R/FI_train.R

In FastImputation: Learn from Training Data then Quickly Fill in Missing Data

#' @name FI_train
#' @docType data
#' @title Imputation Training Data
#' @description Larger simulated dataset drawn from the same distribution as FI_test and FI_true and used
#'   to train the imputation algorithm. 5\% of the values are missing.  Used with TrainFastImputation.
#' @source All columns start as multivariate normal draws.  Columns 2, 5, and 6 are transformed.
#'   Column 9 is the result of three multivariate normal columns being interpreted as one-hot encoding
#'   of a three-valued categorical variable.
#' @author Stephen R. Haptonstahl \email{srh@haptonstahl.org}
#' @format A data frame with 9 variables and 10000 observations. \describe{
#'   \item{\code{user_id_1}}{Sequential user ids}
#'   \item{\code{bounded_below_2}}{Multivariate normal, transformed using \code{exp(x)}}
#'   \item{\code{unbounded_3}}{Multivariate normal}
#'   \item{\code{unbounded_4}}{Multivariate normal}
#'   \item{\code{bounded_above_5}}{Multivariate normal, transformed using \code{-exp(x)}}
#'   \item{\code{bounded_above_and_below_6}}{Multivariate normal, transformed using \code{pnorm(x)}}
#'   \item{\code{unbounded_7}}{Multivariate normal}
#'   \item{\code{unbounded_8}}{Multivariate normal}
#'   \item{\code{categorical_9}}{"A" if the first of three multivariate normal draws is greatest; "B" if the
#'   second is greatest; "C" if the third is greatest}
#'   }
#' @usage data(FI_train)
#' @keywords datasets 
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