R/complete.R

In mice: Multivariate Imputation by Chained Equations

#' Extracts the completed data from a \code{mids} object
#'
#' Takes an object of class \code{mids}, fills in the missing data, and returns
#' the completed data in a specified format.
#'
#' @aliases complete
#' @param data An object of class \code{mids} as created by the function
#' \code{mice()}.
#' @param action A numeric vector or a keyword. Numeric
#' values between 1 and \code{data$m} return the data with
#' imputation number \code{action} filled in. The value of \code{action = 0}
#' return the original data, with missing values. \code{action} can
#' also be one of the following keywords: \code{"all"}, \code{"long"},
#' \code{"broad"} and \code{"repeated"}. See the Details section
#' for the interpretation.
#' The default is \code{action = 1L} returns the first imputed data set.
#' @param include A logical to indicate whether the original data with the missing
#' values should be included.
#' @param mild A logical indicating whether the return value should
#' always be an object of class \code{mild}. Setting \code{mild = TRUE}
#' overrides \code{action} keywords \code{"long"}, \code{"broad"}
#' and \code{"repeated"}. The default is \code{FALSE}.
#' @param \dots Additional arguments. Not used.
#' @return Complete data set with missing values replaced by imputations.
#' A \code{data.frame}, or a list of data frames of class \code{mild}.
#' @details
#' The argument \code{action} can be length-1 character, which is
#' matched to one of the following keywords:
#' \describe{
#' \item{\code{"all"}}{produces a \code{mild} object of imputed data sets. When
#' \code{include = TRUE}, then the original data are appended as the first list
#' element;}
#' \item{\code{"long"}}{ produces a data set where imputed data sets
#' are stacked vertically. The columns are added: 1) \code{.imp}, integer,
#' referring the imputation number, and 2) \code{.id}, character, the row
#' names of \code{data$data};}
#' \item{\code{"stacked"}}{ same as \code{"long"} but without the two
#' additional columns;}
#' \item{\code{"broad"}}{ produces a data set with where imputed data sets
#' are stacked horizontally. Columns are ordered as in the original data.
#' The imputation number is appended to each column name;}
#' \item{\code{"repeated"}}{ same as \code{"broad"}, but with
#' columns in a different order.}
#' }
#' @note
#' Technical note: \code{mice 3.7.5} renamed the \code{complete()} function
#' to \code{complete.mids()} and exported it as an S3 method of the
#' generic \code{tidyr::complete()}. Name clashes between
#' \code{mice::complete()} and \code{tidyr::complete()} should no
#' longer occur.
#' @seealso \code{\link{mice}}, \code{\link[=mids-class]{mids}}
#' @keywords manip
#' @examples
#'
#' # obtain first imputed data set
#' sum(is.na(nhanes2))
#' imp <- mice(nhanes2, print = FALSE, maxit = 1)
#' dat <- complete(imp)
#' sum(is.na(dat))
#'
#' # obtain stacked third and fifth imputation
#' dat <- complete(imp, c(3, 5))
#'
#' # obtain all datasets, with additional identifiers
#' head(complete(imp, "long"))
#'
#' # same, but now as list, mild object
#' dslist <- complete(imp, "all")
#' length(dslist)
#'
#' # same, but also include the original data
#' dslist <- complete(imp, "all", include = TRUE)
#' length(dslist)
#'
#' # select original + 3 + 5, store as mild
#' dslist <- complete(imp, c(0, 3, 5), mild = TRUE)
#' names(dslist)
#' @export
complete.mids <- function(data, action = 1L, include = FALSE,
                          mild = FALSE, ...) {
  if (!is.mids(data)) stop("'data' not of class 'mids'")

  m <- as.integer(data$m)
  if (is.numeric(action)) {
    action <- as.integer(action)
    idx <- action[action >= 0L & action <= m]
    if (include && all(idx != 0L)) idx <- c(0L, idx)
    shape <- ifelse(mild, "mild", "stacked")
  } else if (is.character(action)) {
    if (include) idx <- 0L:m else idx <- 1L:m
    shape <- match.arg(action, c("all", "long", "broad", "repeated", "stacked"))
    shape <- ifelse(shape == "all" || mild, "mild", shape)
  } else {
    stop("'action' not recognized")
  }

  mylist <- vector("list", length = length(idx))
  for (j in seq_along(idx)) {
    mylist[[j]] <- single.complete(data$data, data$where, data$imp, idx[j])
  }

  if (shape == "stacked") {
    return(bind_rows(mylist))
  }
  if (shape == "mild") {
    names(mylist) <- as.character(idx)
    class(mylist) <- c("mild", "list")
    return(mylist)
  }
  if (shape == "long") {
    cmp <- bind_rows(mylist)
    cmp <- data.frame(
      .imp = rep(idx, each = nrow(data$data)),
      .id = rep.int(1L:nrow(data$data), length(idx)),
      cmp
    )
    if (is.integer(attr(data$data, "row.names"))) {
      row.names(cmp) <- seq_len(nrow(cmp))
    } else {
      row.names(cmp) <- as.character(seq_len(nrow(cmp)))
    }
    return(cmp)
  }
  # must be broad or repeated
  cmp <- bind_cols(mylist)
  names(cmp) <- paste(rep.int(names(data$data), m),
    rep.int(idx, rep.int(ncol(data$data), length(idx))),
    sep = "."
  )
  if (shape == "broad") {
    return(cmp)
  } else {
    return(cmp[, order(rep.int(seq_len(ncol(data$data)), length(idx)))])
  }
}

single.complete <- function(data, where, imp, ell) {
  if (ell == 0L) {
    return(data)
  }
  if (is.null(where)) {
    where <- is.na(data)
  }
  idx <- seq_len(ncol(data))[apply(where, 2, any)]
  for (j in idx) {
    if (is.null(imp[[j]])) {
      data[where[, j], j] <- NA
    } else {
      data[where[, j], j] <- imp[[j]][, ell]
    }
  }
  data
}