R/potthoffroy.R

In stefvanbuuren/mice: Multivariate Imputation by Chained Equations

#' Potthoff-Roy data
#'
#' Data from Potthoff-Roy (1964) with repeated measures on dental fissures.
#'
#' This data set is the famous Potthoff-Roy data, used to demonstrate MANOVA on
#' repeated measure data. Potthoff and Roy (1964) published classic data on a
#' study in 16 boys and 11 girls, who at ages 8, 10, 12, and 14 had the distance
#' (mm) from the center of the pituitary gland to the pteryomaxillary fissure
#' measured. Changes in pituitary-pteryomaxillary distances during growth is
#' important in orthodontic therapy. The goals of the study were to describe the
#' distance in boys and girls as simple functions of age, and then to compare
#' the functions for boys and girls. The data have been reanalyzed by many
#' authors including Jennrich and Schluchter (1986), Little and Rubin (1987),
#' Pinheiro and Bates (2000), Verbeke and Molenberghs (2000) and Molenberghs and
#' Kenward (2007). See Chapter 9 of Van Buuren (2012) for a challenging
#' exercise using these data.
#'
#' @name potthoffroy
#' @docType data
#' @format \code{tbs} is a data frame with 27 rows and 6 columns:
#' \describe{
#' \item{id}{Person number}
#' \item{sex}{Sex M/F}
#' \item{d8}{Distance at age 8 years}
#' \item{d10}{Distance at age 10 years}
#' \item{d12}{Distance at age 12 years}
#' \item{d14}{Distance at age 14 years}
#' }
#' @source Potthoff, R. F., Roy, S. N. (1964).  A generalized multivariate
#' analysis of variance model usefully especially for growth curve problems.
#' \emph{Biometrika}, \emph{51}(3), 313-326.
#'
#' Little, R. J. A., Rubin, D. B. (1987).  \emph{Statistical Analysis with
#' Missing Data.} New York: John Wiley & Sons.
#'
#' Van Buuren, S. (2018).
#' \href{https://stefvanbuuren.name/fimd/ex-ch-longitudinal.html}{\emph{Flexible Imputation of Missing Data. Second Edition.}}
#' Chapman & Hall/CRC. Boca Raton, FL.
#' @keywords datasets
#' @examples
#' ### create missing values at age 10 as in Little and Rubin (1987)
#'
#' phr <- potthoffroy
#' idmis <- c(3, 6, 9, 10, 13, 16, 23, 24, 27)
#' phr[idmis, 4] <- NA
#' phr
#'
#' md.pattern(phr)
NULL