R/pattern1.R

In stefvanbuuren/mice: Multivariate Imputation by Chained Equations

#' Datasets with various missing data patterns
#'
#' Four simple datasets with various missing data patterns
#'
#' Van Buuren (2012) uses these four artificial datasets to illustrate various
#' missing data patterns.
#'
#' @name pattern
#' @aliases pattern1 pattern2 pattern3 pattern4
#' @docType data
#' @format \describe{ \item{list("pattern1")}{Data with a univariate missing
#' data pattern} \item{list("pattern2")}{Data with a monotone missing data
#' pattern} \item{list("pattern3")}{Data with a file matching missing data
#' pattern} \item{list("pattern4")}{Data with a general missing data pattern} }
#' Van Buuren, S. (2018).
#' \href{https://stefvanbuuren.name/fimd/missing-data-pattern.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}}
#' Chapman & Hall/CRC. Boca Raton, FL.
#' @keywords datasets
#' @examples
#' pattern4
#'
#' data <- rbind(pattern1, pattern2, pattern3, pattern4)
#' mdpat <- cbind(expand.grid(rec = 8:1, pat = 1:4, var = 1:3), r = as.numeric(as.vector(is.na(data))))
#'
#' types <- c("Univariate", "Monotone", "File matching", "General")
#' tp41 <- lattice::levelplot(r ~ var + rec | as.factor(pat),
#'   data = mdpat,
#'   as.table = TRUE, aspect = "iso",
#'   shrink = c(0.9),
#'   col.regions = mdc(1:2),
#'   colorkey = FALSE,
#'   scales = list(draw = FALSE),
#'   xlab = "", ylab = "",
#'   between = list(x = 1, y = 0),
#'   strip = lattice::strip.custom(
#'     bg = "grey95", style = 1,
#'     factor.levels = types
#'   )
#' )
#' print(tp41)
#'
#' md.pattern(pattern4)
#' p <- md.pairs(pattern4)
#' p
#'
#' ### proportion of usable cases
#' p$mr / (p$mr + p$mm)
#'
#' ### outbound statistics
#' p$rm / (p$rm + p$rr)
#'
#'
#' fluxplot(pattern2)
NULL