X)), Z) correlations <- matrix(NA_real_, l,l) if(!skip_correlation_check) for(i in 1:(l - 1)) { ## FIXME: Put SMCs in the lower triangle if(is(.Object@variables[[i]], "irrelevant")) next x <- try(rank(xtfrm(.Object@variables[[i]]@raw_data)), silent = TRUE) if(!is.numeric(x)) next for(j in (i + 1):l) { if(is(.Object@variables[[j]], "irrelevant")) next y <- try(rank(xtfrm(.Object@variables[[j]]@raw_data))) if(!is.numeric(y)) next rho <- cor(x, y, use = "pair", method = "pearson") # on ranks if(is.finite(rho) && abs(rho) == 1) { warning(paste(names(.Object@variables)[i], "and", names(.Object@variables)[j], "have the same rank ordering.\n", "Please verify whether they are in fact distinct variables.\n")) } if(is.finite(rho)) correlations[i,j] <- rho } } .Object@correlations <- correlations .Object@priors <- .set_priors(.Object@variables) .Object }) setClass("allcategorical_missing_data.frame", representation("missing_data.frame", "Hstar" = "integer", "parameters" = "list","latents" = "unordered-categorical"), prototype = prototype(Hstar = 20L), validity = function(object) { out <- TRUE types <- sapply(object@variables, FUN = function(y) is(y, "irrelevant") | is(y, "categorical")) if(!all(types)) { out <- "all variable classes must be 'irrelevant' or 'categorical'" } else if(length(object@Hstar) && object@Hstar < 1) { out <- "'Hstar' must be >= 1" } return(out) }) setMethod("initialize", "allcategorical_missing_data.frame", def = function(.Object, include_missingness = TRUE, ...) { .Object <- callNextMethod() l <- length(.Object@variables) n <- nrow(.Object) uc <- factor(rep(NA_integer_, n)) .Object@latents <- new("unordered-categorical", raw_data = rep(NA_integer_, n)) .Object@priors <- list(a = rep(1, ncol(.Object)), a_alpha = 1, b_alpha = 1) names(.Object@priors$a) <- colnames(.Object) return(.Object) }) setClass("experiment_missing_data.frame", representation("missing_data.frame", concept = "factor", case = "character"), validity = function(object) { out <- TRUE l <- length(object@concept) if(l != length(object@variables)) { out <- "length of 'concept' must equal the number of variables" } else if(!all(levels(object@concept) %in% c("outcome", "covariate", "treatment"))) { out <- "all elements of 'concept' must be exactly one of 'outcome', 'covariate', or 'treatment'" } else if(sum(object@concept == "treatment") != 1) { out <- "there must be exactly one variable designated 'treatment'" } else if(!is(object@variables[[which(object@concept == "treatment")]], "binary")) { out <- "the 'treatment' variable must be of class 'binary'" } else if(object@variables[[which(object@concept == "treatment")]]@n_miss) { out <- "'treatment' variable cannot have any missingness" } else if(length(object@case) > 1) { out <- "'case' must be exactly one of 'outcomes', 'covariates', or 'both'" } else if(length(object@case) && !(object@case %in% c("outcomes", "covariates", "both"))) { out <- "'case' must be exactly one of 'outcomes', 'covariates', or 'both'" } return(out) }) setMethod("initialize", "experiment_missing_data.frame", def = function(.Object, include_missingness = TRUE, ...) { .Object <- callNextMethod() l <- 1 ## FIXME if(l == 0) return(.Object) names(.Object@concept) <- .Object@DIMNAMES[[2]] outcomes <- any(!.Object@no_missing[.Object@concept == "outcomes"]) covariates <- any(!.Object@no_missing[.Object@concept == "covariates"]) .Object@case <- if(outcomes & covariates) "both" else if(outcomes) "covariates" else "outcomes" return(.Object) }) .empty_mdf_list <- list() class(.empty_mdf_list) <- "mdf_list" setClass("multilevel_missing_data.frame", representation("missing_data.frame", groups = "character", mdf_list = "mdf_list"), prototype( mdf_list = .empty_mdf_list ), validity = function(object) { out <- TRUE return(out) } ) setMethod("initialize", "multilevel_missing_data.frame", def = function(.Object, include_missingness = TRUE, ...) { .Object <- callNextMethod() classes <- sapply(.Object@variables, class) for(i in .Object@groups) classes[names(classes) == i] <- "fixed" df <- complete(.Object, m = 0L) mdf_list <- missing_data.frame(df, by = .Object@groups, types = classes) .Object@mdf_list <- mdf_list return(.Object) }) ## an object of class mi merely holds the results of a call to mi(), primary the list of missing_data.frames setClass("mi", representation( call = "call", data = "list", # of missing_data.frames total_iters = "integer"), # how many iterations were conducted (can be a vector) ) ## an object of class pooled has regression results using the Rubin rules setClass("pooled", representation( formula = "formula", fit = "character", models = "list", coefficients = "numeric", ses = "numeric", pooled_summary = "ANY", call = "language"), ) r-cran-mi-1.0/R/AllGeneric.R000066400000000000000000000043711275731226000155020ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. setGeneric("change", def = function(data, y, to, what, ...) standardGeneric("change")) setGeneric("change_family", def = function(data, y, to, ...) standardGeneric("change_family")) setGeneric("change_imputation_method", def = function(data, y, to, ...) standardGeneric("change_imputation_method")) setGeneric("change_link", def = function(data, y, to, ...) standardGeneric("change_link")) setGeneric("change_model", def = function(data, y, to, ...) standardGeneric("change_model")) setGeneric("change_size", def = function(data, y, to, ...) standardGeneric("change_size")) setGeneric("change_transformation", def = function(data, y, to, ...) standardGeneric("change_transformation")) setGeneric("change_type", def = function(data, y, to, ...) standardGeneric("change_type")) setGeneric("complete", def = function(y, m, ...) standardGeneric("complete")) setGeneric("fit_model", def = function(y, data, ...) standardGeneric("fit_model")) setGeneric("get_parameters", def = function(object, ...) standardGeneric("get_parameters")) setGeneric("hist", def = function(x, ...) standardGeneric("hist")) setGeneric("mi", def = function(y, model, ...) standardGeneric("mi")) setGeneric("missing_variable", def = function(y, type, ...) standardGeneric("missing_variable")) setGeneric("missing_data.frame", def = function(y, ...) standardGeneric("missing_data.frame")) ## FIXME: acount for the other stuff in the original AllGeneric.R r-cran-mi-1.0/R/change.R000066400000000000000000000144711275731226000147240ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. setMethod("change", signature(data = "missing_data.frame", y = "ANY", to = "ANY", what = "character"), def = function(data, y, to, what, ...) { if(length(what) > 1) stop("'what' must have length one") # what <- match.arg(what, c("family", "imputation_method", "model", "size", "transformation", # "type", "class", "link", "method")) if(what == "class") what <- "type" if(what == "method") what <- "imputation_method" if(is.character(y) && !(what %in% c("family", "imputation_method", "link", "model", "size", "transformation", "type"))) { if(length(y) > 1) stop("'y' must have length one") if(length(to) > 1) stop("'to' must have length one") if(is.logical(y) | is.numeric(y)) y <- colnames(data)[y] if(to == "unpossible") { mv <- data@variables[[y]] unpossible <- which(mv@raw_data == what) mv@n_unpossible <- length(unpossible) mv@which_unpossible <- unpossible mv@which_obs <- mv@which_obs[!(mv@which_obs %in% mv@which_unpossible)] mv@n_obs <- length(mv@which_obs) mv@which_miss <- mv@which_miss[!(mv@which_miss %in% mv@which_unpossible)] mv@n_miss <- length(mv@which_miss) data@variables[[y]] <- mv if(!length(data@weights)) { data@weights <- lapply(data@variables, FUN = function(y) { if(y@n_unpossible) { w <- rep(1, y@n_total) w[y@which_unpossible] <- 0 return(w) } else return(NULL) }) } else data@weights[[y]][mv@which_unpossible] <- 0 return(data) } mv <- data@variables[[y]] mv@raw_data[mv@raw_data == what] <- to if(is.na(what) | is.na(to)) mv <- new(class(mv), raw_data = mv@raw_data, variable_name = mv@variable_name) data@variables[[y]] <- mv return(data) } if(what == "family") return(change_family(data = data, y = y, to = to)) else if(what == "link") return(change_link(data = data, y = y, to = to)) else if(what == "imputation_method") return(change_imputation_method(data = data, y = y, to = to)) else if(what == "model") return(change_model(data = data, y = y, to = to)) else if(what == "size") return(change_size(data = data, n = y)) else if(what == "transformation") { if(missing(to)) return(change_transformation(data = data, y = y)) else return(change_transformation(data = data, y = y, to = to, ...)) } else if(what == "type") { if(missing(to)) return(change_type(data = data, y = y)) else return(change_type(data = data, y = y, to = to, ...)) } else stop("this should never happen") }) setMethod("change", signature(data = "missing_data.frame", y = "ANY", to = "numeric", what = "numeric"), def = function(data, y, to, what) { if(length(to) > 1) stop("'to' must be a scalar") if(length(what) > 1) stop("'what' must be a scalar") if(is.logical(y) | is.numeric(y)) y <- colnames(data)[y] mv <- data@variables[[y]] mv@raw_data[mv@raw_data == what] <- to # NOTE: exception to "never change the raw_data slot rule" if(is(mv, "categorical")) { values <- unique(mv@raw_data) values <- values[!is.na(values)] if(length(values) == 2) mv <- new("binary", raw_data = mv@raw_data, variable_name = mv@variable_name) } else if(is.na(what) | is.na(to)) mv <- new(class(mv), raw_data = mv@raw_data, variable_name = mv@variable_name) else if(is(mv, "continuous")) mv@data <- mv@transformation(mv@raw_data) data@variables[[y]] <- mv return(data) ## FIXME: maybe reinitialize data? }) setMethod("change", signature(data = "missing_data.frame", y = "ANY", to = "logical", what = "numeric"), def = function(data, y, to, what) { change(data = data, y = y, what = what, to = as.numeric(to)) }) setMethod("change", signature(data = "missing_data.frame", y = "ANY", to = "character", what = "numeric"), def = function(data, y, to, what) { if(length(to) > 1) stop("'to' must be a scalar") if(to != "unpossible") stop("'to' must be 'unpossible'") if(length(what) > 1) stop("'what' must be have length one") if(is.logical(y) | is.numeric(y)) y <- colnames(data)[y] mv <- data@variables[[y]] unpossible <- which(mv@raw_data == what) mv@n_unpossible <- length(unpossible) mv@which_unpossible <- unpossible mv@which_obs <- mv@which_obs[!(mv@which_obs %in% mv@which_unpossible)] mv@n_obs <- length(mv@which_obs) mv@which_miss <- mv@which_miss[!(mv@which_miss %in% mv@which_unpossible)] mv@n_miss <- length(mv@which_miss) data@variables[[y]] <- mv if(!length(data@weights)) { data@weights <- lapply(data@variables, FUN = function(y) { if(y@n_unpossible) { w <- rep(1, y@n_total) w[y@which_unpossible] <- 0 return(w) } else return(NULL) }) } else data@weights[[y]][mv@which_unpossible] <- 0 return(data) }) setMethod("change", signature(data = "missing_data.frame", y = "ANY", to = "logical", what = "character"), def = function(data, y, to, what) { change(data = data, y = y, what = what, to = as.numeric(to)) }) setMethod("change", signature(data = "mdf_list", y = "ANY", to = "ANY", what = "ANY"), def = function(data, y, to, what, ...) { out <- lapply(data, FUN = change, y = y, to = to, what = what, ...) class(out) <- "mdf_list" return(out) }) r-cran-mi-1.0/R/change_family.R000066400000000000000000000137161275731226000162660ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these change the parametric family used in the imputation process setMethod("change_family", signature(data = "missing", y = "missing_variable", to = "family"), def = function(y, to) { y@family <- to validObject(y, complete = TRUE) return(y) }) setMethod("change_family", signature(data = "missing", y = "missing_variable", to = "missing"), def = function(y, to) { cat("Likely families include:", y@known_families, sep = "\n") return(invisible(NULL)) }) setMethod("change_family", signature(data = "missing", y = "proportion", to = "family"), def = function(y, to) { y@family <- to if(y@family$family == "gaussian") { y@transformation <- qnorm y@inverse_transformation <- pnorm } else if(y@family$family == "binomial") { y@transformation <- .identity_transformation y@transformation <- .identity_transformation } y@data <- y@transformation(y@data) validObject(y, complete = TRUE) return(y) }) setMethod("change_family", signature(data = "missing", y = "unordered-categorical", to = "family"), def = function(y, to) { y@family <- to y@estimator <- if(y@family$family == "binomial") "RNL" else "MNL" return(y) } ) setMethod("change_family", signature(data = "missing_data.frame", y = "character", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) # else y <- y[1] } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) { # link <- data@variables[[y[i]]]@family$link if(i <= length(to)) fam <- do.call(to[i], args = list()) data@variables[[y[i]]] <- change_family(y = data@variables[[y[i]]], to = fam) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "character", to = "list"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) # else y <- y[1] } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) { data@variables[[y[i]]] <- change_family(y = data@variables[[y[i]]], to = to[[i]]) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "character", to = "family"), def = function(data, y, to) { if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) { data@variables[[y[i]]] <- change_family(y = data@variables[[y[i]]], to = to) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "numeric", to = "character"), def = function(data, y, to, ...) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { data@variables[[y]] <- change_family(y = data@variables[[y]], to = do.call(to[i], args = list(...))) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "numeric", to = "list"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { data@variables[[y]] <- change_family(y = data@variables[[y]], to = to[[i]]) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "numeric", to = "family"), def = function(data, y, to) { for(i in 1:length(y)) { data@variables[[y]] <- change_family(y = data@variables[[y]], to = to) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "logical", to = "character"), def = function(data, y, to) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_family(data, which(y), to)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "logical", to = "family"), def = function(data, y, to) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_family(data, which(y), to)) }) r-cran-mi-1.0/R/change_imputation_method.R000066400000000000000000000060141275731226000205270ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these change the imputation method setMethod("change_imputation_method", signature(data = "missing", y = "missing_variable", to = "character"), def = function(y, to) { to <- match.arg(tolower(to) , getClass(class(y))@prototype@imputation_method) y@imputation_method <- to validObject(y, complete = TRUE) return(y) }) setMethod("change_imputation_method", signature(data = "missing", y = "missing_variable", to = "missing"), def = function(y, to) { cat("Possible methods include:", getClass(class(y))@prototype@imputation_method, sep = "\n") return(invisible(NULL)) }) setMethod("change_imputation_method", signature(data = "missing_data.frame", y = "character", to = "character"), def = function(data, y, to) { if(all(y %in% c("missing_variable", names(getClass("missing_variable")@subclasses)))) { mark <- sapply(colnames(data), FUN = function(x) { if(data@variables[[x]]@all_obs) return(FALSE) is(data@variables[[x]], y) }) if(!any(mark)) stop(paste("no variables with missingness have class", y)) else y <- names(mark)[mark] } y <- match.arg(y, colnames(data), several.ok = TRUE) if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { data@variables[[y[i]]] <- change_imputation_method(y = data@variables[[y[i]]], to = to[i]) } return(data) }) setMethod("change_imputation_method", signature(data = "missing_data.frame", y = "numeric", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { data@variables[[y]] <- change_imputation_method(y = data@variables[[y]], to = to[i]) } return(data) }) setMethod("change_imputation_method", signature(data = "missing_data.frame", y = "logical", to = "character"), def = function(data, y, to) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_imputation_method(data, which(y), to)) }) r-cran-mi-1.0/R/change_link.R000066400000000000000000000055371275731226000157440ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these change the link function used in the imputation process setMethod("change_link", signature(data = "missing", y = "missing_variable", to = "character"), def = function(y, to) { fam <- do.call(y@family$family, args = list(link = to)) y@family <- fam validObject(y, complete = TRUE) return(y) }) setMethod("change_link", signature(data = "missing", y = "missing_variable", to = "missing"), def = function(y, to) { cat("Likely choices include:", y@known_links, sep = "\n") return(invisible(NULL)) }) setMethod("change_link", signature(data = "missing_data.frame", y = "character", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) else y <- y[1] } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) { data@variables[[y[i]]] <- change_link(y = data@variables[[y[i]]], to = to[i]) } return(invisible(data)) }) setMethod("change_link", signature(data = "missing_data.frame", y = "numeric", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { data@variables[[y]] <- change_link(y = data@variables[[y]], to = to[i]) } return(invisible(data)) }) setMethod("change_family", signature(data = "missing_data.frame", y = "logical", to = "character"), def = function(data, y, to) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_link(data, which(y), to)) }) r-cran-mi-1.0/R/change_model.R000066400000000000000000000153231275731226000161010ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these are convience functions that implicitly change something else by changing the model buzzword setMethod("change_model", signature(data = "missing", y = "missing_variable", to = "character"), def = function(y, to) { switch(to, "logit" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = binomial(link = "logit")), "probit" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = binomial(link = "probit")), "cauchit" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = binomial(link = "cauchit")), "cloglog" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = binomial(link = "cloglog")), "qlogit" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = quasibinomial(link = "logit")), "qprobit" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = quasibinomial(link = "probit")), "qcauchit" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = quasibinomial(link = "cauchit")), "qcloglog" = new("binary", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = quasibinomial(link = "cloglog")), "ologit" = new("ordered-categorical", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = multinomial(link = "logit")), "oprobit" = new("ordered-categorical", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = multinomial(link = "probit")), "ocauchit" = new("ordered-categorical", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = multinomial(link = "cauchit")), "ocloglog" = new("ordered-categorical", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = multinomial(link = "cloglog")), "mlogit" = new("unordered-categorical", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = multinomial(link = "logit")), "RNL" = new("unordered-categorical", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = binomial(link = "logit")), "qpoisson" = new("count", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = quasipoisson(link = "log")), "poisson" = new("count", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = poisson(link = "log")), "linear" = new("continuous", variable_name = y@variable_name, raw_data = y@raw_data, imputation_method = y@imputation_method, family = gaussian(link = "identity")), stop("model not recognized") ) }) setMethod("change_model", signature(data = "missing_data.frame", y = "character", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) to <- rep(to[1], length(y)) } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) check <- FALSE for(i in 1:length(y)) { categorical <- is(data@variables[[y[i]]], "categorical") data@variables[[y[i]]] <- change_model(y = data@variables[[y[i]]], to = to[i]) if(categorical & !is(data@variables[[y[i]]], "categorical")) check <- TRUE if(!categorical & is(data@variables[[y[i]]], "categorical")) check <- TRUE } if(check) return(new(class(data), variables = data@variables)) else return(data) }) setMethod("change_model", signature(data = "missing_data.frame", y = "numeric", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { categorical <- is(data@variables[[y[i]]], "categorical") data@variables[[y[i]]] <- change_model(y = data@variables[[y[i]]], to = to[[i]]) if(categorical & !is(data@variables[[y[i]]], "categorical")) check <- TRUE if(!categorical & is(data@variables[[y[i]]], "categorical")) check <- TRUE } if(check) return(new(class(data), variables = data@variables)) else return(data) }) setMethod("change_model", signature(data = "missing_data.frame", y = "logical", to = "character"), def = function(data, y, to) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_model(data, which(y), to)) }) r-cran-mi-1.0/R/change_size.R000066400000000000000000000052531275731226000157540ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. setMethod("change_size", signature(data = "missing", y = "missing_variable", to = "integer"), def = function(y, to) { n <- to if(n <= 0) { y@data <- y@data[-y@which_extra] y@which_extra <- integer(0) y@n_total <- y@n_total - y@n_extra y@n_extra <- NA_integer_ return(y) } end <- y@n_total SEQ <- (end+1):(end+n) y@data <- c(y@data, rep(NA, n)) y@which_extra <- c(y@which_extra, SEQ) y@n_extra <- y@n_extra + n y@n_total <- y@n_total + n return(y) }) setMethod("change_size", signature(data = "missing", y = "categorical", to = "integer"), def = function(y, to) { n <- to if(n <= 0) { y@data <- y@data[-y@which_extra] y@which_extra <- integer(0) y@n_total <- y@n_total - y@n_extra y@n_extra <- NA_integer_ return(y) } end <- y@n_total SEQ <- (end+1):(end+n) y@data <- c(y@data, rep(NA, n)) y@which_extra <- c(y@which_extra, SEQ) y@n_extra <- y@n_extra + n y@n_total <- y@n_total + n return(y) }) setMethod("change_size", signature(data = "missing", y = "fixed", to = "integer"), def = function(y, to) { n <- to if(n <= 0) { y@data <- y@data[-y@which_extra] y@which_extra <- integer(0) y@n_total <- y@n_total - y@n_extra y@n_extra <- NA_integer_ return(y) } end <- y@n_total SEQ <- (end+1):(end+n) y@data <- c(y@data, rep(y@data[1], n)) y@which_extra <- c(y@which_extra, SEQ) y@n_extra <- y@n_extra + n y@n_total <- y@n_total + n return(y) }) setMethod("change_size", signature(data = "missing_data.frame", y = "missing", to = "integer"), def = function(data, to) { n <- to data@variables <- lapply(data@variables, FUN = function(x) change_size(x, n)) data@DIM[1] <- data@variables[[1]]@n_total return(data) }) r-cran-mi-1.0/R/change_transformation.R000066400000000000000000000163011275731226000200440ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these change the transformation and inverse_transformation slots of a continuous variable setMethod("change_transformation", signature(data = "missing", y = "missing_variable", to = "function"), def = function(y, to, inverse = FALSE) { if(!is(y, "continuous")) stop(paste(y@variable_name, "is not a continuous variable and hence has no transformation")) else if(is(y, "SC_proportion")) stop(paste(y@variable_name, "is a SC_proportion and cannot change its transformation (yet)")) if(inverse) { if(identical(to, .standardize_transform)) { formals(to)$mean <- mean(y@raw_data, na.rm = TRUE) formals(to)$sd <- sd(y@raw_data, na.rm = TRUE) } else if(identical(to, .logshift)) { yy <- y@raw_data if(any(yy < 0, na.rm = TRUE)) a <- - min(yy, na.rm = TRUE) else a <- 0 a <- (a + min(yy[yy > 0], na.rm = TRUE)) / 2 formals(to)$a <- a } if("inverse" %in% names(formals(to))) formals(to)$inverse <- TRUE y@inverse_transformation <- to } else { if(identical(to, .standardize_transform)) { formals(to)$mean <- mean(y@raw_data, na.rm = TRUE) formals(to)$sd <- sd(y@raw_data, na.rm = TRUE) } else if(identical(to, .logshift)) { yy <- y@raw_data if(any(yy < 0, na.rm = TRUE)) a <- - min(yy, na.rm = TRUE) else a <- 0 a <- (a + min(yy[yy > 0], na.rm = TRUE)) / 2 formals(to)$a <- a } y@transformation <- to y@data <- y@transformation(y@raw_data) } return(y) }) setMethod("change_transformation", signature(data = "missing", y = "missing_variable", to = "missing"), def = function(y) { if(is(y, "continuous")) cat("Likely choices include:", y@known_transformations, sep = "\n") else cat("No transformation possible for non-continuous variables\n") return(invisible(NULL)) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "character", to = "missing"), def = function(data, y) { if(all(y %in% c("continuous", names(getClass("continuous")@subclasses)))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) else y <- y[1] } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) change_transformation(y = data@variables[[y[i]]]) return(data) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "character", to = "character"), def = function(data, y, to) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") if(all(y %in% c("continuous", names(getClass("continuous")@subclasses)))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) to <- rep(to[1], length(y)) } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) trans <- lapply(to, FUN = function(x) { switch(x, "identity" = .identity_transform, "standardize" = .standardize_transform, "squeeze" = .squeeze_transform, "logshift" = .logshift, "log" = log, "sqrt" = sqrt, "cuberoot" = .cuberoot, function(...) stop(paste("must replace the transformation slot for", x))) }) inverse <- lapply(to, FUN = function(x) { switch(x, "identity" = .identity_transform, "standardize" = .standardize_transform, "squeeze" = .squeeze_transform, "logshift" = .logshift, "log" = exp, "sqrt" = function(y, ...) y^2, "cuberoot" = .cuberoot, function(...) stop(paste("must replace the inverse_transformation slot for", x))) }) for(i in 1:length(y)) { data@variables[[y[i]]] <- change_transformation(y = data@variables[[y[i]]], to = trans[[i]]) data@variables[[y[i]]] <- change_transformation(y = data@variables[[y[i]]], to = inverse[[i]], inverse = TRUE) mark <- data@index[[y[i]]][1] data@X[,mark] <- data@variables[[y[i]]]@data } # initialize(data) return(data) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "numeric", to = "character"), def = function(data, y, to) { return(change_transformation(data = data, y = colnames(data)[y], to = to)) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "logical", to = "character"), def = function(data, y, to) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_transformation(data = data, y = names(data@variables)[y], to = to)) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "character", to = "function"), def = function(data, y, to, inverse = stop("you must specify 'inverse = FALSE' or 'inverse = TRUE'")) { if(all(y %in% c("continuous", names(getClass("continuous")@subclasses)))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) { if(inverse) data@variables[[y[i]]] <- change_transformation(y = data@variables[[y[i]]], to = to, inverse = TRUE) else data@variables[[y[i]]] <- change_transformation(y = data@variables[[y[i]]], to = to, inverse = FALSE) mark <- data@index[[y[i]]][1] data@X[,mark] <- data@variables[[y[i]]]@data } return(data) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "numeric", to = "function"), def = function(data, y, to, inverse) { y <- names(data@variables)[y] return(change_transformation(data = data, y = y, to = to, inverse)) }) setMethod("change_transformation", signature(data = "missing_data.frame", y = "logical", to = "function"), def = function(data, y, to, inverse) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_transformation(data = data, y = names(data@variables)[y], to = to, inverse = inverse)) }) r-cran-mi-1.0/R/change_type.R000066400000000000000000000102121275731226000157520ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these coerce a missing_variable to a different type of missing_variable setMethod("change_type", signature(data = "missing", y = "missing_variable", to = "character"), def = function(y, to, ...) { to <- match.arg(to, names(getClass("missing_variable")@subclasses)) if(to %in% c("ordered-categorical", "binary")) raw <- as.ordered(y@raw_data) else if(to == "unordered-categorical") raw <- factor(y@raw_data, ordered = FALSE) else raw <- as.numeric(y@raw_data) vals <- unique(raw) vals <- vals[!is.na(vals)] if(length(vals) <= 1) { warning(paste(y@variable_name, ": cannot change type because only one unique value")) return(y) } else return(new(to, variable_name = y@variable_name, raw_data = raw, imputation_method = y@imputation_method, ...)) }) setMethod("change_type", signature(data = "missing", y = "missing_variable", to = "missing"), def = function(y, to) { classes <- .possible_missing_variable(y@raw_data) classes <- names(classes[classes]) cat("Likely choices include:", classes, sep = "\n") return(invisible(NULL)) }) setMethod("change_type", signature(data = "missing_data.frame", y = "character", to = "missing"), def = function(data, y, to) { if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) else y <- y[1] } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) change_type(y = data@variables[[y[i]]]) return(data) }) setMethod("change_type", signature(data = "missing_data.frame", y = "character", to = "character"), def = function(data, y, to, ...) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") if(all(y %in% names(getClass("missing_variable")@subclasses))) { classes <- sapply(data@variables, class) y <- c(sapply(y, FUN = function(x) { names(classes[which(classes == x)]) })) if(is.list(y)) stop(paste("no variables of class", names(y)[1])) to <- rep(to[1], length(y)) } y <- match.arg(y, data@DIMNAMES[[2]], several.ok = TRUE) for(i in 1:length(y)) { data@variables[[y[i]]] <- change_type(y = data@variables[[y[i]]], to = to[i], ...) data@variables[[y[i]]]@variable_name = y[i] } return(new(class(data), variables = data@variables)) }) setMethod("change_type", signature(data = "missing_data.frame", y = "numeric", to = "character"), def = function(data, y, to, ...) { if(length(to) == 1) to <- rep(to, length(y)) else if(length(to) != length(y)) stop("'y' and 'to' must have the same length") for(i in 1:length(y)) { data@variables[[y]] <- change_type(y = data@variables[[y]], to = to[[i]], ...) } return(new(class(data), variables = data@variables)) }) setMethod("change_type", signature(data = "missing_data.frame", y = "logical", to = "character"), def = function(data, y, to, ...) { if(length(y) != data@DIM[2]) { stop("the length of 'y' must equal the number of variables in 'data'") } return(change_type(data, which(y), to, ...)) }) r-cran-mi-1.0/R/complete.R000066400000000000000000000141371275731226000153060ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## These functions extract completed data setMethod("complete", signature(y = "missing_variable", m = "integer"), def = function(y, m, ...) { out <- y@data if(m > 0 & y@n_drawn) out[y@which_drawn] <- as.numeric(y@imputations[m,]) return(out) }) setMethod("complete", signature(y = "irrelevant", m = "integer"), def = function(y, m, ...) { return(y@raw_data) }) setMethod("complete", signature(y = "categorical", m = "integer"), def = function(y, m, to_factor = TRUE, ...) { out <- y@data if(m > 0 & y@n_drawn) out[y@which_drawn] <- as.numeric(y@imputations[m,]) if(to_factor) { out <- factor(out, ordered = is(y, "ordered-categorical")) levels(out) <- y@levels } return(out) }) setMethod("complete", signature(y = "binary", m = "integer"), def = function(y, m, to_factor = TRUE, ...) { out <- y@data if(m > 0 & y@n_drawn) out[y@which_drawn] <- as.numeric(y@imputations[m,]) if(to_factor) { out <- factor(out, ordered = FALSE) levels(out) <- y@levels } return(out) }) setMethod("complete", signature(y = "continuous", m = "integer"), def = function(y, m, transform = TRUE, ...) { out <- y@data if(m > 0 & y@n_drawn) out[y@which_drawn] <- as.numeric(y@imputations[m,]) if(transform) out <- y@inverse_transformation(out) return(out) }) setMethod("complete", signature(y = "nonnegative-continuous", m = "integer"), def = function(y, m, transform = TRUE, ...) { out <- y@data if(m > 0 & y@n_drawn) out[y@which_drawn] <- as.numeric(y@imputations[m,]) if(transform) { out <- y@inverse_transformation(out) out[y@raw_data == 0] <- 0 } return(out) }) setMethod("complete", signature(y = "SC_proportion", m = "integer"), def = function(y, m, transform = TRUE, ...) { out <- y@data if(m > 0 & y@n_drawn) out[y@which_drawn] <- as.numeric(y@imputations[m,]) if(transform) out <- y@inverse_transformation(out) out[y@raw_data == 0] <- 0 out[y@raw_data == 1] <- 1 return(out) }) setMethod("complete", signature(y = "missing_data.frame", m = "integer"), def = function(y, m, to_matrix = FALSE, include_missing = TRUE) { if(to_matrix) out <- sapply(y@variables, complete, m = m, to_factor = FALSE, transform = FALSE) else out <- as.data.frame(lapply(y@variables, complete, m = m, to_factor = TRUE, transform = TRUE)) if(is(y, "allcategorical_missing_data.frame")) { out <- cbind(out, latents = complete(y@latents, m = m, to_factor = !to_matrix)) } if(include_missing) { M <- is.na(y)[,!sapply(y@variables, FUN = function(y) y@all_obs), drop = FALSE] colnames(M) <- paste("missing", colnames(M), sep = "_") out <- cbind(out, M) } return(out) }) setMethod("complete", signature(y = "mi", m = "numeric"), def = function(y, m = length(y), to_matrix = FALSE, include_missing = TRUE) { stopifnot(m == as.integer(m)) m <- as.integer(m) l <- length(y@data) draws <- sum(y@total_iters) if(length(m) > 1) out <- lapply(y@data[m], complete, m = 0L, to_matrix = to_matrix, include_missing = include_missing) else if(m == 1) out <- complete(y@data[[1]], m = 0L, to_matrix = to_matrix, include_missing = include_missing) # not a list else if(m <= l) out <- lapply(y@data[1:m], complete, m = 0L, to_matrix = to_matrix, include_missing = include_missing) else { # wants more completed datasets than chains quotient <- m %/% l remainder <- m %% l num <- quotient + (1:l <= remainder) out <- vector("list", m) count <- 1 for(i in seq_along(y@data)) { if(num[i] == 1) { out[[count]] <- complete(y@data[[i]], m = 0L, to_matrix = to_matrix, include_missing = include_missing) count <- count + 1 } else { # double-dip from a chain SEQ <- seq(from = ceiling(draws / 2), to = draws, length.out = num[i]) temp <- sapply(SEQ, FUN = function(j) complete(y@data[[i]], m = as.integer(j), to_matrix = to_matrix, include_missing = include_missing), simplify = FALSE) for(j in seq_along(temp)) { out[[count]] <- temp[[j]] count <- count + 1 } } } } return(out) }) setMethod("complete", signature(y = "mi", m = "missing"), def = function(y, to_matrix = FALSE, include_missing = TRUE) { return(complete(y, m = length(y), to_matrix = to_matrix, include_missing = include_missing)) }) setMethod("complete", signature(y = "mi_list", m = "numeric"), def = function(y, m = length(y[[1]]), to_matrix = FALSE, include_missing = TRUE) { temp <- lapply(y, FUN = complete, m = m, to_matrix = to_matrix, include_missing = include_missing) dfs <- temp[[1]] if(length(m) == 1 && m == 1 && length(temp) > 1) for(i in 2:length(temp)) { dfs <- rbind(dfs, temp[[i]]) } else if(length(temp) > 1) for(i in 2:length(temp)) for(j in 1:length(dfs)) { dfs[[j]] <- rbind(dfs[[j]], temp[[i]][[j]]) } return(dfs) }) setMethod("complete", signature(y = "mi_list", m = "missing"), def = function(y, to_matrix = FALSE, include_missing = TRUE) { return(complete(y, m = length(y[[1]]), to_matrix = to_matrix, include_missing = include_missing)) }) r-cran-mi-1.0/R/convenience.R000066400000000000000000000104501275731226000157640ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## Some S3 methods for convenience as.double.missing_variable <- function(x, ...) { stop("you must write an 'as.double' method for the", class(x), "class") } as.double.categorical <- function(x, ...) { x@data } as.double.continuous <- function(x, transformed = TRUE, ...) { if(transformed) x@data else x@inverse_transformation(x@data) } as.double.count <- function(x, ...) { x@data } as.double.irrelevant <- function(x, ...) { as.double(x@raw_data) } as.double.missing_data.frame <- function(x, transformed = TRUE, ...) { sapply(x@variables, as.double, transformed = transformed) } as.data.frame.missing_data.frame <- function(x, row.names = NULL, optional = FALSE, ...) { as.data.frame(lapply(x@variables, FUN = function(y) y@raw_data), row.names = if(is.null(row.names)) rownames(x) else row.names) } dim.missing_data.frame <- function(x) { x@DIM } dimnames.missing_data.frame <- function(x) { x@DIMNAMES } names.missing_data.frame <- function(x) { x@DIMNAMES[[2]] } dim.mi <- function(x) { if(isS4(x)) x@data[[1]]@DIM else { class(x) <- "list" return(dim(x)) } } dimnames.mi <- function(x) { if(isS4(x)) x@data[[1]]@DIMNAMES else { class(x) <- "list" return(dimnames(x)) } } names.mi <- function(x) { if(isS4(x)) x@data[[1]]@DIMNAMES[[2]] else { class(x) <- "list" return(names(x)) } } is.na.missing_variable <- function(x) { out <- rep(FALSE, x@n_total) out[x@which_miss] <- TRUE return(out) } is.na.missing_data.frame <- function(x) { sapply(x@variables, is.na) } is.na.mi <- function(x) { if(isS4(x)) is.na(x@data[[1]]) else { class(x) <- "list" return(is.na(x)) } } length.missing_variable <- function(x) { x@n_total } length.missing_data.frame <- function(x) { ncol(x) } length.mi <- function(x) { if(isS4(x)) length(x@data) else { class(x) <- "list" return(length(x)) } } print.mdf_list <- function(x ,...) { show(x) } print.mi_list <- function(x, ...) { show(x) } "[.missing_data.frame" <- function(x, i, j, drop = if (missing(i)) TRUE else length(j) == 1) { if(!missing(i)) { cdf <- complete(x, m = 0L) if(!missing(j)) return(cdf[i,j,drop = drop]) else return(cdf[i,,drop = drop]) } else if(length(j) > 1) return(new(class(x), variables = x@variables[j])) else if(is.numeric(j) && j < 0) return(new(class(x), variables = x@variables[j])) else return(x@variables[[j]]) } "[<-.missing_data.frame" <- function (x, i, j, value) { if(!missing(i)) { if(!missing(j)) x@variables[[j]]@raw_data[i,] <- value else stop("a variable (column) must be specified when replacing") } else if(is.null(value)) x@variables[j] <- value else if(is(value, "missing_variable")) x@variables[[j]] <- value else stop("replacement must be 'NULL' or a 'missing_variable'") return(new(class(x), variables = x@variables)) } "[[.missing_data.frame" <- function(x, ..., exact = TRUE) { return(x[,...]) } "[[<-.missing_data.frame" <- function (x, i, j, value) { if(missing(j)) x[,i] <- value else x[i,j] <- value return(x) } "$.missing_data.frame" <- function(x, name) { return(x[,name]) } "$<-.missing_data.frame" <- function(x, name, value) { # this never gets dispatched for some reason x[,name] <- value return(x) } r-cran-mi-1.0/R/debug.R000066400000000000000000000022451275731226000145610ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## FIXME: Make damn sure .MI_DEBUG is FALSE before pushing to CRAN .MI_DEBUG <- FALSE ## FIXME: Also, make all if(.MI_DEBUG) statements one-liners in other files ## and do sed s/if(.MI_DEBUG)/#if(.MI_DEBUG)/g *.R if(TRUE && .MI_DEBUG) { # define multi-line debugging functions in here options(error = recover) } r-cran-mi-1.0/R/fit_model.R000066400000000000000000000731131275731226000154370ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these fit a regression and return the model # note, helper functions are good because they are checked more rigorously by R CMD check setMethod("fit_model", signature(y = "missing_variable", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { stop("This method should not have been called. You need to define the relevant fit_model() S4 method") }) setMethod("fit_model", signature(y = "irrelevant", data = "missing_data.frame"), def = function(y, data, ...) { stop("'fit_model' should not have been called on an 'irrelevant' variable") }) setMethod("fit_model", signature(y = "binary", data = "missing_data.frame"), def = function(y, data, s, warn, X = NULL, ...) { if(is.null(X)) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] if(is(data, "experiment_missing_data.frame")) { treatment <- names(which(data@concept == "treatment")) if(data@concept[y@variable_name] == "outcome") { X <- cbind(X, interaction = X * data@variables[[treatment]]@data) } } } if(s > 1) start <- y@parameters[s-1,] else if(s < -1) start <- y@parameters[1,] else start <- NULL start <- NULL weights <- if(length(data@weights) == 1) data@weights[[1]] else data@weights[[y@variable_name]] CONTROL <- list(epsilon = max(1e-8, exp(-abs(s))), maxit = 25, trace = FALSE) priors <- data@priors[[y@variable_name]] out <- bayesglm.fit(X, y@data - 1L, weights = weights, prior.mean = priors[[1]], prior.scale = priors[[2]], prior.df = priors[[3]], prior.mean.for.intercept = priors[[4]], prior.scale.for.intercept = priors[[5]], prior.df.for.intercept = priors[[6]], start = start, family = y@family, Warning = FALSE, control = CONTROL) if(warn && !out$converged) { warning(paste("bayesglm() did not converge for variable", y@variable_name, "on iteration", abs(s))) } if(any(abs(coef(out)) > 100)) { warning(paste(y@variable_name, ": separation on iteration", abs(s))) } out$x <- X class(out) <- c("bayesglm", "glm", "lm") return(out) }) .fit_MNL <- function(y, X, weights) { model<-nnet::multinom(y@data ~ X -1, weights = weights, Hess = y@imputation_method == "ppd", model = TRUE, trace = FALSE, MaxNWts = 10000) return(model) } .fit_RNL <- function(y, X, weights, CONTROL) { if (y@use_NA==TRUE) values <- c(-.Machine$integer.max, 1:length(y@levels)) else values <- 1:length(y@levels) out <- sapply(values, simplify = FALSE, FUN = function(l) { model <- bayesglm.fit(X, y@data == l, weights = weights, family = y@family, control=CONTROL) model$x <- X # bayesglm.fit() by default does not retain the model matrix it uses class(model) <- c("bayesglm", "glm", "lm") return(model) }) class(out) <- "RNL" return(out) } setMethod("fit_model", signature(y = "unordered-categorical", data = "missing_data.frame"), def = function(y, data, warn, s, ...) { to_drop <- data@index[[y@variable_name]] if (y@use_NA) { y@data[y@which_drawn] <- -.Machine$integer.max # make NAs the smallest possible signed integer } if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] if(is(data, "experiment_missing_data.frame")) { treatment <- names(which(data@concept == "treatment")) if(data@concept[y@variable_name] == "outcome") { X <- cbind(X, interaction = X * data@variables[[treatment]]@data) } } weights <- if(length(data@weights) == 1) data@weights[[1]] else data@weights[[y@variable_name]] if(y@estimator == "MNL") { out <- .fit_MNL(y, X, weights) data@X } else if(y@estimator == "RNL"){ CONTROL <- list(epsilon = max(1e-8, exp(-abs(s))), maxit = 25, trace = FALSE) out <- .fit_RNL(y, X, weights, CONTROL) data@X } else stop("estimator not recognized") return(out) }) .clogit <- # similar to the survival::clogit function function(formula, data, n, method, weights, subset, x = TRUE, na.action = "na.exclude") { coxcall <- match.call() coxcall[[1]] <- as.name("coxph") newformula <- formula newformula[[2]] <- substitute(survival::Surv(rep(1, nn), case), list(case = formula[[2]], nn = n)) environment(newformula) <- environment(formula) coxcall$formula <- newformula coxcall$n <- NULL coxcall <- eval(coxcall, sys.frame(sys.parent())) coxcall$userCall <- sys.call() class(coxcall) <- c("clogit", "coxph") coxcall } setMethod("fit_model", signature(y = "grouped-binary", data = "missing_data.frame"), def = function(y, data, s, warn) { # see http://www.stata.com/support/faqs/stat/clogitcl.html for a good explanation of this model to_drop <- data@index[[y@variable_name]] X <- data@X[,-to_drop] weights <- if(length(data@weights) == 1) data@weights[[1]] else data@weights[[y@variable_name]] groups <- sapply(y@strata, FUN = function(x) complete(data@variables[[x]], m = 0L), simplify = FALSE) out <- .clogit(y@data ~ X + strata(groups), method = "breslow", weights = weights, n = nrow(X)) out$x <- X return(out) }) setMethod("fit_model", signature(y = "ordered-categorical", data = "missing_data.frame"), def = function(y, data, s, warn, X = NULL, ...) { if(is.null(X)) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] if(is(data, "experiment_missing_data.frame")) { treatment <- names(which(data@concept == "treatment")) if(data@concept[y@variable_name] == "outcome") { X <- cbind(X, interaction = X * data@variables[[treatment]]@data) } } X <- X[,-1] } method <- if(y@family$link == "logit") "logistic" else y@family$link start <- NULL start <- c(rep(0, ncol(X)), qlogis(cumsum(table(y@data)) / nrow(X))) start <- start[-length(start)] weights <- if(length(data@weights) == 1) data@weights[[1]] else data@weights[[y@variable_name]] CONTROL <- list(reltol = max(1e-8, exp(-abs(s)))) priors <- data@priors[[y@variable_name]] out <- bayespolr(as.ordered(y@data) ~ X, weights = weights, method = method, prior.mean = priors[[1]], prior.scale = priors[[2]], prior.df = priors[[3]], prior.counts.for.bins = priors[[4]], control = list(reltol = max(1e-8, exp(-abs(s)))), ...) if(warn && out$convergence != 0) { warning(paste("bayespolr() did not converge for variable", y@variable_name, "on iteration", abs(s))) } out$x <- X return(out) }) setMethod("fit_model", signature(y = "interval", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { stop("FIXME: write this method") }) ## helper function .fit_continuous <- function(y, data, s, warn, X, subset = 1:nrow(X)) { weights <- if(length(data@weights) == 1) data@weights[[1]] else data@weights[[y@variable_name]] if(!is.null(weights)) weights <- weights[subset] if(s > 1) start <- y@parameters[s-1,] else if(s < -1) start <- y@parameters[1,] else start <- NULL start <- NULL mark <- c(TRUE, apply(X[subset,-1, drop = FALSE], 2, FUN = function(x) length(unique(x)) > 1)) if(!all(mark)) { if(abs(s) == 1) { stop(paste(y@variable_name, ": imputed values on iteration 0 randomly inadmissible; try mi() again with different seed")) } X <- X[,mark] if(!is.null(start)) start <- start[mark] } CONTROL <- list(epsilon = max(1e-8, exp(-abs(s))), maxit = 25, trace = FALSE) priors <- data@priors[[y@variable_name]] out <- bayesglm.fit(X[subset,], y@data[subset], weights = weights, start = start, family = y@family, prior.mean = priors[[1]], prior.scale = priors[[2]], prior.df = priors[[3]], prior.mean.for.intercept = priors[[4]], prior.scale.for.intercept = priors[[5]], prior.df.for.intercept = priors[[6]], Warning = FALSE, control = CONTROL) if(warn && !out$converged) { warning(paste("bayesglm() did not converge for variable", y@variable_name, "on iteration", abs(s))) } out$x <- X class(out) <- c("bayesglm", "glm", "lm") return(out) } setMethod("fit_model", signature(y = "continuous", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] if(is(data, "experiment_missing_data.frame")) { treatment <- names(which(data@concept == "treatment")) if(data@concept[y@variable_name] == "outcome") { X <- cbind(X, interaction = X * data@variables[[treatment]]@data) } } return(.fit_continuous(y, data, s, warn, X)) }) # setMethod("fit_model", signature(y = "truncated-continuous", data = "missing_data.frame"), def = # function(y, data, s, warn, ...) { # stop("FIXME: write this method using library(survival)") # }) # # setMethod("fit_model", signature(y = "censored-continuous", data = "missing_data.frame"), def = # function(y, data, s, warn, ...) { # stop("FIXME: mi does not do censored-continuous variables yet") # to_drop <- data@index[[y@variable_name]] # X <- cbind(y@raw_data, data@X[,-to_drop]) # if(is(data, "experiment_missing_data.frame")) { # treatment <- names(which(data@concept == "treatment")) # if(data@concept[y@variable_name] == "outcome") { # X <- cbind(X, interaction = X * data@variables[[treatment]]@data) # } # } # }) setMethod("fit_model", signature(y = "semi-continuous", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { stop("the semi-continuous fit_model() method should not have been called") }) setMethod("fit_model", signature(y = "nonnegative-continuous", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] model <- fit_model(y@indicator, data, s, warn, X) if(abs(s) > 1) subset <- complete(y@indicator, m = 0L, to_factor = TRUE) == 0 else subset <- 1:nrow(X) return(.fit_continuous(y = y, data = data, s = s, warn = warn, X = X, subset = subset)) }) setMethod("fit_model", signature(y = "SC_proportion", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] model <- fit_model(y@indicator, data, s, warn, X) if(abs(s) > 1) subset <- complete(y@indicator, m = 0L, to_factor = TRUE) == 0 else subset <- 1:nrow(X) return(.fit_proportion(y = y, data = data, s = s, warn = warn, X = X, subset = subset)) }) ## helper function .fit_proportion <- function(y, data, s, warn, X, subset = 1:nrow(X)) { weights <- if(length(data@weights) == 1) data@weights[[1]] else data@weights[[y@variable_name]] if(!is.null(weights)) weights <- weights[subset] if(s > 1) start <- y@parameters[s-1,] else if(s < -1) start <- y@parameters[1,] else start <- NULL start <- NULL mark <- c(TRUE, apply(X[subset,-1, drop = FALSE], 2, FUN = function(x) length(unique(x)) > 1)) if(!all(mark)) { if(abs(s) == 1) { stop(paste(y@variable_name, ": imputed values on iteration 0 randomly inadmissible; try mi() again with a different seed")) } X <- X[,mark] if(!is.null(start)) start <- start[c(mark, TRUE)] } out <- betareg::betareg.fit(X[subset,], y@data[subset], weights = if(!is.null(weights)) weights[subset], link = y@family$link, link.phi = y@link.phi, control = betareg::betareg.control(reltol = 1e-8, start = start, fsmaxit = 0)) if(warn && !out$converged) { warning(paste("betareg() did not converge for variable", y@variable_name, "on iteration", abs(s))) } out$x <- X class(out) <- c("betareg") return(out) } setMethod("fit_model", signature(y = "proportion", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] if(y@family$family == "gaussian") out <- .fit_continuous(y, data, s, warn, X) else out <- .fit_proportion(y, data, s, warn, X) return(out) }) setMethod("fit_model", signature(y = "count", data = "missing_data.frame"), def = function(y, data, s, warn, ...) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] return(.fit_continuous(y, data, s, warn, X)) # even though counts are not continuous }) ## experiments setMethod("fit_model", signature(y = "missing_variable", data = "experiment_missing_data.frame"), def = function(y, data, ...) { stop("you need to write a specific fit_model() method for the", class(y), "class") }) setMethod("fit_model", signature(y = "continuous", data = "experiment_missing_data.frame"), def = function(y, data, s, warn, ...) { to_drop <- data@index[[y@variable_name]] ## For each case, make an X matrix based on the giant matrix in data@X if(data@case == "outcomes") { # missingness on outcome(s) only if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] treatment_name <- names(data@concept[data@concept == "treatment"]) X <- cbind(X, interaction = X[,!(colnames(X) %in% c("(Intercept)", treatment_name))] * X[,treatment_name]) } else if(data@case == "covariates") { # missingness on covariate(s) only to_drop <- c(to_drop, which(data@concept == "treatment")) if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] } else { # missing on both outcome(s) and covariate(s) if(data@concept[y@variable_name] == "covariate") { to_drop <- c(to_drop, which(data@concept == "treatment")) } if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] } return(mi::.fit_continuous(y, data, s, warn, X)) }) ## here y indicates which variable to model setMethod("fit_model", signature(y = "character", data = "mi"), def = function(y, data, m = length(data@data), ...) { s <- sum(data@total_iters) + 1 if(length(m) == 1) { models <- vector("list", m) for(i in 1:m) { model <- data@data[[i]]@variables[[y]]@model if(is.null(model)) { model <- fit_model(y = data@data[[i]]@variables[[y]], data = data@data[[i]], s = s, warn = TRUE, ...) if(!isS4(model)) model$x <- model$X <- model$y <- NULL } models[[i]] <- model } } else { models <- vector("list", length(m)) models <- for(i in 1:length(m)) { if(is.null(data@data[[i]]@variables[[y]]@model)) { models[[m[i]]] <- fit_model(y = data@data[[m[i]]]@variables[[y]], data = data@data[[m[i]]], s = s, warn = TRUE, ...) } else models[[i]] <- data@data[[i]]@variables[[y]]@model } } return(models) }) ## fit all variables with missingness setMethod("fit_model", signature(y = "missing", data = "mi"), def = function(data, m = length(data@data)) { varnames <- names(data@data[[1]]@variables) exclude <- data@data[[1]]@no_missing | sapply(data@data[[1]], FUN = function(y) is(y, "irrelevant")) models <- sapply(varnames, simplify = FALSE, FUN = function(v) { if(v %in% exclude) paste(v, "not modeled") ## maybe just skip these? else fit_model(y = v, data = data, m = m) }) return(models) }) ## fit all elements of a mdf_list setMethod("fit_model", signature(y = "missing", data = "mdf_list"), def = function(data, s = -1, verbose = FALSE, warn = FALSE, ...) { out <- lapply(data, fit_model, s = s, verbose = verbose, warn = warn, ...) class(out) <- "mdf_list" return(out) }) .fit_model_y <- function(y, data, s, verbose, warn, ...) { if(s != 0 && y@imputation_method != "mcar") { if(is(y, "semi-continuous")) { to_drop <- data@index[[y@variable_name]] if(length(to_drop)) X <- data@X[,-to_drop] else X <- data@X[,] model <- fit_model(y = y@indicator, data = data, s = s, warn = warn, X = X) indicator <- mi(y = y@indicator, model = model, s = ifelse(s < 0, 1L, s)) if(s > 1) indicator@parameters[s,] <- get_parameters(model) else if(abs(s) == 1) { parameters <- get_parameters(model) rows <- if(s == 1) nrow(indicator@parameters) else 1 if(ncol(indicator@parameters) == 0) { temp <- matrix(NA_real_, nrow = rows, ncol = length(parameters)) } temp[1,] <- parameters indicator@parameters <- temp } else indicator@parameters[1,] <- get_parameters(model) y@indicator <- indicator } model <- fit_model(y = y, data = data, s = s, warn = warn) y <- mi(y = y, model = model, s = ifelse(s < 0, 1L, s)) } else y <- mi(y = y) if(y@imputation_method == "mcar") { # do nothing } else if(s > 1) { parameters <- get_parameters(model) if(length(parameters) != ncol(y@parameters)) parameters <- y@parameters[s-1,] # scary y@parameters[s,] <- parameters } else if(abs(s) == 1) { parameters <- get_parameters(model) rows <- if(s == 1) nrow(y@parameters) else 1 if(ncol(y@parameters) == 0) { temp <- matrix(NA_real_, nrow = rows, ncol = length(parameters)) } temp[1,] <- parameters y@parameters <- temp } else if(s != 0) { parameters <- get_parameters(model) if(length(parameters) == ncol(y@parameters)) y@parameters[s,] <- parameters } return(y) } .update_X <- function(y, data) { which_drawn <- y@which_drawn varname <- y@variable_name if(is(y, "categorical")) { dummies <- .cat2dummies(y)[which_drawn,,drop = FALSE] data@X[ which_drawn, data@index[[varname]][1:NCOL(dummies)]] <- dummies } else if(is(y, "semi-continuous")) { mark <- data@index[[varname]] data@X[ which_drawn, mark[1] ] <- y@data[which_drawn] dummies <- .cat2dummies(y@indicator) data@X[ which_drawn, mark[1 + 1:NCOL(dummies)] ] <- dummies[which_drawn,,drop = FALSE] } else if(is(y, "censored_continuous")) { temp <- y@data[which_drawn] if(y@n_lower) temp <- cbind(temp, lower = y@lower_indicator@data[y@which_drawn]) if(y@n_upper) temp <- cbind(temp, upper = y@upper_indicator@data[y@which_drawn]) data@X[ which_drawn, data@index[[varname]][1:NCOL(temp)]] <- temp data@X[ y@which_censored, data@index[[varname]][1] ] <- y@data[y@which_censored] } else if(is(y, "truncated_continuous")) { temp <- y@data[which_drawn] if(y@n_lower) temp <- cbind(temp, lower = y@lower_indicator@data[y@which_drawn]) if(y@n_upper) temp <- cbind(temp, upper = y@upper_indicator@data[y@which_drawn]) data@X[ which_drawn, data@index[[varname]][1:NCOL(temp)]] <- temp data@X[ y@which_truncated, data@index[[varname]][1] ] <- y@data[y@which_truncated] } else data@X[ which_drawn, data@index[[varname]][1] ] <- y@data[which_drawn] return(data) } .fit_model_mdf <- function(data, s, verbose, warn, ...) { if(verbose) { txt <- paste("Iteration:", abs(s)) if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat("\n", txt) else cat("
", txt, file = file.path(data@workpath, "mi.html"), append = TRUE) } on.exit(print("the problematic variable is")) on.exit(show(y), add = TRUE) for(jj in sample(1:ncol(data), ncol(data), replace = FALSE)) { y <- data@variables[[jj]] if(y@all_obs) next if(is(y, "irrelevant")) next y <- .fit_model_y(y, data, s, verbose, warn, ...) data <- .update_X(y, data) data@variables[[jj]] <- y if(verbose) { txt <- "." if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat(txt) else cat(txt, file = file.path(data@workpath, "mi.html"), append = TRUE) } } if(verbose) { if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat(" ") else cat("
", file = file.path(data@workpath, "mi.html"), append = TRUE) } if(.MI_DEBUG) sapply(data@variables, validObject, complete = TRUE) on.exit() return(data) } ## unlike the above methods, these return a (modified) missing_data.frame setMethod("fit_model", signature(y = "missing", data = "missing_data.frame"), def = function(data, s = -1, verbose = FALSE, warn = FALSE, ...) { return(.fit_model_mdf(data = data, s = s, verbose = verbose, warn = warn, ...)) }) setMethod("fit_model", signature(y = "missing", data = "allcategorical_missing_data.frame"), def = function(data, s = -1, verbose = FALSE, warn = FALSE, ...) { if(verbose) { txt <- paste("Iteration:", abs(s)) if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat("\n", txt) else cat("
", txt, file = file.path(data@workpath, "mi.html"), append = TRUE) } Hstar <- data@Hstar if(abs(s) == 0) { # starting iteration V_h <- c(runif(Hstar - 1), 1) c_prod <- cumprod(1 - V_h) data@parameters$pi <- V_h * c(1, c_prod[-Hstar]) data@variables <- lapply(data@variables, FUN = function(y) { if(is(y, "irrelevant")) return(y) if(y@all_obs) return(y) return(mi(y)) # bootstrapping }) data@X <- do.call(cbind, args = lapply(data@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NULL) else return(y@data) })) phi <- lapply(1:Hstar, FUN = function(h) { lapply(data@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NULL) return(c(tabulate(y@data, nbins = length(y@levels)) / y@n_total)) })}) data@parameters$phi <- phi data@parameters$alpha <- 1 cols <- Hstar rows <- nrow(data@latents@imputations) if(ncol(data@latents@parameters) == 0) { temp <- matrix(NA_real_, nrow = rows, ncol = cols) } data@latents@parameters <- temp return(data) } # S1: Update latent class membership pi <- data@parameters$pi phi <- data@parameters$phi probs <- sapply(1:Hstar, FUN = function(h) { phi_h <- phi[[h]] numerators <- rep(1, nrow(data)) for(j in 1:ncol(data)) { y <- data@variables[[j]] if(is(y, "irrelevant")) next phi_hj <- phi_h[[y@variable_name]] numerators <- numerators * data@X[,y@variable_name] * phi_hj[data@X[,y@variable_name]] } numerators <- numerators * pi[h] return(numerators) }) z <- apply(probs, 1, FUN = function(prob) { which(rmultinom(1,1,prob) == 1) # rmultinom normalizes internally }) data@latents@data[] <- z data@latents@imputations[s,] <- z data@latents@parameters[s,] <- pi # S2: Update V_h n_h <- c(tabulate(z, nbins = Hstar)) V_h <- sapply( 1:(Hstar - 1), FUN = function(h) { a <- 1 + n_h[h] b <- data@parameters$alpha + sum(n_h[-c(1:h)]) if(b == 0) return(1) rbeta(1, a, b) }) V_h <- c(V_h, 1) c_prod <- cumprod(1 - V_h) data@parameters$pi <- V_h * c(1, c_prod[-Hstar]) # S3: Update choice probabilities phi <- lapply(1:Hstar, FUN = function(h) lapply(data@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NULL) mark <- z == h tab <- tabulate(y@data[mark], nbins = length(y@levels)) return(.rdirichlet(1, data@priors$a[y@variable_name] + c(tab))) })) data@parameters$phi <- phi # S4: Update alpha alpha <- rgamma(1, data@priors$a_alpha + Hstar - 1, data@priors$b_alpha - log(pi[Hstar])) data@parameters$alpha <- alpha # S5: Impute data@variables <- lapply(data@variables, FUN = function(y) { if(is(y, "irrelevant")) return(y) if(y@all_obs) return(y) if(verbose) { txt <- "." if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat(txt) else cat(txt, file = file.path(data@workpath, "mi.html"), append = TRUE) } classes <- z[y@which_drawn] uc <- unique(classes) Pr <- t(sapply(uc, FUN = function(c) phi[[c]][[y@variable_name]])) rownames(Pr) <- uc y <- mi(y, Pr[as.character(classes),,drop=FALSE]) }) data@X <- do.call(cbind, args = lapply(data@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NULL) else return(y@data) })) return(data) }) .fit_model_Sophie <- function(y, data, s = -1, verbose = FALSE, warn = FALSE, ...) { classes <- data@latents@data uc <- unique(classes) Pr <- t(sapply(uc, FUN = function(c) data@parameters$phi[[c]][[y@variable_name]])) rownames(Pr) <- uc # Pr <- Pr[as.character(classes),,drop=FALSE] Pr <- Pr / rowSums(Pr) return(list(fitted = Pr)) } setMethod("fit_model", signature(y = "unordered-categorical", data = "allcategorical_missing_data.frame"), def = function(y, data, s = -1, verbose = FALSE, warn = FALSE, ...) { return(.fit_model_Sophie(y, data, s, verbose, warn, ...)) }) setMethod("fit_model", signature(y = "ordered-categorical", data = "allcategorical_missing_data.frame"), def = function(y, data, s = -1, verbose = FALSE, warn = FALSE, ...) { return(.fit_model_Sophie(y, data, s, verbose, warn, ...)) }) setMethod("fit_model", signature(y = "binary", data = "allcategorical_missing_data.frame"), def = function(y, data, s = -1, verbose = FALSE, warn = FALSE, ...) { return(.fit_model_Sophie(y, data, s, verbose, warn, ...)) }) setMethod("fit_model", signature(y = "missing_data.frame", data = "missing_data.frame"), def = function(y, data, s = -1, verbose = FALSE, warn = FALSE, ...) { if(verbose) { txt <- paste("Iteration:", abs(s)) if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat("\n", txt) else cat("
", txt, file = file.path(data@workpath, "mi.html"), append = TRUE) } for(jj in sample(1:ncol(y), ncol(y), replace = FALSE)) { z <- y@variables[[jj]] if(z@all_obs) next if(is(z, "irrelevant")) next y@variables[[jj]] <- .fit_model_y(z, data, s, verbose, warn, ...) if(verbose) { txt <- "." if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat(txt) else cat(txt, file = file.path(data@workpath, "mi.html"), append = TRUE) } } if(verbose) { if(isatty(stdout()) && !(any(search() == "package:gWidgets"))) cat(" ") else cat("
", file = file.path(data@workpath, "mi.html"), append = TRUE) } if(.MI_DEBUG) sapply(data@variables, validObject, complete = TRUE) return(y) }) setMethod("fit_model", signature(y = "missing", data = "multilevel_missing_data.frame"), def = function(data, s = -1, verbose = FALSE, warn = FALSE, ...) { data@mdf_list <- fit_model(data = data@mdf_list, s = s, verbose = verbose, warn = warn, ...) if(s == 0) return(data) ## FIXME: Implement 3+ levels recursively # update group means means <- sapply(data@mdf_list, FUN = function(x) colMeans(x@X[,-1])) if(is.list(means)) { } else means <- t(means) mark <- 0L ## FIXME data@X[,mark] <- means # impute the group level variables if necessary data <- .fit_model_mdf(data = data, s = s, verbose = verbose, warn = warn, ...) # model the individual level estimates for(i in seq_along(ncol(data))) { if(is(data@variables[[i]], "irrelevant")) next # if(data@no_missing[i]) next mark <- if(s < 0) 1 else s fish <- sapply(data@mdf_list, FUN = function(d) d@variables[[i]]@parameters[mark,]) if(is.list(fish)) { ## FIXME: may be a list } else fish <- t(fish) for(j in seq_along(ncol(fish))) { model <- bayesglm.fit(data@X, y = fish[,j]) # group-level regression class(model) <- c("bayesglm", "glm", "lm") params <- arm::sim(model, 1) beta <- params@coef sigma <- params@sigma yhats <- rnorm(nrow(X), data@X %*% beta, sd = sigma) # change the priors for each element of the mdf_list accordingly for(k in seq_along(data@mdf_list)) { data@mdf_list[[k]]$mean[colnames(data)[j]] <- yhats[k] data@mdf_list[[k]]$sd[colnames(data)[j]] <- sigma } } } return(data) }) r-cran-mi-1.0/R/get_parameters.R000066400000000000000000000052061275731226000164750ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## these extract parameters from an estimated object setMethod("get_parameters", signature(object = "ANY"), def = function(object, ...) { return(c(coef(object))) }) setOldClass("polr") setMethod("get_parameters", signature(object = "polr"), def = function(object, ...) { return(c(coef(object), object$zeta)) }) setOldClass("multinom") setMethod("get_parameters", signature(object = "multinom"), def = function(object, ...) { return(c(t(coef(object)))) }) setMethod("get_parameters", signature(object = "missing_variable"), def = function(object, latest = FALSE, ...) { if(latest) { if(is.logical(latest)) { mark <- !apply(object@parameters, 1, FUN = function(x) any(is.na(x))) mark <- mark[length(mark)] } else mark <- latest return(object@parameters[mark,]) } else return(object@parameters) }) setMethod("get_parameters", signature(object = "missing_data.frame"), def = function(object, latest = FALSE, ...) { mini_list <- lapply(object@variables, get_parameters, latest = latest, ...) out <- matrix(NA_real_, nrow(mini_list[[1]]), ncol = 0) for(i in seq_along(mini_list)) out <- cbind(out, mini_list[[i]]) return(out) }) setMethod("get_parameters", signature(object = "mi"), def = function(object, latest = FALSE, ...) { mini_list <- lapply(object@data, get_parameters, latest = latest, ...) dims <- dim(mini_list) out <- array(NA_real_, c(dims[1], length(mini_list), dims[2]), dimnames = list(NULL, NULL, colnames(mini_list[[1]]))) for(i in 1:NCOL(out)) out[,i,] <- mini_list[[i]] return(out) }) setMethod("get_parameters", signature(object = "mi_list"), def = function(object, latest = FALSE, ...) { lapply(object, get_parameters, latest = latest, ...) }) r-cran-mi-1.0/R/hist_methods.R000066400000000000000000000251121275731226000161630ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. setMethod("hist", signature(x = "missing_variable"), def = function(x, ...) { y <- x@data NAs <- is.na(x) h_all <- hist(y, plot = FALSE) plot(h_all, border = "lightgray", main = "", xlab = if(x@done) "Completed" else "Observed", axes = FALSE, mgp = c(2, 1, 0), tcl = .05, col = if(x@done) "lightgray" else "blue", freq = TRUE, ...) axis(1, lwd = 0) axis(2) if(x@done) { h_obs <- hist(y[!NAs], breaks = h_all$breaks, plot = FALSE) h_miss <- hist(y[NAs], breaks = h_all$breaks, plot = FALSE) segments(h_obs$breaks[1], 0, y1 = h_obs$counts[1], col = "blue") segments(h_miss$breaks[1], 0, y1 = h_miss$counts[1], col = "red") segments(h_obs$breaks[1], y0 = h_obs$counts[1], x1 = h_obs$breaks[2], col = "blue") segments(h_miss$breaks[1], y0 = h_miss$counts[1], x1 = h_miss$breaks[2], col = "red") for(i in 2:(length(h_obs$breaks)-1)) { segments(x0 = h_obs$breaks[i], y0 = h_obs$counts[i-1], y1 = h_obs$counts[i], col = "blue") segments(x0 = h_miss$breaks[i], y0 = h_miss$counts[i-1], y1 = h_miss$counts[i], col = "red") segments(x0 = h_obs$breaks[i], y0 = h_obs$counts[i], x1 = h_obs$breaks[i+1], col = "blue") segments(x0 = h_miss$breaks[i], y0 = h_miss$counts[i], x1 = h_miss$breaks[i+1], col = "red") } segments(x0 = h_obs$breaks[i+1], y0 = h_obs$counts[i], y1 = 0, col = "blue") segments(x0 = h_miss$breaks[i+1], y0 = h_miss$counts[i], y1 = 0, col = "blue") if(.MI_DEBUG) stopifnot(all(h_all$counts == (h_obs$counts + h_miss$counts))) } return(invisible(NULL)) }) setMethod("hist", signature(x = "semi-continuous"), def = function(x, ...) { con <- complete(x@indicator, 0L) == 0 y <- x@data[con] NAs <- is.na(x)[con] h_all <- hist(y, plot = FALSE) plot(h_all, freq = TRUE, border = "lightgray", main = "", xlab = if(x@done) "Completed" else "Observed", axes = FALSE, mgp = c(2, 1, 0), tcl = .05, col = if(x@done) "lightgray" else "blue", xlim = range(x@data, na.rm = TRUE), ...) axis(1, lwd = 0) axis(2) if(x@done) { h_obs <- hist(y[!NAs], breaks = h_all$breaks, plot = FALSE) h_miss <- hist(y[NAs], breaks = h_all$breaks, plot = FALSE) segments(h_obs$breaks[1], 0, y1 = h_obs$counts[1], col = "blue") segments(h_miss$breaks[1], 0, y1 = h_miss$counts[1], col = "red") segments(h_obs$breaks[1], y0 = h_obs$counts[1], x1 = h_obs$breaks[2], col = "blue") segments(h_miss$breaks[1], y0 = h_miss$counts[1], x1 = h_miss$breaks[2], col = "red") for(i in 2:(length(h_obs$breaks)-1)) { segments(x0 = h_obs$breaks[i], y0 = h_obs$counts[i-1], y1 = h_obs$counts[i], col = "blue") segments(x0 = h_miss$breaks[i], y0 = h_miss$counts[i-1], y1 = h_miss$counts[i], col = "red") segments(x0 = h_obs$breaks[i], y0 = h_obs$counts[i], x1 = h_obs$breaks[i+1], col = "blue") segments(x0 = h_miss$breaks[i], y0 = h_miss$counts[i], x1 = h_miss$breaks[i+1], col = "red") } segments(x0 = h_obs$breaks[i+1], y0 = h_obs$counts[i], y1 = 0, col = "blue") segments(x0 = h_miss$breaks[i+1], y0 = h_miss$counts[i], y1 = 0, col = "blue") NAs <- is.na(x)[!con] tab <- table(x@data[!con], NAs) for(i in 1:NROW(tab)) { segments(x0 = as.numeric(rownames(tab)[i]), y0 = 0, y1 = sum(tab[i,]), col = "lightgray", lty = "dashed") segments(x0 = as.numeric(rownames(tab)[i]), y0 = 0, y1 = tab[i,1], col = "blue", lty = "dashed") if(ncol(tab) == 2) segments(x0 = as.numeric(rownames(tab)[i]), y0 = 0, y1 = tab[i,2], col = "red", lty = "dashed") } if(.MI_DEBUG) stopifnot(all(h_all$counts == (h_obs$counts + h_miss$counts))) } else { tab <- table(x@data[!con]) for(i in 1:NCOL(tab)) segments(x0 = as.numeric(names(tab)[i]), y0 = 0, y1 = tab[i], col = "blue", lty = "dashed") } return(invisible(NULL)) }) setMethod("hist", signature(x = "categorical"), def = function(x, ...) { y <- x@data values <- sort(unique(y)) breaks <- c(min(values) - 0.5, values + 0.5) values <- unique(y) values <- sort(values[!is.na(values)]) breaks <- c(sapply(values, FUN = function(x) c(x - .25, x + .25))) NAs <- is.na(x) h_all <- hist(y, breaks, plot = FALSE) # h_all$counts[h_all$counts == 0] <- NA_integer_ plot(h_all, border = "lightgray", axes = FALSE, main = "", xlab = if(x@done) "Completed" else "Observed", mgp = c(2, 1, 0), tcl = .05, col = if(x@done) "lightgray" else "blue", freq = TRUE, ylim = range(h_all$counts, na.rm = TRUE), ...) axis(1, at = values, labels = levels(x@raw_data), lwd = 0) axis(2) if(x@done) { h_obs <- hist(y[!NAs], breaks, plot = FALSE) h_miss <- hist(y[NAs], breaks, plot = FALSE) counts_obs <- h_obs$counts counts_obs <- counts_obs counts_miss <- h_miss$counts counts_miss <- counts_miss segments(breaks[1], 0, y1 = counts_obs[1], col = "blue") segments(breaks[1], 0, y1 = counts_miss[1], col = "red") if(counts_obs[1]) segments(breaks[1], y0 = counts_obs[1], x1 = breaks[2], col = "blue") if(counts_miss[1]) segments(breaks[1], y0 = counts_miss[1], x1 = breaks[2], col = "red") for(i in 2:(length(breaks)-1)) { segments(x0 = breaks[i], y0 = counts_obs[i-1], y1 = counts_obs[i], col = "blue") segments(x0 = breaks[i], y0 = counts_miss[i-1], y1 = counts_miss[i], col = "red") if(counts_obs[i]) segments(x0 = breaks[i], y0 = counts_obs[i], x1 = breaks[i+1], col = "blue") if(counts_miss[i]) segments(x0 = breaks[i], y0 = counts_miss[i], x1 = breaks[i+1], col = "red") } segments(x0 = breaks[i+1], y0 = counts_obs[i], y1 = 0, col = "blue") segments(x0 = breaks[i+1], y0 = counts_miss[i], y1 = 0, col = "red") if(.MI_DEBUG) stopifnot(all(h_all$counts == (h_obs$counts + h_miss$counts))) } return(invisible(NULL)) }) setMethod("hist", signature(x = "binary"), def = function(x, ...) { y <- x@data if(max(y, na.rm = TRUE) > 1) y <- y - 1L values <- 0:1 breaks <- c(-.5, .5, 1.5) breaks <- c(-.25, .25, .75, 1.25) NAs <- is.na(x) h_all <- hist(y, breaks, plot = FALSE) # h_all$counts[h_all$counts == 0] <- NA_integer_ plot(h_all, border = "lightgray", axes = FALSE, main = "", xlab = if(x@done) "Completed" else "Observed", mgp = c(2, 1, 0), tcl = .05, col = if(x@done) "lightgray" else "blue", freq = TRUE, ylim = range(h_all$counts, na.rm = TRUE), ...) axis(1, at = values, lwd = 0) axis(2) if(x@done) { h_obs <- hist(y[!NAs], breaks, plot = FALSE) h_miss <- hist(y[NAs], breaks, plot = FALSE) counts_obs <- h_obs$counts counts_obs <- counts_obs counts_miss <- h_miss$counts counts_miss <- counts_miss segments(breaks[1], 0, y1 = counts_obs[1], col = "blue") segments(breaks[1], 0, y1 = counts_miss[1], col = "red") if(counts_obs[1]) segments(breaks[1], y0 = counts_obs[1], x1 = breaks[2], col = "blue") if(counts_miss[1]) segments(breaks[1], y0 = counts_miss[1], x1 = breaks[2], col = "red") for(i in 2:(length(breaks)-1)) { segments(x0 = breaks[i], y0 = counts_obs[i-1], y1 = counts_obs[i], col = "blue") segments(x0 = breaks[i], y0 = counts_miss[i-1], y1 = counts_miss[i], col = "red") if(counts_obs[i]) segments(x0 = breaks[i], y0 = counts_obs[i], x1 = breaks[i+1], col = "blue") if(counts_miss[i]) segments(x0 = breaks[i], y0 = counts_miss[i], x1 = breaks[i+1], col = "red") } segments(x0 = breaks[i+1], y0 = counts_obs[i], y1 = 0, col = "blue") segments(x0 = breaks[i+1], y0 = counts_miss[i], y1 = 0, col = "red") if(.MI_DEBUG) stopifnot(all(h_all$counts == (h_obs$counts + h_miss$counts))) } return(invisible(NULL)) }) setMethod("hist", signature(x = "missing_data.frame"), def = function(x, ask = TRUE, ...) { k <- sum(!x@no_missing) if (.Device != "null device" && x@done) { oldask <- grDevices::devAskNewPage(ask = ask) if (!oldask) on.exit(grDevices::devAskNewPage(oldask), add = TRUE) op <- options(device.ask.default = TRUE) on.exit(options(op), add = TRUE) } par(mfrow = n2mfrow(k)) for(i in 1:x@DIM[2]) { if(x@no_missing[i]) next hist(x@variables[[i]]) header <- x@variables[[i]]@variable_name if(is(x@variables[[i]], "continuous")) { trans <- .show_helper(x@variables[[i]])$transformation[1] header <- paste("\n", header, " (", trans, ")", sep = "") } title(main = header) } return(invisible(NULL)) }) setMethod("hist", signature(x = "mdf_list"), def = function(x, ask = TRUE, ...) { if (.Device != "null device") { oldask <- grDevices::devAskNewPage(ask = ask) if (!oldask) on.exit(grDevices::devAskNewPage(oldask), add = TRUE) op <- options(device.ask.default = ask) on.exit(options(op), add = TRUE) } sapply(x, FUN = hist, ...) return(invisible(NULL)) }) setMethod("hist", signature(x = "mi"), def = function(x, m = 1:length(x), ask = TRUE, ...) { for(i in m) hist(x@data[[i]], ask = ask, ...) return(invisible(NULL)) }) setMethod("hist", signature(x = "mi_list"), def = function(x, m = 1:length(x), ask = TRUE, ...) { if (.Device != "null device") { oldask <- grDevices::devAskNewPage(ask = ask) if (!oldask) on.exit(grDevices::devAskNewPage(oldask), add = TRUE) op <- options(device.ask.default = ask) on.exit(options(op), add = TRUE) } sapply(x, FUN = hist, m = m, ask = ask, ...) return(invisible(NULL)) }) r-cran-mi-1.0/R/mi.R000066400000000000000000001205271275731226000141040ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. .prune_missing_variable <- function(y, s) { if(!is(y, "missing_variable")) stop("'y' must inherit from the 'missing_variable' class") if(!y@all_obs) { y@parameters <- y@parameters[1:s,,drop = FALSE] y@imputations <- y@imputations[1:s,,drop = FALSE] } return(y) } .MPinverse <- function(eta, tol = sqrt(.Machine$double.eps)) { cov_eta <- cov(eta) ev <- eigen(cov_eta, TRUE) ev$values <- ifelse(ev$values > tol, 1/ev$values, 0) Sigma_inv <- crossprod(sqrt(ev$values)*(t(ev$vectors))) return(Sigma_inv) } .mi <- function(i, y, verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models) { mdf <- y if(verbose) message("Chain ", i, "\n") for(s in s_start:s_end) { if(verbose) message("Chain ", i, " Iteration ", s, "\n") mdf <- fit_model(data = mdf, s = s, verbose = FALSE, warn = s == s_end) if(s > 0) { pars <- unlist(sapply(mdf@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NA_real_) else if(y@all_obs) return(NA_real_) else return(y@parameters[s,,drop=TRUE]) })) pars <- t(pars[!is.na(pars)]) fp <- file.path(mdf@workpath, paste0("pars_", i, ".csv")) write.table(pars, file = fp, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE) imps <- unlist(sapply(mdf@variables, FUN = function(y) { if(is(y, "irrelevant")) return(NA_real_) else if(y@all_obs) return(NA_real_) else return(y@imputations[s,,drop=TRUE]) })) imps <- t(imps[!is.na(imps)]) fp <- file.path(mdf@workpath, paste0("imps_", i, ".csv")) write.table(imps, file = fp, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE) } Time.Elapsed <- proc.time() - ProcStart if(((Time.Elapsed)/60)[3] > max.minutes) { warning("'max.minutes' threshold exceeded") break } } if(((Time.Elapsed)/60)[3] > max.minutes) mdf@variables <- lapply(mdf@variables, .prune_missing_variable, s = s) if(verbose) message("Estimating models on completed data for chain ", i, "\n") mdf@variables <- lapply(mdf@variables, FUN = function(y) { if(!y@all_obs & !is(y, "irrelevant")) { model <- fit_model(y, mdf, s = s + 1, warn = TRUE) y@fitted <- fitted(model) if(!isS4(model)) model$x <- model$X <- model$y <- model$model <- NULL if(save_models) y@model <- model } else y@model <- NULL return(y) }) mdf@done <- TRUE if(verbose) message("Done with chain ", i, "\n") return(mdf) } .mi_split <- function(i, y, data, verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models) { mdf <- y if(verbose) message("Chain ", i, "\n") data@priors <- mdf@priors for(s in s_start:s_end) { if(verbose) message("Chain ", i, " Iteration ", s, "\n") mdf <- fit_model(mdf, data, s = s, verbose = FALSE, warn = s == s_end) Time.Elapsed <- proc.time() - ProcStart if(((Time.Elapsed)/60)[3] > max.minutes) { warning("'max.minutes' threshold exceeded") break } } if(((Time.Elapsed)/60)[3] > max.minutes) mdf@variables <- lapply(mdf@variables, .prune_missing_variable, s = s) if(verbose) message("Estimating models on completed data for chain ", i, "\n") mdf@variables <- lapply(mdf@variables, FUN = function(y) { if(!y@all_obs & !is(y, "irrelevant")) { model <- fit_model(y, data, s = s + 1, warn = TRUE) y@fitted <- fitted(model) if(!isS4(model)) model$x <- model$X <- model$y <- model$model <- NULL if(save_models) y@model <- model } else y@model <- NULL return(y) }) mdf@done <- TRUE if(verbose) message("Done with chain ", i, "\n") return(mdf) } setMethod("mi", signature(y = "missing_data.frame", model = "missing"), def = function(y, n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, save_models = FALSE, parallel = .Platform$OS.type != "Windows") { call <- match.call() if(!is.na(seed)) set.seed(seed) if(n.iter < 0) stop(message="number of iterations must be non-negative") ProcStart <- proc.time() s_start <- 0 s_end <- n.iter Time.Elapsed <- proc.time() - ProcStart y@variables <- lapply(y@variables, FUN = function(x) { if(!x@all_obs & !is(x, "irrelevant")) { x@parameters <- matrix(NA_real_, nrow = n.iter, ncol = 0) x@imputations <- matrix(NA_real_, nrow = n.iter, ncol = x@n_drawn) if(is(x, "semi-continuous")) { x@indicator@parameters <- matrix(NA_real_, nrow = n.iter, ncol = 0) x@indicator@imputations <- matrix(NA_real_, nrow = n.iter, ncol = x@n_drawn) } } x@done <- TRUE return(x) }) if(is(y, "allcategorical_missing_data.frame")) { y@latents@imputations <- matrix(NA_integer_, nrow = n.iter, ncol = nrow(y)) y@latents@levels <- as.character(1:y@Hstar) } if(n.chains <= 0) return(y) if(is.logical(parallel) && parallel) { cores <- getOption("mc.cores", 2L) cl <- parallel::makeCluster(cores, outfile = "") on.exit(parallel::stopCluster(cl)) } if(!parallel) { mdfs <- vector("list", n.chains) for(i in seq_along(mdfs)) { ProcStart <- proc.time() mdfs[[i]] <- .mi(i, y, verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models) } } else { mdfs <- parallel::parLapply(cl, X = as.list(1:n.chains), fun = function(i) .mi(i, y, verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models)) } # # else mdfs <- mclapply(as.list(1:n.chains), # FUN = function(i) .mi(i, y, verbose, s_start, s_end, # ProcStart, max.minutes, parallel, save_models)) names(mdfs) <- paste("chain", 1:length(mdfs), sep = ":") object <- new("mi", call = call, data = mdfs, total_iters = as.integer(s_end)) return(object) }) setMethod("mi", signature(y = "data.frame", model = "missing"), def = function(y, n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, save_models = FALSE, parallel = .Platform$OS.type != "Windows") { y <- as(y, "missing_data.frame") return(mi(y, n.iter = n.iter, n.chains = n.chains, max.minutes = max.minutes, seed = seed, verbose = verbose, save_models = save_models, parallel = parallel)) }) setMethod("mi", signature(y = "matrix", model = "missing"), def = function(y, n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, save_models = FALSE, parallel = .Platform$OS.type != "Windows") { y <- as(y, "missing_data.frame") return(mi(y, n.iter, n.chains, max.minutes, seed, verbose, save_models, parallel)) }) setMethod("mi", signature(y = "mi", model = "missing"), function(y, n.iter = 30, max.minutes = Inf, seed = NA, verbose = TRUE, save_models = FALSE, parallel = .Platform$OS.type != "Windows") { call <- match.call() if(!is.na(seed)) set.seed(seed) if(n.iter < 1) stop(message="number of iterations must be at least 1") ProcStart <- proc.time() total_iters <- y@total_iters s_start <- sum(total_iters) + 1 s_end <- s_start + n.iter - 1 mdfs <- y@data n.chains <- length(mdfs) for(i in 1:n.chains) { y <- mdfs[[i]] if(TRUE) y@variables <- lapply(y@variables, FUN = function(x) { if(x@all_obs & is(x, "irrelevant")) return(x) x@imputations <- rbind(x@imputations, matrix(NA_integer_, n.iter, x@n_drawn)) x@parameters <- rbind(x@parameters, matrix(NA_real_, n.iter, ncol(x@parameters))) if(is(x, "semi-continuous")) { x@indicator@imputations <- rbind(x@indicator@imputations, matrix(NA_integer_, n.iter, x@indicator@n_drawn)) x@indicator@parameters <- rbind(x@indicator@parameters, matrix(NA_real_, n.iter, ncol(x@indicator@parameters))) } return(x) }) } if(is.logical(parallel) && parallel) { cores <- getOption("mc.cores", 2L) cl <- parallel::makeCluster(cores, outfile = "") on.exit(parallel::stopCluster(cl)) } if(!parallel) { mdfs <- vector("list", n.chains) for(i in seq_along(mdfs)) { ProcStart <- proc.time() mdfs[[i]] <- .mi(i, y, verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models) } } else { mdfs <- parallel::parLapply(cl, as.list(1:n.chains), fun = function(i) .mi(i, y, verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models)) } # else mdfs <- mclapply(as.list(1:n.chains), # FUN = function(i) .mi(i, y, verbose, s_start, s_end, # ProcStart, max.minutes, parallel, save_models)) object <- new("mi", call = call, data = mdfs, total_iters = as.integer(c(total_iters, n.iter))) return(object) }) setMethod("mi", signature(y = "missing_data.frame", model = "mi"), def = function(y, model, n.iter = sum(model@total_iters), max.minutes = 20, seed = NA, verbose = TRUE, save_models = FALSE, parallel = .Platform$OS.type != "Windows") { n.chains <- length(model) call <- match.call() if(!is.na(seed)) set.seed(seed) y <- mi(y, n.chains = 0L, n.iter = n.iter) ProcStart <- proc.time() s_start <- 0 s_end <- n.iter if(is.logical(parallel) && parallel) { cores <- getOption("mc.cores", 2L) cl <- parallel::makeCluster(cores, outfile = "") on.exit(parallel::stopCluster(cl)) } mdfs <- model@data if(!parallel) { for(i in seq_along(mdfs)) { ProcStart <- proc.time() mdfs[[i]] <- .mi_split(i, y, mdfs[[i]], verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models) } } else { mdfs <- parallel::parLapply(cl, as.list(1:n.chains), fun = function(i) .mi_split(i, y, mdfs[[i]], verbose, s_start, s_end, ProcStart, max.minutes, parallel, save_models)) } # else mdfs <- mclapply(as.list(1:n.chains), # FUN = function(i) .mi_split(i, y, mdfs[[i]], verbose, s_start, s_end, # ProcStart, max.minutes, parallel, save_models)) names(mdfs) <- paste("chain", 1:length(mdfs), sep = ":") to_drop <- 1:ncol(model@data[[1]]@X) for(i in 1:n.chains) { model@data[[i]]@variables <- c(model@data[[i]]@variables, mdfs[[i]]@variables) model@data[[i]]@no_missing <- c(model@data[[i]]@no_missing, mdfs[[i]]@no_missing) # leave patterns as is I guess model@data[[i]]@DIM[2] <- model@data[[i]]@DIM[2] + mdfs[[i]]@DIM[2] model@data[[i]]@DIMNAMES[[2]] <- c(model@data[[i]]@DIMNAMES[[2]], mdfs[[i]]@DIMNAMES[[2]]) mdfs[[i]]@index <- lapply(mdfs[[i]]@index, FUN = function(x) if(is.null(x)) x else to_drop) model@data[[i]]@index <- c(model@data[[i]]@index, mdfs[[i]]@index) model@data[[i]]@weights <- c(model@data[[i]]@weights, mdfs[[i]]@weights) model@data[[i]]@priors <- c(model@data[[i]]@priors, mdfs[[i]]@priors) } object <- new("mi", call = call, data = model@data, total_iters = as.integer(s_end)) return(object) }) setMethod("mi", signature(y = "mdf_list", model = "missing"), def = function (y, ...) { out <- lapply(y, FUN = mi, ...) class(out) <- "mi_list" return(out) }) setMethod("mi", signature(y = "list", model = "missing"), def = function (y, ...) { if(!all(sapply(y, is, class2 = "mi"))) { stop("all elements of 'y' must be mi objects or missing_data.frame objects") } ## FIXME: should probably check that all the mi objects are based on the same missing_data.frame mdfs <- lapply(mi, FUN = function(x) return(x@data)) object <- new("mi", call = y[[1]]@call, data = mdfs, total_iters = y[[1]]@total_iters) return(object) }) setMethod("mi", signature(y = "mdf_list", model = "missing"), function (y, n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, save_models = FALSE, parallel = .Platform$OS.type != "Windows") { out <- lapply(y, mi, n.iter = n.iter, n.chains = n.chains, max.minutes = max.minutes, seed = seed, verbose = verbose, save_models = save_models, parallel = parallel) class(out) <- "mi_list" return(out) }) setMethod("mi", signature(y = "mi_list", model = "missing"), def = function (y, ...) { out <- lapply(y, FUN = mi, ...) class(out) <- "mi_list" return(out) }) setMethod("show", signature(object = "mi"), def = function(object) { cat("Object of class", class(object), "with", length(object@data), "chains, each with", sum(object@total_iters), "iterations.\n") cat("Each chain is the evolution of an object of", class(object@data[[1]]), "class with", nrow(object@data[[1]]), "observations on", ncol(object@data[[1]]), "variables.\n") return(invisible(NULL)) }) setMethod("show", signature(object = "mi_list"), def = function(object) { sapply(object, show) return(invisible(NULL)) }) setMethod("summary", signature(object = "mi"), def = function(object) { mdf <- object@data[[1]] matrices <- complete(object, to_matrix = TRUE, include_missing = FALSE) chains <- length(matrices) matrices <- array(unlist(matrices), dim = c(dim(mdf), chains), dimnames = c(dimnames(mdf), NULL)) out <- vector("list", ncol(mdf)) names(out) <- colnames(mdf) for(i in seq_along(out)) { if(mdf@no_missing[i]) { if(is(mdf@variables[[i]], "categorical")) { mat <- table(matrices[,i,1]) lev <- mdf@variables[[i]]@levels if(length(lev) && length(dim(mat)) > 1) colnames(mat) <- lev } else mat <- summary(matrices[,i,1]) out[[i]] <- list(is_missing = "all values observed", observed = mat) } else if(is(mdf@variables[[i]], "categorical")) { mark <- is.na(mdf@variables[[i]]) mat <- table(c(matrices[,i,]), rep(mark, times = chains)) lev <- mdf@variables[[i]]@levels if(length(lev)) rownames(mat) <- lev colnames(mat) <- c("observed", "imputed") out[[i]] <- list(crosstab = mat) } else { missing <- is.na(mdf@variables[[i]]@raw_data) out[[i]] <- list(is_missing = table(missing), imputed = summary(c(matrices[missing,i,])), observed = summary(c(matrices[!missing,i,]))) } } return(out) }) setMethod("traceplot", signature(x = "mi"), def = function(x, ...) { traceplot(mi2BUGS, ...) }) setMethod("traceplot", signature(x = "mi_list"), def = function(x, ...) { traceplot(lapply(x, mi2BUGS, ...)) }) ## all the mi() methods below should return the missing_variable after imputing ## need to explicitly write out methods instead of doing poor man's S4 setMethod("mi", signature(y = "missing_variable", model = "ANY"), def = function(y, model, ...) { stop("This method should not have been called. You need to define the relevant mi() S4 method") }) setMethod("mi", signature(y = "missing_variable", model = "missing"), def = function(y) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") draws <- sample(y@data[y@which_obs], size = y@n_drawn, replace = TRUE) y@data[y@which_drawn] <- draws return(y) }) setMethod("mi", signature(y = "semi-continuous", model = "missing"), def = function(y) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") y@indicator <- mi(y@indicator) draws <- sample(y@data[y@which_obs], size = y@n_drawn, replace = TRUE) if(is(y, "SC_proportion")) { n <- y@n_total if(is(y@indicator, "binary")) { mark <- which(complete(y@indicator, m = 0L)[y@which_miss] == 1) if(any(y@raw_data == 0, na.rm = TRUE)) draws[mark] <- .5 / n else draws[mark] <- (n - .5) / n } else { mark <- which(complete(y@indicator, m = 0L)[y@which_miss] != 0) draws[mark] <- (draws[mark] * (n - 1) + .5) / n } } else if(is(y, "nonnegative-continuous")) { mark <- which(y@indicator@data[y@which_miss] == 1) if(length(mark)) draws[mark] <- y@transformation(rep(0, length(mark))) } else stop("FIXME: semi-continuous is not supported yet") y@data[y@which_drawn] <- draws return(y) }) # setMethod("mi", signature(y = "semi-continuous", model = "missing"), def = # function(y) { # if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") # # categories <- 1:(ncol(y@indicator@dummies) + 1) # draws <- sample(categories, size = y@n_drawn, replace = TRUE) # dummies <- t(sapply(draws, FUN = function(x) x == categories))[,-1,drop = FALSE] # y@indicator@dummies[y@which_drawn,] <- dummies # y@indicator@data[y@which_drawn] <- draws # # draws <- sample(y@data[y@which_obs], size = y@n_drawn, replace = TRUE) # if(is(y, "SC_proportion")) { # n <- y@n_total # if(is(y@indicator, "binary")) { # mark <- which(complete(y@indicator, m = 0L)[y@which_miss] == 1) # if(any(y@raw_data == 0, na.rm = TRUE)) draws[mark] <- .5 / n # else draws[mark] <- (n - .5) / n # } # else { # mark <- which(complete(y@indicator, m = 0L)[y@which_miss] != 0) # draws[mark] <- (draws[mark] * (n - 1) + .5) / n # } # } # else if(is(y, "nonnegative-continuous")) { # mark <- which(y@indicator@data[y@which_miss] == 1) # if(length(mark)) draws[mark] <- y@transformation(rep(0, length(mark))) # } # # the_range <- range(y@data, na.rm = TRUE) # free <- y@data[y@which_obs] # free <- free[free != the_range[1] & free != the_range[2]] # draws <- sample(free, size = y@n_drawn, replace = TRUE) # y@data[y@which_drawn] <- draws # return(y) # }) setMethod("mi", signature(y = "bounded-continuous", model = "missing"), def = function(y) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") a <- if(length(y@lower) == 1) y@lower else y@lower[y@which_drawn] a <- ifelse(a == -Inf, min(y@data, na.rm = TRUE), a) a <- ifelse(a == Inf, max(y@data, na.rm = TRUE), a) b <- if(length(y@upper) == 1) y@upper else y@upper[y@which_drawn] b <- ifelse(b == -Inf, min(y@data, na.rm = TRUE), b) b <- ifelse(b == Inf, max(y@data, na.rm = TRUE), b) draws <- runif(y@n_drawn, min = a, max = b) y@data[y@which_drawn] <- draws return(y) }) setMethod("mi", signature(y = "categorical", model = "missing"), def = function(y) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") draws <- sample(y@data[y@which_obs], size = y@n_drawn, replace = TRUE) y@data[y@which_drawn] <- draws return(y) }) .draw_parameters <- function(means, ev) { if(any(ev$values <= 0)) return(means) else return(means + (ev$vectors %*% (sqrt(ev$values) * rnorm(length(means))))[,1]) } .pmm <- function(y, eta, Sigma_inv = NULL, strata = NULL) { if(is(y, "unordered-categorical")) { if(is.null(Sigma_inv)) Sigma_inv <- .MPinverse(eta) MD <- mahalanobis(eta, colMeans(eta), Sigma_inv, inverted=TRUE) MD_observed <- MD[y@which_obs] y_observed <- y@data[y@which_obs] draws <- sapply(MD[y@which_drawn], FUN = function(x) { mark <- which.min(abs(MD_observed - x)) drawmark <- c(y_observed[mark], mark) return(drawmark) }) } else if(is(y, "grouped-binary")) { draws <- sapply(y@which_drawn, FUN = function(i) { which_same <- which(strata == strata[i]) candidates <- intersect(which_same, y@which_obs) if(length(candidates) == 0) { msg <- paste(y@variable_name, ": must have some observed values in each group") stop(msg) } eta_can <- eta[candidates] y_can <- y@data[candidates] mark <- which.min(abs(eta_can - eta[i])) drawmark <- c(y_can[mark], mark) return(drawmark) }) } else { eta_obs <- eta[y@which_obs] y_obs <- y@data[y@which_obs] draws <- sapply(eta[y@which_drawn], FUN = function(x) { if(is.na(x)) return(NA_real_) # happens with semi-continuous mark <- which.min(abs(eta_obs - x)) drawmark <- c(y_obs[mark], mark) return(drawmark) }) } return(t(draws)) } setOldClass("polr") setMethod("mi", signature(y = "ordered-categorical", model = "polr"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(!is.element(y@imputation_method, c("ppd", "pmm"))) badHessian <- FALSE else if(is.null(model$Hessian)) badHessian <- FALSE else if(!all(is.finite(model$Hessian))) badHessian <- TRUE else { means <- c(coef(model), model$zeta) ev <- eigen(vcov(model), symmetric = TRUE) badHessian <- any(ev$values <= 0) parameters <- .draw_parameters(means, ev) while(!badHessian && any(diff(parameters[-(1:ncol(model$x))]) <= 0)) { # rejection sampling on cutpoints parameters <- .draw_parameters(means, ev) } } if(badHessian && y@imputation_method == "ppd") { warning(paste("predictive mean matching used for", y@variable_name, "on iteration", s, "as a fallback due to Hessian error")) old_method <- y@imputation_method y@imputation_method <- "pmm" y <- mi(y, model, s, ...) y@imputation_method <- old_method return(y) } else if(y@imputation_method == "ppd") { eta <- as.vector(model$x[y@which_drawn,,drop=FALSE] %*% head(parameters, ncol(model$x))) pfun <- switch(y@family$link, logit = plogis, probit = pnorm, cloglog = function(q) exp(-exp(-q)), cauchit = pcauchy) zeta <- parameters[-(1:ncol(model$x))] draws <- sapply(eta, FUN = function(x) { which(rmultinom(1, 1, diff(c(0,pfun(zeta - x),1))) == 1) }) } else if(y@imputation_method == "pmm") { parameters <- c(coef(model), model$zeta) eta <- model$x %*% parameters[1:ncol(model$x)] pmm <- .pmm(y, eta) draws <- pmm[,1] y@fitted[y@which_drawn,] <- y@fitted[y@which_obs,][pmm[,2],] } else if(y@imputation_method == "median") { predictions <- predict(model, type = "class") draws <- rep(floor(median(predictions[y@which_obs])), y@n_drawn) } else if(y@imputation_method == "mode") draws <- predict(model, type = "class")[y@which_drawn] else stop("'imputation_method' not recognized") y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setOldClass("multinom") setMethod("mi", signature(y = "unordered-categorical", model = "multinom"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(t(coef(model)), ev) if (ncol(model.matrix(model)) != nrow(parameters)) parameters <- t(parameters) eta <- model.matrix(model) %*% parameters if(y@imputation_method == "ppd") { exp_eta <- matrix(pmin(.Machine$double.xmax / ncol(eta), cbind(1, exp(eta[y@which_drawn,,drop = FALSE]))), ncol = ncol(eta) + 1) denom <- rowSums(exp_eta) Pr <- exp_eta / denom if (y@use_NA) { Pr <- Pr[,-1]/rowSums(Pr[,-1]) badrows <- apply(is.na(Pr), 1, all) if(any(badrows)) { warning("Some rows of Pr are all 0 after dropping the missingness category") Pr[badrows,] <- 1/(ncol(Pr) - 1) } } draws <- apply(Pr, 1, FUN = function(p) which(rmultinom(1, 1, p) == 1)) } else if(y@imputation_method == "pmm"){ pmm <- .pmm(y, eta) draws <- pmm[,1] y@fitted[y@which_drawn,,drop=FALSE] <- y@fitted[y@which_obs,,drop=FALSE][pmm[,2]] } else if(y@imputation_method == "mode") draws <- predict(model, type = "class")[y@which_drawn] else stop("'imputation_method' not recognized") y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setOldClass("RNL") setMethod("mi", signature(y = "unordered-categorical", model = "RNL"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(y@imputation_method == "ppd") { # imputating from the posterior predictive distribution Pr <- sapply(model, FUN = function(m) { ev <- eigen(vcov(m), symmetric = TRUE) parameters <- .draw_parameters(coef(m), ev) eta <- m$x[y@which_drawn,,drop=FALSE] %*% parameters pred <- m$family$linkinv(eta) return(pred) }) if(y@use_NA) { Pr <- Pr[,-1]/rowSums(Pr[,-1]) badrows <- apply(is.na(Pr), 1, all) if(any(badrows)) { warning("Some rows of Pr are all 0 after dropping the missingness category") Pr[badrows,] <- 1/(ncol(Pr) - 1) } } draws <- apply(Pr, 1, FUN = function(p) which(rmultinom(1, 1, p) == 1)) } else if(y@imputation_method == "pmm") { eta <- sapply(model, FUN = function(m) { ev <- eigen(vcov(m), symmetric = TRUE) parameters <- .draw_parameters(coef(m), ev) eta <- m$x %*% parameters return(eta) }) pmm <- .pmm(y, eta) draws <- pmm[,1] y@fitted[y@which_drawn,,drop=FALSE] <- y@fitted[y@which_obs,,drop=FALSE][pmm[,2]] } else stop("only ppd and pmm are supported imputation methods in the RNL case") y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setOldClass("glm") setMethod("mi", signature(y = "binary", model = "glm"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(y@imputation_method == "ppd") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) eta <- model$x[y@which_drawn,,drop=FALSE] %*% parameters pred <- model$family$linkinv(eta) draws <- rbinom(y@n_drawn, 1, pred) + 1L } else if(y@imputation_method == "pmm") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) eta <- model$x %*% parameters pmm <- .pmm(y, eta) draws <- pmm[,1] y@fitted[y@which_drawn] <- y@fitted[y@which_obs][pmm[,2]] } else if(y@imputation_method == "median") { predictions <- predict(model, type = "class") draws <- rep(floor(median(predictions[y@which_obs])), y@n_drawn) } else if(y@imputation_method == "mode") draws <- predict(model, type = "class")[y@which_drawn] else if(y@imputation_method == "mean") stop("'mean' is not a supported 'imputation_method' for binary variables") else if(y@imputation_method == "expectation") stop("'expectation' is not a supported 'imputation_method' for binary variables") else stop("'imputation_method' not recognized") draws <- as.integer(draws) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setOldClass("clogit") setMethod("mi", signature(y = "grouped-binary", model = "clogit"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") # reconstruc the strata Terms <- model$terms temp <- untangle.specials(Terms, "strata") mf <- model.frame(model) strata <- strata(mf[, temp$vars], shortlabel = TRUE) if(y@imputation_method == "pmm") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) eta <- model$x %*% parameters draws <- .pmm(y, eta, strata = strata)[,1] #FIXME: haven't adjusted fitted values } else stop("only 'pmm' is supported for 'grouped-binary' variables") draws <- as.integer(draws) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setMethod("mi", signature(y = "interval", model = "glm"), def = function(y, model, s, ...) { stop("FIXME: write this method") if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(y@imputation_method == "ppd") { stop("FIXME") } else stop("only ppd is supported as an imputation method for interval variables") return(y) }) setMethod("mi", signature(y = "categorical", model = "matrix"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(y@imputation_method != "ppd") stop("only ppd is supported in this case") if(nrow(model) != y@n_drawn) stop("matrix of probabilities has the wrong number of rows") draws <- apply(model, 1, FUN = function(p) which(rmultinom(1, 1, p) == 1)) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) ## helper function .mi_continuous <- function(y, model) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(y@imputation_method == "ppd") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) if(model$family$family == "gaussian") { eta <- model$x[y@which_drawn,,drop=FALSE] %*% parameters pred <- model$family$linkinv(eta) if(is(y, "bounded-continuous")) { a <- if(length(y@lower) > 1) y@lower[y@which_drawn] else y@lower b <- if(length(y@upper) > 1) y@upper[y@which_drawn] else y@upper draws <- truncnorm::rtruncnorm(y@n_drawn, mean = pred, sd = sqrt(model$dispersion), a = a, b = b) } else draws <- rnorm(y@n_drawn, pred, sqrt(model$dispersion)) } else { eta <- model$x %*% parameters model$fitted <- model$family$linkinv(eta) # model$dispersion <- parameters@sigma^2 draws <- y@family$sim(model, nsim = 1)[y@which_drawn] } } else if(y@imputation_method == "pmm") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) if(is(y, "semi-continuous")) { eta <- rep(NA_real_, y@n_total) mark <- complete(y@indicator, 0L) == 0 eta[mark] <- model$x[mark,] %*% parameters } else eta <- model$x %*% parameters draws <- .pmm(y, eta)[,1] #FIXME: haven't adjusted fitted values using pmm for continuous } else if(y@imputation_method == "mean") { eta <- predict(model, type = "response") eta_observed <- eta[y@which_obs] eta_mean <- mean(eta_observed) draws <- rep(eta_mean, y@n_drawn) } else if(y@imputation_method == "median") { eta <- predict(model, type = "response") eta_observed <- eta[y@which_obs] eta_median <- median(eta_observed) draws <- rep(eta_median, y@n_drawn) } else if(y@imputation_method == "expectation") draws <- predict(model, type = "response")[y@which_drawn] else stop("'imputation_method' not recognized") return(draws) } setMethod("mi", signature(y = "continuous", model = "glm"), def = function(y, model, s, ...) { draws <- .mi_continuous(y, model) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) # setMethod("mi", signature(y = "censored-continuous", model = "glm"), def = # function(y, model, s, ...) { # not_obs <- c(y@which_drawn, y@which_censored) # if(y@imputation_method == "ppd") { # parameters <- arm::sim(model, 1) # eta <- model$x[not_obs,,drop=FALSE] %*% parameters@coef[1,] # pred <- model$family$linkinv(eta) # draws <- rnorm(y@n_drawn, pred, parameters@sigma) # } # else if(y@imputation_method == "pmm") { # eta <- predict(model, type = "link") # eta_observed <- eta[y@which_obs] # y_observed <- y@data[y@which_obs] # draws <- sapply(eta[nob_obs], FUN = function(x) { # mark <- which.min(abs(eta_observed - x)) # return(y_observed[mark]) # }) # } # else if(y@imputation_method == "mean") { # eta <- predict(model, type = "response") # eta_observed <- eta[y@which_obs] # eta_mean <- mean(eta_observed) # draws <- rep(eta_mean, length(not_obs)) # } # else if(y@imputation_method == "median") { # eta <- predict(model, type = "response") # eta_observed <- eta[y@which_obs] # eta_median <- median(eta_observed) # draws <- rep(floor(eta_median), length(not_obs)) # } # else if(y@imputation_method == "expectation") draws <- predict(model, type = "response")[not_obs] # else stop("'imputation_method' not recognized") # # y@data[not_obs] <- draws # y@imputations[s,] <- draws # return(y) # }) setMethod("mi", signature(y = "semi-continuous", model = "glm"), def = function(y, model, s, ...) { stop("the semi-continuous mi() method should not have been called") }) setMethod("mi", signature(y = "nonnegative-continuous", model = "glm"), def = function(y, model, s, ...) { draws <- .mi_continuous(y, model) # now account for the fact that some draws were determined to be 0 in step 1 mark <- which(complete(y@indicator, 0L)[y@which_miss] == 1) if(length(mark)) draws[mark] <- y@transformation(rep(0, length(mark))) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) ## helper function .mi_proportion <- function(y, model) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(!is.element(y@imputation_method, c("ppd", "pmm"))) badHessian <- FALSE else if(is.null(model$vcov)) badHessian <- FALSE else if(!all(is.finite(model$vcov))) badHessian <- TRUE else { ev <- eigen(vcov(model), TRUE) badHessian <- any(ev$values <= 0) means <- coef(model) parameters <- .draw_parameters(means, ev) # while(!badHessian && parameters[length(parameters)] <= 0) { # parameters <- .draw_parameters(means, ev) # } } if(badHessian && y@imputation_method == "ppd") { warning(paste("predictive mean matching used for", y@variable_name, "as a fallback due to Hessian error")) old_method <- y@imputation_method y@imputation_method <- "pmm" y <- mi(y, model) return(y@data[y@which_miss]) } else if(y@imputation_method == "ppd") { eta <- model$x[y@which_drawn,,drop=FALSE] %*% parameters[1:NCOL(model$x)] mu <- model$link$mean$linkinv(eta) phi <- model$link$precision$linkinv(parameters[length(parameters)]) ## FIXME: in the parameterized case shape1 <- mu * phi shape2 <- phi - shape1 draws <- rbeta(y@n_drawn, shape1, shape2) } else if(y@imputation_method == "pmm") { eta <- model$x %*% parameters[-length(parameters)] draws <- .pmm(y, eta)[,1] #FIXME: haven't adjusted fitted values for pmm } else if(y@imputation_method == "mean") { mu <- predict(model) mu_observed <- mu[y@which_obs] mu_mean <- mean(mu_observed) draws <- rep(mu_mean, y@n_drawn) } else if(y@imputation_method == "median") { mu <- predict(model) mu_observed <- mu[y@which_obs] mu_median <- median(mu_observed) draws <- rep(mu_median, y@n_drawn) } else if(y@imputation_method == "expectation") draws <- predict(model)[y@which_drawn] else stop("'imputation_method' not recognized") return(draws) } setOldClass("betareg") setMethod("mi", signature(y = "proportion", model = "betareg"), def = function(y, model, s, ...) { draws <- .mi_proportion(y, model) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setMethod("mi", signature(y = "proportion", model = "glm"), def = function(y, model, s, ...) { draws <- .mi_continuous(y, model) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setMethod("mi", signature(y = "SC_proportion", model = "betareg"), def = function(y, model, s, ...) { draws <- .mi_proportion(y, model) n <- y@n_total if(is(y@indicator, "binary")) { mark <- which(complete(y@indicator, 0L)[y@which_miss] == 1) if(any(y@raw_data == 0, na.rm = TRUE)) draws[mark] <- .5 / n else draws[mark] <- (n - .5) / n } else { signs <- complete(y@indicator, 0L)[y@which_drawn] draws[signs < 0] <- .5 / n draws[signs > 0] <- (n - .5) / n } y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) ## draw from overdispersed Poisson distribution .rpois.od <- function(n, lambda, dispersion = 1) { if (dispersion <= 1) ans <- rpois(n, lambda) else { B <- 1/(dispersion-1) A <- lambda * B ans <- rnbinom(n, size= A , mu = lambda) } return(ans) } setMethod("mi", signature(y = "count", model = "glm"), def = function(y, model, s, ...) { if(y@n_drawn == 0) stop("'impute' should not have been called because there are no missing data") if(y@imputation_method == "ppd") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) eta <- model$x[y@which_drawn,,drop=FALSE] %*% parameters pred <- model$family$linkinv(eta) draws <- .rpois.od(y@n_drawn, pred, model$dispersion) } else if(y@imputation_method == "pmm") { ev <- eigen(vcov(model), symmetric = TRUE) parameters <- .draw_parameters(coef(model), ev) eta <- model$x %*% parameters draws <- .pmm(y, eta)[,1] #FIXME: haven't adjusted fitted values for pmm } else if(y@imputation_method == "mean") { eta <- predict(model, type = "response") eta_observed <- eta[y@which_obs] eta_mean <- mean(eta_observed) draws <- rep(round(eta_mean), y@n_drawn) } else if(y@imputation_method == "median") { eta <- predict(model, type = "response") eta_observed <- eta[y@which_obs] eta_median <- median(eta_observed) draws <- rep(floor(eta_median), y@n_drawn) } else if(y@imputation_method == "expectation") draws <- round(predict(model, type = "response")[y@which_drawn]) else stop("'imputation_method' not recognized") draws <- as.integer(draws) y@data[y@which_drawn] <- draws y@imputations[s,] <- draws return(y) }) setMethod("mi", signature(y = "irrelevant", model = "ANY"), def = function(y, model, ...) { stop("The mi() method should not have been called on an 'irrelevant' variable") }) ## FIXME: account for the other stuff at the bottom of the original mi.R file r-cran-mi-1.0/R/misc.R000066400000000000000000000273441275731226000144350ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## like sapply but for objects of mi class mipply <- ## FIXME: should probably be a generic function instead of poor man's S4 function(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, columnwise = TRUE, to.matrix = FALSE) { if(is(X, "mi_list")) { out <- lapply(X, mipply, ..., simplify = simplify, USE.NAMES = USE.NAMES, columnwise = columnwise, to.matrix = to.matrix) } else if(is(X, "mi")) { X <- complete(X, to_matrix = to.matrix) if(columnwise) out <- sapply(X, FUN = function(x) apply(x, 2, FUN, ...), simplify = simplify, USE.NAMES = USE.NAMES) else out <- sapply(X, FUN, ..., simplify = simplify, USE.NAMES = USE.NAMES) } else if(is(X, "mdf_list")) { out <- lapply(X, mipply, ..., simplify = simplify, USE.NAMES = USE.NAMES, columnwise = columnwise, to.matrix = to.matrix) } else if(is(X, "missing_data.frame")) { if(columnwise) out <- sapply(X, FUN = function(x) apply(x, 2, FUN, ...), simplify = simplify, USE.NAMES = USE.NAMES) else out <- sapply(X, FUN, ..., simplify = simplify, USE.NAMES = USE.NAMES) } else if(is(X, "missing_variable")) { out <- FUN(X@data, ...) } else if(is(X, "mi_list")) { out <- lapply(X, FUN = mipply, ..., simplify = simplify, USE.NAMES = USE.NAMES, columnwise = columnwise, to.matrix = to.matrix) } else stop("'X' must be of class 'mi', 'missing_data.frame', 'missing_variable', or 'mi_list'") return(out) } ## create a bugs array from an mi object mi2BUGS <- function(imputations, statistic = c("moments", "imputations", "parameters")) { if(is(imputations, "mi_list")) return(lapply(imputations, FUN = mi2BUGS, statistic = statistic)) else if(!is(imputations, "mi")) stop("imputations must be an object of class 'mi' or 'mi_list'") statistic <- match.arg(statistic) if(statistic == "moments") { iterations <- sum(imputations@total_iters) mark <- !imputations@data[[1]]@no_missing & !sapply(imputations@data[[1]]@variables, is, class2 = "irrelevant") means <- lapply(1:iterations, FUN = function(m) { matrices <- lapply(imputations@data, FUN = complete, m = m, to_matrix = TRUE, include_missing = FALSE) out <- sapply(matrices, colMeans)[mark,,drop = FALSE] return(out) }) sds <- lapply(1:iterations, FUN = function(m) { matrices <- lapply(imputations@data, FUN = complete, m = m, to_matrix = TRUE, include_missing = FALSE) out <- sapply(matrices, FUN = function(x) apply(x, 2, sd))[mark,,drop = FALSE] return(out) }) dims <- dim(means[[1]]) arr <- array(NA_real_, c(iterations, dims[2], 2 * dims[1]), list(NULL, NULL, c(paste("mean", rownames(means[[1]]), sep = "_"), paste("sd", rownames(means[[1]]), sep = "_")))) for(i in seq_along(means)) for(j in 1:ncol(arr)) { arr[i,j, 1:dims[1]] <- means[[i]][,j] arr[i,j,-c(1:dims[1])] <- sds[[i]][,j] } } else if(statistic == "imputations") { imp_list <- lapply(imputations@data, function(x) lapply(x@variables, function(y) y@imputations)) n.parameters <- rapply(imp_list, ncol) arr <- array(NA_real_, c(sum(imputations@total_iters), length(imp_list), n.parameters)) ## FIXME: names? for(i in seq_along(imp_list)) arr[,i,] <- unlist(imp_list[[i]]) } else arr <- get_parameters(imputations) return(arr) # compatible with R2WinBUGS } ##Outputs completed data in either Stata (.dta) format or comma-separated (.csv) format mi2stata <- function(imputations, m, file, missing.ind=FALSE, ...) { if(grepl("\\.csv$", file)) type <- "csv" if(grepl("\\.dta$", file)) type <- "dta" else if(!is(imputations, "mi")) stop("imputations must be an object of class 'mi'") else if(!is(file, "character")) stop("filename must be specified as a character object") else if(type!="dta" & type!="csv") stop("file type must be 'dta' for stata format or 'csv' for comma-separated format") message("Note: after loading the data into Stata, version 11 or later, type 'mi import ice' to register the data as being multiply imputed. For Stata 10 and earlier, install MIM by typing 'findit mim' and include 'mim:' as a prefix for any command using the MI data.") unpos <- sum(sapply(imputations@data[[1]]@variables, FUN=function(x){x@n_unpossible})) if (unpos>0 & !missing.ind) { missing.ind <- TRUE warning("There are legitimately skipped values in the data that were not imputed. Including variables to indicate which missing values were imputed. Values which are still missing but are not indicated are legitimate skips.") } if (unpos>0 & missing.ind) { warning("There are legitimately skipped values in the data that were not imputed. Values which are still missing but are not indicated are legitimate skips.") } data.list <- complete(imputations, m) if (missing.ind) miss.indic <- data.list[[1]][,which(!is.element(colnames(data.list[[1]]), names(imputations@data[[1]]@variables)))] vars <- which(is.element(colnames(data.list[[1]]), names(imputations@data[[1]]@variables))) stata.data <- data.list[[1]][,vars] stata.miss <- sapply(imputations@data[[1]]@variables, FUN=function(x){ v <- is.element(1:x@n_total, x@which_drawn) return(v) }, simplify=TRUE) is.na(stata.data) <- stata.miss if (missing.ind) stata.data <- cbind(stata.data, miss.indic) stata.data$mi <- 1:nrow(stata.data); stata.data$mj <- 0 for(i in seq_along(data.list)){ dl <- data.list[[i]] if(!missing.ind) dl <- dl[,vars] dl$mi <- 1:nrow(dl) dl$mj <- i stata.data <- rbind(stata.data, dl) } colnames(stata.data)[which(colnames(stata.data)=="mi")] <- "_mi" colnames(stata.data)[which(colnames(stata.data)=="mj")] <- "_mj" if(type=="dta") foreign::write.dta(stata.data, file=file, version = 7L, ...) else if(type=="csv") write.table(stata.data, file=file, sep=",", col.names=TRUE, row.names=FALSE) } ## Returns the Gelman statistic Rhats <- function(imputations, statistic = c("moments", "imputations", "parameters")) { BUGS <- mi2BUGS(imputations, statistic) make_Rhat <- function(x) { m <- ncol(x) if(m < 2) stop("need at least 2 chains to calculate an R-hat") iter <- nrow(x) xbars <- colMeans(x) variances <- apply(x, MARGIN = 2:3, FUN = sd)^2 W <- colMeans(variances) B <- iter * apply(xbars, MARGIN = 2, FUN = var) R <- sqrt( (iter - 1) / iter + 1 / iter * B / W ) return(R) } if(is(imputations, "mi")) return(make_Rhat(BUGS)) else return(sapply(BUGS, FUN = make_Rhat)) } ## tests whether a method is the one defined in my (as opposed to a user-defined method in .GlobalEnv) is.method_in_mi <- function(generic, ...) { method <- selectMethod(generic, signature(...)) return(environmentName(environment(method@.Data)) == "mi") } ## cube root transformation .cuberoot <- function(y, inverse = FALSE) { if(inverse) y^3 else y^(1/3) } .parse_trans <- function(trans) { if(identical(names(formals(trans)), c("y", "mean", "sd", "inverse"))) return("standardize") if(identical(names(formals(trans)), c("y", "a", "inverse"))) return("logshift") if(identical(body(trans), body(.squeeze_transform))) return("squeeze") if(identical(body(trans), body(.identity_transform))) return("identity") if(identical(body(trans), body(log))) return("log") if(identical(body(trans), body(sqrt))) return("sqrt") if(identical(body(trans), body(.cuberoot))) return("cuberoot") if(identical(body(trans), body(qnorm))) return("qnorm") return("user-defined") } .prune <- function(class) { classes <- names(getClass(class, where = "mi")@subclasses) classes <- classes[!sapply(classes, isVirtualClass, where = "mi")] if(!isVirtualClass(class, where = "mi")) classes <- c(class, classes) return(classes) } .possible_missing_variable <- function(y) { ## FIXME: update this function whenever you tweak the missing_variable tree mvs <- .prune("missing_variable") maybe <- rep(TRUE, length(mvs)) names(maybe) <- mvs if(is.factor(y)) y <- factor(y) # to drop unused levels vals <- unique(y) vals <- sort(vals[!is.na(vals)]) if(length(vals) == 1) { maybe[] <- FALSE maybe["irrelevant"] <- TRUE maybe[.prune("fixed")] <- TRUE return(maybe) } else maybe[.prune("fixed")] <- FALSE if(!all(table(y) > 1)) maybe[.prune("categorical")] <- FALSE if(length(vals) == 2) { # permit binary plus children but not other kinds of categorical maybe[.prune("categorical")] <- FALSE maybe[.prune("binary")] <- TRUE maybe[.prune("semi-continuous")] <- FALSE } else { maybe[.prune("binary")] <- FALSE } if(!is.numeric(vals)) { maybe[.prune("continuous")] <- FALSE maybe[.prune("count")] <- FALSE return(maybe) } if(any(vals < 0)) { maybe[.prune("nonnegative-continuous")] <- FALSE maybe[.prune("positive-continuous")] <- FALSE maybe[.prune("count")] <- FALSE return(maybe) } if(any(vals == 0)) maybe[.prune("positive-continuous")] <- FALSE else maybe[.prune("nonnegative-continuous")] <- FALSE # unless SC_proportion if(!any(vals < 1 && vals > 0)) { maybe[.prune("SC_proportion")] <- FALSE maybe[.prune("proportion")] <- FALSE } else if(any(vals >= 1)) { maybe[.prune("proportion")] <- FALSE if(any(vals > 1)) maybe[.prune("SC_proportion")] <- FALSE else maybe[.prune("SC_proportion")] <- TRUE } if(any(vals != as.integer(vals))) { maybe[.prune("count")] <- FALSE maybe[.prune("categorical")] <- FALSE } return(maybe) } .cat2dummies <- function(y) { if(!is(y, "categorical")) stop("must be a categorical variable") if(is(y, "binary")) out <- as.matrix(as.integer(y@data == 1)) else { levels <- sort(unique(y@data)) out <- t(sapply(y@data, FUN = function(x) as.integer(x == levels)[-1])) } return(out) } setMethod("fitted", signature(object = "RNL"), def = function(object, ...) { Pr <- sapply(object, FUN = function(m) { eta <- m$x %*% coef(m) pred <- m$family$linkinv(eta) return(pred) }) Pr <- Pr / rowSums(Pr) return(Pr) }) setMethod("fitted", signature(object = "clogit"), def = function(object, ...) { target <- mean(as.numeric(object$y)) lp <- object$linear.predictors foo <- function(par) { intercept <- qlogis(par) mean(plogis(intercept + lp)) - target } opt <- uniroot(foo, lower = 0, upper = 1) return(plogis(qlogis(opt$root) + lp)) }) # Borrowed from library(MCMCpack) .rdirichlet <- function(n, alpha) { l <- length(alpha) x <- matrix(rgamma(l * n, alpha), ncol = l, byrow = TRUE) sm <- rowSums(x) return(x / sm) } r-cran-mi-1.0/R/missing_data.frame.R000066400000000000000000000163601275731226000172310ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. setMethod("missing_data.frame", signature(y = "data.frame"), def = function(y, subclass = NA_character_, by = NULL, types = NULL, favor_ordered = TRUE, favor_positive = FALSE, threshold = 5, ...) { if(!is.na(subclass) && subclass == "allcategorical") threshold <- Inf if(!is.null(by)) { mdfs <- by(y, lapply(by, FUN = function(b) y[,b]), FUN = function(d) { missing_data.frame(d, favor_ordered = favor_ordered, threshold = threshold, favor_positive = favor_positive, subclass = subclass, types = types, ...) }) class(mdfs) <- "mdf_list" return(mdfs) } variables <- vector("list", length = ncol(y)) if(is.null(types)) for(i in seq_along(variables)) { variables[[i]] <- missing_variable(y[,i], favor_ordered = favor_ordered, favor_positive = favor_positive, variable_name = colnames(y)[i], threshold = threshold) } else for(i in seq_along(variables)) { variables[[i]] <- new(types[i], variable_name = colnames(y)[i], raw_data = y[,i]) } if(is.na(subclass)) new("missing_data.frame", variables = variables, DIMNAMES = dimnames(y), ...) else new(paste(subclass, "missing_data.frame", sep = "_"), variables = variables, DIMNAMES = dimnames(y), ...) } ) setMethod("missing_data.frame", signature(y = "matrix"), def = function(y, ...) { return(missing_data.frame(y = as.data.frame(y), ...)) } ) setMethod("missing_data.frame", signature(y = "list"), def = function(y, ...) { if(!all(sapply(y, is, class2 = "missing_variable"))) { stop("all list elements must inherit from the 'missing_variable' class") } return(new("missing_data.frame", variables = y)) } ) setAs(from = "data.frame", to = "missing_data.frame", def = function(from) { missing_data.frame(from) } ) setAs(from = "matrix", to = "missing_data.frame", def = function(from) { missing_data.frame(as.data.frame(from)) } ) setAs(from = "missing_data.frame", to = "data.frame", def = function(from) { return(complete(from, m = 0L)) } ) setAs(from = "missing_data.frame", to = "matrix", def = function(from) { return(complete(from, m = 0L, to_matrix = TRUE)) } ) ## FIXME: Probably need to add a boatload of methods to mimic the behavior of data.frames .default_model <- function(y, data) { if(is(data, "allcategorical_missing_data.frame")) return("Gibbs") if(y@all_obs) { if(is(y, "semi-continuous")) return(rep(NA_character_, 2)) else return(NA_character_) } if(is(y, "irrelevant")) return(NA_character_) if(y@imputation_method == "mcar") return(NA_character_) if(!is.method_in_mi("fit_model", y = class(y), data = class(data))) { if(is(y, "semi-continuous")) return(rep("user-defined", 2)) else return("user-defined") } fam <- y@family$family link <- y@family$link if(is(y, "count")) { if(fam == "quasipoisson" && link == "log") return("qpoisson") else if(fam == "poisson" && link == "log") return("poisson") else return("****") } else if(is(y, "binary")) { if(is(y, "grouped-binary")) return("clogit") if(fam == "quasibinomial") return(paste("q", link, sep = "")) else if(fam == "binomial") return(link) else return("****") } else if(is(y, "interval")) return("survreg") else if(is(y, "ordered-categorical")) return(paste("o", link, sep = "")) else if(is(y, "unordered-categorical")) { if(fam == "binomial") out <- "RN" else out <- "m" return(paste(out, link, sep = "")) } else if(is(y, "proportion")) return(if(fam == "gaussian") "linear" else "betareg") else if(is(y, "SC_proportion")) { out <- .default_model(y@indicator, data) return(c("betareg", out)) } else if(is(y, "semi-continuous")) { out <- .default_model(y@indicator, data) if(fam == "gaussian") { if(link == "identity") return(c("linear", out)) else if(link == "log") return(c("loglinear", out)) else if(link == "inverse") return(c("inverselinear", out)) else return(c("****", out)) } else if(fam == "Gamma") return(c("****", out)) else if(fam == "inverse.gaussian") return(c("****", out)) else if(fam == "quasi") return(c("quasi", out)) else return(c("****", out)) } else if(is(y, "continuous")) { if(fam == "gaussian") { if(link == "identity") return("linear") else if(link == "log") return("loglinear") else if(link == "inverse") return("inverselinear") else return("****") } else if(fam == "Gamma") return("****") else if(fam == "inverse.gaussian") return("****") else if(fam == "quasi") return("quasi") else return("****") } else return("user-defined") } setMethod("show", "missing_data.frame", def = function(object) { k <- object@DIM[2] df <- .show_helper(object@variables[[1]]) for(i in 2:k) { df <- rbind(df, .show_helper(object@variables[[i]])) } df1 <- cbind(df[,1:3], model = unlist(sapply(object@variables, FUN = function(y) .default_model(y, object)))) if(is(object, "experiment_missing_data.frame")) df1$concept[names(object@concept)] <- object@concept df2 <- df[,-c(1:3)] cat("Object of class", class(object), "with", nrow(object), "observations on", ncol(object), "variables\n") if(length(object@patterns)) { npatterns <- nlevels(object@patterns) cat("\nThere are", npatterns, "missing data patterns\n") # print(table(as.integer(object@patterns))) # mat <- as.matrix(levels(object@patterns)) # colnames(mat) <- "missing" # print(mat) cat("\nAppend '@patterns' to this", class(object), "to access the corresponding pattern for every observation or perhaps use table()\n\n") } print(df1) cat("\n") print(df2) if(any(df1$model == "****", na.rm = TRUE)) { cat("\n**** The model lacks a widely-recognized name but is determined by the chosen type, family, and link.\n") } return(invisible(NULL)) }) setMethod("show", "mdf_list", def = function(object) { for(i in seq_along(object)) { cat("\n", names(object)[i], "\n") show(object[[i]]) } return(invisible(NULL)) }) setMethod("summary", "missing_data.frame", def = function(object) { summary(as.data.frame(object)) }) r-cran-mi-1.0/R/missing_variable.R000066400000000000000000000151411275731226000170100ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. .guess_type <- function(y, favor_ordered = TRUE, favor_positive = FALSE, threshold = 5, variable_name = deparse(substitute(y))) { if(!is.null(dim(y))) stop(paste(variable_name, ": must be a vector")) if(is.factor(y)) y <- factor(y) # to drop unused levels values <- unique(y) values <- sort(values[!is.na(values)]) len <- length(values) if(len == 0) { warning(paste(variable_name, ": cannot infer variable type when all values are NA, guessing 'irrelevant'")) type <- "irrelevant" } else if(len == 1) type <- "fixed" else if(grepl("^[[:punct:]]", variable_name)) type <- "irrelevant" else if(identical("id", tolower(variable_name))) type <- "irrelevant" else if(len == 2) { if(!is.numeric(values)) type <- "binary" else if(all(values == as.integer(values))) type <- "binary" else if(favor_positive) { if(all(values > 0)) type <- "positive-continuous" else if(all(values >= 0)) type <- "nonnegative-continuous" else type <- "continuous" } else type <- "continuous" } else if(is.ts(y)) { if(favor_positive) { if(all(values > 0)) type <- "positive-continuous" else if(all(values >= 0)) type <- "nonnegative-continuous" else type <- "continuous" } else type <- "continuous" } else if(is.ordered(y)) type <- "ordered-categorical" else if(is.factor(y)) type <- "unordered-categorical" else if(is.character(y)) type <- "unordered-categorical" else if(is.numeric(y)) { if(all(values >= 0) && all(values <= 1)) { if(any(values %in% 0:1)) type <- "SC_proportion" else type <- "proportion" } else if(len <= threshold && all(values == as.integer(values))) type <- if(favor_ordered) "ordered-categorical" else "unordered-categorical" else if(favor_positive) { if(all(values > 0)) type <- "positive-continuous" else if(all(values >= 0)) type <- "nonnegative-continuous" else type <- "continuous" } else type <- "continuous" } else stop(paste("cannot infer variable type for", variable_name)) return(type) } ## this constructor largely supplants typecast in previous versions of library(mi) setMethod("missing_variable", signature(y = "ANY", type = "missing"), def = function(y, favor_ordered = TRUE, favor_positive = FALSE, threshold = 5, variable_name = deparse(substitute(y))) { type <- .guess_type(y, favor_ordered, favor_positive, threshold, variable_name) return(missing_variable(y = y, type = type, variable_name = variable_name)) }) setMethod("missing_variable", signature(y = "ANY", type = "character"), def = function(y, type, variable_name = deparse(substitute(y)), ...) { return(new(type, raw_data = y, variable_name = variable_name, ...)) }) .show_helper <- function(object) { type <- class(object) missingness <- object@n_miss meth <- object@imputation_method if(object@n_miss) { if(is.character(object@family)) { fam <- object@family link <- NA_character_ } else { fam <- object@family$family link <- object@family$link } } else fam <- link <- NA_character_ if(is(object, "continuous")) trans <- .parse_trans(object@transformation) else trans <- NA_character_ df <- data.frame(type = type, missing = missingness, method = meth, family = fam, link = link, transformation = trans) rownames(df) <- object@variable_name if(is(object, "semi-continuous")) df <- rbind(df, .show_helper(object@indicator)) return(df) } setMethod("show", signature(object = "missing_variable"), def = function(object) { df <- .show_helper(object) print(df) }) ## setAs methods cause subtle problems with auto-coercion # setAs(from = "unordered-categorical", to = "ordered-categorical", def = # function(from) { # class(from) <- "ordered-categorical" # return(from) # }) # # setAs(from = "ordered-categorical", to = "unordered-categorical", def = # function(from) { # class(from) <- "unordered-categorical" # return(from) # }) # # setAs(from = "binary", to = "unordered-categorical", def = # function(from) { # stop("not possible or necessary to coerce from 'binary' to 'unordered-categorical'") # }) # setAs(from = "binary", to = "ordered-categorical", def = # function(from) { # stop("not possible or necessary to coerce from 'binary' to 'unordered-categorical'") # }) # setAs(from = "nonnegative-continuous", to = "continuous", def = # function(from) { # mean <- mean(from@raw_data, na.rm = TRUE) # sd <- sd(from@raw_data, na.rm = TRUE) # from@transformation <- .standardize_transform # formals(from@transformation)$mean <- mean # formals(from@transformation)$sd <- sd # from@inverse_transformation <- .standardize_transform # formals(from@inverse_transformation)$mean <- mean # formals(from@inverse_transformation)$sd <- sd # formals(from@inverse_transformation)$inverse <- TRUE # from@data <- from@transformation(from@raw_data) # class(from) <- "continuous" # return(from) # }) # # setAs(from = "continuous", to = "positive-continuous", def = # function(from) { # from@transformation <- log # from@inverse_transformation <- exp # from@data <- from@transformation(from@raw_data) # class(from) <- "positive-continuous" # validObject(from) # return(from) # }) # ## maybe add more methods ## NOTE: If you change something here, look also at the change_type.R file r-cran-mi-1.0/R/plot_methods.R000066400000000000000000000506221275731226000161760ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # Copyright (C) 2011 Douglas Bates and Martin Maechler # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. setMethod("image", "dgTMatrix", # slight hack of a method in the library(Matrix) function(x, xlim = .5 + c(0, di[2]), ylim = .5 + c(di[1], 0), aspect = "iso", ## was default "fill" sub = sprintf("Dimensions: %d x %d", di[1], di[2]), xlab = "Column", ylab = "Row", cuts = 15, useAbs = NULL, colorkey = !useAbs, col.regions = NULL, lwd = NULL, ...) { di <- x@Dim xx <- x@x if(missing(useAbs)) ## use abs() when all values are non-neg useAbs <- min(xx, na.rm=TRUE) >= 0 else if(useAbs) xx <- abs(xx) rx <- range(xx, finite=TRUE) if(is.null(col.regions)) col.regions <- if(useAbs) { grey(seq(from = 0.7, to = 0, length = 100)) } else { ## no abs(.), rx[1] < 0 nn <- 100 n0 <- min(nn, max(0, round((0 - rx[1])/(rx[2]-rx[1]) * nn))) col.regions <- c(colorRampPalette(c("blue3", "gray80"))(n0), colorRampPalette(c("gray75","red3"))(nn - n0)) } if(!is.null(lwd) && !(is.numeric(lwd) && all(lwd >= 0))) # allow lwd=0 stop("'lwd' must be NULL or non-negative numeric") lattice::levelplot(x@x ~ (x@j + 1L) * (x@i + 1L), sub = sub, xlab = xlab, ylab = ylab, xlim = xlim, ylim = ylim, aspect = aspect, colorkey = colorkey, col.regions = col.regions, cuts = cuts, # par.settings = list(background = list(col = "transparent")), panel = function(x, y, z, subscripts, at, ..., col.regions) { x <- as.numeric(x[subscripts]) y <- as.numeric(y[subscripts]) numcol <- length(at) - 1 num.r <- length(col.regions) col.regions <- if (num.r <= numcol) rep(col.regions, length = numcol) else col.regions[1+ ((1:numcol-1)*(num.r-1)) %/% (numcol-1)] zcol <- rep.int(NA_integer_, length(z)) for (i in seq_along(col.regions)) zcol[!is.na(x) & !is.na(y) & !is.na(z) & at[i] <= z & z < at[i+1]] <- i zcol <- zcol[subscripts] if (any(subscripts)) { if(is.null(lwd)) { wh <- grid::current.viewport()[c("width", "height")] ## wh : current viewport dimension in pixel wh <- c(grid::convertWidth(wh$width, "inches", valueOnly=TRUE), grid::convertHeight(wh$height, "inches", valueOnly=TRUE)) * par("cra") / par("cin") pSize <- wh/di ## size of one matrix-entry in pixels pA <- prod(pSize) # the "area" p1 <- min(pSize) lwd <- ## crude for now if(p1 < 2 || pA < 6) 0.01 # effectively 0 else if(p1 >= 4) 1 else if(p1 > 3) 0.5 else 0.2 } else stopifnot(is.numeric(lwd), all(lwd >= 0)) # allow 0 grid::grid.rect(x = x, y = y, width = 1, height = 1, default.units = "native", gp = grid::gpar(fill = ifelse(is.na(zcol), "black", col.regions[zcol]), lwd = lwd, col = if(lwd < .01) NA else NA)) } }, ...) }) setMethod("image", signature(x = "missing_data.frame"), def = function (x, y.order = FALSE, x.order = FALSE, clustered = TRUE, grayscale = FALSE, ...) { data <- lapply(x@variables, FUN = function(z) if(is(z, "irrelevant")) NULL else is.na(z) * 1) data <- as.matrix(as.data.frame(data[!sapply(data, is.null)])) index <- seq(nrow(data)) x.at <- 1:nrow( data ) x.lab <- index if( x.order ) { orderIndex <- order(colSums(data), decreasing = TRUE) sub <- "Ordered by number of missing items per variable" } if( y.order ) { orderIndex <- order(rowSums(data), decreasing = FALSE) index <- row.names( data ) sub <- "Ordered by number of missing items per observation" x.at <- NULL x.lab <- FALSE } if(clustered){ orderIndex <- order.dendrogram(as.dendrogram(hclust(dist(data, method = "binary"), method="mcquitty"))) sub <- "Clustered by missingness" } if(!grayscale) { data <- lapply(x@variables, FUN = function(z) { y <- z@data if(is(z, "irrelevant")) return(NULL) else if(is(z, "continuous")) return( (y - mean(y, na.rm = TRUE)) / (2 * sd(y, na.rm = TRUE)) ) else if(is(z, "count")) return( (y - mean(y, na.rm = TRUE)) / (2 * sd(y, na.rm = TRUE)) ) else if(is(z, "categorical")) { y <- z@data if(is(z, "binary")) { y <- y == max(y, na.rm = TRUE) return( (y - 0.5) * 2 ) } else { the_range <- seq(from = -.99, to = 1, length.out = length(unique(na.omit(y)))) return(the_range[as.integer(as.factor(y))]) } } else return( (y - mean(y, na.rm = TRUE)) / (2 * sd(y, na.rm = TRUE)) ) }) data <- as.matrix(as.data.frame(data[!sapply(data, is.null)])) } if(y.order) X <- Matrix(data[,orderIndex]) else X <- Matrix(data[orderIndex,]) if(grayscale) { plot(image(X, aspect = "fill", xlab = "Standardized Variable", ylab = "Observation Number", sub = sub, scales = list(x = list(at = 1:ncol(data), labels = colnames(data), rot = 90, abbreviate = TRUE, minlength = 8)), main = "Dark represents missing data", colorkey = FALSE, alpha.regions = 1, ...)) return(invisible(NULL)) } nn <- 100 rx <- range(X, finite = TRUE) n0 <- min(nn, max(0, round((0 - rx[1])/(rx[2]-rx[1]) * nn))) col.regions <- heat.colors(17) breaks <- seq(from = rx[1] - 1e-8, to = rx[2] + 1e-8, length.out = 16) plot(image(X, aspect = "fill", xlab = "Standardized Variable", ylab = "Observation Number", sub = sub, at = breaks, scales = list(x = list(at = 1:ncol(data), labels = colnames(data), rot = 90, abbreviate = TRUE, minlength = 8)), main = "Dark represents missing data", colorkey = TRUE, col.regions = col.regions, alpha.regions = 1, ...)) return(invisible(NULL)) }) setMethod("image", signature(x = "mdf_list"), def = function (x, y.order = FALSE, x.order = FALSE, clustered = TRUE, grayscale = FALSE, ask = TRUE, ...) { if (.Device != "null device") { oldask <- grDevices::devAskNewPage(ask = ask) if (!oldask) on.exit(grDevices::devAskNewPage(oldask), add = TRUE) op <- options(device.ask.default = ask) on.exit(options(op), add = TRUE) } sapply(x, FUN = image, y.order = y.order, x.order = x.order, clustered = clustered, grayscale = grayscale, ...) return(invisible(NULL)) }) setMethod("image", signature(x = "mi"), def = function (x, y.order = FALSE, x.order = FALSE, clustered = TRUE, ...) { data <- lapply(x@data[[1]]@variables, FUN = function(z) if(is(z, "irrelevant")) NULL else is.na(z) * 1) data <- as.matrix(as.data.frame(data[!sapply(data, is.null)])) if( x.order ) { orderIndex <- order(colSums(data), decreasing = TRUE) sub <- "Ordered by number of missing items per variable" } if( y.order ) { orderIndex <- order(rowSums(data), decreasing = FALSE) index <- row.names( data ) sub <- "Ordered by number of missing items per observation" } if(clustered){ orderIndex <- order.dendrogram(as.dendrogram(hclust(dist(data, method = "binary"), method="mcquitty"))) sub <- "Clustered by missingness" } foo <- function(z, raw = FALSE) { y <- if(raw) z@raw_data else z@data # y <- z@data if(is(z, "irrelevant")) return(NULL) else if(is(z, "continuous")) return( (y - mean(y, na.rm = TRUE)) / (2 * sd(y, na.rm = TRUE)) ) else if(is(z, "count")) return( (y - mean(y, na.rm = TRUE)) / (2 * sd(y, na.rm = TRUE)) ) else if(is(z, "categorical")) { y <- if(raw) as.numeric(z@raw_data) else z@data if(is(z, "binary")) { y <- y == max(y, na.rm = TRUE) return( (y - 0.5) * 2 ) } else { the_range <- seq(from = -.99, to = 1, length.out = length(unique(na.omit(y)))) return(the_range[as.integer(as.factor(y))]) } } else return( (y - mean(y, na.rm = TRUE)) / (2 * sd(y, na.rm = TRUE)) ) } temp <- lapply(x@data[[1]]@variables, FUN = foo) temp <- as.matrix(as.data.frame(temp[!sapply(temp, is.null)])) temp[data == 1] <- NA_real_ data <- temp if(y.order) data <- data[,orderIndex] else data <- data[orderIndex,] X0 <- Matrix(data) data <- 0 chains <- min(3, length(x@data)) for(i in seq_along(x@data)) { temp <- lapply(x@data[[i]]@variables, FUN = foo, raw = FALSE) temp <- as.matrix(as.data.frame(temp[!sapply(temp, is.null)])) data <- data + temp / chains } if(y.order) data <- data[,orderIndex] else data <- data[orderIndex,] X1 <- Matrix(data) X <- rbind2(X0, X1) breaks <- seq(from = min(X, na.rm = TRUE), to = max(X, na.rm = TRUE), length.out = 15) plot(image(X0, aspect = "fill", xlab = "", ylab = "Observation Number", sub = "", at = breaks, scales = list(x = list(at = 1:ncol(data), labels = colnames(data), rot = 90, abbreviate = TRUE, minlength = 5)), main = "Original data", colorkey = TRUE, col.regions = heat.colors(17), ...), split = c(1,1,1,2)) plot(image(X1, aspect = "fill", xlab = "", ylab = "Observation Number", sub = "", at = breaks, scales = list(x = list(at = 1:ncol(data), labels = colnames(data), rot = 90, abbreviate = TRUE, minlength = 5)), main = "Average completed data", colorkey = TRUE, col.regions = heat.colors(17), ...), newpage = FALSE, split = c(1,2,1,2)) return(invisible(NULL)) }) setMethod("image", signature(x = "mi_list"), def = function (x, y.order = FALSE, x.order = FALSE, clustered = TRUE, grayscale = FALSE, ask = TRUE, ...) { if (.Device != "null device") { oldask <- grDevices::devAskNewPage(ask = ask) if (!oldask) on.exit(grDevices::devAskNewPage(oldask), add = TRUE) op <- options(device.ask.default = ask) on.exit(options(op), add = TRUE) } sapply(x, FUN = image, y.order = y.order, x.order = x.order, clustered = clustered, grayscale = grayscale, ...) return(invisible(NULL)) }) .binnedplot <- function (x, y, nclass = NULL, xlab = "Expected Values", ylab = "Average residual", main = "", cex.pts = 0.8, col.pts = "blue", col.int = "gray") { n <- length(x) if (is.null(nclass)) { if (n >= 100) { nclass = floor(sqrt(length(x))) } if (n > 10 & n < 100) { nclass = 10 } if (n <= 10) { nclass = floor(n/2) } } aa <- data.frame(arm::binned.resids(x, y, nclass)$binned) # aa <- aa[!is.na(aa$X2se),] ## FIXME: remove once Yu-Sung fixes arm::binned.resids plot(range(aa$xbar), range(aa$ybar, aa$X2se, -aa$X2se), xlab = xlab, ylab = ylab, type = "n", main = main, mgp = c(2, 1, 0), tcl = .05) abline(0, 0, lty = 2) lines(aa$xbar, aa$X2se, col = col.int) lines(aa$xbar, -aa$X2se, col = col.int) points(aa$xbar, aa$ybar, pch = 19, cex = cex.pts, col = col.pts) } .binnedpoints <- function (x, y, nclass = NULL, cex.pts = 0.8, col.pts = "red") { n <- length(x) if (is.null(nclass)) { if (n >= 100) { nclass = floor(sqrt(length(x))) } if (n > 10 & n < 100) { nclass = 10 } if (n <= 10) { nclass = floor(n/2) } } if(n > 5) { aa <- data.frame(arm::binned.resids(x, y, nclass)$binned) points(aa$xbar, aa$ybar, pch = 19, cex = cex.pts, col = col.pts) } return(invisible(NULL)) } setMethod("plot", signature(x = "missing_data.frame", y = "missing_variable"), def = function(x, y, ...) { NAs <- is.na(y@raw_data) z <- y@data hist(y) yhat <- y@fitted the_range <- range(c(yhat, z)) plot(the_range, the_range, type = "n", xlab = "Expected Values", ylab = "Completed", mgp = c(2, 1, 0), tcl = .05) abline(0, 1, lty = 2, col = "lightgray") points(yhat, z, col = ifelse(NAs, "red", "blue"), pch = ".", cex = 2) lines(lowess(x = yhat[!NAs], y = z[!NAs]), col = "blue") .binnedplot(yhat[!NAs], (y@data - yhat)[!NAs]) .binnedpoints(yhat[NAs], (y@data - yhat)[NAs]) return(invisible(NULL)) }) setMethod("plot", signature(x = "missing_data.frame", y = "categorical"), def = function(x, y, ...) { NAs <- is.na(y@raw_data) z <- y@data hist(y) s <- nrow(y@parameters) + 1 yhat <- y@fitted if(length(yhat) == 0) { # embedded varname <- y@variable_name varname <- strsplit(varname, ":")[[1]][1] to_drop <- x@index[[varname]] X <- x@X[,-to_drop] s <- nrow(y@parameters) + 1 model <- fit_model(y, data = x, s = s, warn = TRUE, X = X) yhat <- fitted(model) } if(is.matrix(yhat)) yhat <- yhat %*% (1:ncol(yhat)) the_range <- range(c(yhat, z)) #+ c(-.1, .1) plot(the_range, the_range, type = "n", xlab = "Expected Values", ylab = "Completed (jittered)", mgp = c(2, 1, 0), tcl = .05) abline(0, 1, lty = 2, col = "lightgray") points(yhat, jitter(z), col = ifelse(NAs, "red", "blue"), pch = ".", cex = 2) .binnedplot(yhat[!NAs], (y@data - yhat)[!NAs]) .binnedpoints(yhat[NAs], (y@data - yhat)[NAs]) return(invisible(NULL)) }) setMethod("plot", signature(x = "missing_data.frame", y = "binary"), def = function(x, y, ...) { NAs <- is.na(y@raw_data) z <- y@data - 1L hist(y) s <- nrow(y@parameters) + 1 yhat <- y@fitted if(length(yhat) == 0) { # embedded varname <- y@variable_name varname <- strsplit(varname, ":")[[1]][1] to_drop <- x@index[[varname]] X <- x@X[,-to_drop] model <- fit_model(y = y, data = x, s = s, warn = TRUE, X = X) yhat <- fitted(model) } the_range <- range(c(yhat, z)) #+ c(-.1, .1) plot(the_range, the_range, type = "n", xlab = "Expected Values", ylab = "Completed (jittered)", mgp = c(2, 1, 0), tcl = .05) abline(0, 1, lty = 2, col = "lightgray") points(yhat, jitter(z), col = ifelse(NAs, "red", "blue"), pch = ".", cex = 2) .binnedplot(yhat[!NAs], (y@data - 1 - yhat)[!NAs]) .binnedpoints(yhat[NAs], (y@data - 1 - yhat)[NAs]) return(invisible(NULL)) }) setMethod("plot", signature(x = "allcategorical_missing_data.frame", y = "categorical"), def = function(x, y, ...) { NAs <- is.na(y@raw_data) z <- y@raw_data hist(y) latents <- x@latents@data yhat <- t(sapply(latents[!NAs], FUN = function(l) y@fitted[l,])) tab_obs <- table(z[!NAs]) tab_model <- table(apply(yhat, 1, FUN = function(p) which(rmultinom(1, 1, p) == 1))) the_range <- c(0, max(c(tab_obs, tab_model))) barplot(tab_obs, beside = TRUE, xlab = "Observed Values", ylim = the_range) names(tab_model) <- levels(z) barplot(tab_model, beside = TRUE, xlab = "Expected Values", ylim = the_range) return(invisible(NULL)) }) setMethod("plot", signature(x = "allcategorical_missing_data.frame", y = "binary"), def = function(x, y, ...) { NAs <- is.na(y@raw_data) z <- y@raw_data hist(y) latents <- x@latents@data yhat <- t(sapply(latents[!NAs], FUN = function(l) y@fitted[l,])) tab_obs <- table(z[!NAs]) tab_model <- table(apply(yhat, 1, FUN = function(p) which(rmultinom(1, 1, p) == 1))) the_range <- c(0, max(c(tab_obs, tab_model))) barplot(tab_obs, beside = TRUE, xlab = "Observed Values", ylim = the_range) names(tab_model) <- levels(z) barplot(tab_model, beside = TRUE, xlab = "Expected Values", ylim = the_range) return(invisible(NULL)) }) setMethod("plot", signature(x = "missing_data.frame", y = "semi-continuous"), def = function(x, y, ...) { NAs <- is.na(y@raw_data) z <- y@data hist(y) s <- nrow(y@parameters) + 1 yhat <- z yhat[complete(y@indicator, m = 0L, to_factor = TRUE) == 0] <- y@fitted #fitted(model) the_range <- range(c(yhat, z)) plot(the_range, the_range, type = "n", xlab = "Expected Values", ylab = "Completed", mgp = c(2, 1, 0), tcl = .05) abline(0, 1, lty = 2, col = "lightgray") points(yhat, z, col = ifelse(NAs, "red", "blue"), pch = ".", cex = 2) lines(lowess(x = yhat[!NAs], y = z[!NAs]), col = "blue") .binnedplot(yhat[!NAs], (y@data - yhat)[!NAs]) .binnedpoints(yhat[NAs], (y@data - yhat)[NAs]) return(invisible(NULL)) }) setMethod("plot", signature(x = "mi", y = "ANY"), def = function(x, y, ask = TRUE, header = character(0), ...) { if(missing(y)) select <- 1:ncol(x@data[[1]]) else if(is.logical(y)) select <- which(y) else if(is.character(y)) select <- which(colnames(x@data[[1]]) %in% y) else if(is.numeric(y)) select <- which(1:nrow(x@data[[1]]) %in% y) for(i in seq_along(x@data[[1]]@variables)) { if(x@data[[1]]@no_missing[i]) next else if(is(x@data[[1]]@variables[[i]], "irrelevant")) next else if(x@data[[1]]@variables[[i]]@imputation_method == "mcar") { warning(x@data[[1]]@variables[[i]]@variable_name, " not plotted because it assumes MCAR") next } if(!(i %in% select)) next l <- min(3, length(x@data)) if (.Device != "null device") { oldask <- grDevices::devAskNewPage(ask = ask) if (!oldask) on.exit(grDevices::devAskNewPage(oldask), add = TRUE) op <- options(device.ask.default = ask) on.exit(options(op), add = TRUE) } par(mfrow = c(l,3), mar = c(5,4,1,1) + .1) if(is(x@data[[1]]@variables[[i]], "semi-continuous")) { for(j in 1:l) plot(x@data[[j]], x@data[[j]]@variables[[i]]@indicator, ...) title(main = paste("\n", header, x@data[[1]]@variables[[i]]@indicator@variable_name, sep = ""), outer = TRUE) } for(j in 1:l) plot(x@data[[j]], x@data[[j]]@variables[[i]], ...) new_header <- paste(header, x@data[[1]]@variables[[i]]@variable_name) if(is(x@data[[1]]@variables[[i]], "continuous")) { trans <- .show_helper(x@data[[1]]@variables[[i]])$transformation[1] new_header <- paste("\n", new_header, " (", trans, ")", sep = "") } else new_header <- paste("\n", new_header, sep = "") title(main = new_header, outer = TRUE) } return(invisible(NULL)) }) setMethod("plot", signature(x = "mi_list", y = "ANY"), def = function(x, y, ask = TRUE, ...) { if(missing(y)) for(i in seq_along(x)) plot(x[[i]], ask = ask, header = paste(names(x)[i], ": ", sep = ""), ...) else for(i in seq_along(x)) plot(x[[i]], y = y, ask = ask, header = paste(names(x)[i], ": ", sep = ""), ...) return(invisible(NULL)) }) r-cran-mi-1.0/R/pool.R000066400000000000000000000206501275731226000144440ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. pool <- function(formula, data, m = NULL, FUN = NULL, ...) { if(is.list(data)) { if(all(sapply(data, is, class2 = "mi"))) { if(is.null(m)) m <- length(data[[1]]@data) else m <- as.integer(m) dfs <- complete(data[[1]], m = m, to_matrix = FALSE) l <- length(data) if(l > 1) for(i in 2:l) { temp <- complete(data[[l]], m = m, to_matrix = FALSE) for(j in seq_along(temp)) dfs[[j]] <- rbind(dfs[[j]], temp[[j]]) } data <- data[[1]] } else if(all(sapply(data, is.data.frame))) { dfs <- data m <- length(dfs) } else { stop("if 'data' is a list it must be a list of mi objects or data.frames") } } else if(is(data, "mi")) { if(is.null(m)) m <- length(data@data) else m <- as.integer(m) dfs <- complete(data, m = m, to_matrix = FALSE) } if(!is(formula, "formula")) stop("'formula' must be a formula") dots <- list(...) if(is.null(FUN)) { if(!is(data, "mi")) stop("if 'data' is not of class 'mi', 'FUN' must be specified") yname <- as.character(formula)[2] if(!(yname %in% colnames(data@data[[1]]))) { stop(paste("no variable called", yname, "possibly due to typo or transformation,", "in which case you need to specify 'FUN' explicitly")) } else y <- data@data[[1]]@variables[[yname]] if(!is.method_in_mi("fit_model", y = class(y), data = class(data@data[[1]]))) { stop(paste(yname, "seems to have a user-defined 'fit_model' method,", "in which case 'FUN' must be specified explicitly")) } if(is(y, "unordered-categorical")) { FUN <- nnet::multinom fit <- "multinom" } else if(is(y, "binary") | is(y, "count") | is(y, "continuous")) { FUN <- arm::bayesglm fit <- "bayesglm" if(!("family" %in% names(dots))) dots$family <- y@family } else if(is(y, "interval")) { FUN <- survival::survreg fit <- "survreg" } else if(is(y, "ordered-categorical")) { FUN <- arm::bayespolr fit <- "bayespolr" if(!("method" %in% names(dots))) dots$method <- if(y@family$link == "logit") "logistic" else y@family$link } } else if(!is(FUN, "function")) stop("'FUN' must be a function or NULL") else fit <- deparse(substitute(FUN)) models <- lapply(dfs, FUN = function(d) { dots$data <- d dots$formula <- formula do.call(FUN, args = dots) }) summaries <- lapply(models, summary) pooled_summary <- summaries[[1]] if(is.list(pooled_summary)) for(i in seq_along(pooled_summary)) { if(is.numeric(pooled_summary[[i]])) { num <- lapply(summaries, FUN = function(x) x[[i]]) if(is.matrix(pooled_summary[[i]])) { mat <- pooled_summary[[i]] arr <- array(unlist(num), dim = c(dim(mat), m)) arr <- apply(arr, 1:2, mean) colnames(arr) <- colnames(mat) rownames(arr) <- rownames(mat) pooled_summary[[i]] <- arr } else if(length(pooled_summary[[i]]) > 1) { arr <- rowMeans(matrix(unlist(num), ncol = m)) names(arr) <- names(pooled_summary[[i]]) pooled_summary[[i]] <- arr } else pooled_summary[[i]] <- mean(unlist(num)) } } else { pooled_summary <- list() warning("could not construct pooled_summary") } coefs <- sapply(models, get_parameters) variances <- sapply(models, FUN = function(x) diag(vcov(x))) W <- rowMeans(variances) B <- apply(coefs, 1, var) ses <- sqrt(W + B * (1 + 1/m)) if(is(pooled_summary, "summary.glm") | is(pooled_summary, "summary.polr")) { pooled_summary$call <- match.call() pooled_summary$coefficients[,1:2] <- cbind(rowMeans(coefs), ses) } else if(is(pooled_summary, "summary.multinom")) { pooled_summary$call <- match.call() pooled_summary$coefficients <- cbind(coef = rowMeans(coefs), ses, z = NA_real_, p = NA_real_) } else warning("pooled_summary is probably bogus") if(ncol(pooled_summary$coefficients) >= 3) { if(colnames(pooled_summary$coefficients)[3] == "t value") { pooled_summary$coefficients[,3] <- tvalue <- pooled_summary$coefficients[,1] / ses if(TRUE) { gamma <- (1 + 1/m) * B / ses^2 df.r <- pooled_summary$df.residual v <- (m - 1) * (1 + m/(m + 1) * W / B)^2 v_obs <- (1 - gamma) * (df.r + 1) / (df.r + 3) * df.r df.star <- 1/(1/v + 1/v_obs) if(ncol(pooled_summary$coefficients) == 4) { pooled_summary$coefficients[,4] <- 2 * pt(-abs(tvalue), df.star) } else pooled_summary$coefficients <- cbind(pooled_summary$coefficients, "p-value" = 2 * pt(-abs(tvalue), df.star)) } } else { pooled_summary$coefficients[,3] <- zvalue <- pooled_summary$coefficients[,1] / ses if(ncol(pooled_summary$coefficients) == 4) { pooled_summary$coefficients[,4] <- 2 * pnorm(-abs(zvalue)) } else pooled_summary$coefficients <- cbind(pooled_summary$coefficients, "p-value" = 2 * pnorm(-abs(zvalue)) ) } } kall <- match.call() kall[1] <- call(fit) out <- new("pooled", formula = formula, fit = fit, models = models, coefficients = rowMeans(coefs), ses = ses, pooled_summary = pooled_summary, call = kall) return(out) } setMethod("display", signature(object = "pooled"), def = function(object, digits = 2, ...) { call <- object@call summ <- summary(object) coef <- object@pooled_summary$coefficients[,1:2] colnames(coef) <- c("coef.est", "coef.se") n <- summ$df.residual k <- summ$df[1] k.intercepts <- length(summ$zeta) print(call) pfround(coef, digits) if(k.intercepts > 0) { cat(paste("n = ", n, ", k = ", k, " (including ", k.intercepts, " intercepts)\nresidual deviance = ", fround(summ$deviance, 1), ", null deviance is not computed by polr", "\n", sep = "")) return(invisible(NULL)) } cat(paste("n = ", n, ", k = ", k, "\nresidual deviance = ", fround(summ$deviance, 1), ", null deviance = ", fround(summ$null.deviance, 1), " (difference = ", fround(summ$null.deviance - summ$deviance, 1), ")", "\n", sep = "")) dispersion <- summ$dispersion if (dispersion != 1) { cat(paste("overdispersion parameter = ", fround(dispersion, 1), "\n", sep = "")) if (summ$family$family == "gaussian") { cat(paste("residual sd is sqrt(overdispersion) = ", fround(sqrt(dispersion), digits), "\n", sep = "")) } } return(invisible(NULL)) }) setMethod("show", signature(object = "pooled"), def = function(object) { display(object) return(invisible(NULL)) }) setMethod("summary", signature(object = "pooled"), def = function(object, ...) { return(object@pooled_summary) }) setMethod("coef", signature(object = "pooled"), def = function(object, ...) { return(object@coefficients) }) setMethod("vcov", signature(object = "pooled"), def = function(object, ...) { return(object@vcov) }) setMethod("residuals", signature(object = "pooled"), def = function(object, ...) { return(rowMeans(sapply(object@models, residuals))) }) setMethod("fitted", signature(object = "pooled"), def = function(object, ...) { return(rowMeans(sapply(object@models, fitted, ...))) }) r-cran-mi-1.0/R/random_df.R000066400000000000000000000576121275731226000154340ustar00rootroot00000000000000# Part of the mi package for multiple imputation of missing data # Copyright (C) 2008, 2009, 2010, 2011 Andrew Gelman # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ## Function to draw from the relevant symmetric generalized beta distribution .rgbeta <- function(num, shape) { if(shape > 0) -1 + 2 * rbeta(num, shape, shape) else if(shape == 0) -1 + 2 * rbinom(num, 1, 0.5) else stop("shape must be non-negative") } ## Function to draw a Cholesky factor of a random correlation matrix ## as a function of canonical partial correlations (CPCs) .rcorvine <- function(n_full, n_partial, n_cat, eta, restrictions, strong, experiment, treatment_cor, last_CPC) { nom <- !is.null(n_cat) n <- n_full + 2 * n_partial if(nom) { n <- n + sum(n_cat) - length(n_cat) nc <- 2 * n_partial + sum(n_cat) - length(n_cat) holder <- matrix(NA_real_, nrow = nc, ncol = nc) } else holder <- matrix(NA_real_, nrow = 2 * n_partial, ncol = 2 * n_partial) count <- 1 if(eta <= 0) stop("'eta' must be positive") alpha <- eta + (n - 2) / 2 # if eta == 1, then tcrossprod(L) is uniform over correlation matrices L <- matrix(NA_real_, n, n) L[upper.tri(L)] <- 0 L[1,1] <- 1 L <- .rcorvine_helper(L, holder, n_full, n_partial, n_cat, alpha, restrictions, strong, experiment, treatment_cor, nom, last_CPC) if(restrictions == "MARish") L <- .MAR_opt(L, n_full, n_partial) return(L) } .rcorvine_helper <- function(L, holder, n_full, n_partial, n_cat, alpha, restrictions, strong, experiment, treatment_cor, nom, last_CPC) { n <- nrow(L) mark <- is.na(L[,1]) sum_mark <- sum(mark) CPCs <- .rgbeta(sum(mark), alpha) count <- 1 if(experiment) { len <- length(treatment_cor) if(len == 1 && treatment_cor == 0) treatment_cor <- rep(0, sum_mark) else if(len != sum_mark) { stop(paste("length of 'treatment_cor' must be", sum_mark)) } treatment_mark <- is.na(treatment_cor) treatment_cor[treatment_mark] <- CPCs[treatment_mark] CPCs <- treatment_cor # treatment variable is first } if(n_full == 0 && restrictions != "none") { CPCs[sum_mark:(sum_mark - n_partial)] <- 0 holder[1,mark] <- CPCs count <- count + 1 } else if(n_full == 0) { holder[1,mark] <- CPCs count <- count + 1 } L[mark,1] <- CPCs W <- log(1 - CPCs^2) ## NOTE: order of variables is: ## all fully observed (with the treatment first if applicable) ## all partially observed but not nominal variables (if any) ## the components of the nominal variable(s) (if any) ## all missingness indicators # fully observed variables have arbitrary CPCs start <- 2 end <- n_full if(n_full >= 2) for(i in start:end) { L[i,i] <- exp(0.5 * W[i-1]) gap <- which(is.na(L[,i])) gap1 <- gap - 1 alpha <- alpha - 0.5 CPCs <- .rgbeta(length(gap), alpha) if(restrictions == "MCAR") CPCs[length(gap):(length(gap) - n_partial + 1)] <- 0 L[gap,i] <- CPCs * exp(0.5 * W[gap1]) W[gap1] <- W[gap1] + log(1 - CPCs^2) } # partially observed variables have arbitrary CPCs among themselves # but are conditionally uncorrelated with all missingness indicators under MAR # note: we condition on all fully observed variables and all previous partially observed variables # this triangle scheme implies that the errors when predicting the partially observed variables are uncorrelated if(n_full >= 2) start <- end + 1 end <- start + n_partial - 1 if(nom) end <- end - length(n_cat) if(start <= end) for(i in start:end) { L[i,i] <- exp(0.5 * W[i-1]) gap <- which(is.na(L[,i])) gap1 <- gap - 1 alpha <- alpha - 0.5 CPCs <- .rgbeta(length(gap), alpha) if(restrictions %in% c("triangular", "stratified")) { CPCs[length(gap):(length(gap) - n_partial + 1)] <- 0 if(i == end && !is.na(last_CPC)) CPCs[length(CPCs)] <- last_CPC } else if(restrictions == "MCAR") CPCs[] <- 0 L[gap,i] <- CPCs * exp(0.5 * W[gap1]) W[gap1] <- W[gap1] + log(1 - CPCs^2) holder[count,(count+1):ncol(holder)] <- CPCs count <- count + 1 } # if there are nominal partially observed variables, make the category residuals uncorrelated (MNL assumption) if(nom) { #if(n_full >= 2) start <- end + 1 end <- start + sum(n_cat) - 1 for(i in start:end) { L[i,i] <- exp(0.5 * W[i-1]) gap <- which(is.na(L[,i])) gap1 <- gap - 1 alpha <- alpha - 0.5 if(restrictions != "none") CPCs <- rep(0, length(gap)) else { CPCs <- .rgbeta(length(gap), alpha) CPCs[-(length(gap):(length(gap) - n_partial + 1))] <- 0 } L[gap,i] <- CPCs * exp(0.5 * W[gap1]) W[gap1] <- W[gap1] + log(1 - CPCs^2) holder[count,(count+1):ncol(holder)] <- CPCs count <- count + 1 } } # missingness indicators can be constructed to be instruments if MAR holds whose strength can be manipulated if(n_partial > 1) { start <- end + 1 end <- n - 1 count <- if(n_full > 0) 1 else 2 if(start <= end) for(i in start:end) { L[i,i] <- exp(0.5 * W[i-1]) gap <- which(is.na(L[,i])) gap1 <- gap - 1 alpha <- alpha - 0.5 if(restrictions %in% c("none", "MARish")) CPCs <- .rgbeta(length(gap), alpha) else if(restrictions %in% c("stratified", "MCAR")) CPCs <- rep(0, length(gap)) else if(strong == 2) CPCs <- holder[count,(count+1):(count + length(gap))] else if(strong == 1) CPCs <- .rgbeta(length(gap), alpha) else if(strong == 0) CPCs <- rep(0, length(gap)) L[gap,i] <- CPCs * exp(0.5 * W[gap1]) W[gap1] <- W[gap1] + log(1 - CPCs^2) } } L[n,n] <- exp(0.5 * W[n-1]) return(L) } ## Function to draw a Cholesky factor of a random correlation matrix ## as a function of canonical partial correlations (CPCs) .rcorvine_partial <- function(Sigma, n_partial, n_cat, eta, restrictions, strong, experiment, treatment_cor) { n <- nrow(Sigma) n_full <- n - n_partial ldlt <- LDLt(Sigma) U <- t(ldlt$L) holder <- matrix(NA_real_, n, n) holder[1,-1] <- U[1,-1] W <- c(NA_real_, 1 - holder[1,-1]^2) for(i in 2:(n-1)) { denominator <- W[i] gap <- (i+1):n temp <- U[i,gap] / sqrt(W[gap] / denominator) invalid <- is.na(temp) temp[invalid] <- sign(U[i,gap][invalid]) invalid <- abs(temp) > 1 temp[invalid] <- sign(temp[invalid]) holder[i,gap] <- temp W[gap] <- W[gap] * (1 - holder[i,gap]^2) } nom <- !is.null(n_cat) n <- n + n_partial if(nom) { n <- n + sum(n_cat) - length(n_cat) } L <- t(U) * sqrt(diag(ldlt$D)) if(eta <= 0) stop("'eta' must be positive") diff <- n - nrow(L) alpha <- eta + diff / 2 L <- cbind(L, matrix(0, nrow(L), diff)) L <- rbind(L, matrix(NA_real_, diff, n)) L[upper.tri(L)] <- 0 holder <- matrix(NA_real_, nrow = diff, ncol = ncol(L)) L <- .rcorvine_helper(L, holder, n_full, n_partial, n_cat, alpha, restrictions, strong, experiment, treatment_cor, nom) if(restrictions == "MARish") L <- .MAR_opt(L, n_full, n_partial) return(L) } .MAR_opt <- function(L, n_full, n_partial) { n_p2 <- n_partial^2 lowers <- lower.tri(L) cell_mark <- tail(which(lowers), n_p2) lowers[] <- FALSE lowers[cell_mark] <- TRUE row_mark <- which(apply(lowers, 1, any)) diag(L)[row_mark] <- NA_real_ partials <- (n_full + 1):(n_full + n_partial) missingness <- nrow(L):(nrow(L) - n_partial + 1) block_mark <- which( row(L) %in% partials & col(L) %in% missingness ) foo <- function(theta) { L[cell_mark] <- theta diags <- 1 - rowSums(L[row_mark,,drop=FALSE]^2, na.rm = TRUE) if(any(diags < 0)) return(NA_real_) diag(L)[row_mark] <- sqrt(diags) Sigma_inv <- chol2inv(t(L)) return(c(crossprod(Sigma_inv[block_mark]))) } opt <- optim(L[cell_mark], foo, method = "BFGS") L[cell_mark] <- opt$par diag(L)[row_mark] <- sqrt(1 - rowSums(L[row_mark,,drop=FALSE]^2, na.rm = TRUE)) return(L) } .NMARness <- function(L) { xs <- grep("^x_", rownames(L), value = TRUE) ys <- grep("^y_", rownames(L), value = TRUE) us <- grep("^u_", rownames(L), value = TRUE) sapply(ys, FUN = function(y) { i <- y sapply(us, FUN = function(u) { j <- u cons <- c(xs, setdiff(us, u)) mark <- c(i,j,cons) D_ijcons <- det(tcrossprod(L[mark,,drop = FALSE])) mark <- cons D_cons <- det(tcrossprod(L[mark,,drop = FALSE])) mark <- c(i,cons) D_icons <- det(tcrossprod(L[mark,,drop = FALSE])) mark <- c(j,cons) D_jcons <- det(tcrossprod(L[mark,,drop = FALSE])) return(1 - D_ijcons * D_cons / (D_icons * D_jcons)) }) }) } ## Function to construct a random data.frame with tunable missingness rdata.frame <- function(N = 1000, restrictions = c("none", "MARish", "triangular", "stratified", "MCAR"), last_CPC = NA_real_, strong = FALSE, pr_miss = .25, Sigma = NULL, alpha = NULL, experiment = FALSE, treatment_cor = c(rep(0, n_full - 1), rep(NA, 2 * n_partial)), n_full = 1, n_partial = 1, n_cat = NULL, eta = 1, df = Inf, types = "continuous", estimate_CPCs = TRUE) { if(length(N) != 1) stop("length of 'N' must be 1") if(N <= 0) stop("'N' must be positive") restrictions <- match.arg(restrictions) if(strong && restrictions == "none") warning("instruments are not valid unless the MAR assumption is enforced") if(n_full < 0) stop("'n_full' must be >= 0") if(n_partial < 0) stop("'n_partial must be >= 0") n <- n_partial + n_full if(n == 0) stop("at least one of 'n_full' or 'n_partial' must be positive") if(length(pr_miss) == 1) pr_miss <- rep(pr_miss, n_partial) if(any(pr_miss <= 0)) stop("all elements of 'pr_miss' must be > 0") if(any(pr_miss >= 1)) stop("all elements of 'pr_miss' must be < 1") if(length(df) != 1) stop("'df' must be of length 1") if(df <= 0) stop("'df' must be a positive") if(length(types) == 1) types <- rep(types, n) types <- match.arg(types, c("continuous", "count", "binary", "treatment", "ordinal", "nominal", "proportion", "positive"), several.ok = TRUE) if(any(types[1:n_full] == "nominal")) { warning("fully observed nominal variables not supported, changing them to ordinal without loss of generality") types <- ifelse(types == "nominal" & 1:length(types) <= n_full, "ordinal", types) } # else if(!is.null(n_cat)) types[n:(n - length(n_cat) + 1)] <- "nominal" if(all( c("ordinal", "nominal") %in% types[-(1:n_full)] )) { stop("including both ordinal and nominal partially observed variables is not supported yet") } if(any(types == "nominal")) { has_nominal <- TRUE if(is.null(n_cat)) { if(types[n] != "nominal") { warning("assuming the last partially observed variable is nominal with 3 categories") types[n] <- "nominal" } n_cat <- 3 } } else has_nominal <- FALSE if(has_nominal) { if(any(n_cat < 3)) stop("nominal variables must have more than 2 categories") types <- c(types[types != "nominal"], types[types == "nominal"]) } if(experiment) { if(types[1] != "treatment") stop("the first variable must be the treatment variable") if(any(types[-1] == "treatment")) stop("only one treatment variable is permitted") } if(is.null(Sigma)) L <- .rcorvine(n_full, n_partial, if(has_nominal) n_cat else NULL, eta, restrictions, strong, experiment, treatment_cor, last_CPC) else { if(!isSymmetric(Sigma)) stop("'Sigma' must be symmetric") if(ncol(Sigma) != (n_full + 2 * n_partial)) stop("'Sigma' must be of order 'n_full + 2 * n_partial'") if(any(types == "nominal")) stop("nominal variables not supported when 'Sigma' is given") if(experiment) stop("treatment variables not supported when 'Sigma' is given") L <- chol(Sigma) } if(is.null(alpha)) { Z <- matrix(rnorm(N * nrow(L)), nrow = nrow(L)) X <- as.data.frame(t(Z) %*% t(L)) } else { if(length(alpha) == 1 && is.na(alpha)) alpha <- rt(ncol(L), df) else if(length(alpha) != ncol(L)) stop(paste("length of alpha must be", ncol(L))) Sigma <- tcrossprod(L) result <- find_Omega(Sigma, alpha, control = list(maxit = 1000)) X <- as.data.frame(sn::rmsn(N, Omega = result$Omega, alpha = alpha)) } if(df < Inf) X <- X / sqrt(rchisq(N, df) / df) if(!has_nominal) colnames(X) <- c(if(n_full) paste("x", 1:n_full, sep = "_"), if(n_partial) paste("y", 1:n_partial, sep = "_"), if(n_partial) paste("u", 1:n_partial, sep = "_") ) else { if(length(n_cat) > 23) stop("number of nominal variables must be <= 23") cn <- as.character(NULL) for(i in seq_along(n_cat)) cn <- c(cn, paste(letters[i], 1:n_cat[i], sep = "_")) colnames(X) <- c(if(n_full) paste("x", 1:n_full, sep = "_"), if(n_partial > length(n_cat)) paste("y", 1:(n_partial - length(n_cat)), sep = "_") else NULL, cn, paste("u", 1:n_partial, sep = "_") ) } if(experiment) { row_mark <- X[,1] == 1 col_mark <- c(FALSE, is.na(treatment_cor)) col_mark[grepl("^u_", colnames(X))] <- FALSE if(any(col_mark)) X[row_mark,col_mark] <- X[row_mark,col_mark] + 1 # ATT } X_obs <- X correlations <- rep(NA_real_, if(!has_nominal) n_partial else n_partial - length(n_cat) + sum(n_cat)) end <- n_partial - length(n_cat) * has_nominal if(end > 0) for(i in 1:end) { y_var <- paste("y", i, sep = "_") u_var <- paste("u", i, sep = "_") X_obs[X[,u_var] < quantile(X[,u_var], probs = pr_miss[i]), y_var] <- NA_real_ X_obs[[u_var]] <- NULL if(!estimate_CPCs) next f_miss <- colnames(X) if(n_full > 0) f_miss <- f_miss[1:(n_full + i - 1)] else f_miss <- "1" f_miss <- paste(f_miss, collapse = " + ") f_miss <- as.formula(paste(u_var, "~", f_miss)) ols_u <- lm(f_miss, data = X) f_true <- colnames(X) if(n_full > 0) f_true <- f_true[1:(n_full + i - 1)] else f_true <- "1" f_true <- paste(f_true, collapse = " + ") f_true <- as.formula(paste(y_var, "~", f_true)) ols_y <- lm(f_true, data = X) correlations[i] <- cor(residuals(ols_u), residuals(ols_y)) # this differs only randomly from 0 under MAR due to finite N } letter_mark <- 1 if(has_nominal) for(i in (end + 1):n_partial) { y_var <- paste("y", i, sep = "_") u_var <- paste("u", i, sep = "_") mark <- grepl(paste("^", letters[letter_mark], "_", sep = ""), colnames(X)) lev <- as.character(NULL) for(j in 1:ceiling(n_cat[letter_mark] / 26)) lev <- c(lev, rep(letters, each = j)) lev <- lev[1:n_cat[letter_mark]] X_obs[[y_var]] <- X[[y_var]] <- factor(max.col(X[,mark]), labels = lev) X_obs[X[,u_var] < quantile(X[,u_var], probs = pr_miss[i]), y_var] <- NA if(!estimate_CPCs) { letter_mark <- letter_mark + 1 next } f_miss <- colnames(X) if(letter_mark == 1) f_miss <- f_miss[1:(n_full + n_partial - length(n_cat))] else f_miss <- f_miss[1:(n_full + n_partial - length(n_cat) + sum(n_cat[1:(letter_mark - 1)]))] f_miss <- paste(f_miss, collapse = " + ") f_miss <- as.formula(paste(u_var, "~", f_miss)) ols_u <- lm(f_miss, data = X) for(j in 1:n_cat[letter_mark]) { f_true <- colnames(X) if(letter_mark == 1) f_true <- f_true[1:(n_full + n_partial - length(n_cat))] else f_true <- f_true[1:(n_full + n_partial - length(n_cat) + sum(n_cat[1:(letter_mark - 1)]))] f_true <- paste(f_true, collapse = " + ") n_var <- paste(letters[letter_mark], j, sep = "_") f_true <- as.formula(paste(n_var, "~", f_true)) ols_n <- lm(f_true, data = X) correlations[which(is.na(correlations))[1]] <- cor(residuals(ols_u), residuals(ols_n)) # this differs only randomly from 0 under MAR } letter_mark <- letter_mark + 1 } if(!has_nominal) names(correlations) <- if(n_partial) paste("e", 1:n_partial, sep = "_") else NULL else { cn <- if(n_partial > length(n_cat)) paste("e", 1:(n_partial - length(n_cat)), sep = "_") else as.character(NULL) for(i in seq_along(n_cat)) cn <- c(cn, paste("e:", letters[i], "_", 1:n_cat[i], sep = "")) names(correlations) <- cn } X_obs <- X_obs[,grepl("^[xy]_", colnames(X_obs))] mark_ord <- 1 for(i in seq_along(types)) { mark <- is.na(X_obs[,i]) if(types[i] %in% c("binary", "treatment")) { if(i == 1 && experiment) { X_obs[,i] <- X[,i] <- as.factor(X[,i] > 0) colnames(X_obs)[1] <- colnames(X)[1] <- "treatment" } else { X[[toupper(colnames(X)[i])]] <- X[,i] X_obs[,i] <- X[,i] <- cut(X[,i], breaks = 2, labels = c("FALSE", "TRUE")) } } else if(types[i] == "ordinal") { X[[toupper(colnames(X)[i])]] <- X[,i] breaks <- 3 if(length(n_cat) == 1) breaks <- n_cat else if(length(n_cat) > 1) { breaks <- n_cat[mark_ord] mark_ord <- mark_ord + 1 } qs <- quantile(X[,i], prob = seq(from = 0, to = 1, length.out = breaks + 1)) qs[1] <- -Inf qs[length(qs)] <- Inf X_obs[,i] <- X[,i] <- cut(X[,i], breaks = qs, ordered_result = TRUE, labels = LETTERS[1:breaks]) } else if(types[i] == "count") { # this is not quite consistent with the DGP X[[toupper(colnames(X)[i])]] <- X[,i] X_obs[,i] <- X[,i] <- as.integer(qpois(pt(X[,i], df = df), lambda = 5)) } else if(types[i] == "proportion") { # this is not quite consistent with the DGP X[[toupper(colnames(X)[i])]] <- X[,i] X_obs[,i] <- X[,i] <- pt(X[,i], df = df) } else if(types[i] == "positive") { X[[toupper(colnames(X)[i])]] <- X[,i] X_obs[,i] <- X[,i] <- exp(X[,i]) } X_obs[mark,i] <- NA } ord <- c(colnames(X_obs), grep("^u_", colnames(X), value = TRUE)) extras <- colnames(X) extras <- extras[!(extras %in% ord)] ord <- c(ord, extras) X <- X[,ord] cn <- colnames(X) cn <- cn[sapply(1:ncol(X), FUN = function(i) { !is.factor(X[,i]) && !(toupper(cn[i]) %in% cn[-i]) })] resort <- function(s) { ord <- order(as.integer(gsub("^[a-z,A-Z]_", "", s))) return(s[ord]) } cn <- c(if(experiment) "treatment_propensity", resort(grep("^x", cn, ignore.case = TRUE, value = TRUE)), resort(grep("^y", cn, ignore.case = TRUE, value = TRUE)), grep("^[a-t]_", cn, ignore.case = FALSE, value = TRUE), grep("^u", cn, ignore.case = FALSE, value = TRUE)) rownames(L) <- colnames(L) <- cn out <- list(true = X, obs = X_obs, empirical_CPCs = correlations, L = L) if(!is.null(alpha)) out <- c(out, list(alpha = alpha, skewness = result$sn_skewness, kurtosis = result$sn_kurtosis)) return(out) } ## this function makes a positive definite correlation matrix given choose(n,2) unbounded parameters make_O.cor <- function(theta) { n <- (1 + sqrt(1 + 8 * length(theta))) / 2 CPCs <- exp(2 * theta) CPCs <- (CPCs - 1) / (CPCs + 1) L <- matrix(0, n, n) L[1,1] <- 1 start <- 1 end <- n - 1 L[-1,1] <- partials <- CPCs[start:end] W <- log(1 - partials^2) for(i in 2:(n-1)) { start <- end + 1 end <- start + n - i - 1 gap <- (i+1):n gap1 <- i:(n-1) partials <- CPCs[start:end] L[i,i] <- exp(0.5 * W[i-1]) L[gap,i] <- partials * exp(0.5 * W[gap1]) W[gap1] <- W[gap1] + log(1 - partials^2) } L[n,n] <- exp(0.5 * W[n-1]) return(tcrossprod(L)) } ## this objective function is the Frobenius norm of the difference between Sigma and Sigma_proposed fmin <- function(theta, Sigma, alpha, final = FALSE, ...) { n <- nrow(Sigma) omega <- exp(theta[1:n]) # standard deviations of the implicit Omega matrix O.cor <- make_O.cor(theta[-(1:n)]) alphaTO.cor <- alpha %*% O.cor Sigma_proposed <- ( O.cor - 2 / (pi * c(1 + alphaTO.cor %*% alpha)) * crossprod(alphaTO.cor) ) * tcrossprod(omega) if(final) return(Sigma_proposed) return(crossprod( c(Sigma - Sigma_proposed) )[1]) } ## this function makes a 3-factor Cholesky factorization of a PSD A matrix LDLt <- function(A) { n <- nrow(A) L <- diag(n) D <- matrix(0, n, n) for(j in 1:n) { s <- 0 if(j > 1) for(k in 1:(j-1)) s <- s + L[j,k]^2 * D[k,k] D[j,j] <- A[j,j] - s if(D[j,j] < 1e-15) { D[j,j] <- 0 break } if(j < n) for(i in (j+1):n) { s <- 0 if(j > 1) for(k in 1:(j-1)) s <- s + L[i,k] * L[j,k] * D[k,k] L[i,j] <- (A[i,j] - s) / D[j,j] } } return(list(L = L, D = D)) } ## this function makes plausible starting values (basically treating alpha is if it were a zero vector) make_start <- function(Sigma) { log_omega <- log(sqrt(diag(Sigma))) Sigma <- cov2cor(Sigma) n <- nrow(Sigma) U <- t(LDLt(Sigma)$L) holder <- matrix(NA_real_, n, n) holder[1,-1] <- U[1,-1] W <- c(NA_real_, 1 - holder[1,-1]^2) for(i in 2:(n-1)) { denominator <- W[i] gap <- (i+1):n temp <- U[i,gap] / sqrt(W[gap] / denominator) invalid <- is.na(temp) temp[invalid] <- sign(U[i,gap][invalid]) invalid <- abs(temp) > 1 temp[invalid] <- sign(temp[invalid]) holder[i,gap] <- temp W[gap] <- W[gap] * (1 - holder[i,gap]^2) } holder <- t(holder) CPCs <- holder[lower.tri(holder)] return(c(log_omega, atanh(CPCs))) } ## this function finds Omega via optim() and returns it as part of a list with find_Omega <- function(Sigma, alpha, method = "BFGS", start = make_start(Sigma), ...) { stopifnot(isSymmetric(Sigma)) # Sigma is the intended covariance matrix of the multivariate skew-normal variable stopifnot(all(eigen(Sigma, TRUE, TRUE)$values > 0)) n <- nrow(Sigma) alpha <- c(alpha) stopifnot(length(alpha) == n) # alpha is a shape parameter for the multivariate skew-normal variable opt <- optim(start, fmin, method = method, Sigma = Sigma, alpha = alpha, ...) if(opt$convergence != 0) { gradients <- opt$counts["gradient"] warning(paste("Convergence problem. Pass something like 'control = list(maxit = ", 5 * gradients, ")' if alpha is far from a zero vector", sep = "")) } theta <- opt$par omega <- exp(theta[1:n]) O.cor <- make_O.cor(theta[-(1:n)]) opt$Omega <- O.cor * tcrossprod(omega) alphaTO.cor <- c(alpha %*% O.cor) delta <- c( (O.cor %*% alpha) / sqrt(1 + alphaTO.cor %*% alpha)[1] ) mu_z <- sqrt(2/pi) * delta num <- c( mu_z %*% chol2inv(chol(O.cor)) %*% mu_z ) opt$delta <- delta opt$sn_skewness <- ( (4 - pi) / 4 )^2 * ( num / (1 - num) )^3 opt$sn_kurtosis <- 2 * (pi - 3) * ( num / (1 - num) )^2 return(opt) } r-cran-mi-1.0/R/sysdata.rda000066400000000000000000010532601275731226000155140ustar00rootroot00000000000000ý7zXZi"Þ6!ÏXÌæ?çïþ])TW"änRÊŸ’Øá´7iÈ|MújÞä{¼è/Þgíˆ<óË+£ÖŽ|Æ&cG))=F‡Å0ú&Ÿ…¿Ù²ýñHŸð•€ÓÝÙ-î1þÚÒ)7õ%Ȫǔ¢ÿ 9\ÞµÌX»/oî ­hè][N;TÄÏg›šh–캆Pš.£Ðè¨R`Þã5v²:~xÈ<æIþùð/ñ^´¹¿x’‡¹_¼ Só'¤c°±¥ˆÞÚÒéÉ”aiá½":ñÝýÁâ€Üе­C^O«|¿¿Œzû.©t› Õ,$,Dâ¿ëmGN³¨E£î‡Ê‰„¡¢’“Y?ã‚èçFŒ;ÇM 8,wŸ(*·?(™F|¢µl,¥càëŒ8n$_¤ßñêU–Á"¯~šLâô{Þ¹?ðºÐ²ÕV§%áœiÓÊÊ«èªJ÷ "Õ)/ˆý²Þv;,z5óŸ 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Initially, users must get their data into R. There are several ways to do so, including the `read.table`, `read.csv`, `read.fwf` functions plus several functions in the __foreign__ package. All of these functions will generate a `data.frame`, which is a bit like a spreadsheet of data. http://cran.r-project.org/doc/manuals/R-data.html for more information. ```{r step0} options(width = 65) suppressMessages(library(mi)) data(nlsyV, package = "mi") ``` From there, the first step is to convert the `data.frame` to a `missing_data.frame`, which is an enhanced version of a `data.frame` that includes metadata about the variables that is essential in a missing data context. ```{r step1} mdf <- missing_data.frame(nlsyV) ``` The `missing_data.frame` constructor function creates a `missing_data.frame` called `mdf`, which in turn contains seven `missing_variable`s, one for each column of the `nlsyV` dataset. The most important aspect of a `missing_variable` is its class, such as `continuous`, `binary`, and `count` among many others (see the table in the Slots section of the help page for `missing_variable-class`. The `missing_data.frame` constructor function will try to guess the appropriate class for each `missing_variable`, but rarely will it correspond perfectly to the user's intent. Thus, it is very important to call the `show` method on a `missing_data.frame` to see the initial guesses ```{r step1.5} show(mdf) # momrace is guessed to be ordered ``` and to modify them, if necessary, using the `change` function, which can be used to change many things about a`missing_variable`, so see its help page for more details. In the example below, we change the class of the _momrace_ (race of the mother) variable from the initial guess of `ordered-categorical` to a more appropriate `unordered-categorical` and change the income `nonnegative-continuous`. ```{r, step2} mdf <- change(mdf, y = c("income", "momrace"), what = "type", to = c("non", "un")) show(mdf) ``` Once all of the `missing_variable`s are set appropriately, it is useful to get a sense of the raw data, which can be accomplished by looking at the `summary`, `image`, and / or `hist` of a `missing_data.frame` ```{r, step3} summary(mdf) image(mdf) hist(mdf) ``` Next we use the `mi` function to do the actual imputation, which has several extra arguments that, for example, govern how many independent chains to utilize, how many iterations to conduct, and the maximum amount of time the user is willing to wait for all the iterations of all the chains to finish. The imputation step can be quite time consuming, particularly if there are many `missing_variable`s and if many of them are categorical. One important way in which the computation time can be reduced is by imputing in parallel, which is highly recommended and is implemented in the mi function by default on non-Windows machines. If users encounter problems running `mi` with parallel processing, the problems are likely due to the machine exceeding available RAM. Sequential processing can be used instead for `mi` by using the `parallel=FALSE` option. ```{r, step4} rm(nlsyV) # good to remove large unnecessary objects to save RAM options(mc.cores = 2) imputations <- mi(mdf, n.iter = 30, n.chains = 4, max.minutes = 20) show(imputations) ``` The next step is very important and essentially verifies whether enough iterations were conducted. We want the mean of each completed variable to be roughly the same for each of the 4 chains. ```{r, step5A} round(mipply(imputations, mean, to.matrix = TRUE), 3) Rhats(imputations) ``` If so --- and when it does in the example depends on the pseudo-random number seed --- we can procede to diagnosing other problems. For the sake of example, we continue our 4 chains for another 5 iterations by calling ```{r, step5B} imputations <- mi(imputations, n.iter = 5) ``` to illustrate that this process can be continued until convergence is reached. Next, the `plot` of an object produced by `mi` displays, for all `missing_variable`s (or some subset thereof), a histogram of the observed, imputed, and completed data, a comparison of the completed data to the fitted values implied by the model for the completed data, and a plot of the associated binned residuals. There will be one set of plots on a page for the first three chains, so that the user can get some sense of the sampling variability of the imputations. The `hist` function yields the same histograms as `plot`, but groups the histograms for all variables (within a chain) on the same plot. The `image`function gives a sense of the missingness patterns in the data. ```{r, step6} plot(imputations) plot(imputations, y = c("ppvtr.36", "momrace")) hist(imputations) image(imputations) summary(imputations) ``` Finally, we pool over `m = 5` imputed datasets -- pulled from across the 4 chains -- in order to estimate a descriptive linear regression of test scores (_ppvtr.36_) at 36 months on a variety of demographic variables pertaining to the mother of the child. ```{r, step7} analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace, data = imputations, m = 5) display(analysis) ``` The rest is optional and only necessary if you want to perform some operation that is not supported by the __mi__ package, perhaps outside of R. Here we create a list of `data.frame`s, which can be saved to the hard disk and / or exported in a variety of formats with the __foreign__ package. Imputed data can be exported to Stata by using the `mi2stata` function instead of `complete`. ```{r, step8} dfs <- complete(imputations, m = 2) ``` r-cran-mi-1.0/inst/doc/mi_vignette.pdf000066400000000000000000015461761275731226000177170ustar00rootroot00000000000000%PDF-1.5 %ÐÔÅØ 6 0 obj << /Length 2767 /Filter /FlateDecode >> stream xÚÍYëoã¸ÿž¿ÂØûp2`3")QÒáZtؽÞ÷ènZ Ø[ŠMǺի’œ\ú×w”#im%û(Ú‰(Šóàp83¿±¿¸Yø‹ï/|÷üîêâò¥LÊƨpqµƒ¡Ré…Iá›dqµ]¼õž—˵ö•÷â÷´¨sË/ÕŽŸE†Oéý­MoìòÝÕ«Ë—@/¥HÂP!˵T¡0<‘(Ã,¿³È3ô½ï«%ü§Û&Ûìy6-·ß\Js©|ðÑI³€¯?ïRø¾OñrIa¹0R ‹«ˆ¯ö¶AoÐlHåµöv©bÏ6iî&:»”^Ýò[Vºõ%¿ƒuòû–Î fɧ4úTÛfdL˜Ü¦]*–ë ÐÞeÖeižß/cí­xí¡…#ã•ÅREÞ¤µ»±nÐíaÖf ¿!G¦u€¤«øíµàï“}¡ñ|‘Ľ%ÔÂø¾tÂV8îDð¦µw‡3éR%Þ}Ë3,d»gKî«Pa“¶n·¨i‰F'ád+e@3V ±éVtéunOœá@q%BÅɘx5'-…2ê„´M{;/K«HH?|º,°‘ð£ø„¬ÝÝîYQ,$˜éîPn:w“Á¬u~hÏ8èd%{¨DtB]p‚0ŽEk'®jlvSÎ+*ýDDI4¦¬Ó êó¢'zy¨¼ç9ifÜu0¨Jky8RÖxwY¿öÆ–°¥Î-Kç,-}é‰þtÉvMZ<âCÚÀ1i3&&OŽ¼»}F[Ù³YÛëBë¬ãAž½§}mk<ëvom7Ü{[âéaÕ.XæÆ)ôúû‹Ñ¡DF„*f½ö(§ëêo./7MZŠf½\CL¬¸ƒðå7»éD…×öærËS›KŒ–08¤y{ùš×“m˜W‘;8.Þ‚Ž‰Ë@à3EÅqCƒÁd‘âQ9µE©P/¦OÜÈ™O°µO£zquñ¯‹Þ(¹”‹PišÅ¦ 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Multiple Imputation from Conditional Distributions } \description{ The mi package performs multiple imputation for data with missing values. The algorithm iteratively draws imputed values from the conditional distribution for each variable given the observed and imputed values of the other variables in the data. The process approximates a Bayesian framework; multiple chains are run and convergence is assessed after a pre-specified number of iterations within each chain. The package allows customization of the conditional model and the treatment of missing values for each variable. In addition, the package provides graphics to visualize missing data patterns, to diagnose the models used to generate the imputations, and to assess convergence. Functions are included to run statistical models post-imputation with the appropriate degree of sampling uncertainty. } \details{ \tabular{ll}{ Package: \tab mi\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab \Sexpr[eval=TRUE,results=rd,stage=build]{date()} \cr License: \tab GPL (>= 2) \cr LazyLoad: \tab yes\cr } See the vignette for an example of typical usage. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima,Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_data.frame}}, \code{\link{change}}, \code{\link{mi}}, \code{\link{Rhats}}, \code{\link{pool}}, \code{\link{complete}} } \keyword{package} \keyword{AimedAtusers} r-cran-mi-1.0/man/01missing_variable.Rd000066400000000000000000000366121275731226000176750ustar00rootroot00000000000000\name{01missing_variable} \Rdversion{1.1} \docType{class} \alias{01missing_variable} \alias{missing_variable} \alias{missing_variable-class} \alias{MatrixTypeThing-class} \alias{WeAreFamily-class} \title{Class "missing_variable" and Inherited Classes} \description{ The missing_variable class is essentially the data comprising a variable plus all the metadata needed to understand how its missing values will be imputed. However, no variable is merely of missing_variable class; rather every variable is of a class that inherits from the missing_variable class. Even if a variable has no missing values, it needs to be coerced to a class that inherits from the missing_variable class before it can be used to impute values of other missing_variables. Understanding the properties of different subclasses of the missing_variable class is essential for modeling and imputing them. The \code{\link{missing_data.frame-class}} is essentially a list of objects that inherit from the missing_variable class, plus metadata need to understand how these missing_variables relate to each other. Most users will never need to call \code{missing_variable} directly since it is called by \code{\link{missing_data.frame}}. } \section{Objects from the Classes}{The missing_variable class is virtual, so no objects may be created from it. However, the missing_variable generic function can be used to instantiate an object that inherits from the missing_variable class by specifying its \code{type} argument. A user would call the \code{\link{missing_data.frame}} function on a \code{\link{data.frame}}, which in turn calls the missing_variable function on each column of the \code{\link{data.frame}} using various heuristics to guess the \code{type} argument. } \usage{ missing_variable(y, type, ...) ## Hidden arugments not included in the signature: ## favor_ordered = TRUE, favor_positive = FALSE, ## variable_name = deparse(substitute(y)) } \arguments{ \item{y}{Can be any vector, some of whose values may be \code{\link{NA}}, which will comprise the \bold{raw_data} slot of a missing_variable (see the Slots section). It is recommended that this vector \emph{not} have any transformations, such as a log-transformation. Any continuous variable can be transformed using the function in its \bold{transformation} slot. The transformations and other discretionary aspects of a missing_variable are typically changed by calling the \code{\link{change}} function on a \code{\link{missing_data.frame}} See the Slots section for more details. } \item{type}{Missing or a character string among the classes that inherit from the missing_variable class. If missing, the constructor will guess (sometimes incorrectly) based on the characteristics of the variable. The best way to improve the guessing of categorical variables is to use the \code{\link{factor}} function --- possibly with \code{ordered = TRUE} --- to create (possibly ordered) factors that will correctly be coerced to objects of \code{\link{unordered-categorical-class}} and \code{\link{ordered-categorical-class}} respectively. If you fail to do so, the hidden arguments that are not included in the signature affect the guesses. If \code{favor_ordered = TRUE}, which is the default, it will tend to guess that variables with few unique values are should be coerced to \code{\link{ordered-categorical-class}} and \code{\link{unordered-categorical-class}} otherwise. If \code{favor_positive = FALSE}, which is the default, it will tend to guess that variables with many unique values are merely continuous, whether or not all the observed values are positive. If \code{favor_positive = TRUE} nonnegative or positive variables will get coerced to \code{\link{nonnegative-continuous-class}} or \code{\link{positive-continuous-class}}. See the Slots section and the specific help pages for more details on the subclasses. } \item{\dots}{Further hidden arguments that are not in the signature. The \code{favor_ordered} and \code{favor_positive} arguments are documented immediately above. The \code{variable} name argument can be used to control what gets put in the \bold{variable_name} slot, see the Slots section below. } } \section{Slots}{ In the following table, indentation indicates inheritance from the class with less indentation, and italics indicates that the class is virtual so no variables can be created with that class. Inherited classes inherit the transformations, families, link functions, and \code{\link{fit_model-methods}} from their parent class, although these are often superceeded by analogues that are tailored for the inherited class. Also note, the default transformation for the continuous class is a standardization using \emph{twice} the standard deviation of the observed values. The distinction between the transformation entailed by the \code{\link{family}} and the transformation entailed by the function in the \bold{tranformation} slot may be confusing at this point. The former pertains to how the linear predictor of a variable is mapped to the space of a variable when it is on the left-hand side of a generalized linear model. The latter pertains --- for continuous variables only --- to how the values in the \bold{raw_data} slot are mapped into those in the \bold{data} and thus affects how a continuous variable enters into the model whether it is on the left or right-hand side. The classes are discussed in much more detail below. \tabular{lll}{ \bold{Class name [transformation]} \tab \bold{Default family and link} \tab \bold{Default \code{\link{fit_model}}} \cr \emph{missing_variable} \tab none \tab throws error \cr \code{ } \emph{categorical} \tab none \tab throws error \cr \code{ } \code{ } unordered-categorical \tab \code{binomial(link = 'logit')} \tab \code{\link[nnet]{multinom}} \cr \code{ } \code{ } ordered-categorical \tab \code{binomial(link = 'logit')} \tab \code{\link[arm]{bayespolr}} \cr \code{ } \code{ } \code{ } binary \tab \code{binomial(link = 'logit')} \tab \code{\link[arm]{bayesglm}} \cr \code{ } \code{ } \code{ } interval \tab \code{gaussian{link = 'identity'}} \tab \code{\link[survival]{survreg}} \cr \code{ } continuous[standardize] \tab \code{gaussian{link = 'identity'}} \tab \code{\link[arm]{bayesglm}} \cr \code{ } \code{ } semi-continuous[identity] \tab \tab \cr \code{ } \code{ } \code{ } nonnegative-continuous[logshift] \tab \tab \cr \code{ } \code{ } \code{ } \code{ } SC_proportion[squeeze] \tab \code{binomial(link = 'logit')} \tab \code{\link[betareg]{betareg}} \cr \code{ } \code{ } positive-continuous[\code{\link{log}}] \tab \tab \cr \code{ } \code{ } \code{ } proportion[identity] \tab \code{binomial(link = 'logit')} \tab \code{\link[betareg]{betareg}} \cr \code{ } \code{ } bounded-continuous[identity] \tab \tab \cr \code{ } count \tab \code{quasipoisson{link = 'log'}} \tab \code{\link[arm]{bayesglm}} \cr \code{ } irrelevant \tab \tab throws error \cr \code{ } \code{ } fixed \tab \tab throws error \cr } The missing_variable class is virtual and has the following slots (this information is primarily directed at developeRs): \describe{ \item{\code{variable_name}:}{Object of class \code{\link{character}} of length one naming the variable} \item{\code{raw_data}:}{Object of class \code{"ANY"} representing the observations on a variable, some of which may be \code{\link{NA}}. No method should ever change this slot at all. Instead, methods should change the \bold{data} slot.} \item{\code{data}:}{Object of class \code{"ANY"}, which is initially a copy of the \bold{raw_data} slot --- transformed by the function in the \bold{transformation} slot for continuous variables only --- and whose \code{\link{NA}} values are replaced during the multiple imputation process. See \code{\link{mi}}} \item{\code{n_total}:}{Object of class \code{"integer"} which is the \code{\link{length}} of the \bold{data} slot} \item{\code{all_obs}:}{Object of class \code{"logical"} of length one indicating whether all values of the \bold{data} slot are observed and thus not \code{\link{NA}} } \item{\code{n_obs}:}{Object of class \code{"integer"} of length one indicating the number of values of the \bold{data} slot that are observed and thus not \code{\link{NA}} } \item{\code{which_obs}:}{Object of class \code{"integer"}, which is a vector indicating the positions of the observed values in the \bold{data} slot} \item{\code{all_miss}:}{Object of class \code{"logical"} of length one indicating whether all values of the \bold{data} slot are \code{\link{NA}} } \item{\code{n_miss}:}{Object of class \code{"integer"} of length one indicating the number of values of the \bold{data} slot that are \code{\link{NA}} } \item{\code{which_miss}:}{Object of class \code{"integer"}, which is a vector indicating the positions of the missing values in the \bold{data} slot } \item{\code{n_extra}:}{Object of class \code{"integer"} of length one indicating how many (missing) observations have been added to the end of the \bold{data} slot that are not included in the \bold{raw_data} slot. Although the extra values will be imputed, they are not considered to be \dQuote{missing} for the purposes of defining the previous three slots} \item{\code{which_extra}:}{Object of class \code{"integer"}, which is a vector indicating the positions of the extra values at the end of the \bold{data} slot } \item{\code{n_unpossible}:}{Object of class \code{"integer"} of length one indicating the number of values that are logically or structurally unobservable} \item{\code{which_unpossible}:}{Object of class \code{"integer"} indicating the positions of the unpossible values in the \bold{data} slot } \item{\code{n_drawn}:}{Object of class \code{"integer"} of length one which is the sum of the \bold{n_miss} and \bold{n_extra} slots} \item{\code{which_drawn}:}{Object of class \code{"integer"} which is a vector concatinating the \bold{which_miss} and \bold{which_extra} slots } \item{\code{imputation_method}:}{Object of class \code{"character"} of length one indicating how the \code{\link{NA}} values are to be imputed. Possibilities include \dQuote{ppd} for imputation from the posterior predictive distribution, \dQuote{pmm} for imputation via predictive mean matching, \dQuote{mean} for mean-imputation, \dQuote{median} for median-imputation, \dQuote{expectation} for conditional mean-imputation. With enough programming effort, other kinds of imputation can be defined and specified here.} \item{\code{family}:}{Object of class \code{"WeAreFamily"} that will typically be passed to \code{\link{glm}} and similar functions during the multiple imputation process} \item{\code{known_families}:}{Object of class \code{\link{character}} indicating the families that are known to be supported for a class; see \code{\link{family}}} \item{\code{known_links}:}{Object of class \code{\link{character}} indicating what link functions are known to be supported by the elements of the \bold{known_families} slot; see \code{\link{family}}} \item{\code{imputations}:}{Object of class \code{"MatrixTypeThing"} with rows equal to the number of iterations (initially zero) of the multiple imputation algorithm and columns equal to the \bold{n_drawn} slot. The rows are appropriately extended and then filled by the \code{\link{mi}} function} \item{\code{done}:}{Object of class \code{"logical"} of length one indicating whether the \code{\link{NA}} values in the \bold{data} slot have been replaced by imputed values} \item{\code{parameters}:}{Object of class \code{"MatrixTypeThing"} with rows equal to the number of iterations (initially zero) of the multiple imputation algorithm and columns equal to the number of estimated parameters when modeling the \bold{data} slot. The rows are appropriately extended and then filled by the \code{\link{mi}} function} \item{\code{model}:}{Object of class \code{"ANY"} which can be filled by an object that is output by one of the \code{\link{fit_model-methods}}, which is done by default by \code{\link{mi}} when all the iterations have completed} \item{\code{fitted}:}{Object of class \code{"ANY"} although typically a vector or matrix that contains the fitted values of the model in the slot immediately above. Note that the \bold{fitted} slot is filled by default by \code{\link{mi}}, although the \bold{model} slot is left empty by default to save RAM.} \item{\code{estimator}:}{Object of class \code{"character"} of length one indicating which pre-existing \code{\link{fit_model}} to use for an unordered-categorical variable. Options are \code{"mnl"}, in which \code{\link[nnet]{multinom}} from the \pkg{nnet} package is used to fit the values of the unordered categorical variable; and \code{"rnl"}, in which each category is separately modeled as the positive binary outcome against all other categories using a \code{\link[arm]{bayesglm}} \code{fit_model} and the probabilities of each category are normalized to sum to 1 after each model is run. In general, \code{"rnl"} is slightly less accurate than \code{"mnl"}, but runs much more quickly especially when the unordered categorical variable has many unique categories.} } The WeAreFamily class is a class union of \code{\link{character}} and \code{\link{family}}, while the MatrixTypeThing class is a class union of \code{\link{matrix}} only at the moment. } \value{ The missing_variable function returns an object that inherits from the missing_variable class. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_data.frame}}, \code{\link{categorical-class}}, \code{\link{unordered-categorical-class}}, \code{\link{ordered-categorical-class}}, \code{\link{binary-class}}, \code{\link{interval-class}}, \code{\link{continuous-class}}, \code{\link{semi-continuous-class}}, \code{\link{nonnegative-continuous-class}}, \code{\link{SC_proportion-class}}, \code{\link{censored-continuous-class}}, \code{\link{truncated-continuous-class}}, \code{\link{bounded-continuous-class}}, \code{\link{positive-continuous-class}}, \code{\link{proportion-class}}, \code{\link{count-class}} } \examples{ # STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "continuous") show(income) # STEP 1: CONVERT IT TO A missing_data.frame mdf <- missing_data.frame(nlsyV) # this calls missing_variable() internally show(mdf) } \keyword{classes} \keyword{AimedAtUseRs} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/02missing_data.frame.Rd000066400000000000000000000250101275731226000201010ustar00rootroot00000000000000\name{02missing_data.frame} \Rdversion{1.1} \docType{class} \alias{02missing_data.frame} \alias{missing_data.frame-class} \alias{missing_data.frame} \title{Class "missing_data.frame"} \description{ This class is similar to a \code{\link{data.frame}} but is customized for the situation in which variables with missing data are being modeled for multiple imputation. This class primarily consists of a list of \code{\link{missing_variable}}s plus slots containing metadata indicating how the \code{\link{missing_variable}}s relate to each other. Most operations that work for a \code{\link{data.frame}} also work for a missing_data.frame. } \section{Objects from the Class}{ Objects can be created by calls of the form \code{new("missing_data.frame", ...)}. However, useRs almost always will pass a \code{\link{data.frame}} to the missing_data.frame constructor function to produce an object of missing_data.frame class. } \usage{ missing_data.frame(y, ...) ## Hidden arguments not included in the signature ## favor_ordered = TRUE, favor_positive = FALSE, ## subclass = NA_character_, ## include_missingness = TRUE, skip_correlation_check = FALSE } \arguments{ \item{y}{Usually a \code{\link{data.frame}}, possibly a numeric matrix, possibly a list of \code{\link{missing_variable}}s.} \item{\dots}{Hidden arguments. The \code{favor_ordered} and \code{favor_positive} arguments are passed to the \code{\link{missing_variable}} function and are documented under the \code{type} argument. Briefly, they affect the heuristics that are used to guess what class a variable should be coerced to. The \code{subclass} argument defaults to \code{\link{NA}} and can be used to specify that the resulting object should inherit from the missing_data.frame class rather than be an object of \code{missing_data.frame} class. Any further arguments are passed to the \code{\link{initialize-methods}} for a missing_data.frame. They currently are \code{include_missingness}, which defaults to \code{TRUE} and indicates that the missingness pattern of the other variables should be included when modeling a particular \code{\link{missing_variable}}, and \code{skip_correlation_check}, which defaults to FALSE and indicates whether to skip the default check for whether the observed values of each pair of \code{\link{missing_variable}}s has a perfect absolute Spearman \code{\link{cor}}relation. } } \section{Slots}{ This section is primarily aimed at developeRs. A missing_data.frame inherits from \code{\link{data.frame}} but has the following additional slots: \describe{ \item{\code{variables}:}{Object of class \code{"list"} and each list element is an object that inherits from the \code{\link{missing_variable-class}} } \item{\code{no_missing}:}{Object of class \code{"logical"}, which is a vector whose length is the same as the length of the \bold{variables} slot indicating whether the corresponding \code{\link{missing_variable}} is fully observed } \item{\code{patterns}:}{Object of class \code{\link{factor}} whose length is equal to the number of observation and whose elements indicate the missingness pattern for that observation} \item{\code{DIM}:}{Object of class \code{"integer"} of length two indicating first the number of observations and second the length of the \bold{variables} slot } \item{\code{DIMNAMES}:}{Object of class \code{"list"} of length two providing the appropriate number rownames and column names } \item{\code{postprocess}:}{Object of class \code{"function"} used to create additional variables from existing variables, such as interactions between two \code{\link{missing_variable}}s once their missing values have been imputed. Does not work at the moment} \item{\code{index}:}{Object of class \code{"list"} whose length is equal to the number of \code{\link{missing_variable}}s with some missing values. Each list element is an integer vector indicating which columns of the \bold{X} slot must be dropped when modeling the corresponding \code{\link{missing_variable}} } \item{\code{X}:}{Object of \code{\link{MatrixTypeThing-class}} with rows equal to the number of observations and is loosely related to a \code{\link{model.matrix}}. Rather than repeatedly parsing a \code{\link{formula}} during the multiple imputation process, this \bold{X} matrix is created once and some of its columns are dropped when modeling a \code{\link{missing_variable}} utilizing the \bold{index} slot. The columns of the \bold{X} matrix consists of numeric representations of the \code{\link{missing_variable}}s plus (by default) the unique missingness patterns } \item{\code{weights}:}{Object of class \code{"list"} whose length is equal to one or the number of \code{\link{missing_variable}}s with some missing values. Each list element is passed to the corresponding argument of \code{\link{bayesglm}} and similar functions. In particular, some observations can be given a weight of zero, which should drop them when modeling some \code{\link{missing_variable}}s} \item{\code{priors}:}{Object of class \code{"list"} whose length is equal to the number of \code{\link{missing_variable}}s and whose elements give appropriate values for the priors used by the model fitting function wraped by the \code{\link{fit_model-methods}}; see, e.g., \code{\link[arm]{bayesglm}}} \item{\code{correlations}:}{Object of class \code{"matrix"} with rows and columns equal to the length of the \bold{variables} slot. Its strict upper triangle contains Spearman \code{\link{cor}}relations between pairs of variables (ignoring missing values), and its strict lower triangle contains Squared Multiple Correlations (SMCs) between a variable and all other variables (ignoring missing values). If either a Spearman correlation or a SMC is very close to unity, there may be difficulty or error messages during the multiple imputation process.} \item{\code{done}:}{Object of class \code{"logical"} of length one indicating whether the missing values have been imputed} \item{\code{workpath}:}{Object of class \code{\link{character}} of length one indicating the path to a working directory that is used to store some objects} } } \details{ In most cases, the first step of an analysis is for a useR to call the \code{missing_data.frame} function on a \code{\link{data.frame}} whose variables have some \code{\link{NA}} values, which will call the \code{\link{missing_variable}} function on each column of the \code{\link{data.frame}} and return the \code{\link{list}} that fills the \bold{variable} slot. The classes of the list elements will depend on the nature of the column of the \code{\link{data.frame}} and various fallible heuristics. The success rate can be enhanced by making sure that columns of the original \code{\link{data.frame}} that are intended to be categorical variables are (ordered if appropriate) \code{\link{factor}}s with labels. Even in the best case scenario, it will often be necessary to utlize the \code{\link{change}} function to modify various discretionary aspects of the \code{\link{missing_variable}}s in the \bold{variables} slot of the missing_data.frame. The \code{\link{show}} method for a missing_data.frame should be utilized to get a quick overview of the \code{\link{missing_variable}}s in a missing_data.frame and recognized what needs to be \code{\link{change}}d. } \section{Methods}{ There are many methods that are defined for a missing_data.frame, although some are primarily intended for developers. The most relevant ones for users are: \describe{ \item{change}{\code{signature(data = "missing_data.frame", y = "ANY", what = "character", to = "ANY")} which is used to change discretionary aspects of the \code{\link{missing_variable}}s in the \bold{variables} slot of a missing_data.frame} \item{hist}{\code{signature(x = "missing_data.frame")} which shows histograms of the observed variables that have missingness} \item{image}{\code{signature(x = "missing_data.frame")} which plots an image of the \bold{missingness} slot to visualize the pattern of missingness when \code{grayscale = FALSE} or the pattern of missingness in light of the observed values (\code{grayscale = TRUE}, the default)} \item{mi}{\code{signature(y = "missing_data.frame", model = "missing")} which multiply imputes the missing values} \item{show}{\code{signature(object = "missing_data.frame")} which gives an overview of the salient characteristics of the \code{\link{missing_variable}}s in the \bold{variables} slot of a missing_data.frame } \item{summary}{\code{signature(object = "missing_data.frame")} which produces the same result as the \code{\link{summary}} method for a \code{\link{data.frame}}} } There are also S3 methods for the \code{\link{dim}}, \code{\link{dimnames}}, and \code{\link{names}} generics, which allow functions like \code{\link{nrow}}, \code{\link{ncol}}, \code{\link{rownames}}, \code{\link{colnames}}, etc. to work as expected on \code{missing_data.frame}s. Also, accessing and changing elements for a \code{missing_data.frame} mostly works the same way as for a \code{\link{data.frame}} } \value{ The \code{missing_data.frame} constructor function returns an object of class \code{missing_data.frame} or that inherits from the \code{missing_data.frame} class. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{change}}, \code{\link{missing_variable}}, \code{\link{mi}}, \code{\link{experiment_missing_data.frame}}, \code{\link{multilevel_missing_data.frame}} } \examples{ # STEP 0: Get data data(CHAIN, package = "mi") # STEP 1: Convert to a missing_data.frame mdf <- missing_data.frame(CHAIN) # warnings about missingness patterns show(mdf) # STEP 2: change things mdf <- change(mdf, y = "log_virus", what = "transformation", to = "identity") # STEP 3: look deeper summary(mdf) hist(mdf) image(mdf) # STEP 4: impute \dontrun{ imputations <- mi(mdf) } ## An example with subsetting on a fully observed variable data(nlsyV, package = "mi") mdfs <- missing_data.frame(nlsyV, favor_positive = TRUE, favor_ordered = FALSE, by = "first") mdfs <- change(mdfs, y = "momed", what = "type", to = "ord") show(mdfs) } \keyword{classes} \keyword{manip} \keyword{AimedAtUseRs}r-cran-mi-1.0/man/03change.Rd000066400000000000000000000235551275731226000156100ustar00rootroot00000000000000\name{03change} \docType{methods} \alias{03change} \alias{change} \alias{change-methods} \alias{change_family} \alias{change_imputation_method} \alias{change_link} \alias{change_model} \alias{change_size} \alias{change_transformation} \alias{change_type} \title{Make Changes to Discretionary Characteristics of Missing Variables} \description{ These methods change the family, imputation method, size, type, and so forth of a \code{\link{missing_variable}}. They are typically called immediately before calling \code{\link{mi}} because they affect how the conditional expectation of each \code{\link{missing_variable}} is modeled. } \usage{ change(data, y, to, what, ...) change_family(data, y, to, ...) change_imputation_method(data, y, to, ...) change_link(data, y, to, ...) change_model(data, y, to, ...) change_size(data, y, to, ...) change_transformation(data, y, to, ...) change_type(data, y, to, ...) } \arguments{ \item{data}{A \code{\link{missing_data.frame}} (typically) but can be missing for all but the \code{change} function } \item{y}{A character vector (typically) naming one or more \code{\link{missing_variable}}s within the \code{\link{missing_data.frame}} specified by the \bold{data} argument. Alternatively, \bold{y} can be the name of a class that inherits from \code{\link{missing_variable}}, in which case all \code{\link{missing_variable}}s of that class within \code{data} will be changed. Can also be an vector of integers or a logical vector indicating which \code{\link{missing_variable}}s to change. } \item{what}{Typically a character string naming what is to be changed, such as \code{"family"}, \code{"imputation_method"}, \code{"size"}, \code{"transformation"}, \code{"type"}, \code{"link"}, or \code{"model"}. Alternatively, it can be a scalar value, in which case all occurances of that value for the variable indicated by \code{y} will be changed to the value indicated by \code{to} } \item{to}{Typically a character string naming what \code{y} should be changed to, such as one of the admissible families, imputation methods, transformations, or types. If missing, then possible choices for the \code{to} argument will be helpfully printed on the screen. If \code{what} is a number, then \code{to} should be the number (or \code{NA}) that the value designated by \code{what} will be recoded to. See the Details section for more information. } \item{\dots}{Other arguments, not currently utilized} } \details{ In order to run \code{\link{mi}} correctly, data must first be specified to be ready for multiple imputation using the \code{\link{missing_data.frame}} function. For each variable, \code{missing_data.frame} will record information required by \code{mi}: the variable's type, distribution family, and link function; whether a variable should be standardized or tranformed by a log function or square root; what specific model to use for the conditional distribution of the variable in the \code{mi} algorithm and how to draw imputed values from this model; and whether additional rows (for the purposes of prediction) are required. \code{missing_data.frame} will attempt to guess the correct type, family, and link for each variable based on its class in a regular \code{data.frame}. These guesses can be checked with \code{show} and adjusted if necessary with \code{change}. Any further additions to the model in regards to variable transformations, custom conditional models, or extra non-observed predictive cases must be specified with \code{change} before \code{mi} is run. In general, most users will only use the \code{change} command. \code{change} will then call \code{change_family}, \code{change_imputation_method}, \code{change_link}, \code{change_model}, \code{change_size}, \code{change_transformation}, or \code{change_type} depending on what characteristic is specified with the \code{what} option. The other change_* functions can be called directly but are primarily intended to be called indirectly by the change function. \describe{ \item{\code{what = "type"}}{Change the subclass of variable(s) \code{y}. \code{to} should be a character vector whose elements are subclasses of the \code{\link{missing_variable-class}} and are documented further there. Among the most commonly used subclasses are \code{"unordered-categorical"}, \code{"ordered-categorical"}, \code{"binary"}, \code{"interval"}, \code{"continuous"}, \code{"count"}, and \code{"irrelevant"}.} \item{\code{what = "family"}}{Change the distribution family for variable(s) \code{y}. \code{to} must be of class \code{\link{family}} or a list where each element is of class \code{\link{family}}. If a variable is of \code{\link{binary-class}}, then the family must be \code{\link{binomial}} (the default) or possibly \code{\link{quasibinomial}}. If a variable is of \code{\link{ordered-categorical-class}} or \code{\link{unordered-categorical-class}}, use the \code{\link{multinomial}} family. If a variable is of \code{\link{count-class}}, then the family must be \code{\link{quasipoisson}} (the default) or \code{\link{poisson}}. If a variable is continuous, there are more choices for its family, but \code{\link{gaussian}} is the default and the others are not supported yet.} \item{\code{what = "link"}}{Change the link function for variable(s) \code{y}. \code{to} can be any of the supported link functions for the existing \bold{family}. See \code{\link{family}} for details; however, not all of these link functions have appropriate \code{\link{fit_model}} and \code{\link{mi-methods}} yet.} \item{\code{what = "model"}}{Change the conditional model for variable \code{y}. It usually is not necessary to change the model, since it is actually determined by the class, family, and link function of the variable. This option can be used, however, to employ models that are not among those listed above.\code{to} should be a character vector of length one indicating what model should be used during the imputation process. Valid choices for binary variables include \code{"logit"}, \code{"probit"} \code{"cauchit"}, \code{"cloglog"}, or quasilikelihoods \code{"qlogit"}, \code{"qprobit"}, \code{"qcauchit"}, \code{"qcloglog"}. For ordinal variables, valid choices include \code{"ologit"}, \code{"oprobit"}, \code{"ocauchit"}, and \code{"ocloglog"}. For count variables, valid choices include \code{"qpoisson"} and \code{"poisson"}. Currently the only valid option for gaussian variables is \code{"linear"}. To change the model for unordered-categorical variables, see the estimator slot in \code{\link{missing_variable}}.} \item{\code{what = "imputation_method"}}{Change the method for drawing imputed values from the conditional model specified for variable(s) \code{y}. \code{to} should be a character vector of length one or of the same length as \code{y} naming one of the following imputation methods: \code{"ppd"} (posterior predictive distribution), \code{"pmm"} (predictive mean matching), \code{"mean"} (mean imputation), \code{"median"} (median imputation), \code{"expectation"} (conditional expectation imputation).} \item{\code{what = "size"}}{Optionally add additional rows for the purposes of prediction. \code{to} should be a single integer. If \code{to} is non-negative but less than the number of rows in the \code{\link{missing_data.frame}} given by the \code{data} argument, then \code{\link{missing_data.frame}} is augmented with \code{to} more rows, where all the additional observations are missing. If \code{to} is greater than the number of rows in the \code{\link{missing_data.frame}}given by the \code{data} argument, then the \code{\link{missing_data.frame}} is extended to have \code{to} rows, where the observations in the surplus rows are missing. If \code{to} is negative, then any additional rows in the \code{\link{missing_data.frame}} given by the \code{data} argument are removed to restore it to its original size.} \item{\code{what = "transformation"}}{Specify a particular transformation to be applied to variable(s) \code{y}. \code{to} should be a character vector of length one or of the same length as \code{y} indicating what transformation function to use. Valid choices are \code{"identity"} for no transformation, \code{"standardize"} for standardization (using twice the standard deviation of the observed values), \code{"log"} for natural logarithm transformation, \code{"logshift"} for a \code{log(y + a)} transformation where \code{a} is a small constant, or \code{"sqrt"} for square-root transformation. Changing the transformation will also change the inverse transformation in the appropriate way. Any other value of \code{to} will produce an informative error message indicating that the transformation and inverse transformation need to be changed manually.} \item{what = a value}{Finally, if both \code{what} and \code{to} are values then the former is recoded to the latter for all occurances within the missing variable indicated by \code{y}.} } } \value{ If the \bold{data} argument is not missing, then the method returns this argument with the specified changes. If \bold{data} is missing, then the method returns an object that inherits from the \code{\link{missing_variable-class}} with the specified changes. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{missing_data.frame}} } \examples{ # STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 1: CONVERT IT TO A missing_data.frame mdf <- missing_data.frame(nlsyV) show(mdf) # STEP 2: CHANGE WHATEVER IS WRONG WITH IT mdf <- change(mdf, y = "momrace", what = "type", to = "un") mdf <- change(mdf, y = "income", what = "imputation_method", to = "pmm") mdf <- change(mdf, y = "binary", what = "family", to = binomial(link = "probit")) mdf <- change(mdf, y = 5, what = "transformation", to = "identity") show(mdf) } \keyword{manip} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/04mi.Rd000066400000000000000000000163461275731226000147710ustar00rootroot00000000000000\name{04mi} \Rdversion{1.1} \docType{class} \alias{04mi} \alias{mi} \alias{mi-class} \alias{mi-methods} \title{Multiple Imputation } \description{ The \code{mi} function cannot be run in isolation. It is the most important step of a multi-step process to perform multiple imputation. The data must be specified as a \code{\link{missing_data.frame}} before \code{mi} is used to impute missing values for one or more \code{\link{missing_variable}}s. An iterative algorithm is used where each \code{\link{missing_variable}} is modeled (using \code{\link{fit_model}}) as a function of all the other \code{\link{missing_variable}}s and their missingness patterns. This documentation outlines the technical uses of the \code{mi} function. For a more general discussion of how to use \code{mi} for multiple imputation, see \code{\link{mi-package}}. } \usage{ mi(y, model, ...) ## Hidden arguments: ## n.iter = 30, n.chains = 4, max.minutes = Inf, seed = NA, verbose = TRUE, ## save_models = FALSE, parallel = .Platform$OS.type != "windows" } \arguments{ \item{y}{Typically an object that inherits from the \code{\link{missing_data.frame-class}}, although many methods are defined for subclasses of the \code{\link{missing_variable-class}}. Alternatively, \code{y = "parallel"} the appropriate parallel backend will be registered but no imputation performed. See the Details section. } \item{model}{Missing when \code{y = "parallel"} or when \code{y} inherits from the \code{\link{missing_data.frame-class}} but otherwise should be the result of a call to \code{\link{fit_model}}. } \item{\dots}{Further arguments, the most important of which are \describe{ \item{\code{n.iter}}{number of iterations to perform, defaulting to 30} \item{\code{n.chains}}{number of chains to use, ideally equal to the number of virtual cores available for use, and defaulting to 4} \item{\code{max.minutes}}{hard time limit that defaults to 20} \item{\code{seed}}{either \code{NA}, which is the default, or a psuedo-random number seed} \item{\code{verbose}}{logical scalar that is \code{TRUE} by default, indicating that progress of the iterative algorithm should be printed to the screen, which does not work under Windows when the chains are executed in parallel} \item{\code{save_models}}{logical scalar that defaults to \code{FALSE} but if \code{TRUE} indicates that the models estimated on a frozen completed dataset should be saved. This option should be used if the user is interested in evaluating the quality of the models run after the last iteration of the \code{mi} algorithm, but saving these models consumes much more RAM} \item{\code{debug}}{logical scalar indicating whether to run in debug mode, which forces the processing to be sequential, and allows developers to capture errors within chains} \item{\code{parallel}}{if TRUE, then parallel processing is used, if available. If FALSE, sequential processing is used. In addition, ths argument may be an object produced by \code{\link[parallel]{makeCluster}}} } } } \details{ It is important to distinguish the two \code{mi} methods that are most relevant to users from the many \code{mi} methods that are less relevant. The primary \code{mi} method is that where \code{y} inherits from the \code{\link{missing_data.frame-class}} and \code{model} is omitted. This method \dQuote{does} the imputation according to the additional arguments described under \dots above and returns an object of class \code{"mi"}. Executing two or more independent chains is important for monitoring the convergence of each chain, see \code{\link{Rhats}}. If the chains have not converged in the amount of iterations or time specified, the second important \code{mi} method is that where \code{y} is an object of class \code{"mi"} and \code{model} is omitted, which continues a previous run of the iterative imputation algorithm. All the arguments described under \dots above remain applicable, except for \code{n.chains} and \code{save_RAM} because these are established by the previous run that is being continued. The numerous remaining methods are of less importance to users. One \code{mi} method is called when \code{y = "parallel"} and \code{model} is omitted. This method merely sets up the parallel backend so that the chains can be executed in parallel on the local machine. We use the \code{\link{mclapply}} function in the \pkg{parallel} package to implement parallel processing on non-Windows machines, and we use the \pkg{snow} package to implement parallel processing on Windows machines; we refer users to the documentation for these packages for more detail about parallel processing. Parallel processing is used by default on machines with multiple processors, but sequential processing can be used instead by using the \code{parallel=FALSE} option. If the user is not using a mulitcore computer, sequential processing is used instead of parallel processing. The first two \code{mi} methods described above in turn call a \code{mi} method where \code{y} inherits from the \code{\link{missing_data.frame-class}} and \code{model} is that which is returned by one of the \code{\link{fit_model-methods}}. The methods impute values for the originally missing values of a \code{\link{missing_variable}} given a fitted model, according to the \bold{imputation_method} slot of the \code{\link{missing_variable}} in question. Advanced users could define new subclasses of the \code{\link{missing_variable-class}} in which case it may be necessary to write such a \code{mi} method for the new class. It will almost certainly be necessary to add to the \code{\link{fit_model-methods}}. The existing \code{mi} and \code{fit-model-methods} should provide a template for doing so. } \value{ If \code{model} is missing and \code{n.chains} is positive, then the \code{mi} method will return an object of class \code{"mi"}, which has the following slots: \describe{ \item{call}{the call to \code{mi}} \item{data}{a list of \code{\link{missing_data.frame}}s, one for each chain} \item{total_iters}{an integer vector that records how many iterations have been performed} } There are a few methods for such an object, such as \code{\link{show}}, \code{\link{summary}}, \code{\link{dimnames}}, \code{\link{nrow}}, \code{\link{ncol}}, etc. If \code{mi} is called on a \code{\link{missing_data.frame}} with \code{model} missing and a nonpositive \code{n.chains}, then the \code{\link{missing_data.frame}} will be returned after allocating storeage. If \code{model} is not missing, then the \code{mi} method will impute missing values for the \code{y} argument and return it. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_data.frame}}, \code{\link{fit_model}} } \examples{ # STEP 0: Get data data(CHAIN, package = "mi") # STEP 1: Convert to a missing_data.frame mdf <- missing_data.frame(CHAIN) # warnings about missingness patterns show(mdf) # STEP 2: change things mdf <- change(mdf, y = "log_virus", what = "transformation", to = "identity") # STEP 3: look deeper summary(mdf) # STEP 4: impute \dontrun{ imputations <- mi(mdf) } } \keyword{classes} \keyword{regression} \keyword{AimedAtusers} r-cran-mi-1.0/man/05Rhats.Rd000066400000000000000000000041531275731226000154370ustar00rootroot00000000000000\name{05Rhats} \alias{Rhats} \alias{05Rhats} \alias{mi2BUGS} \title{Convergence Diagnostics} \description{ These functions are used to gauge whether \code{\link{mi}} has converged. } \usage{ Rhats(imputations, statistic = c("moments", "imputations", "parameters")) mi2BUGS(imputations, statistic = c("moments", "imputations", "parameters")) } \arguments{ \item{imputations}{an object of \code{\link{mi-class}} } \item{statistic}{single character string among \code{"moments"}, \code{"imputations"}, and \code{"parameters"} indicating what statistic to monitor for convergence } } \details{ If \code{statistic = "moments"} (the default), then the mean and standard deviation of each variable will be monitored over the iterations. If \code{statistic = "imputations"}, then the imputed values will be monitored, which may be quite large and quite slow and is not possible if the \code{save_RAM = TRUE} flag was set in the call to the \code{\link{mi}} function. If \code{statistic = "parameters"}, then the estimated coefficients and ancillary parameters extracted by the \code{\link{get_parameters-methods}} will be monitored. \code{Rhats} produces a vector of R-hat convergence statistics that compare the variance between chains to the variance across chains. Values closer to 1.0 indicate little is to be gained by running the chains longer, and in general, values greater than 1.1 indicate that the chains should be run longer. See Gelman, Carlin, Stern, and Rubin, "Bayesian Data Analysis", Second Edition, 2009, p.304 for more information about the R-hat statistic. \code{mi2BUGS} outputs the history of the indicated statistic } \value{ \code{mi2BUGS} returns an array while \code{Rhats} a vector of R-hat convergence statistics. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \examples{ if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } dim(mi2BUGS(imputations)) Rhats(imputations) } \keyword{manip} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/06pool.Rd000066400000000000000000000034221275731226000153260ustar00rootroot00000000000000\name{06pool} \alias{06pool} \Rdversion{1.1} \docType{class} \alias{pool} \alias{pooled-class} \alias{pooled-methods} \alias{display,pooled-method} \title{Estimate a Model Pooling Over the Imputed Datasets} \description{ This function estimates a chosen model, taking into account the additional uncertainty that arises due to a finite number of imputations of the missing data. } \usage{ pool(formula, data, m = NULL, FUN = NULL, ...) } \arguments{ \item{formula}{a \code{\link{formula}} in the same syntax as used by \code{\link{glm}} } \item{data}{an object of \code{\link{mi-class}} } \item{m}{number of completed datasets to average over, which if \code{NULL} defaults to the number of chains used in \code{\link{mi}} } \item{FUN}{Function to estimate models or \code{NULL} which uses the same function as used in the \code{\link{fit_model-methods}} for the dependent variable } \item{\dots}{further arguments passed to \code{FUN} } } \details{ \code{FUN} is estimated on each of the \code{m} completed datasets according to the given \code{formula} and the results are combined using the Rubin Rules. } \value{ An object of class \code{"pooled"} whose definition is subject to change but it has a \code{\link{summary}} and \code{\link[arm]{display}} method. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{mi}} } \examples{ if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace, data = imputations) display(analysis) } \keyword{regression} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/07complete.Rd000066400000000000000000000041371275731226000161720ustar00rootroot00000000000000\name{07complete} \docType{methods} \alias{07complete} \alias{complete} \alias{complete-methods} \title{Extract the Completed Data} \description{ This function extracts several multiply imputed \code{\link{data.frame}}s from an object of \code{\link{mi-class}}. } \usage{ complete(y, m, ...) } \arguments{ \item{y}{An object of \code{\link{mi-class}} (typically) or \code{\link{missing_data.frame-class}} or \code{\link{missing_variable-class}} } \item{m}{If \bold{y} is an object of \code{\link{mi-class}}, then \code{m} must be a specified integer indicating how many multiply imputed \code{\link{data.frame}}s to return or, if missing, the number of \code{\link{data.frame}}s will be equal to the length of the \bold{data} slot in \code{y}. If \code{y} is not an object of \code{\link{mi-class}}, then \bold{m} must be a specified integer indicating which iteration to use in the resulting \code{\link{data.frame}}, where any non-positive integer is a short hand for the last iteration. } \item{\dots}{Other arguments, not currently utilized} } \details{ Several functions within \pkg{mi} use \code{complete}, although the only reason in principle why a user should need to call \code{complete} is to create \code{\link{data.frame}}s to export to another program. For analysis, it is better to use the \code{\link{pool}} function, although currently \code{\link{pool}} might not offer all the necessary functionality. } \value{ If \bold{y} is an object of \code{\link{mi-class}} and \code{m > 1}, a \code{\link{list}} of \code{m} \code{\link{data.frame}}s is returned. Otherwise, a single \code{\link{data.frame}} is returned. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{mi-class}} } \examples{ if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } data.frames <- complete(imputations, 3) lapply(data.frames, summary) } \keyword{manip} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/CHAIN.Rd000066400000000000000000000040051275731226000150270ustar00rootroot00000000000000\name{CHAIN} \docType{data} \alias{CHAIN} \title{ Subset of variables from the CHAIN project } \description{ The CHAIN project was a longitudinal cohort study of people living with HIV in New York City, which was recruited in 1994 from a large number of medical care and social service agencies serving HIV in New York City. This subset of data pertain to the sixth round of interviews. } \usage{data(CHAIN)} \format{ A \code{\link{data.frame}} with 532 observations on the following 8 variables. \describe{ \item{\code{log_virus}}{ log of self reported viral load level, where zero represents an undetectable level. } \item{\code{age}}{ age at time of the interview } \item{\code{income}}{ annual family income in 10 intervals } \item{\code{healthy}}{ a continuous scale of physical health with a theoretical range between 0 and 100 where better health is associated with higher scale values } \item{\code{mental}}{ a binary measure of poor mental health ( 1=Yes, 0=No ) } \item{\code{damage}}{ ordered interval for the CD4 count, which is an indicator of how much damage HIV has caused to the immune system } \item{\code{treatment}}{ a three-level ordered variable: 0=Not currently taking HAART (Highly Active AntiretRoviral Therapy) 1=taking HAART but nonadherent, 2=taking HAART and adherent } } } \details{ A missing value in the log virus load level was assigned to individuals who either could not recall their viral load level, did not have a viral load test in the six month preceding the interview, or reported their viral loads as "good" or "bad". } \source{ http://cchps.columbia.edu/research.cfm } \references{ Messeri P, Lee G, Abramson DA, Aidala A, Chiasson MA, Jones JD. (2003). \dQuote{Antiretroviral therapy and declining AIDS mortality in New York City}. \emph{Medical Care} 41:512--521. } \keyword{datasets} r-cran-mi-1.0/man/allcategorical_missing_data.frame.Rd000066400000000000000000000035161275731226000227740ustar00rootroot00000000000000\name{allcategorical_missing_data.frame} \Rdversion{1.1} \docType{class} \alias{allcategorical_missing_data.frame} \alias{allcategorical_missing_data.frame-class} \title{Class "allcategorical_missing_data.frame"} \description{ This class inherits from the \code{\link{missing_data.frame-class}} but is customized for the situation where all the variables are categorical. } \section{Objects from the Class}{ Objects can be created by calls of the form \code{new("allcategorical_missing_data.frame", ...)}. However, its users almost always will pass a \code{\link{data.frame}} to the \code{\link{missing_data.frame}} function and specify the \code{subclass} argument. } \section{Slots}{ The allcategorical_missing_data.frame class inherits from the \code{\link{missing_data.frame-class}} and has three additional slots \describe{ \item{Hstar}{Positive integer indicating the maximum number of latent classes} \item{parameters}{A list that holds the current realization of the unknown parameters} \item{latents}{An object of \code{\link{unordered-categorical-class}} that contains the current realization of the latent classes} } } \details{ The \code{\link{fit_model-methods}} for the allcategorical_missing_data.frame class implement a Gibbs sampler. However, it does not utilize any ordinal information that may be available. Continuous variables should be made into factors using the \code{\link{cut}} command before calling \code{\link{missing_data.frame}}. } \author{ Sophie Si for the algorithm and Ben Goodrich for the R implementation } \seealso{ \code{\link{missing_data.frame}} } \examples{ rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "stratified", types = "ord") mdf <- missing_data.frame(rdf$obs, subclass = "allcategorical") } \keyword{classes} \keyword{manip} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/bounded.Rd000066400000000000000000000053131275731226000156300ustar00rootroot00000000000000\name{bounded-continuous-class} \Rdversion{1.1} \docType{class} \alias{bounded-continuous-class} \alias{bounded-continuous} \title{Class "bounded-continuous"} \description{ The bounded-continuous class inherits from the \code{\link{continuous-class}} and is intended for variables whose observations fall within open intervals that have \emph{known} boundaries. Although proportions satisfy this definition, the \code{\link{proportion-class}} should be used in that case. At the moment, a bounded continuous variable is modeled as if it were simply a continuous variable, but its \code{\link{mi-methods}} impute the missing values from a truncated normal distribution using the \code{\link[truncnorm]{rtruncnorm}} function in the \pkg{truncnorm} package. Note that the default transformation is the identity so if another transformation is used, the bounds must be specified on the transformed data. Aside from these facts, the rest of the documentation here is primarily directed toward developers. } \section{Objects from the Classes}{Objects can be created that are of bounded-continuous class via the the \code{\link{missing_variable}} generic function by specifying \code{type = "bounded-continuous"} as well as \code{lower} and / or \code{upper} } \section{Slots}{ The bounded-continuous class inherits from the continuous class and is intended for variables that are supported on a known interval. Its default transformation function is the identity transformation and its \code{imputation_method} must be \code{"ppd"}. It has two additional slots: \describe{ \item{upper}{a numeric vector whose length is either one or the value of the \code{n_total} slot giving the upper bound for \emph{every} observation; \code{NA}s are not allowed} \item{lower}{a numeric vector whose length is either one or the value of the \code{n_total} slot giving the lower bound for \emph{every} observation; \code{NA}s are not allowed} } } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{continuous-class}}, \code{\link{positive-continuous-class}}, \code{\link{proportion-class}} } \examples{ # STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) lo_bound <- 0 hi_bound <- rep(Inf, nrow(CHAIN)) hi_bound[CHAIN$log_virus == 0] <- 6 log_virus <- missing_variable(ifelse(CHAIN$log_virus == 0, NA, CHAIN$log_virus), type = "bounded-continuous", lower = lo_bound, upper = hi_bound) show(log_virus) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/categorical.Rd000066400000000000000000000071071275731226000164700ustar00rootroot00000000000000\name{categorical} \Rdversion{1.1} \docType{class} \alias{categorical} \alias{categorical-class} \alias{unordered-categorical-class} \alias{ordered-categorical-class} \alias{interval-class} \alias{binary-class} \alias{grouped-binary-class} \title{Class "categorical" and Inherited Classes} \description{ The categorical class is a virtual class that inherits from the \code{\link{missing_variable-class}} and is the parent of the unordered-categorical and ordered-categorical classes. The ordered-categorical class is the parent of both the binary and interval classes. Aside from these facts, the rest of the documentation here is primarily directed toward developers. } \section{Objects from the Classes}{The categorical class is virtual, so no objects may be created from it. However, the \code{\link{missing_variable}} generic function can be used to instantiate an object that inherits from the categorical class by specifying \code{type = "unordered-categorical"}, \code{type = "ordered-categorical"}, \code{type = "binary"}, \code{type = "grouped-binary"}, or \code{type = "interval"}. } \section{Slots}{ The unordered-categorical class inherits from the categorical class and has no additional slots but must have more than two uniquely observed values in its \code{raw_data} slot. The default \code{\link{fit_model}} method is a wrapper for the \code{\link[nnet]{multinom}} function in the \pkg{nnet} package. The ordered-categorical class inherits from the categorical class and has one additional slot: \describe{ \item{cutpoints}{Object of class \code{"numeric"} which is a vector of thresholds (sometimes estimated) that govern how an assumed latent variable is divided into observed ordered categories} } The \code{\link{fit_model}} method for an ordered-categorical variable is, by default, a wrapper for \code{\link[arm]{bayespolr}}. The binary class inherits from the ordered-categorical class and has no additional slots. It must have exactly two uniquely observed values in its \code{raw_data} slot and its \code{\link{fit_model}} method is, by default, a wrapper for \code{\link[arm]{bayespolr}}. The grouped-binary class inherits from the binary class and has one additional slot: \describe{ \item{strata}{Object of class \code{"character"} which is a vector (possibly of length one) of variable names that group the observations into strata. The named external variables should also be categorical.} } The default \code{\link{fit_model}} method for a grouped-binary variable is a wrapper for the \code{\link[survival]{clogit}} function in the \pkg{survival} package and the variables named in the \bold{strata} slot are passed to the \code{\link[survival]{strata}} function. The interval class inherits from the ordered-categorical class, has no additional slots, and is intended for variables whose observed values are only known up to orderable intervals. Its \code{\link{fit_model}} method is, by default, a wrapper for \code{\link[survival]{survreg}} even though it may or may not be a \dQuote{survival} model in any meaningful sense. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}} } \examples{ # STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) momrace <- missing_variable(as.factor(nlsyV$momrace), type = "unordered-categorical") show(momrace) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/censored-continuous.Rd000066400000000000000000000101271275731226000202150ustar00rootroot00000000000000\name{censored-continuous-class} \Rdversion{1.1} \docType{class} \alias{truncated-continuous-class} \alias{truncated-continuous} \alias{FF_truncated-continuous-class} \alias{FN_truncated-continuous-class} \alias{NF_truncated-continuous-class} \alias{NN_truncated-continuous-class} \alias{censored-continuous-class} \alias{censored-continuous} \alias{FF_censored-continuous-class} \alias{FN_censored-continuous-class} \alias{NF_censored-continuous-class} \alias{NN_censored-continuous-class} \title{The "censored-continuous" Class, the "truncated-continuous" Class and Inherited Classes} \description{ The censored-continuous class and the truncated-continuous class are both virtual and both inherit from the \code{\link{continuous-class}} and each is the parent of four classes that differ depending on whether the lower and upper bounds are numeric vectors or functions. A censored observation is one whose exact value is not observed. A truncated observation is one whose exact value is not observed and which implies that values on some \emph{other} variables are not observed for that unit of observation. An example of truncation might be that some taxation forms are not required when a person's income falls below a certain threshold. The methods for these classes are not working yet. Aside from these facts, the rest of the documentation here is primarily directed toward developeRs. } \section{Objects from the Classes}{Both the censored-continuous class and the truncated-continuous class are virtual, so no objects can be created with these classes. However, the \code{\link{missing_variable}} generic function can be used to create an object that inherits from one of their subclasses by specifying \code{type = "NNcensored-continuous"}, \code{type = "NFcensored-continuous"}, \code{type = "FNcensored-continuous"}, \code{type = "FFcensored-continuous"}, \code{type = "NNtruncated-continuous"}, \code{type = "NFtruncated-continuous"}, \code{type = "FNtruncated-continuous"}, \code{type = "FFtruncated-continuous"}. When doing so, the lower and upper slots need to be specified appropriately. } \section{Slots}{ The censored-continuous class and the truncated-continuous class are both virtual, both inherit from the continuous class, both use the identity transformation by default, and both have two additional slots: \describe{ \item{upper}{The upper bound for each observation} \item{lower}{The lower bound for each observation} } Both the censored-continuous class and the truncated-continuous class have four subclasses that differ depending on whether the upper and / or lower bounds are numeric vectors or functions that output numeric vectors (scalars are recycled and can be \code{Inf}). These subclasses are \describe{ \item{NN_censored-continuous}{where both the lower and upper bounds are numeric vectors} \item{FN_censored-continuous}{where the lower bound is a function and the upper bound is a numeric vector} \item{NF_censored-continuous}{where the lower bound is a numeric vector and the upper bound is a function} \item{FF_censored-continuous}{where both the lower and upper bounds are functions} \item{NN_truncated-continuous}{where both the lower and upper bounds are numeric vectors} \item{FN_truncated-continuous}{where the lower bound is a function and the upper bound is a numeric vector} \item{NF_truncated-continuous}{where the lower bound is a numeric vector and the upper bound is a function} \item{FF_truncated-continuous}{where both the lower and upper bounds are functions} } } \author{ Ben Goodrich, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{continuous-class}} } \examples{ # STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) #log_virus <- missing_variable(CHAIN$log_virus, type = "NN_censored-continuous", # lower = 0, upper = Inf) #show(log_virus) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/continuous.Rd000066400000000000000000000050131275731226000164130ustar00rootroot00000000000000\name{continuous} \Rdversion{1.1} \docType{class} \alias{continuous} \alias{continuous-class} \title{Class "continuous"} \description{ The continuous class inherits from the \code{\link{missing_variable-class}} and is the parent of the following classes: \code{\link{semi-continuous}}, \code{\link{censored-continuous}}, \code{\link{truncated-continuous}}, and \code{\link{bounded-continuous}}. The distinctions among these subclasses are given on their respective help pages. Aside from these facts, the rest of the documentation here is primarily directed toward developers. } \section{Objects from the Classes}{Objects can be created that are of class continuous via the \code{\link{missing_variable}} generic function by specifying \code{type = "continuous"} } \section{Slots}{ The continuous class inherits from the \code{\link{missing_variable}} class and has the following additional slots: \describe{ \item{transformation}{Object of class \code{"function"} which is passed the \code{raw_data} slot and whose returned value is assigned to the \code{data} slot. By default, this function is the \dQuote{standardize} transformation, using the mean and \emph{twice} the standard deviation of the observed values} \item{inverse_transformation}{Object of class \code{"function"} which is the inverse of the function in the \code{transformation} slot.} \item{transformed}{Object of class \code{"logical"} of length one indicating whether the \code{data} slot is in the \dQuote{transformed} state or the \dQuote{untransformed} state} \item{known_transformations}{Object of class \code{"character"} indicating which transformations are possible for this variable} } The \code{\link{fit_model}} method for a continuous variable is, by default, a wrapper for \code{\link[arm]{bayesglm}} and its \code{family} slot is, by default, \code{\link{gaussian}} } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{semi-continuous-class}}, \code{\link{censored-continuous-class}}, \code{\link{truncated-continuous-class}}, \code{\link{bounded-continuous-class}} } \examples{ # STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "continuous") show(income) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/count.Rd000066400000000000000000000027371275731226000153470ustar00rootroot00000000000000\name{count-class} \Rdversion{1.1} \docType{class} \alias{count-class} \title{Class "count"} \description{ The count class inherits from the \code{\link{missing_variable-class}} and is intended for count data. Aside from these facts, the rest of the documentation here is primarily directed toward developers. } \section{Objects from the Classes}{Objects can be created that are of count class via the \code{\link{missing_variable}} generic function by specifying \code{type = "count"} } \section{Slots}{ The count class inherits from the missing_variable class and its \code{raw_data} slot must consist of nonnegative integers. Its default family is \code{\link{quasipoisson}} and its default \code{\link{fit_model}} method is a wrapper for \code{\link[arm]{bayesglm}}. The other possibility for the family is \code{\link{poisson}} but is not recommended due to its overly-restrictive nature. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{continuous-class}}, \code{\link{positive-continuous-class}}, \code{\link{proportion-class}} } \examples{ # STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) age <- missing_variable(as.integer(CHAIN$age), type = "count") show(age) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/experiment_missing_data.frame.Rd000066400000000000000000000045651275731226000222130ustar00rootroot00000000000000\name{experiment_missing_data.frame} \Rdversion{1.1} \docType{class} \alias{experiment_missing_data.frame} \alias{experiment_missing_data.frame-class} \title{Class "experiment_missing_data.frame"} \description{ This class inherits from the \code{\link{missing_data.frame-class}} but is customized for the situation where the sample is a randomized experiment. } \section{Objects from the Class}{ Objects can be created by calls of the form \code{new("experiment_missing_data.frame", ...)}. However, its users almost always will pass a \code{\link{data.frame}} to the \code{\link{missing_data.frame}} function and specify the \code{subclass} and \code{concept} arguments. } \section{Slots}{ The experiment_missing_data.frame class inherits from the \code{\link{missing_data.frame-class}} and has two additional slots \describe{ \item{concept}{Object of class \code{\link{factor}} whose length is equal to the number of variables and whose levels are \code{"treatment"}, \code{"covariate"} and \code{"outcome"}} \item{case}{Object of class \code{\link{character}} of length one, indicating whether the missingness is in the outcomes only, in the covariates only, or in both the outcomes and covariates. This slot is filled automatically by the \code{\link{initialize}} method} } } \details{ The \code{\link{fit_model-methods}} for the experiment_missing_data.frame class take into account the special nature of a randomized experiment. At the moment, the treatment variable must be binary and fully observed. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_data.frame}} } \examples{ rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "stratified", experiment = TRUE, types = c("t", "ord", "con", "pos"), treatment_cor = c(0, 0, NA, 0, NA)) Sigma <- tcrossprod(rdf$L) rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2", "missing_y_1", "missing_Y_2") print(round(Sigma, 3)) concept <- as.factor(c("treatment", "covariate", "covariate", "outcome")) mdf <- missing_data.frame(rdf$obs, subclass = "experiment", concept = concept) } \keyword{classes} \keyword{manip} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/fit_model.Rd000066400000000000000000000067021275731226000161550ustar00rootroot00000000000000\name{fit_model} \docType{methods} \alias{fit_model} \alias{fit_model-methods} \title{Wrappers To Fit a Model} \description{ The methods are called by the \code{\link{mi}} function to model a given \code{\link{missing_variable}} as a function of all the other \code{\link{missing_variable}}s and also their missingness pattern. By overwriting these methods, users can change the way a \code{\link{missing_variable}} is modeled for the purposes of imputing its missing values. See also the table in \code{\link{missing_variable}}. } \usage{ fit_model(y, data, ...) } \arguments{ \item{y}{An object that inherits from \code{\link{missing_variable-class}} or missing } \item{data}{A \code{\link{missing_data.frame}} } \item{\dots}{Additional arguments, not currently utilized } } \details{ In \code{\link{mi}}, each \code{\link{missing_variable}} is modeled as a function of all the other \code{\link{missing_variable}}s plus their missingness pattern. The \code{fit_model} methods are typically short wrappers around a statistical model fitting function and return the estimated model. The model is then passed to one of the \code{\link{mi-methods}} to impute the missing values of that \code{\link{missing_variable}}. Users can easily overwrite these methods to estimate a different model, such as wrapping \code{\link{glm}} instead of \code{\link[arm]{bayesglm}}. See the source code for examples, but the basic outline is to first extract the \code{X} slot of the \code{\link{missing_data.frame}}, then drop some of its columns using the \code{index} slot of the \code{\link{missing_data.frame}}, next pass the result along with the \code{data} slot of \code{y} to a statistical fitting function, and finally returned the appropriately classed result (along with the subset of \code{X} used in the model). Many of the optional arguments to a statistical fitting function can be specified using the slots of \code{y} (e.g. its \code{family} slot) or the slots of \bold{data} (e.g. its \code{weights} slot). The exception is the method where \code{y} is missing, which is used internally by \code{\link{mi}}, and should \emph{not} be overwritten unless great care is taken to understand its role. } \value{ If \code{y} is missing, then the modified \code{\link{missing_data.frame}} passed to \code{data} is returned. Otherwise, the estimated model is returned as a classed list object. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{mi}}, \code{\link{get_parameters}} } \examples{ getMethod("fit_model", signature(y = "binary", data = "missing_data.frame")) setMethod("fit_model", signature(y = "binary", data = "missing_data.frame"), def = function(y, data, ...) { to_drop <- data@index[[y@variable_name]] X <- data@X[, -to_drop] start <- NULL # using glm.fit() instead of bayesglm.fit() out <- glm.fit(X, y@data, weights = data@weights[[y@variable_name]], start = start, family = y@family, Warning = FALSE, ...) out$x <- X class(out) <- c("glm", "lm") # not "bayesglm" class anymore return(out) }) \dontrun{ if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } imputations <- mi(imputations) # will use new fit_model() method for binary variables } } \keyword{regression} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/get_parameters.Rd000066400000000000000000000027161275731226000172160ustar00rootroot00000000000000\name{get_parameters} \docType{methods} \alias{get_parameters} \alias{get_parameters-methods} \title{An Extractor Function for Model Parameters} \description{ This function is not intended to be called directly by users. During the multiple imputation process, the \code{\link{mi}} function estimates models and stores the estimated parameters in the \code{parameters} slot of an object that inherits from the \code{\link{missing_variable-class}}. The \code{get_parameter} function simply extracts these parameters for storeage, which are usually the estimated coefficients but may also include ancillary parameters. } \usage{ get_parameters(object, ...) } \arguments{ \item{object}{Usually an estimated model, such as that produced by \code{\link{glm}} } \item{\dots}{Additional arguments, currently not used } } \details{ There is method for the object produced by \code{\link{polr}}, which also returns the estimated cutpoints in a proportional odds model. However, the default method simply calls \code{\link{coef}} and returns the result. If users implement their own models, it may be necessary to write a short \code{get_parameters} method. } \value{ A numeric vector of estimated parameters } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{fit_model}} } \examples{ showMethods("get_parameters") } \keyword{methods}r-cran-mi-1.0/man/hist.Rd000066400000000000000000000025671275731226000151670ustar00rootroot00000000000000\name{hist} \Rdversion{1.1} \docType{methods} \alias{hist} \alias{hist-methods} \title{Histograms of Multiply Imputed Data } \description{ This function creates a histogram from an object of \code{\link{missing_data.frame-class}} or \code{\link{mi-class}} } \usage{ hist(x, ...) } \arguments{ \item{x}{an object of \code{\link{missing_data.frame-class}} or \code{\link{mi-class}} } \item{\dots}{further arguments passed to \code{\link{plot.histogram}} } } \details{ When called on an object of \code{\link{missing_data.frame-class}}, the histograms of the observed data are generated, one for each \code{\link{missing_variable}} but grouped on a single page. When called on an object of \code{\link{mi-class}}, the histograms of the observed, imputed, and completed data are generated, one for each \code{\link{missing_variable}}, grouped on a single page for each chain. } \value{ An invisible \code{NULL} is returned with a side-effect of creating a plot } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link[graphics]{hist}} } \examples{ if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } hist(imputations) } \keyword{hplot} \keyword{AimedAtUseRs} \keyword{methods}r-cran-mi-1.0/man/irrelevant.Rd000066400000000000000000000036731275731226000163720ustar00rootroot00000000000000\name{irrelevant} \Rdversion{1.1} \docType{class} \alias{irrelevant} \alias{irrelevant-class} \alias{fixed-class} \alias{group-class} \title{Class "irrelevant" and Inherited Classes} \description{ The irrelevant class inherits from the \code{\link{missing_variable-class}} and is used to designate variables that are excluded from the models used to impute the missing values of \dQuote{relevant} variables. For example, if a survey has an \dQuote{id} variable that simply distinguishes observations, the user should designate it as irrelevant, although it will automatically be classified so if its name is either \dQuote{id} or starts with punctuation (including underscores). The fixed class inherits from the irrelevant class and is used for variables that are constant (within a sample). A variable that is instantiated from the fixed class cannot have any missing values. The group class inherits from the fixed class and is used like a \code{\link{factor}} to spit samples in multilevel modeling; see \code{\link{multilevel_missing_data.frame-class}}. None of these classes have an additional slots. Aside from these facts, the rest of the documentation here is primarily directed toward developeRs. } \section{Objects from the Classes}{The \code{\link{missing_variable}} generic function can be used to instantiate an object that inherits from the irrelevant class by specifying \code{type = "irrelevant"}, \code{type = "fixed"}, or \code{type = "group"}. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable-class}} } \examples{ # STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) first <- missing_variable(as.factor(nlsyV$first), type = "group") show(first) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/mi-internal.Rd000066400000000000000000000223441275731226000164320ustar00rootroot00000000000000\name{mi-internal} \alias{mi-internal} \alias{change,missing_data.frame,ANY,ANY,character-method} \alias{mi,missing_data.frame,missing-method} \alias{plot,missing_data.frame,missing-method} \alias{plot,mi,ANY-method} \alias{show,missing_data.frame-method} \alias{show,missing_variable-method} \alias{summary,missing_data.frame-method} \alias{summary,mi-method} \alias{show,mi-method} \alias{change_family,missing,missing_variable,family-method} \alias{change_family,missing,proportion,family-method} \alias{change_family,missing,unordered-categorical,family-method} \alias{change_imputation_method,missing,missing_variable,character-method} \alias{change_imputation_method,missing,missing_variable,missing-method} \alias{change_link,missing,missing_variable,character-method} \alias{change_link,missing,missing_variable,missing-method} \alias{change_link,missing_data.frame,character,character-method} \alias{change_link,missing_data.frame,numeric,character-method} \alias{change_link,missing_data.frame,logical,character-method} \alias{change_model,missing,missing_variable,character-method} \alias{change_model,missing_data.frame,character,character-method} \alias{change_model,missing_data.frame,numeric,character-method} \alias{change_model,missing_data.frame,logical,character-method} \alias{change_size,missing,missing_variable,integer-method} \alias{change_size,missing,categorical,integer-method} \alias{change_size,missing,fixed,integer-method} \alias{change_size,missing_data.frame,missing,integer-method} \alias{change_type,missing,missing_variable,character-method} \alias{change_imputation_method,missing_data.frame,logical,character-method} \alias{change_imputation_method,missing_data.frame,numeric,character-method} \alias{change_transformation,missing,missing_variable,missing-method} \alias{change_transformation,missing_data.frame,character,missing-method} \alias{change_transformation,missing_data.frame,character,function-method} \alias{change_transformation,missing_data.frame,numeric,function-method} \alias{change_transformation,missing_data.frame,logical,function-method} \alias{change_type,missing,missing_variable,missing-method} \alias{change_type,missing_data.frame,character,missing-method} \alias{complete,irrelevant,integer-method} \alias{complete,categorical,integer-method} \alias{complete,binary,integer-method} \alias{complete,continuous,integer-method} \alias{complete,nonnegative-continuous,integer-method} \alias{complete,SC_proportion,integer-method} \alias{complete,mi,integer-method} \alias{complete,missing_data.frame,numeric-method} \alias{complete,missing_variable,integer-method} \alias{complete,mi,missing-method} \alias{complete,mi_list,numeric-method} \alias{complete,mi_list,missing-method} \alias{fit_model,missing_variable,missing_data.frame-method} \alias{fit_model,missing,missing_data.frame-method} \alias{fit_model,continuous,missing_data.frame-method} \alias{fit_model,semi-continuous,missing_data.frame-method} \alias{fit_model,nonnegative-continuous,missing_data.frame-method} \alias{fit_model,SC_proportion,missing_data.frame-method} \alias{fit_model,proportion,missing_data.frame-method} \alias{fit_model,truncated-continuous,missing_data.frame-method} \alias{fit_model,censored-continuous,missing_data.frame-method} \alias{fit_model,missing_variable,experiment_missing_data.frame-method} \alias{fit_model,continuous,experiment_missing_data.frame-method} \alias{fit_model,missing,multilevel_missing_data.frame-method} \alias{fit_model,missing,mdf_list-method} \alias{fit_model,binary,allcategorical_missing_data.frame-method} \alias{fit_model,missing,allcategorical_missing_data.frame-method} \alias{fit_model,ordered-categorical,allcategorical_missing_data.frame-method} \alias{fit_model,unordered-categorical,allcategorical_missing_data.frame-method} \alias{get_parameters,ANY-method} \alias{get_parameters,polr-method} \alias{get_parameters,multinom-method} \alias{get_parameters,mi-method} \alias{get_parameters,mi_list-method} \alias{get_parameters,missing_data.frame-method} \alias{get_parameters,missing_variable-method} \alias{hist,mi-method} \alias{hist,missing_variable-method} \alias{hist,semi-continuous-method} \alias{hist,binary-method} \alias{hist,categorical-method} \alias{initialize,missing_variable-method} \alias{image,mi-method} \alias{image,mi_list-method} \alias{image,mdf_list-method} \alias{image,missing_data.frame-method} \alias{image,dgTMatrix-method} \alias{mi,character,missing-method} \alias{mi,missing_variable,ANY-method} \alias{mi,missing_variable,missing-method} \alias{mi,semi-continuous,missing-method} \alias{mi,bounded-continuous,missing-method} \alias{mi,binary,glm-method} \alias{mi,grouped-binary,clogit-method} \alias{mi,continuous,glm-method} \alias{mi,bounded-continuous,glm-method} \alias{mi,SC_proportion,betareg-method} \alias{mi,proportion,betareg-method} \alias{mi,nonnegative-continuous,glm-method} \alias{mi,censored-continuous,glm-method} \alias{mi,semi-continuous,glm-method} \alias{mi,categorical,missing-method} \alias{mi,count,glm-method} \alias{mi,data.frame,missing-method} \alias{mi,irrelevant,ANY-method} \alias{mi,interval,glm-method} \alias{mi,mdf_list,missing-method} \alias{mi,mi_list,missing-method} \alias{mi,list,missing-method} \alias{mi,missing_data.frame,mi-method} \alias{mi,matrix,missing-method} \alias{mi,mi,missing-method} \alias{mi,by,missing-method} \alias{mi,missing_variable,ANY-method} \alias{mi,missing_variable,missing-method} \alias{mi,nonnegative-continuous,missing-method} \alias{mi,ordered-categorical,polr-method} \alias{mi,proportion,glm-method} \alias{mi,unordered-categorical,multinom-method} \alias{mi,unordered-categorical,RNL-method} \alias{mi,categorical,matrix-method} \alias{missing_data.frame,data.frame-method} \alias{missing_data.frame,list-method} \alias{missing_data.frame,matrix-method} \alias{missing_variable,ANY,character-method} \alias{missing_variable,ANY,missing-method} \alias{betareg-class} \alias{clogit-class} \alias{mdf_list-class} \alias{mi_list-class} \alias{family-class} \alias{multinom-class} \alias{RNL-class} \alias{plot,missing_data.frame,missing_variable-method} \alias{plot,mi_list,ANY-method} \alias{plot,allcategorical_missing_data.frame,binary-method} \alias{plot,allcategorical_missing_data.frame,categorical-method} \alias{change_family,missing_data.frame,character,character-method} \alias{change_family,missing,missing_variable,missing-method} \alias{change_family,missing_data.frame,character,family-method} \alias{change_family,missing_data.frame,character,list-method} \alias{change_family,missing_data.frame,logical,character-method} \alias{change_family,missing_data.frame,logical,family-method} \alias{change_family,missing_data.frame,numeric,character-method} \alias{change_family,missing_data.frame,numeric,family-method} \alias{change_family,missing_data.frame,numeric,list-method} \alias{change_imputation_method,missing_data.frame,character,character-method} \alias{change_size,missing_data.frame,numeric-method} \alias{change_transformation,missing,missing_variable,function-method} \alias{change_transformation,missing_data.frame,character,character-method} \alias{change_transformation,missing_data.frame,numeric,character-method} \alias{change_transformation,missing_data.frame,logical,character-method} \alias{change_type,missing_data.frame,character,character-method} \alias{change_type,missing_data.frame,logical,character-method} \alias{change_type,missing_data.frame,numeric,character-method} \alias{change,missing_data.frame,ANY,numeric,numeric-method} \alias{change,missing_data.frame,ANY,logical,numeric-method} \alias{change,missing_data.frame,ANY,character,numeric-method} \alias{change,missing_data.frame,ANY,logical,character-method} \alias{change,mdf_list,ANY,ANY,ANY-method} \alias{coerce,data.frame,missing_data.frame-method} \alias{coerce,matrix,missing_data.frame-method} \alias{coerce,missing_data.frame,data.frame-method} \alias{coerce,missing_data.frame,matrix-method} \alias{complete,missing_data.frame,integer-method} \alias{complete,mi,numeric-method} \alias{fit_model,binary,missing_data.frame-method} \alias{fit_model,grouped-binary,missing_data.frame-method} \alias{fit_model,count,missing_data.frame-method} \alias{fit_model,irrelevant,missing_data.frame-method} \alias{fit_model,interval,missing_data.frame-method} \alias{fit_model,missing_variable,missing_data.frame-method} \alias{fit_model,ordered-categorical,missing_data.frame-method} \alias{fit_model,unordered-categorical,missing_data.frame-method} \alias{fit_model,character,mi-method} \alias{fit_model,missing,mi-method} \alias{fit_model,missing_data.frame,missing_data.frame-method} \alias{hist,missing_data.frame-method} \alias{hist,mdf_list-method} \alias{hist,mi_list-method} \alias{initialize,missing_data.frame-method} \alias{plot,missing_data.frame,binary-method} \alias{plot,missing_data.frame,categorical-method} \alias{plot,missing_data.frame,semi-continuous-method} \alias{plot,missing_data.frame,missing_variable-method} \alias{plot,mi,missing-method} \alias{traceplot,mi} \alias{traceplot,mi_list} \alias{.prune} \alias{.possible_missing_variable} \title{Internal Functions and Methods} \description{ These functions are not intended to be called directly. In the case of methods, they documented elsewhere, either with the associated generic function or with the class of the object that the method is defined for. } \keyword{internal}r-cran-mi-1.0/man/mi2stata.Rd000066400000000000000000000066771275731226000157520ustar00rootroot00000000000000\name{mi2stata} \alias{mi2stata} \title{Exports completed data in Stata (.dta) or comma-separated (.csv) format} \description{ This function exports completed data from an object of \code{\link{mi-class}} in which \code{m} completed \code{\link{data.frame}}s are appended to the end of the raw data. Two additional variables are added which indicate the row number and distinguish the \code{\link{data.frame}}s. The outputed file is either Stata (.dta) or comma-separated (.csv) format, and can be easily registered in Stata as multiply imputed data.} \usage{ mi2stata(imputations, m, file, missing.ind=FALSE, ...) } \arguments{ \item{imputations}{Object of \code{\link{mi-class}}} \item{m}{The number of completed datasets to append onto the raw data} \item{file}{The filename, either a full path or relative to the working directory, where the file will be saved. Filenames must end in either '.dta' or '.csv'. Files with names ending in '.dta' will be saved as a Stata data file, and files with names ending in '.csv' will be saved as a comma-separated file.} \item{missing.ind}{If \code{TRUE}, includes a binary variable for each variable with \code{\link{NA}} values, indicating the observations which were originally missing. Defaults to \code{FALSE}.} \item{\dots}{Further arguments passed to \code{\link{write.dta}} for Stata files, or to \code{\link{write.table}} for .csv files.} } \details{ The function calls \code{\link{complete}} to construct \code{m} completed \code{\link{data.frame}}s, and uses \code{\link{rbind}} to append them to the bottom of the raw data that still contains all of the missing values. Two new variables are added: \code{_mi}, which contains the observation numbers; and \code{_mj}, which indexes the \code{\link{data.frame}}s. To save a Stata .dta file, end the filename with '.dta'. To save a comma-separated file, end the filename with .csv'. Stata files are loaded into Stata using Stata's \code{use} command, and comma-separated files can be loaded by typing \code{insheet using} \emph{filename}\code{, comma names clear}. Once the file is loaded into Stata, the data must be registered as multiply imputed before any subsequent analyses can be performed. In Stata version 11 or later, type \code{mi import mice} to register the data. The \code{_mi} and \code{_mj} variables will be replaced by variables named \code{_mi_id} and \code{_mi_m} respectively. In Stata version 10 or earlier, install the \code{MIM} package by typing \code{findit mim} and installing package \code{st0139_1}. Then the prefix \code{mim:} must be added to any command using the multiply imputed data. Any observations which are unpossible (legitimately skipped, and are not imputed, see \code{\link{missing_variable}}) will remain missing in the complete data, but will not be indicated as missing by these variables. If there are any unpossible values, missing indicators are included automatically. } \value{ \code{NULL} } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{complete}}, \code{\link{mi}}, \code{\link{write.dta}}, \code{\link{write.table}} } \examples{ fn <- paste(tempfile(), "dta", sep = ".") if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } mi2stata(imputations, m=5, file=fn , missing.ind=TRUE) } \keyword{utilities} r-cran-mi-1.0/man/mipply.Rd000066400000000000000000000047111275731226000155230ustar00rootroot00000000000000\name{mipply} \alias{mipply} \title{Apply a Function to a Object of Class mi} \description{ This function is a wrapper around \code{\link{sapply}} that is invoked on the \code{data} slot of an object of \code{\link{mi-class}} and / or on an object of \code{\link{missing_data.frame-class}} after being coerced to a \code{\link{data.frame}} } \usage{ mipply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, columnwise = TRUE, to.matrix = FALSE) } \arguments{ \item{X}{Object of \code{\link{mi-class}}, \code{\link{missing_data.frame-class}}, \code{\link{missing_variable-class}}, \code{\link{mi_list-class}}, or \code{\link{mdf_list-class}} } \item{FUN}{Function to call} \item{\dots}{Further arguments passed to \code{FUN}, currently broken } \item{simplify}{If \code{TRUE}, coerces result to a vector or matrix if possible } \item{USE.NAMES}{ignored but included for compatibility with \code{\link{sapply}} } \item{columnwise}{logical indicating whether to invoke \code{FUN} on the columns of a \code{\link{missing_data.frame}} after coercing it to a \code{\link{data.frame}} or a \code{\link{matrix}} or to invoke \code{FUN} on the \dQuote{whole} \code{\link{data.frame}} or \code{\link{matrix}} } \item{to.matrix}{Logical indicating whether to coerce each \code{\link{missing_data.frame}} to a numeric \code{\link{matrix}} or to a \code{\link{data.frame}}. The default is \code{FALSE}, in which case the \code{\link{data.frame}} will include \code{\link{factor}}s if any of the \code{\link{missing_variable}}s inherit from \code{\link{categorical-class}} } } \details{ The \code{columnwise} and \code{to.matrix} arguments are the only additions to the argument list in \code{\link{sapply}}, see the Examples section for an illustration of their use. Note that functions such as \code{\link{mean}} only accept \code{\link{numeric}} inputs, which can produce errors or warnings when \code{to.matrix = FALSE}. } \value{ A list, vector, or matrix depending on the arguments } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{sapply}} } \examples{ if(!exists("imputations", env = .GlobalEnv)) { imputations <- mi:::imputations # cached from example("mi-package") } round(mipply(imputations, mean, to.matrix = TRUE), 3) mipply(imputations, summary, columnwise = FALSE) } \keyword{utilities} r-cran-mi-1.0/man/multilevel_missing_data.frame.Rd000066400000000000000000000032711275731226000222060ustar00rootroot00000000000000\name{multilevel_missing_data.frame} \Rdversion{1.1} \docType{class} \alias{multilevel_missing_data.frame} \alias{multilevel_missing_data.frame-class} \title{Class "multilevel_missing_data.frame"} \description{ This class inherits from the \code{\link{missing_data.frame-class}} but is customized for the situation where the sample has a multilevel structure. } \section{Objects from the Class}{ Objects can be created by calls of the form \code{new("multilevel_missing_data.frame", ...)}. However, its users almost always will pass a \code{\link{data.frame}} to the \code{\link{missing_data.frame}} function and specify the \code{subclass} and \code{groups} arguments. } \section{Slots}{ The multilevel_missing_data.frame class inherits from the \code{\link{missing_data.frame-class}} and has two additional slots \describe{ \item{groups}{Object of class \code{\link{character}} indicating which variables define the multilevel structure} \item{mdf_list}{Object of class \code{mdf_list} whose elements contain a \code{\link{missing_data.frame}} for each group. This slot is filled automatically by the \code{\link{initialize}} method.} } } \details{ The \code{\link{fit_model-methods}} for the multilevel_missing_data.frame class will, by default, utilize multilevel modeling techniques that shrink the estimated parameters for each group toward their global means. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_data.frame}} } \examples{ ## Write example } \keyword{classes} \keyword{manip} \keyword{AimedAtUseRs} r-cran-mi-1.0/man/multinomial.Rd000066400000000000000000000021761275731226000165460ustar00rootroot00000000000000\name{multinomial} \alias{multinomial} \title{The multinomial family} \description{ This function is a returns a \code{\link{family}} and is a generalization of \code{\link{binomial}}. users would only need to call it when calling \code{\link{change}} with \code{what = "family", to = multinomial(link = 'logit')} } \usage{ multinomial(link = "logit") } \arguments{ \item{link}{character string among those supported by \code{\link{binomial}} } } \details{ This function is mostly cosmetic. The \code{family} slot for an object of \code{\link{unordered-categorical-class}} must be \code{multinomial(link = 'logit')}. For an object of \code{\link{ordered-categorical-class}} but not its subclasses, the \code{family} slot must be \code{multinomial()} but the link function can differ from its default (\code{"logit"}) } \value{ A \code{\link{family}} object } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{family}}, \code{\link{binomial}} } \examples{ multinomial() } \keyword{utilities} r-cran-mi-1.0/man/nlsyV.Rd000066400000000000000000000032261275731226000153240ustar00rootroot00000000000000\name{nlsyV} \alias{nlsyV} \docType{data} \title{ National Longitudinal Survey of Youth Extract } \description{ This dataset pertains to children and their families in the United States and is intended to illustrate missing data issues. Note that although the original data are longitudinal, this extract is not. } \usage{data(nlsyV)} \format{ A data frame with 400 randomly subsampled observations on the following 7 variables. \describe{ \item{\code{ppvtr.36}}{a numeric vector with data on the Peabody Picture Vocabulary Test (Revised) administered at 36 months} \item{\code{first}}{indicator for whether child was first-born} \item{\code{b.marr}}{indicator for whether mother was married when child was born} \item{\code{income}}{a numeric vector with data on family income in year after the child was born} \item{\code{momage}}{a numeric vector with data on the age of the mother when the child was born} \item{\code{momed}}{educational status of mother when child was born (1 = less than high school, 2 = high school graduate, 3 = some college, 4 = college graduate)} \item{\code{momrace}}{race of mother (1 = black, 2 = Hispanic, 3 = white)} } Note that \bold{momed} would typically be an ordered \code{\link{factor}} while \bold{momrace} would typically be an unorderd \code{\link{factor}} but both are \code{\link{numeric}} in this \code{\link{data.frame}} in order to illustrate the mechanism to \code{\link{change}} the type of a \code{\link{missing_variable}} } \source{ National Longitudinal Survey of Youth, 1997, \url{http://www.bls.gov/nls/nlsy97.htm} } \examples{ data(nlsyV) summary(nlsyV) } \keyword{datasets} r-cran-mi-1.0/man/positive.Rd000066400000000000000000000042201275731226000160460ustar00rootroot00000000000000\name{positive-continuous-class} \Rdversion{1.1} \docType{class} \alias{positive-continuous-class} \alias{proportion-class} \title{Class "positive-continuous" and Inherited Classes} \description{ The positive-continuous class inherits from the \code{\link{continuous-class}} and is the parent of the proportion class. In both cases, no observations can be zero, and in the case of the proportion class, no observations can be one. The \code{\link{nonnegative-continuous-class}} and the \code{\link{SC_proportion-class}} are appropriate for those situations. Aside from these facts, the rest of the documentation here is primarily directed toward developeRs. } \section{Objects from the Classes}{Objects can be created that are of positive-continuous or proportion class via the \code{\link{missing_variable}} generic function by specifying \code{type = "positive-continuous"} or \code{type = "proportion"} } \section{Slots}{ The default transformation for the positive-continuous class is the \code{\link{log}} function. The proportion class inherits from the positive-continuous class and has the identity transformation and the \code{\link{binomial}} family as defaults, in which case the \code{\link{fit_model-methods}} call the \code{\link[betareg]{betareg}} function in the \pkg{betareg} package. Alternatively, the transformation could be an inverse CDF like the \code{\link{qnorm}} function and the family could be \code{\link{gaussian}}, in which case the \code{\link{fit_model-methods}} call the \code{\link[arm]{bayesglm}} function in the \pkg{arm} package. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{continuous-class}}, \code{\link{positive-continuous-class}}, \code{\link{proportion-class}} } \examples{ # STEP 0: GET DATA data(CHAIN, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) healthy <- missing_variable(CHAIN$healthy / 100, type = "proportion") show(healthy) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/man/rdata.frame.Rd000066400000000000000000000247271275731226000164060ustar00rootroot00000000000000\name{rdata.frame} \alias{rdata.frame} \title{Generate a random data.frame with tunable characteristics} \description{ This function generates a random \code{\link{data.frame}} with a missingness mechanism that is used to impose a missingness pattern. The primary purpose of this function is for use in simulations } \usage{ rdata.frame(N = 1000, restrictions = c("none", "MARish", "triangular", "stratified", "MCAR"), last_CPC = NA_real_, strong = FALSE, pr_miss = .25, Sigma = NULL, alpha = NULL, experiment = FALSE, treatment_cor = c(rep(0, n_full - 1), rep(NA, 2 * n_partial)), n_full = 1, n_partial = 1, n_cat = NULL, eta = 1, df = Inf, types = "continuous", estimate_CPCs = TRUE) } \arguments{ \item{N}{integer indicating the number of observations} \item{restrictions}{character string indicating what restrictions to impose on the the missing data mechansim, see the Details section} \item{last_CPC}{a numeric scalar between \eqn{-1} and \eqn{1} exclusive or \code{NA_real_} (the default). If not \code{NA_real_}, then this value will be used to construct the correlation matrix from which the data are drawn. This option is useful if restrictions is \code{"triangular"} or \code{"stratified"}, in which case the degree to which \code{last_CPC} is not zero causes a violation of the Missing-At-Random assumption that is confined to the last of the partially observed variables} \item{strong}{Integer among 0, 1, and 2 indicating how strong to make the instruments with multiple partially observed variables, in which case the missingness indicators for each partially observed variable can be used as instruments when predicting missingness on other partially observed variables. Only applies when \code{restrictions = "triangular"}} \item{pr_miss}{numeric scalar on the (0,1) interval or vector of length \code{n_partial} indicating the proportion of observations that are missing on partially observed variables} \item{Sigma}{Either \code{\link{NULL}} (the default) or a correlation matrix of appropriate order for the variables (including the missingness indicators). By default, such a matrix is generated at random.} \item{alpha}{Either \code{\link{NULL}}, \code{\link{NA}}, or a numeric vector of appropriate length that governs the skew of a multivariate skewed normal distribution; see \code{\link[sn]{rmsn}}. The appropriate length is \code{n_full - 1 + 2 * n_partial} iff none of the variable types is nominal. If some of the variable types are nominal, then the appropriate length is \code{n_full - 1 + 2 * n_partial + sum(n_cat) - length(n_cat)}. If \code{\link{NULL}}, \code{alpha} is taken to be zero, in which case the data-generating process has no skew. If \code{\link{NA}}, \code{alpha} is drawn from \code{\link{rt}} with \code{df} degrees of freedom} \item{experiment}{logical indicating whether to simulate a randomized experiment} \item{treatment_cor}{Numeric vector of appropriate length indicating the correlations between the treatment variable and the other variables, which is only relevant if \code{experiment = TRUE}. The appropriate length is \code{n_full - 1 + 2 * n_partial} iff none of the variable types is nominal. If some of the variable types are nominal, then the appropriate length is \code{n_full - 1 + 2 * n_partial + sum(n_cat) - length(n_cat)}. If treatment_cor is of length one and is zero, then it will be recylced to the appropriate length. The treatment variable should be uncorrelated with intended covariates and uncorrelated with missingness on intended covariates. If any elements of treatment_cor are \code{\link{NA}}, then those elements will be replaced with random draws. Note that the order of the random variables is: all fully observed variables,all partially observed but not nominal variables, all partially observed nominal variables, all missingness indicators for partially observed variables.} \item{n_full}{integer indicating the number of fully observed variables} \item{n_partial}{integer indicating the number of partially observed variables} \item{n_cat}{Either \code{\link{NULL}} or an integer vector (possibly of length one) indicating the number of categories in each partially observed nominal or ordinal variable; see the Details section} \item{eta}{Positive numeric scalar which serves as a hyperparameter in the data-generating process. The default value of 1 implies that the correlation matrix among the variables is jointly uniformally distributed, using essentially the same logic as in the \pkg{clusterGeneration} package} \item{df}{positive numeric scalar indicating the degress of freedom for the (possibly skewed) multivariate t distribution, which defaults to \code{\link{Inf}} implying a (possibly skewed) multivariate normal distribution} \item{types}{a character vector (possibly of length one, in which case it is recycled) indicating the type for each fully observed and partially observed variable, which currently can be among \code{"continuous"}, \code{"count"}, \code{"binary"}, \code{"treatment"} (which is binary), \code{"ordinal"}, \code{"nominal"}, \code{"proportion"}, \code{"positive"}. See the Details section. Unique abbreviations are acceptable.} \item{estimate_CPCs}{A logical indicating whether the canonical partial correlations between the partially observed variables and the latent missingnesses should be estimated. The default is \code{TRUE} but considerable wall time can be saved by switching it to \code{FALSE} when there are many partially observed variables.} } \details{ By default, the correlation matrix among the variables and missingness indicators is intended to be close to uniform, although it is often not possible to achieve exactly. If \code{restrictions = "none"}, the data will be Not Missing At Random (NMAR). If \code{restrictions = "MARish"}, the departure from Missing At Random (MAR) will be minimized via a call to \code{\link{optim}}, but generally will not fully achieve MAR. If \code{restrictions = "triangular"}, the MAR assumption will hold but the missingness of each partially observed variable will only depend on the fully observed variables and the other latent missingness indicators. If \code{restrictions = "stratified"}, the MAR assumption will hold but the missingness of each partially observed variable will only depend on the fully observed variables. If \code{restrictions = "MCAR"}, the Missing Completely At Random (MCAR) assumption holds, which is much more restrictive than MAR. There are some rules to follow, particularly when specifying \code{types}. First, if \code{experiment = TRUE}, there must be exactly one treatment variable (taken to be binary) and it must come first to ensure that the elements of \code{treatment_cor} are handled properly. Second, if there are any partially observed nominal variables, they must come last; this is to ensure that they are conditionally uncorrelated with each other. Third, fully observed nominal variables are not supported, but they can be made into ordinal variables and then converted to nominal after the fact. Fourth, including both ordinal and nominal partially observed variables is not supported yet, Finally, if any variable is specified as a count, it will not be exactly consistent with the data-generating process. Essentially, a count variable is constructed from a continuous variable by evaluating \code{\link{pt}} on it and passing that to \code{\link{qpois}} with an intensity parameter of 5. The other non-continuous variables are constructed via some transformation or discretization of a continuous variable. If some partially observed variables are either ordinal or nominal (but not both), then the \code{n_cat} argument governs how many categories there are. If \code{n_cat} is \code{NULL}, then the number of categories defaults to three. If \code{n_cat} has length one, then that number of categories will be used for all categorical variables but must be greater than two. Otherwise, the length of \code{n_cat} must match the number of partially observed categorical variables and the number of categories for the \eqn{i}th such variable will be the \eqn{i}th element of \code{n_cat}. } \value{ A list with the following elements: \enumerate{ \item{true}{ a \code{\link{data.frame}} containing no \code{\link{NA}} values} \item{obs}{ a \code{\link{data.frame}} derived from the previous with some \code{\link{NA}} values that represents a dataset that could be observed} \item{empirical_CPCs}{ a numeric vector of empirical Canonical Partial Correlations, which should differ only randomly from zero iff \code{MAR = TRUE} and the data-generating process is multivariate normal} \item{L}{ a Cholesky factor of the correlation matrix used to generate the true data} } In addition, if \code{alpha} is not \code{\link{NULL}}, then the following elements are also included: \enumerate{ \item{alpha}{ the \code{alpha} vector utilized} \item{sn_skewness}{ the skewness of the multivariate skewed normal distribution in the population; note that this value is only an approximation of the skewness when \code{df < Inf}} \item{sn_kurtosis}{ the kurtosis of the multivariate skewed normal distribution in the population; note that this value is only an approximation of the kurtosis when \code{df < Inf}} } } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{data.frame}}, \code{\link{missing_data.frame}} } \examples{ rdf <- rdata.frame(n_partial = 2, df = 5, alpha = rnorm(5)) print(rdf$empirical_CPCs) # not zero rdf <- rdata.frame(n_partial = 2, restrictions = "triangular", alpha = NA) print(rdf$empirical_CPCs) # only randomly different from zero print(rdf$L == 0) # some are exactly zero by construction mdf <- missing_data.frame(rdf$obs) show(mdf) hist(mdf) image(mdf) # a randomized experiment rdf <- rdata.frame(n_full = 2, n_partial = 2, restrictions = "triangular", experiment = TRUE, types = c("t", "ord", "con", "pos"), treatment_cor = c(0, 0, NA, 0, NA)) Sigma <- tcrossprod(rdf$L) rownames(Sigma) <- colnames(Sigma) <- c("treatment", "X_2", "y_1", "Y_2", "missing_y_1", "missing_Y_2") print(round(Sigma, 3)) } \keyword{utilities} r-cran-mi-1.0/man/semi-continuous.Rd000066400000000000000000000104521275731226000173510ustar00rootroot00000000000000\name{semi-continuous-class} \Rdversion{1.1} \docType{class} \alias{semi-continuous} \alias{semi-continuous-class} \alias{semi-continuous} \alias{nonnegative-continuous-class} \alias{nonnegative-continuous} \alias{SC_proportion-class} \alias{SC_proportion} \title{Class "semi-continuous" and Inherited Classes} \description{ The \code{semi-continuous} class inherits from the \code{\link{continuous-class}} and is the parent of the \code{nonnegative-continuous} class, which in turn is the parent of the \code{SC_proportion class} for semi-continuous variables. A semi-continuous variable has support on one or more point masses and a continuous interval. The \code{semi-continuous} class differs from the \code{\link{censored-continuous-class}} and the \code{\link{truncated-continuous-class}} in that observations that fall on the point masses are bonafide data, rather than indicators of censoring or truncation. If there are no observations that fall on a point mass, then either the \code{\link{continuous-class}} or one of its other subclasses should be used. Aside from these facts, the rest of the documentation here is primarily directed toward developers. } \section{Objects from the Classes}{Objects can be created that are of \code{semi-continuous}, \code{nonnegative-continuous}, or \code{SC_proportion} class via the \code{\link{missing_variable}} generic function by specifying \code{type = "semi-continuous"} \code{type = "nonnegative-continuous"}, \code{type = "SC_proportion"}. } \section{Slots}{ The semi-continuous class inherits from the continuous class and is intended for variables that, for example have a point mass at certain points and are continuous in between. Thus, its default transformation is the identity transformation, which is to say no transformation in practice. It has one additional slot. \describe{ \item{indicator}{Object of class \code{"ordered-categorical"} that indicates whether an observed value falls on a point mass or the continuous interval in between. By convention, zero signifies an observation that falls within the continuous interval} } At the moment, there are no methods for the semi-continuous class. However, the basic approach to modeling a semi-continuous variable has two steps. First, the \bold{indicator} is modeled using the methods that are defined for it and its missing values are imputed. Second, the continuous part of the semi-continuous variable is modeled using the same techniques that are used when modeling continuous variables. Note that in the second step, only a subset of the observations are modeled, although this subset possibly includes values that were originally missing in which case they are imputed. The nonnegative-continuous class inherits from the semi-continuous class, which has its point mass at zero and is continuous over the positive real line. By default, the transformation for the positive part of a nonnegative-continuuos variable is \code{log(y + a)}, where \code{a} is a small constant determined by the observed data. If a variable is strictly positive, the \code{\link{positive-continuous-class}} should be used instead. The SC_proportion class inherits from the nonnegative-continuous class. It has no additional slots, and the only supported transformation function is the \code{(y * (n - 1) + .5) / n} function. Its default \code{\link{fit_model}} method is a wrapper for the \code{\link[betareg]{betareg}} function in the \pkg{betareg} package. Its \bold{family} must be \code{\link{binomial}} so that its \code{link} function can be passed to \code{\link[betareg]{betareg}} If the observed values fall strictly on the open unit interval, the \code{\link{proportion-class}} should be used instead. } \author{ Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman. } \seealso{ \code{\link{missing_variable}}, \code{\link{continuous-class}}, \code{\link{positive-continuous-class}}, \code{\link{proportion-class}} } \examples{ # STEP 0: GET DATA data(nlsyV, package = "mi") # STEP 0.5 CREATE A missing_variable (you never need to actually do this) income <- missing_variable(nlsyV$income, type = "nonnegative-continuous") show(income) } \keyword{classes} \keyword{DirectedTowardDevelopeRs} r-cran-mi-1.0/tests/000077500000000000000000000000001275731226000143065ustar00rootroot00000000000000r-cran-mi-1.0/tests/missing_data.frame.R000066400000000000000000000015751275731226000201740ustar00rootroot00000000000000stopifnot(require(mi)) rdf <- rdata.frame(N = 100, n_partial = 2, n_full = 2) mdf <- missing_data.frame(rdf$obs) rdf <- rdata.frame(N = 100, n_partial = 6, n_full = 1, types = c("ordinal", "cont", "count", "binary", "proportion", "positive", "nominal")) mdf <- missing_data.frame(rdf$obs) mdf <- missing_data.frame(rdf$obs, favor_positive = TRUE) rdf <- rdata.frame(N = 100, n_partial = 5, n_full = 1, experiment = TRUE, types = c("treatment", "cont", "count", "binary", "proportion", "positive")) mdf <- missing_data.frame(rdf$obs, subclass = "experiment", concept = as.factor(c("treatment", rep("covariate", 4), "outcome"))) rdf <- rdata.frame(N = 100, n_partial = 5, n_full = 0, types = "ordinal") mdf <- missing_data.frame(rdf$obs, subclass = "allcategorical") r-cran-mi-1.0/tests/missing_variable.R000066400000000000000000000021071275731226000177470ustar00rootroot00000000000000stopifnot(require(mi)) x <- rnorm(10) x[1] <- NA y <- missing_variable(x, type = "continuous") y <- missing_variable(x, type = "irrelevant") x <- rep(1, 10) y <- missing_variable(x, type = "fixed") x <- rep(1:5, each = 2) y <- missing_variable(x, type = "group") x[1] <- NA y <- missing_variable(x, type = "unordered-categorical") y <- missing_variable(x, type = "ordered-categorical") y <- missing_variable(x, type = "interval") x <- rbinom(10, size = 1, prob = 0.5) x[1] <- NA y <- missing_variable(x, type = "binary") y <- missing_variable(x, type = "grouped-binary", strata = rep(c("A", "B"), each = 5)) x <- runif(10) x[1] <- NA y <- missing_variable(x, type = "bounded-continuous", lower = 0, upper = 1) y <- missing_variable(x, type = "positive-continuous") y <- missing_variable(x, type = "proportion") x[which.min(x)] <- 0 y <- missing_variable(x, type = "nonnegative-continuous") y <- missing_variable(x, type = "SC_proportion") x[which.max(x)] <- 1 y <- missing_variable(x, type = "SC_proportion") x <- rpois(10, lambda = 5) x[1] <- NA y <- missing_variable(x, type = "count") r-cran-mi-1.0/vignettes/000077500000000000000000000000001275731226000151545ustar00rootroot00000000000000r-cran-mi-1.0/vignettes/mi_vignette.Rmd000066400000000000000000000137261275731226000201430ustar00rootroot00000000000000--- title: "An Example of mi Usage" author: "Ben Goodrich and Jonathan Kropko, for this version, based on earlier versions written by Yu-Sung Su, Masanao Yajima, Maria Grazia Pittau, Jennifer Hill, and Andrew Gelman" date: "06/16/2014" output: pdf_document --- There are several steps in an analysis of missing data. Initially, users must get their data into R. There are several ways to do so, including the `read.table`, `read.csv`, `read.fwf` functions plus several functions in the __foreign__ package. All of these functions will generate a `data.frame`, which is a bit like a spreadsheet of data. http://cran.r-project.org/doc/manuals/R-data.html for more information. ```{r step0} options(width = 65) suppressMessages(library(mi)) data(nlsyV, package = "mi") ``` From there, the first step is to convert the `data.frame` to a `missing_data.frame`, which is an enhanced version of a `data.frame` that includes metadata about the variables that is essential in a missing data context. ```{r step1} mdf <- missing_data.frame(nlsyV) ``` The `missing_data.frame` constructor function creates a `missing_data.frame` called `mdf`, which in turn contains seven `missing_variable`s, one for each column of the `nlsyV` dataset. The most important aspect of a `missing_variable` is its class, such as `continuous`, `binary`, and `count` among many others (see the table in the Slots section of the help page for `missing_variable-class`. The `missing_data.frame` constructor function will try to guess the appropriate class for each `missing_variable`, but rarely will it correspond perfectly to the user's intent. Thus, it is very important to call the `show` method on a `missing_data.frame` to see the initial guesses ```{r step1.5} show(mdf) # momrace is guessed to be ordered ``` and to modify them, if necessary, using the `change` function, which can be used to change many things about a`missing_variable`, so see its help page for more details. In the example below, we change the class of the _momrace_ (race of the mother) variable from the initial guess of `ordered-categorical` to a more appropriate `unordered-categorical` and change the income `nonnegative-continuous`. ```{r, step2} mdf <- change(mdf, y = c("income", "momrace"), what = "type", to = c("non", "un")) show(mdf) ``` Once all of the `missing_variable`s are set appropriately, it is useful to get a sense of the raw data, which can be accomplished by looking at the `summary`, `image`, and / or `hist` of a `missing_data.frame` ```{r, step3} summary(mdf) image(mdf) hist(mdf) ``` Next we use the `mi` function to do the actual imputation, which has several extra arguments that, for example, govern how many independent chains to utilize, how many iterations to conduct, and the maximum amount of time the user is willing to wait for all the iterations of all the chains to finish. The imputation step can be quite time consuming, particularly if there are many `missing_variable`s and if many of them are categorical. One important way in which the computation time can be reduced is by imputing in parallel, which is highly recommended and is implemented in the mi function by default on non-Windows machines. If users encounter problems running `mi` with parallel processing, the problems are likely due to the machine exceeding available RAM. Sequential processing can be used instead for `mi` by using the `parallel=FALSE` option. ```{r, step4} rm(nlsyV) # good to remove large unnecessary objects to save RAM options(mc.cores = 2) imputations <- mi(mdf, n.iter = 30, n.chains = 4, max.minutes = 20) show(imputations) ``` The next step is very important and essentially verifies whether enough iterations were conducted. We want the mean of each completed variable to be roughly the same for each of the 4 chains. ```{r, step5A} round(mipply(imputations, mean, to.matrix = TRUE), 3) Rhats(imputations) ``` If so --- and when it does in the example depends on the pseudo-random number seed --- we can procede to diagnosing other problems. For the sake of example, we continue our 4 chains for another 5 iterations by calling ```{r, step5B} imputations <- mi(imputations, n.iter = 5) ``` to illustrate that this process can be continued until convergence is reached. Next, the `plot` of an object produced by `mi` displays, for all `missing_variable`s (or some subset thereof), a histogram of the observed, imputed, and completed data, a comparison of the completed data to the fitted values implied by the model for the completed data, and a plot of the associated binned residuals. There will be one set of plots on a page for the first three chains, so that the user can get some sense of the sampling variability of the imputations. The `hist` function yields the same histograms as `plot`, but groups the histograms for all variables (within a chain) on the same plot. The `image`function gives a sense of the missingness patterns in the data. ```{r, step6} plot(imputations) plot(imputations, y = c("ppvtr.36", "momrace")) hist(imputations) image(imputations) summary(imputations) ``` Finally, we pool over `m = 5` imputed datasets -- pulled from across the 4 chains -- in order to estimate a descriptive linear regression of test scores (_ppvtr.36_) at 36 months on a variety of demographic variables pertaining to the mother of the child. ```{r, step7} analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace, data = imputations, m = 5) display(analysis) ``` The rest is optional and only necessary if you want to perform some operation that is not supported by the __mi__ package, perhaps outside of R. Here we create a list of `data.frame`s, which can be saved to the hard disk and / or exported in a variety of formats with the __foreign__ package. Imputed data can be exported to Stata by using the `mi2stata` function instead of `complete`. ```{r, step8} dfs <- complete(imputations, m = 2) ```