The standard definition is to use 60% of the median income.
svyarpt(formula, design, ...)
# S3 method for survey.design
svyarpt(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...)
# S3 method for svyrep.design
svyarpt(formula, design, quantiles = 0.5, percent = 0.6, na.rm = FALSE, ...)
# S3 method for DBIsvydesign
svyarpt(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 quantiles, usually .50 (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 )
svyarpt( ~eqincome , design = des_eusilc )
#> arpt SE
#> eqincome 10859 50.636
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )
svyarpt( ~eqincome , design = des_eusilc_rep )
#> arpt SE
#> eqincome 10859 44.277
if (FALSE) {
# linearized design using a variable with missings
svyarpt( ~ py010n , design = des_eusilc )
svyarpt( ~ py010n , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyarpt( ~ py010n , design = des_eusilc_rep )
svyarpt( ~ 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 )
svyarpt( ~ eqincome , design = dbd_eusilc )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}