R/simulated_data.R

In mvnimpute: Simultaneously Impute the Missing and Censored Values

#' Simulated continuous data with missing and censored values
#'
#' A dataset including simulated data with missing and censored values
#'
#' @format A list including data matrix with 200 rows and 4 variables and associated indicator matrix:
#' \describe{
#'    \item{y}{Outcome variable to be used in the regression model after imputation}
#'    \item{x1}{First covariate variable subject to MAR missing and non-informative censored values}
#'    \item{x2}{Second covariate variable subject to MAR missing and non-informative censored values}
#'    \item{x3}{Third covariate variable that is fully observed}
#' }
#'
#' @details A simulated dataset and its associated indicator matrix are included into a list. In the
#' indicator matrix, 0 stands for the missing values, 1 stands for the observed values, and 3 stands
#' for the left censored values.
#'
"simulated.dat"