################################################################################
# Function: dcdf.size.prop
# Programmer: Tom Kincaid
# Date: December 3, 2002
# Last Revised: June 4, 2008
#
#' Deconvoluted Size-Weighted Cumulative Distribution Function for Proportion
#'
#' This function calculates an estimate of the size-weighted, deconvoluted
#' cumulative distribution function (CDF) for the proportion of a discrete
#' resource. The simulation extrapolation deconvolution method (Stefanski and
#' Bay, 1996) is use to deconvolute measurement error variance from the
#' response. The Horvitz-Thompson ratio estimator, i.e., the ratio of two
#' Horvitz-Thompson estimators, is used to calculate the estimate. The
#' numerator of the ratio estimates the size-weighted total of the resource
#' equal to or less than a specified value. The denominator of the ratio
#' estimates the sum of the size-weights for the resource. The function can
#' accomodate single-stage and two-stage samples.
#'
#' @param g Vector of the values of the deconvolution function g(.) evaluated
#' at a specified value for the response value for each site.
#'
#' @param wgt Vector of the final adjusted weight (inverse of the sample
#' inclusion probability) for each site, which is either the weight for a
#' single- stage sample or the stage two weight for a two-stage sample.
#'
#' @param cluster.ind Logical value that indicates whether the sample is a
#' two- stage sample, where TRUE = a two-stage sample and FALSE = not a
#' two-stage sample.
#'
#' @param cluster Vector of the stage one sampling unit (primary sampling unit
#' or cluster) code for each site.
#'
#' @param wgt1 Vector of the final adjusted stage one weight for each site.
#'
#' @param swgt Vector of the size-weight for each site, which is the stage two
#' size-weight for a two-stage sample.
#'
#' @param swgt1 Vector of the stage one size-weight for each site.
#'
#' @return The deconvoluted CDF estimate.
#'
#' @author Tom Kincaid \email{Kincaid.Tom@epa.gov}
#'
#' @keywords survey
#'
#' @export
################################################################################
dcdf.size.prop <- function(g, wgt, cluster.ind, cluster, wgt1, swgt, swgt1) {
# Calculate additional required values
wgt <- wgt*swgt
if (cluster.ind) {
cluster <- factor(cluster)
ncluster <- length(levels(cluster))
wgt2.lst <- split(wgt, cluster)
wgt1 <- wgt1*swgt1
wgt1.u <- as.vector(tapply(wgt1, cluster, unique))
}
# Calculate the cdf estimate
if (cluster.ind) {
temp <- array(0, c(ncluster, dim(g[[1]])[2]))
for (i in 1:ncluster) {
temp[i,] <- apply(g[[i]]*wgt2.lst[[i]], 2, sum)
}
cdf <- apply(wgt1.u*temp, 2, sum)
} else {
cdf <- apply(wgt*g, 2, sum)
}
if (cluster.ind)
cdf <- cdf/sum(wgt1*wgt)
else
cdf <- cdf/sum(wgt)
# Return the estimate
cdf
}
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