R/NWW.R

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

#' Nadaraya-Watson weights
#' 
#' @description Computes the Nadaraya-Watson weights.
#' @usage NWW(covariate, x, kernel = "gaussian", bw)
#' @param covariate Covariate values for obtaining weights.
#' @param x Covariate value to compute the weight at.
#' @param kernel A character string specifying the desired kernel. See details 
#' below for possible options. 
#' Defaults to "gaussian" where the gaussian density kernel will be used.
#' @param bw A single numeric value to compute a kernel density bandwidth.
#'
#' @details Possible options for argument window are "gaussian", "epanechnikov", 
#' "tricube", "boxcar", "triangular", "quartic" or "cosine". 
#' @return A vector with Nadaraya-Watson weights.
#' @examples
#' 
#' b3state<-multidf(gap1=bladder4state$y1, event1=bladder4state$d1, 
#'                  gap2=bladder4state$y2, status=bladder4state$d2, 
#'                  size=bladder4state$size)
#'                  
#' obj0 <- b3state[[1]]
#' 
#' NWW(covariate = obj0$size, x=3, kernel = "gaussian", bw = 3)
#' 
#' @author Gustavo Soutinho and Luis Meira-Machado


NWW <- function(
  covariate,
  x,
  kernel="gaussian",
  bw
) {
  len <- length(covariate);
  listg <- .C("NWWeightsKernel", as.double(covariate), as.integer(len),
    as.double(x), as.double(bw), as.character(kernel),
    weight = double(len), PACKAGE="survivalREC");
  return(listg$weight);
}