old/get_ecc_quantiles.R

In katerobsau/depPPR: deppR - Dependence restoration of statistical post-processed weather forecasts in R

#' Creates a vector of quantiles
#'
#' This function produces qunatiles for ECC-R (random qunatiles),
#' ECC-Q (uniform  quantiles) or ECC-S (jittered quantiles).
#'
#' @param m number of members in the ensemble (must be an integer)
#' @param ecc_type one of the characters \code{'R'}, \code{'Q'}, \code{'Q1'},
#'  or \code{'S'}. This character corresponds to the desired ECC sampling method.
#'  There are two types of equi-spaced quantiles, 'Q' and 'Q1'.
#'
#' @return a vector of sample quantiles
#'
#' @details If the \code{ecc_type} is \code{'R'} then quantiles are randomly sampled.
#' If the \code{ecc_type} is \code{'Q'} then qunatiles are equally sampled.
#' #' If the \code{ecc_type} is \code{'Q1'} then quantiles are equally sampled (Bröck et al. 2012)
#' If the \code{ecc_type} is \code{'S'} then the quantiles are jittered.
#'
#' @author Kate Saunders and Kirien Whan
#'
#' @references
#'
#' Schefzik, Roman, Thordis L. Thorarinsdottir, and Tilmann Gneiting.
#' "Uncertainty quantification in complex simulation models using ensemble
#' copula coupling." Statistical science 28.4 (2013): 616-640.
#'
#' Hu, Yiming, et al. "A stratified sampling approach for improved sampling
#' from a calibrated ensemble forecast distribution." Journal of Hydrometeorology
#' 17.9 (2016): 2405-2417.
#'
#' @seealso \code{\link{sample_ecc_members}}
#'
#' @examples
#' get_ecc_quantiles(3, "R")
#' get_ecc_quantiles(4, "Q")
#' get_ecc_quantiles(5, "S")
#'
get_ecc_quantiles <- function(m, ecc_type){
  quantiles <- switch(ecc_type,
                      R = runif(m),
                      Q = (1:m)/(m + 1),
                      S = (1:m-1)/m + runif(m)/m,
                      Q1 = (1:m - 0.5)/m)
  return(quantiles)
}