R/kernelwts.R

In rdd: Regression Discontinuity Estimation

#' Kernel Weighting function
#' 
#' This function will calculate the appropriate kernel weights
#' for a vector. This is useful when, for instance, one wishes to 
#' perform local regression.
#' 
#' @param X input x values. This variable represents the axis along which kernel weighting should be performed.
#' @param center the point from which distances should be calculated.
#' @param bw the bandwidth.
#' @param kernel a string indicating the kernel to use. Options are \code{"triangular"} (the default), 
#' \code{"epanechnikov"}, \code{"quartic"}, \code{"triweight"}, \code{"tricube"}, \code{"gaussian"},
#' and \code{"cosine"}.
#' @return A vector of weights with length equal to that of the \code{X} input (one weight per element of \code{X}).
#' @export
#' @author Drew Dimmery <\email{drewd@@nyu.edu}>
#' @examples
#' require(graphics)
#' 
#' X<-seq(-1,1,.01)
#' triang.wts<-kernelwts(X,0,1,kernel="triangular")
#' plot(X,triang.wts,type="l")
#' 
#' cos.wts<-kernelwts(X,0,1,kernel="cosine")
#' plot(X,cos.wts,type="l")


kernelwts<-function(X,center,bw,kernel="triangular"){
  dist<-(X-center)/bw
  if(kernel=="triangular"){
    w<-(1-abs(dist))
  } else if (kernel=="rectangular") {
    w<-1/2
  } else if (kernel=="epanechnikov") {
    w<-3/4*(1-dist^2)
  } else if (kernel=="quartic" | kernel=="biweight") {
    w<-15/16*(1-dist^2)^2
  } else if (kernel=="triweight") {
    w<-35/32*(1-dist^2)^3
  } else if (kernel=="tricube") {
    w<-70/81*(1-abs(dist)^3)^3
  } else if (kernel=="gaussian") {
    w<-1/sqrt(2*pi)*exp(-1/2*dist^2)
  } else if (kernel=="cosine") {
    w<-pi/4*cos(pi/2 * dist)
  } else {
    stop("Invalid kernel selection.")
  }
  w<-ifelse(abs(dist)>1&kernel!="gaussian",0,w)
  w<-w/sum(w)
  return(w)
}