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#' Relative median income ratio
#'
#' Estimate the ratio between the median income of people with age above 65 and the median income of people with age below 65.
#'
#'
#' @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 age formula defining the variable age
#' @param agelim the age cutpoint, the default is 65
#' @param quantiles income quantile, usually .5 (median)
#' @param na.rm Should cases with missing values be dropped?
#' @param med_old return the median income of people older than agelim
#' @param med_young return the median income of people younger than agelim
#' @param ... arguments passed on to `survey::oldsvyquantile`
#'
#' @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 ) )
#'
#' # missing completely at random, missingness rate = .20
#' ind_miss <- rbinom(nrow(eusilc), 1, .20 )
#' eusilc$eqincome_miss <- eusilc$eqincome
#' is.na(eusilc$eqincome_miss)<- ind_miss==1
#'
#' # linearized design
#' des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc )
#' des_eusilc <- convey_prep(des_eusilc)
#'
#' svyrmir( ~eqincome , design = des_eusilc , age = ~age, med_old = TRUE )
#'
#' # replicate-weighted design
#' des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
#' des_eusilc_rep <- convey_prep(des_eusilc_rep)
#'
#' svyrmir( ~eqincome , design = des_eusilc_rep, age= ~age, med_old = TRUE )
#'
#' \dontrun{
#'
#' # linearized design using a variable with missings
#' svyrmir( ~ eqincome_miss , design = des_eusilc,age= ~age)
#' svyrmir( ~ eqincome_miss , design = des_eusilc , age= ~age, na.rm = TRUE )
#' # replicate-weighted design using a variable with missings
#' svyrmir( ~ eqincome_miss , design = des_eusilc_rep,age= ~age )
#' svyrmir( ~ eqincome_miss , design = des_eusilc_rep ,age= ~age, 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 )
#'
#' svyrmir( ~eqincome , design = dbd_eusilc , age = ~age )
#'
#' dbRemoveTable( conn , 'eusilc' )
#'
#' dbDisconnect( conn , shutdown = TRUE )
#'
#' }
#'
#' @export
svyrmir <-
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"
)
UseMethod("svyrmir", design)
}
#' @rdname svyrmir
#' @export
svyrmir.survey.design <-
function(formula,
design,
age,
agelim = 65,
quantiles = 0.5,
na.rm = FALSE,
med_old = FALSE,
med_young = FALSE,
...) {
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."
)
incvar <-
model.frame(formula, design$variables, na.action = na.pass)[[1]]
agevar <-
model.frame(age, design$variables, na.action = na.pass)[[1]]
x <- cbind(incvar, agevar)
if (na.rm) {
nas <- rowSums(is.na(x))
design <- design[nas == 0, ]
if (length(nas) > length(design$prob)) {
incvar <- incvar[nas == 0]
agevar <- agevar[nas == 0]
} else{
incvar[nas > 0] <- 0
agevar[nas > 0] <- 0
}
}
if (is.null(names(design$prob)))
names(design$prob) <- as.character(seq(length(design$prob)))
w <- 1 / design$prob
N <- sum(w)
h <- h_fun(incvar, w)
age.name <- terms.formula(age)[[2]]
dsub1 <-
eval(substitute(
within_function_subset(design , subset = age < agelim) ,
list(age = age.name, agelim = agelim)
))
if (nrow(dsub1) == 0)
stop("zero records in the set of non-elderly people")
if ("DBIsvydesign" %in% class(dsub1)) {
ind1 <- names(design$prob) %in% which(dsub1$prob != Inf)
} else{
ind1 <- names(design$prob) %in% names(dsub1$prob)
}
q_alpha1 <-
survey::oldsvyquantile(
x = formula,
design = dsub1,
quantiles = quantiles,
method = "constant",
na.rm = na.rm,
...
)
q_alpha1 <- as.vector(q_alpha1)
Fprime1 <-
densfun(
formula = formula,
design = dsub1,
q_alpha1,
h = h,
FUN = "F",
na.rm = na.rm
)
N1 <- sum(w * ind1)
linquant1 <-
-(1 / (N1 * Fprime1)) * ind1 * ((incvar <= q_alpha1) - quantiles)
dsub2 <-
eval(substitute(
within_function_subset(design , subset = age >= agelim) ,
list(age = age.name, agelim = agelim)
))
if (nrow(dsub2) == 0)
stop("zero records in the set of elderly people")
if ("DBIsvydesign" %in% class(dsub2)) {
ind2 <- names(design$prob) %in% which(dsub2$prob != Inf)
} else{
ind2 <- names(design$prob) %in% names(dsub2$prob)
}
q_alpha2 <-
survey::oldsvyquantile(
x = formula,
design = dsub2,
quantiles = quantiles,
method = "constant",
na.rm = na.rm,
...
)
q_alpha2 <- as.vector(q_alpha2)
Fprime2 <-
densfun(
formula = formula,
design = dsub2,
q_alpha2,
h = h,
FUN = "F",
na.rm = na.rm
)
N2 <- sum(w * ind2)
linquant2 <-
-(1 / (N2 * Fprime2)) * ind2 * ((incvar <= q_alpha2) - quantiles)
# linearize ratio of medians
MED1 <- list(value = q_alpha1 , lin = linquant1)
MED2 <- list(value = q_alpha2 , lin = linquant2)
list_all <- list(MED1 = MED1, MED2 = MED2)
RMED <- contrastinf(quote(MED2 / MED1), list_all)
rval <- as.vector(RMED$value)
lin <- RMED$lin
variance <-
survey::svyrecvar(
lin / design$prob,
design$cluster,
design$strata,
design$fpc,
postStrata = design$postStrata
)
colnames(variance) <-
rownames(variance) <-
names(rval) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
class(rval) <- c("cvystat" , "svystat")
attr(rval , "var") <- variance
attr(rval, "lin") <- lin
attr(rval , "statistic") <- "rmir"
if (med_old)
attr(rval, "med_old") <- q_alpha2
if (med_young)
attr(rval, "med_young") <- q_alpha1
rval
}
#' @rdname svyrmir
#' @export
svyrmir.svyrep.design <-
function(formula,
design,
age,
agelim = 65,
quantiles = 0.5,
na.rm = FALSE,
med_old = FALSE,
med_young = FALSE,
...) {
if (is.null(attr(design, "full_design")))
stop(
"you must run the ?convey_prep function on your replicate-weighted survey design object immediately after creating it with the svrepdesign() function."
)
df <- model.frame(design)
incvar <-
model.frame(formula, design$variables, na.action = na.pass)[[1]]
agevar <-
model.frame(age, design$variables, na.action = na.pass)[[1]]
x <- cbind(incvar, agevar)
if (na.rm) {
nas <- rowSums(is.na(x))
design <- design[nas == 0, ]
df <- model.frame(design)
incvar <- incvar[nas == 0]
agevar <- agevar[nas == 0]
}
ComputeRmir <-
function(x, w, quantiles, age, agelim) {
indb <- age < agelim
quant_below <- computeQuantiles(x[indb], w[indb], p = quantiles)
inda <- age >= agelim
quant_above <- computeQuantiles(x[inda], w[inda], p = quantiles)
c(quant_above, quant_below, quant_above / quant_below)
}
ws <- weights(design, "sampling")
Rmir_val <-
ComputeRmir(
x = incvar,
w = ws,
quantiles = quantiles,
age = agevar,
agelim = agelim
)
rval <- Rmir_val[3]
ww <- weights(design, "analysis")
qq <-
apply(ww, 2, function(wi)
ComputeRmir(
incvar,
wi,
quantiles = quantiles,
age = agevar,
agelim = agelim
)[3])
if (anyNA(qq))
variance <- NA
else
variance <-
survey::svrVar(qq,
design$scale,
design$rscales,
mse = design$mse,
coef = rval)
variance <- as.matrix(variance)
colnames(variance) <-
rownames(variance) <-
names(rval) <-
strsplit(as.character(formula)[[2]] , ' \\+ ')[[1]]
class(rval) <- c("cvystat" , "svrepstat")
attr(rval , "var") <- variance
attr(rval, "lin") <- NA
attr(rval , "statistic") <- "rmir"
if (med_old)
attr(rval, "med_old") <- Rmir_val[1]
if (med_young)
attr(rval, "med_young") <- Rmir_val[2]
rval
}
#' @rdname svyrmir
#' @export
svyrmir.DBIsvydesign <-
function (formula, design, age, ...) {
if (!("logical" %in% class(attr(design, "full_design")))) {
full_design <- attr(design , "full_design")
full_design$variables <-
cbind(
getvars(
formula,
attr(design , "full_design")$db$connection,
attr(design , "full_design")$db$tablename,
updates = attr(design , "full_design")$updates,
subset = attr(design , "full_design")$subset
),
getvars(
age,
attr(design , "full_design")$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 <-
cbind(
getvars(
formula,
design$db$connection,
design$db$tablename,
updates = design$updates,
subset = design$subset
),
getvars(
age,
design$db$connection,
design$db$tablename,
updates = design$updates,
subset = design$subset
)
)
NextMethod("svyrmir", design)
}
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