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# Function: percentile_est (not exported)
# Programmer: Tom Kincaid
# Date: July 23, 2020
# Revised: May 20, 2021 to eliminate use of the finite population correction
# factor with the local mean variance estimator
# Revised: June 11, 2021 to eliminate estimating means, which will be performed
# by a new function named mean_est
# Revised: September 9, 2021 to remove argument vartype, which is not used
# Revised: September 17 2021 to set argument ties equal to "rounded" when
# calling function oldsvyquantile in order to ensure backward
# compatability with spsurvey prior to version 5.0.0
# Revised: October 12, 2021 to correct an error that occurs when assigning
# results for the case where a subpopulation contain a single value
# Revised: October 25, 2021 to correct an error that occurs when assigning
# results for the case where a domain contains a single subpopulation
#
#' Percentile Estimates for Probability Survey Data
#'
#' This function calculates percentile estimates using the
#' \code{oldsvyquantile()} function in the survey package (\code{svyquantile()}
#' on survey version pre 4.1-1). Upper and lower confidence bounds also are
#' estimated.
#'
#' @param pctsum Data frame containing estimates.
#'
#' @param dframe Data frame containing survey design variables, response
#' variables, and subpopulation (domain) variables.
#'
#' @param itype Character value that identifies a factor variable in the design
#' argument containing subpopulation (domain) values.
#'
#' @param lev_itype Character vector that provides levels of the subpopulation
#' variable.
#'
#' @param nlev_itype Numeric value that provides the number of levels of the
#' subpopulation variable.
#'
#' @param ivar Character value that identifies the response variable.
#'
#' @param design Object of class \code{survey.design} that specifies a complex
#' survey design.
#'
#' @param design_names Character vector that provides names of survey design
#' variables in the \code{design} argument.
#'
#' @param var_nondetect Character value that identifies the name of a logical
#' variable in the \code{dframe} data frame specifying the presence of not
#' detected (nondetect) values for the response variable.
#'
#' @param conf Numeric value for the confidence level.
#'
#' @param mult Numeric value that provides the Normal distribution confidence
#' bound multiplier.
#'
#' @param pctval Vector of the set of values at which percentiles are
#' estimated.
#'
#' @param warn_ind Logical value that indicates whether warning messages were
#' generated.
#'
#' @param warn_df Data frame for storing warning messages.
#'
#' @return A list composed of the following objects:
#' \itemize{
#' \item{\code{pctsum}}{data frame containing the percentile estimates}
#' \item{\code{warn_ind}}{logical variable that indicates whether warning
#' messages were generated}
#' \item{\code{warn_df}}{data frame for storing warning messages}
#' }
#'
#'
#' @author Tom Kincaid \email{Kincaid.Tom@@epa.gov}
#'
#' @keywords survey univar
#'
#' @noRd
################################################################################
percentile_est <- function(pctsum, dframe, itype, lev_itype, nlev_itype, ivar,
design, design_names, var_nondetect, conf, mult,
pctval, warn_ind, warn_df) {
# Assign a value to the function name variable
fname <- "percentile_est"
# Assign the number of perentile values
npctval <- length(pctval)
#
# Calculate percentile estimates
#
if (is.null(var_nondetect)) {
# Calculate percentile estimates, standard error estimates, and confidence
# bound estimates for each combination of subpopulation and response
# variable for the case where nondetects are not present
tst <- !is.na(dframe[, itype])
if (nlev_itype == 1) {
nresp <- sum(!is.na(dframe[tst, ivar]))
if (nresp == 1) {
warn_ind <- TRUE
act <- "Percentiles were not calculated.\n"
warn <- paste0("Percentile estimates were not calculated for subpopulation type \"", itype, "\" \nsince the number of non-missing response values equals one.\n")
warn_df <- rbind(warn_df, data.frame(
func = I(fname), subpoptype = itype, subpop = NA, indicator = ivar,
stratum = NA, warning = I(warn), action = I(act)
))
pctest <- rep(NA, npctval)
nresp <- rep(NA, npctval)
stderr <- rep(NA, npctval)
lbound <- rep(NA, npctval)
ubound <- rep(NA, npctval)
} else {
options(warn = -1)
rslt_svy <- tryCatch(
oldsvyquantile(make.formula(ivar),
design = subset(design, tst), quantiles = pctval / 100,
alpha = (100 - conf) / 100, ci = TRUE, na.rm = TRUE, ties = "rounded"),
error = function(e) oldsvyquantile(make.formula(ivar),
design = subset(design, tst), quantiles = pctval / 100,
alpha = (100 - conf) / 100, ci = TRUE, na.rm = TRUE, ties = "rounded", method = "constant")
)
options(warn = 0)
pctest <- rslt_svy$quantiles
nresp <- cdf_nresp(dframe[, ivar], as.vector(pctest))
stderr <- SE(rslt_svy)
temp <- confint(rslt_svy)
lbound <- temp[, 1]
ubound <- temp[, 2]
}
} else {
pctest <- array(NA, c(nlev_itype, npctval))
nresp <- array(NA, c(nlev_itype, npctval))
stderr <- array(NA, c(nlev_itype, npctval))
lbound <- array(NA, c(nlev_itype, npctval))
ubound <- array(NA, c(nlev_itype, npctval))
nval <- tapply(dframe[tst, ivar], dframe[tst, itype], function(x) {
sum(!is.na(x))
})
subpop_ind <- nval > 1
if (any(!subpop_ind)) {
temp_str <- vecprint(lev_itype[!subpop_ind])
warn_ind <- TRUE
act <- "Percentiles were not calculated.\n"
warn <- paste0("Percentile estimates were not calculated for the following subpopulations of \nsubpopulation type \"", itype, "\" since the number of non-missing response values \nequals one for each subpopulation:\n", temp_str)
warn_df <- rbind(warn_df, data.frame(
func = I(fname),
subpoptype = itype, subpop = NA, indicator = ivar, stratum = NA,
warning = I(warn), action = I(act)
))
}
if (any(subpop_ind)) {
tst <- tst & dframe[, itype] %in% lev_itype[subpop_ind]
levs <- (1:nlev_itype)[subpop_ind]
options(warn = -1)
rslt_svy <- tryCatch(
svyby(make.formula(ivar), make.formula(itype),
design = subset(design, tst), oldsvyquantile,
quantiles = pctval / 100, alpha = (100 - conf) / 100, ci = TRUE,
na.rm = TRUE, ties = "rounded"),
error = function(e) svyby(make.formula(ivar), make.formula(itype),
design = subset(design, tst), oldsvyquantile,
quantiles = pctval / 100, alpha = (100 - conf) / 100, ci = TRUE,
na.rm = TRUE, ties = "rounded", method = "constant")
)
options(warn = 0)
j <- 1
for (i in levs) {
pctest[i, ] <- unlist(rslt_svy[j, 2:(npctval + 1)])
j <- j + 1
}
for (i in levs) {
ind <- dframe[, itype] == lev_itype[i]
nresp[i, ] <- cdf_nresp(dframe[ind, ivar], pctest[i, ])
}
rownames(nresp) <- lev_itype
temp <- SE(rslt_svy)
j <- 1
for (i in levs) {
stderr[i, ] <- unlist(temp[j, 1:npctval])
j <- j + 1
}
temp <- confint(rslt_svy)
j <- 1
for (i in levs) {
lbound[i, ] <- temp[seq(j, by = length(levs), length = npctval), 1]
ubound[i, ] <- temp[seq(j, by = length(levs), length = npctval), 2]
j <- j + 1
}
rownames(lbound) <- lev_itype
rownames(ubound) <- lev_itype
}
}
} else {
# To be implemented
}
# Assign identifiers and estimates to the pctsum data frame
if (is.null(var_nondetect)) {
if (nlev_itype == 1) {
pctsum <- rbind(pctsum, data.frame(
Type = itype,
Subpopulation = lev_itype,
Indicator = ivar,
Statistic = paste0(pctval, "Pct"),
nResp = nresp,
Estimate = pctest[1:npctval],
StdError = stderr,
MarginofError = mult * stderr,
LCB = lbound,
UCB = ubound
))
} else {
for (i in 1:nlev_itype) {
pctsum <- rbind(pctsum, data.frame(
Type = itype,
Subpopulation = lev_itype[i],
Indicator = ivar,
Statistic = paste0(pctval, "Pct"),
nResp = nresp[i, ],
Estimate = pctest[i, ],
StdError = stderr[i, ],
MarginofError = mult * stderr[i, ],
LCB = lbound[i, ],
UCB = ubound[i, ]
))
}
}
} else {
# To be implemented
}
# Return the pctsum data frame, the warn_ind logical value, and the warn_df
# data frame
list(pctsum = pctsum, warn_ind = warn_ind, warn_df = warn_df)
}
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