R/Lwls1D.R

In fdapace: Functional Data Analysis and Empirical Dynamics

#' One dimensional local linear kernel smoother
#' 
#' One dimensional local linear kernel smoother for longitudinal data.
#'
#' @param bw Scalar holding the bandwidth
#' @param kernel_type Character holding the kernel type (see ?FPCA for supported kernels)
#' @param win Vector of length N with weights
#' @param xin Vector of length N with measurement points
#' @param yin Vector of length N with measurement values
#' @param xout Vector of length M with output measurement points
#' @param npoly Scalar (integer) degree of polynomial fitted (default 1)
#' @param nder Scalar (integer) degree of derivative fitted (default 0)
#'
#' @return Vector of length M with measurement values at the the point specified by 'xout'
#'
#' @export


Lwls1D <- function( bw, kernel_type, win=rep(1L, length(xin)), xin, yin, xout, npoly = 1L, nder = 0L){

  if(is.unsorted(xin)){
    stop('`xin` needs to be sorted in increasing order')
  }

  if(is.unsorted(xout)){
    stop('`xout` needs to be sorted in increasing order')
  }

  if(all(is.na(win)) || all(is.na(xin)) || all(is.na(yin))){
    stop(' win, xin or yin contain only NAs!')
  }

  # Deal with NA/NaN measurement values
  NAinY = is.na(xin) | is.na(yin) | is.na(win)
  if(any(NAinY)){
      win = win[!NAinY]
      xin = xin[!NAinY]
      yin = yin[!NAinY]
  } 
  
  return( CPPlwls1d(bw= as.numeric(bw), kernel_type = kernel_type, npoly= as.integer(npoly), nder= as.integer(nder), 
                  xin = as.numeric(xin), yin= as.numeric(yin), xout= as.numeric(xout), win = as.numeric(win)))
}