R/mice.impute.norm.predict.R

In stefvanbuuren/mice: Multivariate Imputation by Chained Equations

#' Imputation by linear regression through prediction
#'
#' Imputes the "best value" according to the linear regression model, also
#' known as \emph{regression imputation}.
#'
#' @aliases mice.impute.norm.predict norm.predict
#' @inheritParams mice.impute.pmm
#' @return Vector with imputed data, same type as \code{y}, and of length
#' \code{sum(wy)}
#' @details
#' Calculates regression weights from the observed data and returns predicted
#' values to as imputations. This
#' method is known as \emph{regression imputation}.
#' @section Warning: THIS METHOD SHOULD NOT BE USED FOR DATA ANALYSIS.
#' This method is seductive because it imputes the most
#' likely value according to the model. However, it ignores the uncertainty
#' of the missing values and artificially
#' amplifies the relations between the columns of the data. Application of
#' richer models having more parameters does not help to evade these issues.
#' Stochastic regression methods, like \code{\link{mice.impute.pmm}} or
#' \code{\link{mice.impute.norm}}, are generally preferred.
#'
#' At best, prediction can give reasonable estimates of the mean, especially
#' if normality assumptions are plausible. See Little and Rubin (2002, p. 62-64)
#' or Van Buuren (2012, p. 11-13, p. 45-46) for a discussion of this method.
#' @author Gerko Vink, Stef van Buuren, 2018
#' @references
#' Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing
#' Data.  New York: John Wiley and Sons.
#'
#' Van Buuren, S. (2018).
#' \href{https://stefvanbuuren.name/fimd/sec-linearnormal.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}}
#' Chapman & Hall/CRC. Boca Raton, FL.
#' @family univariate imputation functions
#' @keywords datagen
#' @export
mice.impute.norm.predict <- function(y, ry, x, wy = NULL, ...) {
  if (is.null(wy)) wy <- !ry
  x <- cbind(1, as.matrix(x))
  p <- estimice(x[ry, , drop = FALSE], y[ry], ...)
  x[wy, , drop = FALSE] %*% p$c
}