R/mice.impute.passive.R

In mice: Multivariate Imputation by Chained Equations

#' Passive imputation
#'
#' Calculate new variable during imputation
#'
#' @param data A data frame
#' @param func A \code{formula} specifying the transformations on data
#' @return The result of applying \code{formula}
#' @details
#' Passive imputation is a special internal imputation function.  Using this
#' facility, the user can specify, at any point in the \code{mice} Gibbs
#' sampling algorithm, a function on the imputed data.  This is useful, for
#' example, to compute a cubic version of a variable, a transformation like
#' \code{Q = W/H^2} based on two variables, or a mean variable like
#' \code{(x_1+x_2+x_3)/3}. The so derived variables might be used in other
#' places in the imputation model. The function allows to dynamically derive
#' virtually any function of the imputed data at virtually any time.
#' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
#' @seealso \code{\link{mice}}
#' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}:
#' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of
#' Statistical Software}, \bold{45}(3), 1-67.
#' \doi{10.18637/jss.v045.i03}
#' @keywords datagen
#' @export
mice.impute.passive <- function(data, func) {
  model.frame(func, data)
}