################################################################################
# Function: mean_est (not exported)
# Programmer: Tom Kincaid
# Date: June 11, 2021
#
#' Mean Estimates for Probability Survey Data
#'
#' This function calculates mean estimates using the svymean function in the
#' survey package. Upper and lower confidence bounds also are estimated.
#'
#' @param meansum 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 vartype Character value providing the choice of the variance
#' estimator, where "Local" = the local mean estimator, \code{"SRS"} = the
#' simple random sampling estimator, \code{"HT"} = the Horvitz-Thompson
#' estimator, and \code{"YG"} = the Yates-Grundy estimator. The default value
#' is \code{"Local"}.
#'
#'
#' @param conf Numeric value for the confidence level.
#'
#' @param mult Numeric value that provides the Normal distribution confidence
#' bound multiplier.
#'
#' @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:
#' \describe{
#' \item{\code{meansum}}{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}
#' }
#'
#' @section Other Functions Required:
#' \describe{
#' \item{\code{\link{SE}}}{extracts standard errors from a survey design
#' object}
#' \item{\code{\link{confint}}}{computes confidence intervals for a survey
#' design object}
#' \item{\code{mean_localmean}}{organizes input and output for
#' calculation of the local mean variance estimator for the estimated
#' mean}
#' \item{\code{\link{svyby}}}{Compute survey statistics on subsets of a
#' survey defined by factors}
#' \item{\code{\link{svymean}}}{calculates the mean for a complex survey
#' design}
#' }
#'
#' @author Tom Kincaid \email{Kincaid.Tom@@epa.gov}
#'
#' @seealso
#' \code{\link{confint}}
#' \code{\link{SE}}
#' \code{\link{svyby}}
#' \code{\link{svymean}}
#'
#' @keywords survey univar
#'
#' @noRd
################################################################################
mean_est <- function(meansum, dframe, itype, lev_itype, nlev_itype, ivar,
design, design_names, var_nondetect, vartype, conf, mult,
warn_ind, warn_df) {
# Assign a value to the function name variable
fname <- "mean_est"
#
# Calculate mean estimates
#
if (is.null(var_nondetect)) {
# Calculate the mean estimate, standard error estimate, 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 <- "Variance of the mean estimate was not calculated.\n"
warn <- paste0("Variance of the mean estimate was 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)
))
temp <- mean(dframe[tst, ivar], na.rm = TRUE)
meanest <- temp
nresp <- 1
stderr <- 0
lbound <- temp
ubound <- temp
} else {
rslt <- svymean(
make.formula(ivar),
design = subset(design, tst), na.rm = TRUE
)
meanest <- rslt
nresp <- sum(!is.na(dframe[, ivar]))
if (vartype == "Local") {
temp <- mean_localmean(
itype, lev_itype, nlev_itype, c(1), ivar, design, design_names,
meanest[1], mult, warn_ind, warn_df
)
stderr <- temp$stderr
lbound <- unlist(temp$confval[1])
ubound <- unlist(temp$confval[2])
warn_ind <- temp$warn_ind
warn_df <- temp$warn_df
} else {
stderr <- SE(rslt)
temp <- confint(rslt, level = conf / 100)
lbound <- temp[1]
ubound <- temp[2]
}
}
} else {
meanest <- rep(NA, nlev_itype)
nresp <- rep(NA, nlev_itype)
stderr <- rep(NA, nlev_itype)
lbound <- rep(NA, nlev_itype)
ubound <- rep(NA, nlev_itype)
nval <- tapply(dframe[tst, ivar], dframe[tst, itype], function(x) {
sum(!is.na(x))
})
subpop_ind <- nval > 1
if (any(!subpop_ind)) {
levs <- (1:nlev_itype)[!subpop_ind]
for (i in levs) {
tst_mean <- tst & dframe[, itype] %in% lev_itype[i]
temp <- mean(dframe[tst_mean, ivar], na.rm = TRUE)
meanest[i] <- temp
nresp[i] <- 1
stderr[i] <- 0
lbound[i] <- temp
ubound[i] <- temp
}
}
if (any(subpop_ind)) {
tst <- tst & dframe[, itype] %in% lev_itype[subpop_ind]
levs <- (1:nlev_itype)[subpop_ind]
rslt <- svyby(
make.formula(ivar), make.formula(itype),
design = subset(design, tst),
svymean, na.rm = TRUE
)
meanest[levs] <- rslt[, 2]
temp <- tapply(dframe[, ivar], dframe[, itype], function(x) {
sum(!is.na(x))
})
nresp[levs] <- temp[levs]
if (vartype == "Local") {
temp <- mean_localmean(
itype, lev_itype, nlev_itype, levs, ivar, design, design_names,
meanest, mult, warn_ind, warn_df
)
stderr[levs] <- temp$stderr[levs]
lbound[levs] <- unlist(temp$confval[levs, 1])
ubound[levs] <- unlist(temp$confval[levs, 2])
warn_ind <- temp$warn_ind
warn_df <- temp$warn_df
} else {
stderr[levs] <- SE(rslt)
temp <- confint(rslt, level = conf / 100)
lbound[levs] <- temp[, 1]
ubound[levs] <- temp[, 2]
}
}
}
} else {
# To be implemented
}
# Assign identifiers and estimates to the meansum data frame
if (is.null(var_nondetect)) {
if (nlev_itype == 1) {
meansum <- rbind(meansum, data.frame(
Type = itype,
Subpopulation = lev_itype,
Indicator = ivar,
nResp = nresp,
Estimate = meanest[1],
StdError = stderr[1],
MarginofError = mult * stderr[1],
LCB = lbound,
UCB = ubound
))
} else {
for (i in 1:nlev_itype) {
meansum <- rbind(meansum, data.frame(
Type = itype,
Subpopulation = lev_itype[i],
Indicator = ivar,
nResp = nresp[i],
Estimate = meanest[i],
StdError = stderr[i],
MarginofError = mult * stderr[i],
LCB = unlist(lbound[i]),
UCB = unlist(ubound[i])
))
}
}
} else {
# To be implemented
}
# Return the meansum data frame, the warn_ind logical value, and the warn_df
# data frame
list(meansum = meansum, warn_ind = warn_ind, warn_df = warn_df)
}
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