R/LIN3df.R

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

#' Lin's estimator for three gap times distribution function.
#' 
#' @description Provides estimates for three gap times distribution function based
#' on the extension the Lin's estimator.
#' @usage LIN3df(object, x, y, z)
#' @param object An object of class multidf.
#' @param x The first time for obtaining estimates for the triviate distribution
#' function.
#' @param y The second time for obtaining estimates for the triviate distribution
#' function.
#' @param z The third time for obtaining estimates for the triviate distribution
#' function.
#' @return Vector with the Lin's estimates for three gapes times distribution
#' function.
#' @references Lin, D. Y., Sun, W. and Ying, Z. (1999). Nonparametric estimation 
#' of the  gap time distributions for serial events with censored data, Biometrika
#' 86, 59-70.
#' @seealso \code{\link{LDM3df}}, \code{\link{KMW3df}} 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]]
#' 
#' LIN3df(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)
#'
#' LIN3df(b4, x=13, y=20, z=40)
#' 
#' @author Gustavo Soutinho and Luis Meira-Machado


LIN3df <-
function(object, x, y, z)
{
 obj <- object[[1]]
  est <- 0
  aux <- rep(0, length(obj$time1))
  for (j in 1:length(obj$time1)) { if (obj$time1[j] <= x & 
                                       obj$time2[j]-obj$time1[j] <= y & 
                                       obj$time[j]-obj$time2[j] > z) 
    aux[j] <- 1/KM(obj$time, 1 - obj$status, t = obj$time1[j] +obj$time2[j] +z)}
    aux <- aux/length(obj$time1)
    
    #db3 <- multidf(time1=obj$time1, event1=obj$event1,
    #               time=obj$time2,status=obj$event2)
    
    db3 <- multidf(gap1=obj$time1, event1=obj$event1,
                   gap2=obj$time2-obj$time1,status=obj$event2)
    
    est <- LINdf(object=db3, x=x, y=y) - sum(aux) 
 
return(est)                    
}