#' @title Show the values at 100 weighted percentiles
#'
#' @description
#' Get a quick look at a weighted distribution by seeing the 100 values that are the weighted percentiles 1-100
#' @details
#' Provides weighted percentiles without using wtd.quantile, see [Hmisc::wtd.Ecdf()] \cr \cr
#' # NOTE: THIS ONLY SHOWS PERCENTILES AND MEAN FOR THE VALID (NOT NA) VALUES !
#' Defining these types as type=1 and type="i/n" will create simple discontinuous quantiles, without interpolation where there are jumps in the values analyzed.
#' *** WARNING: Unless set type=1, the default type=7 in which case stats::quantile() FUNCTION INTERPOLATES, WHICH ISN'T OBVIOUS IN EVERY DATASET!
#' use type=1 to avoid interpolation.
#' and pctiles() rounded results so interpolation would be even less apparent.\cr
#' The quantile function will NOT interpolate between values if type=1:\cr
#' stats::quantile(1:12, probs=(1:10)/10, type=1)\cr
#' 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% \cr
#' 2 3 4 5 6 8 9 10 11 12 \cr
#'###########################\cr
#' **** IMPORTANT ***\cr
#'###########################\cr
#' *** WARNING: The Hmisc::wtd.quantile function DOES interpolate between values, even if type='i/n'\cr
#' There does not seem to be a way to fix that for the Hmisc::wtd.quantile() function.
#' For example, \cr
#' Hmisc::wtd.quantile(1:12, probs=(1:10)/10, type='i/n', weights=rep(1,12))\cr
#' 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% \cr
#' 1.2 2.4 3.6 4.8 6.0 7.2 8.4 9.6 10.8 12.0 \cr
#' @param x Required numeric vector of values whose distribution you want to look at.
#' @param wts NULL by default, or vector of numbers to use as weights in Hmisc::wtd.quantile
#' @param na.rm Logical optional TRUE by default, in which case NA values are removed first.
#' @return Returns a data.frame
#' @seealso [pctiles()] [pctiles.exact()] [pctiles.a.over.b()] [wtd.pctiles.exact()] [wtd.pctiles()] [wtd.pctiles.fast()]
#' @export
wtd.pctiles.fast <- function(x, wts=NULL, na.rm=TRUE) {
wts.as.pcts <- wts / sum(wts, na.rm=TRUE) # done just once if same wts used for many fields
# for each column (field), sort (rank) the raw data alongside wts.as.pcts
i <- order(x) # i is a vector saying which of the x is lowest, which is next, etc. in rank small to large
pctiles <- cumsum(wts.as.pcts[i]) # ranked low to high, and with ties given differing pctiles
# NOW NEED TO FIX THE TIED CASES!
#mytabs <- table(x)
#tied <- mytabs > 1
#x[tied] <- ??? max of that group
#
# make a value's pctile change to be the next higher pctile if corresponding raw equals the next higher raw (tied)
pctiles[ x[1:(length(pctiles) - 1)]==x[2:length(pctiles)]] <- pctiles[2:length(pctiles)]
return( pctiles[order(i)] )
}
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