Nothing
#' Quintile Share Ratio
#'
#' Estimate ratio of the total income received by the highest earners to the total income received by lowest earners, defaulting to 20%.
#'
#'
#' @param formula a formula specifying the income variable
#' @param design a design object of class \code{survey.design} or class \code{svyrep.design} from the \code{survey} library.
#' @param alpha1 order of the lower quintile
#' @param alpha2 order of the upper quintile
#' @param na.rm Should cases with missing values be dropped?
#' @param upper_quant return the lower bound of highest earners
#' @param lower_quant return the upper bound of lowest earners
#' @param upper_tot return the highest earners total
#' @param lower_tot return the lowest earners total
#' @param deff Return the design effect (see \code{survey::svymean})
#' @param linearized Should a matrix of linearized variables be returned
#' @param influence Should a matrix of (weighted) influence functions be returned? (for compatibility with \code{\link[survey]{svyby}})
#' @param return.replicates Return the replicate estimates?
#' @param ... future expansion
#'
#' @details you must run the \code{convey_prep} function on your survey design object immediately after creating it with the \code{svydesign} or \code{svrepdesign} function.
#'
#' @return Object of class "\code{cvystat}", which are vectors with a "\code{var}" attribute giving the variance and a "\code{statistic}" attribute giving the name of the statistic.
#'
#' @author Djalma Pessoa and Anthony Damico
#' @seealso \code{\link{svyarpt}}
#'
#' @references Guillaume Osier (2009). Variance estimation for complex indicators
#' of poverty and inequality. \emph{Journal of the European Survey Research
#' Association}, Vol.3, No.3, pp. 167-195,
#' ISSN 1864-3361, URL \url{https://ojs.ub.uni-konstanz.de/srm/article/view/369}.
#'
#' Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators:
#' linearization and residual techniques. Survey Methodology, 25, 193-203,
#' URL \url{https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882}.
#'
#' @keywords survey
#'
#' @examples
#'
#' library(survey)
#' library(laeken)
#' data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
#'
#' # linearized design
#' des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc )
#' des_eusilc <- convey_prep( des_eusilc )
#'
#' svyqsr( ~eqincome , design = des_eusilc, upper_tot = TRUE, lower_tot = TRUE )
#'
#' # replicate-weighted design
#' des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
#' des_eusilc_rep <- convey_prep( des_eusilc_rep )
#'
#' svyqsr( ~eqincome , design = des_eusilc_rep, upper_tot = TRUE, lower_tot = TRUE )
#'
#' \dontrun{
#'
#' # linearized design using a variable with missings
#' svyqsr( ~ db090 , design = des_eusilc )
#' svyqsr( ~ db090 , design = des_eusilc , na.rm = TRUE )
#' # replicate-weighted design using a variable with missings
#' svyqsr( ~ db090 , design = des_eusilc_rep )
#' svyqsr( ~ db090 , design = des_eusilc_rep , na.rm = TRUE )
#'
#' # database-backed design
#' library(RSQLite)
#' library(DBI)
#' dbfile <- tempfile()
#' conn <- dbConnect( RSQLite::SQLite() , dbfile )
#' dbWriteTable( conn , 'eusilc' , eusilc )
#'
#' dbd_eusilc <-
#' svydesign(
#' ids = ~rb030 ,
#' strata = ~db040 ,
#' weights = ~rb050 ,
#' data="eusilc",
#' dbname=dbfile,
#' dbtype="SQLite"
#' )
#'
#' dbd_eusilc <- convey_prep( dbd_eusilc )
#'
#' svyqsr( ~ eqincome , design = dbd_eusilc )
#'
#' dbRemoveTable( conn , 'eusilc' )
#'
#' dbDisconnect( conn , shutdown = TRUE )
#'
#' }
#'
#' @export
svyqsr <-
function(formula, design, ...) {
if (length(attr(terms.formula(formula) , "term.labels")) > 1)
stop(
"convey package functions currently only support one variable in the `formula=` argument"
)
if ('alpha' %in% names(list(...)) &&
list(...)[["alpha"]] > 0.5)
stop("alpha= cannot be larger than 0.5 (50%)")
UseMethod("svyqsr", design)
}
#' @rdname svyqsr
#' @export
svyqsr.survey.design <-
function(formula,
design,
alpha1 = 0.2 ,
alpha2 = (1 - alpha1) ,
na.rm = FALSE,
upper_quant = FALSE,
lower_quant = FALSE,
upper_tot = FALSE,
lower_tot = FALSE,
deff = FALSE ,
linearized = FALSE ,
influence = FALSE ,
...) {
# test for convey_prep
if (is.null(attr(design, "full_design")))
stop(
"you must run the ?convey_prep function on your linearized survey design object immediately after creating it with the svydesign() function."
)
# collect income data
incvar <-
model.frame(formula, design$variables, na.action = na.pass)[[1]]
# treat missing values
if (na.rm) {
nas <- is.na(incvar)
design <- design[!nas,]
}
# collect domain indices
ind <- names(design$prob)
# Linearization of S20
S20 <-
svyisq(
formula = formula,
design = design,
alpha1,
na.rm = na.rm,
quantile = TRUE ,
deff = FALSE ,
linearized = TRUE
)
qS20 <- attr(S20, "quantile")
totS20 <- coef(S20)
attributes(totS20) <- NULL
S20 <- list(value = totS20[[1]], lin = attr(S20, "linearized")[, 1])
# treat missing
if (is.na(totS20)) {
rval <- as.numeric(NA)
variance <- as.matrix(NA)
colnames(variance) <-
rownames(variance) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
names(rval) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
class(rval) <- c("cvystat" , "svystat")
attr(rval, "var") <- variance
attr(rval, "statistic") <- "qsr"
return(rval)
}
# test division by zero
if (S20$value == 0)
stop(
paste0(
"division by zero. the alpha1=" ,
alpha1 ,
" percentile cannot be zero or svyqsr would return Inf"
)
)
# Linearization of S80C
S80C <-
svyisq(
formula = formula,
design = design,
alpha2 ,
na.rm = na.rm ,
quantile = TRUE ,
upper = TRUE ,
deff = FALSE ,
linearized = TRUE
)
qS80C <- attr(S80C, "quantile")
totS80C <- coef(S80C)
attributes(totS80C) <- NULL
S80C <-
list(value = totS80C[[1]], lin = attr(S80C, "linearized")[, 1])
# ensure consistent lengths
if (length(unique(sapply(lapply(
list(S80C , S20) , `[[` , "lin"
) , length))) != 1)
stop()
# LINEARIZED VARIABLE OF THE SHARE RATIO
list_all <- list(S20 = S20 , S80C = S80C)
QSR <- contrastinf(quote(S80C / S20), list_all)
lin <- as.numeric(QSR$lin)
# compute variance
variance <-
survey::svyrecvar(
lin / design$prob,
design$cluster,
design$strata,
design$fpc,
postStrata = design$postStrata
)
variance[which(is.nan(variance))] <- NA
colnames(variance) <-
rownames(variance) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
# compute deff
if (is.character(deff) || deff) {
nobs <- sum(weights(design , "sampling") > 0)
npop <- sum(weights(design , "sampling"))
if (deff == "replace")
vsrs <- survey::svyvar(lin , design, na.rm = na.rm) * npop ^ 2 / nobs
else
vsrs <-
survey::svyvar(lin , design , na.rm = na.rm) * npop ^ 2 * (npop - nobs) /
(npop * nobs)
deff.estimate <- variance / vsrs
}
# keep necessary linearized functions
names(lin) <- rownames(design$variables)
# coerce to matrix
lin <-
matrix(lin ,
nrow = length(lin) ,
dimnames = list(names(lin) , strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]))
# build result object
rval <- as.numeric(QSR$value)
attributes(rval) <- NULL
names(rval) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
class(rval) <- c("cvystat" , "svystat")
attr(rval, "var") <- variance
attr(rval, "statistic") <- "qsr"
if (upper_quant)
attr(rval, "upper_quant") <- qS80C
if (lower_quant)
attr(rval, "lower_quant") <- qS20
if (upper_tot)
attr(rval, "upper_tot") <- totS80C
if (lower_tot)
attr(rval, "lower_tot") <- totS20
if (is.character(deff) ||
deff)
attr(rval, "deff") <- deff.estimate
if (linearized)
attr(rval, "linearized") <- lin
if (influence)
attr(rval , "influence") <-
sweep(lin , 1 , design$prob , "/")
if (linearized |
influence)
attr(rval , "index") <- as.numeric(rownames(lin))
rval
}
#' @rdname svyqsr
#' @export
svyqsr.svyrep.design <-
function(formula,
design,
alpha1 = 0.2 ,
alpha2 = (1 - alpha1) ,
na.rm = FALSE,
upper_quant = FALSE,
lower_quant = FALSE,
upper_tot = FALSE,
lower_tot = FALSE,
deff = FALSE ,
linearized = FALSE ,
return.replicates = FALSE ,
...) {
# check for convey_prep
if (is.null(attr(design, "full_design")))
stop(
"you must run the ?convey_prep function on your replicate-weighted survey design object immediately after creating it with the svrepdesign() function."
)
# if the class of the full_design attribute is just a TRUE, then the design is
# already the full design. otherwise, pull the full_design from that attribute.
if ("logical" %in% class(attr(design, "full_design")))
full_design <-
design
else
full_design <- attr(design, "full_design")
# collect data
df <- model.frame(design)
incvar <-
model.frame(formula, design$variables, na.action = na.pass)[[1]]
# treat missing values
if (na.rm) {
nas <- is.na(incvar)
design <- design[!nas, ]
df <- model.frame(design)
incvar <- incvar[!nas]
}
# computation function
ComputeQsr <-
function(x, w, alpha1, alpha2) {
quant_inf <- computeQuantiles(x, w, p = alpha1)
quant_sup <- computeQuantiles(x, w, p = alpha2)
rich <- (x > quant_sup) * x
S80 <- sum(rich * w)
poor <- (x <= quant_inf) * x
S20 <- sum(poor * w)
c(quant_sup, quant_inf, S80, S20, S80 / S20)
}
# collect sampling weights
ws <- weights(design, "sampling")
# compute point estimate
Qsr_val <-
ComputeQsr(incvar, ws, alpha1 = alpha1, alpha2 = alpha2)
# treat missing
if (is.na(Qsr_val[[4]])) {
rval <- as.numeric(NA)
variance <- as.matrix(NA)
colnames(variance) <-
rownames(variance) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
names(rval) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
class(rval) <- c("cvystat" , "svystat")
attr(rval, "var") <- variance
attr(rval, "statistic") <- "qsr"
# attr(rval, "linearized") <- lin
return(rval)
}
# test for division by zero
if (Qsr_val[4] == 0)
stop(
paste0(
"division by zero. the alpha1=" ,
alpha1 ,
" percentile cannot be zero or svyqsr would return Inf"
)
)
### variance calculation
# collect analysis weights
ww <- weights(design, "analysis")
# compute replicates
qq <-
apply(ww , 2 , function(wi)
ComputeQsr(
incvar ,
w = wi ,
alpha1 = alpha1 ,
alpha2 = alpha2
)[5])
# compute variance
if (anyNA(qq))
variance <-
NA
else
variance <-
survey::svrVar(qq ,
design$scale ,
design$rscales ,
mse = design$mse ,
coef = Qsr_val[5])
variance <- as.matrix(variance)
colnames(variance) <-
rownames(variance) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
# compute deff
if (is.character(deff) || deff || linearized) {
# Linearization of S20
S20 <-
svyisq(
formula = formula,
design = design,
alpha1,
na.rm = na.rm,
quantile = TRUE ,
deff = FALSE ,
linearized = TRUE
)
qS20 <- attr(S20, "quantile")
totS20 <- coef(S20)
attributes(totS20) <- NULL
S20 <-
list(value = totS20[[1]], lin = attr(S20, "linearized")[, 1])
# Linearization of S80C
S80C <-
svyisq(
formula = formula,
design = design,
alpha2 ,
na.rm = na.rm ,
quantile = TRUE ,
upper = TRUE ,
deff = FALSE ,
linearized = TRUE
)
qS80C <- attr(S80C, "quantile")
totS80C <- coef(S80C)
attributes(totS80C) <- NULL
S80C <-
list(value = totS80C[[1]], lin = attr(S80C, "linearized")[, 1])
# linearizatiion of the ratio
list_all <- list(S20 = S20 , S80C = S80C)
QSR <- contrastinf(quote(S80C / S20) , list_all)
lin <- as.numeric(QSR$lin[, 1])
names(lin) <- rownames(design$variables)
# compute deff
nobs <- length(design$pweights)
npop <- sum(design$pweights)
vsrs <-
unclass(
survey::svyvar(
lin ,
design,
na.rm = na.rm,
return.replicates = FALSE,
estimate.only = TRUE
)
) * npop ^ 2 / nobs
if (deff != "replace")
vsrs <- vsrs * (npop - nobs) / npop
deff.estimate <- variance / vsrs
# filter observation
names(lin) <- rownames(design$variables)
# coerce to matrix
lin <-
matrix(lin ,
nrow = length(lin) ,
dimnames = list(names(lin) , strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]))
}
# build result object
rval <- Qsr_val[[5]]
attributes(rval) <- NULL
names(rval) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
attr(rval, "var") <- variance
attr(rval, "statistic") <- "qsr"
class(rval) <- c("cvystat" , "svrepstat")
if (upper_quant)
attr(rval, "upper_quant") <- Qsr_val[1]
if (lower_quant)
attr(rval, "lower_quant") <- Qsr_val[2]
if (upper_tot)
attr(rval, "upper_tot") <- Qsr_val[3]
if (lower_tot)
attr(rval, "lower_tot") <- Qsr_val[4]
if (linearized)
attr(rval, "linearized") <- lin
if (linearized)
attr(rval , "index") <- as.numeric(rownames(lin))
# keep replicates
if (return.replicates) {
attr(qq , "scale") <- full_design$scale
attr(qq , "rscales") <- full_design$rscales
attr(qq , "mse") <- full_design$mse
rval <- list(mean = rval , replicates = qq)
class(rval) <- c("cvystat" , "svrepstat")
}
# add design effect estimate
if (is.character(deff) ||
deff)
attr(rval , "deff") <- deff.estimate
# return object
rval
}
#' @rdname svyqsr
#' @export
svyqsr.DBIsvydesign <-
function (formula, design, ...) {
if (!("logical" %in% class(attr(design, "full_design")))) {
full_design <- attr(design , "full_design")
full_design$variables <-
getvars(
formula,
attr(design , "full_design")$db$connection,
attr(design , "full_design")$db$tablename,
updates = attr(design , "full_design")$updates,
subset = attr(design , "full_design")$subset
)
attr(design , "full_design") <- full_design
rm(full_design)
}
design$variables <-
getvars(
formula,
design$db$connection,
design$db$tablename,
updates = design$updates,
subset = design$subset
)
NextMethod("svyqsr", design)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.