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#' Kaplan-Meier Weighted estimator for three gap times distribution function.
#'
#' @description Provides estimates for three gap times distribution function
#' based on Kaplan-Meier Weights (KMW).
#' @usage KMW3df(object, x, y, z)
#' @param object An object of class multidf.
#' @param x The first time for obtaining estimates for the trivariate
#' distribution function.
#' @param y The second time for obtaining estimates for the trivariate
#' distribution function.
#' @param z The third time for obtaining estimates for the trivariate
#' distribution function.
#' @return Vector with the Kaplan-Meier Weighted estimates for three gapes times
#' distribution function.
#' @references
#' de Una-Alvarez J, Meira Machado LF (2008). "A Simple Estimator of the
#' Bivariate Distribution Function for Censored Gap Times", Statistical and
#' Probability Letters, 78, 2440-2445.
#'
#' Davison, A.C. and Hinkley, D.V. (1997) "Bootstrap Methods and Their
#' Application", Chapter 5. Cambridge University Press.
#'
#' @seealso \code{\link{LDM3df}}, \code{\link{LIN3df}} and \code{\link{WCH3df}}.
#'
#' @examples
#'
#' data("bladder5state")
#' b4state<-multidf(gap1=bladder5state$y1, event1=bladder4state$d1,
#' gap2=bladder5state$y2, event2=bladder4state$d2,
#' gap3=bladder5state$y3, status=bladder4state$d3)
#'
#' head(b4state)[[1]]
#'
#' KMW3df(b4state, x=13, y=20, z=40)
#'
#' b4<-multidf(gap1=bladder4$t1, event1=bladder4$d1,
#' gap2=bladder4$t2-bladder4$t1, event2=bladder4$d2,
#' gap3=bladder4$t3-bladder4$t2, status=bladder4state$d3)
#'
#' KMW3df(b4, x=13, y=20, z=40)
#'
#' @author Gustavo Soutinho and Luis Meira-Machado
KMW3df <-
function(object, x, y, z)
{
obj <- object[[1]]
#est <- 0
G <- KMW(obj$time, obj$status)
p <- which(obj$time1 <= x & obj$time2 - obj$time1 <= y,
obj$time - obj$time2 <= z)
est <- sum(G[p])
return(est)
}
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