svyisq | R Documentation |
Computes the linearized variable of the total in the lower tail of the distribution of a variable.
svyisq(formula, design, ...) ## S3 method for class 'survey.design' svyisq(formula, design, alpha, quantile = FALSE, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svyisq(formula, design, alpha, quantile = FALSE, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svyisq(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
arguments passed on to 'survey::oldsvyquantile' |
alpha |
the order of the quantile |
quantile |
return the upper bound of the lower tail |
na.rm |
Should cases with missing values be dropped? |
you must run the convey_prep
function on your survey design object immediately after creating it with the svydesign
or svrepdesign
function.
Object of class "cvystat
", which are vectors with a "var
" attribute giving the variance and a "statistic
" attribute giving the name of the statistic.
Djalma Pessoa and Anthony Damico
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.
svyarpr
library(laeken) data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) ) library(survey) des_eusilc <- svydesign(ids = ~rb030, strata =~db040, weights = ~rb050, data = eusilc) des_eusilc <- convey_prep(des_eusilc) svyisq(~eqincome, design=des_eusilc,.20 , quantile = TRUE) # replicate-weighted design des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" ) des_eusilc_rep <- convey_prep(des_eusilc_rep) svyisq( ~eqincome , design = des_eusilc_rep, .20 , quantile = TRUE ) ## Not run: # linearized design using a variable with missings svyisq( ~ py010n , design = des_eusilc, .20 ) svyisq( ~ py010n , design = des_eusilc , .20, na.rm = TRUE ) # replicate-weighted design using a variable with missings svyisq( ~ py010n , design = des_eusilc_rep, .20 ) svyisq( ~ py010n , design = des_eusilc_rep , .20, 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 ) svyisq( ~ eqincome , design = dbd_eusilc, .20 ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
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