Estimate the median of incomes less than the at-risk-of-poverty threshold (arpt).
svypoormed(formula, design, ...)
# S3 method for survey.design
svypoormed(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...)
# S3 method for svyrep.design
svypoormed(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...)
# S3 method for DBIsvydesign
svypoormed(formula, design, ...)a formula specifying the income variable
a design object of class survey.design or class svyrep.design from the survey library.
arguments passed on to `survey::oldsvyquantile`
income quantile, usually .5 (median)
fraction of the quantile, usually .60
Should cases with missing values be dropped?
Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.
you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.
Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, 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 https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.
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 )
svypoormed( ~eqincome , design = des_eusilc )
#> poormed SE
#> eqincome 8803.7 72.88
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )
svypoormed( ~eqincome , design = des_eusilc_rep )
#> poormed SE
#> eqincome 8803.7 81.061
if (FALSE) {
# linearized design using a variable with missings
svypoormed( ~ py010n , design = des_eusilc )
svypoormed( ~ py010n , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svypoormed( ~ py010n , design = des_eusilc_rep )
svypoormed( ~ py010n , 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 )
svypoormed( ~ eqincome , design = dbd_eusilc )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}