R/walking.R

In mice: Multivariate Imputation by Chained Equations

#' Walking disability data
#'
#' Two items YA and YB measuring walking disability in samples A, B and E.
#'
#' Example dataset to demonstrate imputation of two items (YA and YB). Item YA
#' is administered to sample A and sample E, item YB is administered to sample B
#' and sample E, so sample E acts as a bridge study. Imputation using a bridge
#' study is better than simple equating or than imputation under independence.
#'
#' Item YA corresponds to the HAQ8 item, and item YB corresponds to the GAR9
#' items from Van Buuren et al (2005).  Sample E (as well as sample B) is the
#' Euridiss study (n=292), sample A is the ERGOPLUS study (n=306).
#'
#' See Van Buuren (2018) section 9.4 for more details on the imputation
#' methodology.
#'
#' @name walking
#' @aliases walking
#' @docType data
#' @format A data frame with 890 rows on the following 5 variables:
#' \describe{
#' \item{sex}{Sex of respondent (factor)}
#' \item{age}{Age of respondent}
#' \item{YA}{Item administered in samples A and E (factor)}
#' \item{YB}{Item administered in samples B and E (factor)}
#' \item{src}{Source: Sample A, B or E (factor)}
#' }
#' @references van Buuren, S., Eyres, S., Tennant, A., Hopman-Rock, M. (2005).
#' Improving comparability of existing data by Response Conversion.
#' \emph{Journal of Official Statistics}, \bold{21}(1), 53-72.
#'
#' Van Buuren, S. (2018).
#' \href{https://stefvanbuuren.name/fimd/sec-codingsystems.html#sec:impbridge}{\emph{Flexible Imputation of Missing Data. Second Edition.}}
#' Chapman & Hall/CRC. Boca Raton, FL.
#' @keywords datasets
#' @examples
#' md.pattern(walking)
#'
#' micemill <- function(n) {
#'   for (i in 1:n) {
#'     imp <<- mice.mids(imp) # global assignment
#'     cors <- with(imp, cor(as.numeric(YA),
#'       as.numeric(YB),
#'       method = "kendall"
#'     ))
#'     tau <<- rbind(tau, getfit(cors, s = TRUE)) # global assignment
#'   }
#' }
#'
#' plotit <- function() {
#'   matplot(
#'     x = 1:nrow(tau), y = tau,
#'     ylab = expression(paste("Kendall's ", tau)),
#'     xlab = "Iteration", type = "l", lwd = 1,
#'     lty = 1:10, col = "black"
#'   )
#' }
#'
#' tau <- NULL
#' imp <- mice(walking, max = 0, m = 10, seed = 92786)
#' pred <- imp$pred
#' pred[, c("src", "age", "sex")] <- 0
#' imp <- mice(walking, max = 0, m = 3, seed = 92786, pred = pred)
#' micemill(5)
#' plotit()
#'
#' ### to get figure 9.8 van Buuren (2018) use m=10 and micemill(20)
NULL