R/KMWdf.R

In survivalREC: Nonparametric Estimation of the Distribution of Gap Times for Recurrent Events

#' Kaplan-Meier Weighted estimator for the bivariate distribution 
#' function.
#' 
#' @description Provides estimates for the bivariate distribution 
#' function
#' based on Kaplan-Meier Weights (KMW).
#' @usage KMWdf(object, x, y)
#' @param object An object of class multidf.
#' @param x The first time for obtaining estimates for the bivariate 
#' distribution function.
#' @param y The second time for obtaining estimates for the bivariate distribution
#' function.
#' @return Vector with the Kaplan-Meier weights estimates for the bivariate 
#' 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{IPCWdf}}, \code{\link{LDMdf}}, \code{\link{LINdf}} and 
#' \code{\link{WCHdf}}.
#' 
#' @examples
#' data("bladder4state")
#' 
#' b3state<-multidf(gap1=bladder4state$y1, event1=bladder4state$d1, 
#'                  gap2=bladder4state$y2, status=bladder4state$d2, 
#'                  size=bladder4state$size)
#'                  
#' KMWdf(b3state, x=13, y=20)
#' 
#' @author Gustavo Soutinho and Luis Meira-Machado

KMWdf <-
function(object, x, y)
{
 obj <- object[[1]]
 ny <- length(y)
 est <- rep(0,ny)
 G <- KMW(obj$time, obj$status)
 for (i in 1:ny){
     p <- which(obj$time1 <= x & obj$time - obj$time1 <= y[i])
     est[i] <- sum(G[p])
                }  
 return(est)                    
}