R/wins.R

In tsutils: Time Series Exploration, Modelling and Forecasting

#' Winsorise
#'
#' Winsorise either by number or percentage of observations.
#'
#' @param x input data. NAs will be removed.
#' @param p percentage or number of observations to be winsorised. If value is <1 then it is used as a percentages. Otherwise it is the number of observations to winsorise. If the resulting p > floor((length(x)-1)/2), then it is set equal to floor((length(x)-1)/2).
#'
#' @return Winsorised vector.
#'
#' @author Nikolaos Kourentzes, \email{nikolaos@kourentzes.com}.
#'
#' @keywords wins colWins rowWins
#'
#' @examples
#' x <- rnorm(100,mean=0,sd=1)
#' xW <- wins(x)
#'
#' @export wins

wins <- function (x, p=0.05){

  l <- length(x)
  n <- sum(is.na(x))
  c <- l - n

  # Convert percentage to number of observations
  if (p<1){
    p <- ceiling(c*p)
  }

  # At maximum floor((l-1)/2) observations are taken from each side
  if (p>floor((c-1)/2)){
    p <- floor((c-1)/2)
  }

  # Sort and find wmin and wmax
  x.sort <- sort(x)
  x.wmin <- x.sort[p+1]
  x.wmax <- x.sort[c-p]

  # Winsorise (ignore NAs)
  x.out <- x
  idx <- !is.na(x)
  x.w <- x.out[idx]
  x.w[x[idx]<x.wmin] <- x.wmin
  x.w[x[idx]>x.wmax] <- x.wmax
  x.out[idx] <- x.w

  return(x.out)

}

#' @describeIn wins Vectorised version of wins by columns.

colWins <- function (x, p=0.05){

  k <- dim(x)[2]
  v.wins <- Vectorize(function(i){temp <- wins(x[,i], p)})
  x.out <- v.wins(1:k)
  x.out <- x.out + 0*x

  return(x.out)

}

#' @describeIn wins Vectorised version of wins by rows.

rowWins <- function (x, p=0.05){

  d <- dim(x)
  v.wins <- Vectorize(function(i){temp <- wins(x[i,], p)})
  x.out <- v.wins(1:d[1])
  x.out <- array(t(x.out),d)
  x.out <- x.out + 0*x

  return(x.out)

}