Computes the linearized variable of a quantile of variable.
svyiqalpha(formula, design, ...)
# S3 method for survey.design
svyiqalpha(formula, design, alpha, na.rm = FALSE, ...)
# S3 method for svyrep.design
svyiqalpha(formula, design, alpha, na.rm = FALSE, ...)
# S3 method for DBIsvydesign
svyiqalpha(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`
the order of the quantile
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(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
library(survey)
# linearized design
des_eusilc <- svydesign( ids = ~rb030 , strata = ~db040 , weights = ~rb050 , data = eusilc )
des_eusilc <- convey_prep(des_eusilc)
svyiqalpha( ~eqincome , design = des_eusilc, .50 )
#> quantile SE
#> eqincome 18099 84.394
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep(des_eusilc_rep)
svyiqalpha( ~eqincome , design = des_eusilc_rep, .50 )
#> quantile SE
#> eqincome 18099 71.854
if (FALSE) {
# linearized design using a variable with missings
svyiqalpha( ~ py010n , design = des_eusilc, .50 )
svyiqalpha( ~ py010n , design = des_eusilc , .50, na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyiqalpha( ~ py010n , design = des_eusilc_rep, .50 )
svyiqalpha( ~ py010n , design = des_eusilc_rep ,.50, 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 )
svyiqalpha( ~ eqincome , design = dbd_eusilc, .50 )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}