R/is_na_rv.R

In jsta/rv: Simulation-Based Random Variable Objects

#' Missing Data Indicators
#' 
#' \code{is.na.rv} returns the distribution (random variable) of the indicator
#' function of missing data.  \code{rv.all.na} returns \code{TRUE} if all
#' components of the argument vector are completely missing.  \code{rv.any.na}
#' returns \code{TRUE} if any component of the argument vector has missing
#' values.
#' 
#' Internally, \code{is.na.rv} applies the function \code{is.na} to each
#' simulation of each component of the argument vector.
#' 
#' @aliases is.na.rv rv.all.na rv.any.na
#' @param x an rv object
#' @return \code{is.na.rv} returns a "Bernoulli" random vector of the same
#' length and dimension as those of \code{x}.
#' 
#' \code{rv.all.na} and \code{rv.any.na} return \code{TRUE} or \code{FALSE}
#' (single value).
#' @author Jouni Kerman \email{jouni@@kerman.com}
#' @references Kerman, J. and Gelman, A. (2007). Manipulating and Summarizing
#' Posterior Simulations Using Random Variable Objects. Statistics and
#' Computing 17:3, 235-244.
#' 
#' See also \code{vignette("rv")}.
#' @keywords classes
#' @examples 
#'   x <- trunc(rvnorm(1))
#'   y <- !(x==0 & NA) # TRUE if x!=0
#'   x <- y*x
#'   is.na(x)     # 69%: Pr(-1<Z<1)
#'   is.logical(is.na(x)) # TRUE
#'   rv.any.na(x) # TRUE
#'   rv.all.na(x) # FALSE
#'   
#' @export
#' @method is.na rv
is.na.rv <- function(x) {
  simapply(x, is.na)
}