R/boys.R

In mice: Multivariate Imputation by Chained Equations

#' Growth of Dutch boys
#'
#' Height, weight, head circumference and puberty of 748 Dutch boys.
#'
#' Random sample of 10\% from the cross-sectional data used to construct the
#' Dutch growth references 1997. Variables \code{gen} and \code{phb} are ordered
#' factors. \code{reg} is a factor.
#'
#' @name boys
#' @docType data
#' @format A data frame with 748 rows on the following 9 variables: \describe{
#' \item{age}{Decimal age (0-21 years)}
#' \item{hgt}{Height (cm)}
#' \item{wgt}{Weight (kg)}
#' \item{bmi}{Body mass index}
#' \item{hc}{Head circumference (cm)}
#' \item{gen}{Genital Tanner stage (G1-G5)}
#' \item{phb}{Pubic hair (Tanner P1-P6)}
#' \item{tv}{Testicular volume (ml)}
#' \item{reg}{Region (north, east, west, south, city)} }
#' @source Fredriks, A.M,, van Buuren, S., Burgmeijer, R.J., Meulmeester JF,
#' Beuker, R.J., Brugman, E., Roede, M.J., Verloove-Vanhorick, S.P., Wit, J.M.
#' (2000) Continuing positive secular growth change in The Netherlands
#' 1955-1997.  \emph{Pediatric Research}, \bold{47}, 316-323.
#'
#' Fredriks, A.M., van Buuren, S., Wit, J.M., Verloove-Vanhorick, S.P. (2000).
#' Body index measurements in 1996-7 compared with 1980.  \emph{Archives of
#' Disease in Childhood}, \bold{82}, 107-112.
#' @keywords datasets
#' @examples
#'
#' # create two imputed data sets
#' imp <- mice(boys, m = 1, maxit = 2)
#' z <- complete(imp, 1)
#'
#' # create imputations for age <8yrs
#' plot(z$age, z$gen,
#'   col = mdc(1:2)[1 + is.na(boys$gen)],
#'   xlab = "Age (years)", ylab = "Tanner Stage Genital"
#' )
#'
#' # figure to show that the default imputation method does not impute BMI
#' # consistently
#' plot(z$bmi, z$wgt / (z$hgt / 100)^2,
#'   col = mdc(1:2)[1 + is.na(boys$bmi)],
#'   xlab = "Imputed BMI", ylab = "Calculated BMI"
#' )
#'
#' # also, BMI distributions are somewhat different
#' oldpar <- par(mfrow = c(1, 2))
#' MASS::truehist(z$bmi[!is.na(boys$bmi)],
#'   h = 1, xlim = c(10, 30), ymax = 0.25,
#'   col = mdc(1), xlab = "BMI observed"
#' )
#' MASS::truehist(z$bmi[is.na(boys$bmi)],
#'   h = 1, xlim = c(10, 30), ymax = 0.25,
#'   col = mdc(2), xlab = "BMI imputed"
#' )
#' par(oldpar)
#'
#' # repair the inconsistency problem by passive imputation
#' meth <- imp$meth
#' meth["bmi"] <- "~I(wgt/(hgt/100)^2)"
#' pred <- imp$predictorMatrix
#' pred["hgt", "bmi"] <- 0
#' pred["wgt", "bmi"] <- 0
#' imp2 <- mice(boys, m = 1, maxit = 2, meth = meth, pred = pred)
#' z2 <- complete(imp2, 1)
#'
#' # show that new imputations are consistent
#' plot(z2$bmi, z2$wgt / (z2$hgt / 100)^2,
#'   col = mdc(1:2)[1 + is.na(boys$bmi)],
#'   ylab = "Calculated BMI"
#' )
#'
#' # and compare distributions
#' oldpar <- par(mfrow = c(1, 2))
#' MASS::truehist(z2$bmi[!is.na(boys$bmi)],
#'   h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(1),
#'   xlab = "BMI observed"
#' )
#' MASS::truehist(z2$bmi[is.na(boys$bmi)],
#'   h = 1, xlim = c(10, 30), ymax = 0.25, col = mdc(2),
#'   xlab = "BMI imputed"
#' )
#' par(oldpar)
NULL