Computes the linearized variable of the total in the lower tail of the distribution of a variable.
svyisq(formula, design, ...)
# S3 method for survey.design
svyisq(
formula,
design,
alpha,
quantile = FALSE,
upper = FALSE,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
# S3 method for svyrep.design
svyisq(
formula,
design,
alpha,
quantile = FALSE,
upper = FALSE,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
# S3 method for DBIsvydesign
svyisq(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
return the upper bound of the lower tail
return the total in the total in the upper tail. Defaults to FALSE
.
Should cases with missing values be dropped?
Return the design effect (see survey::svymean
)
Should a matrix of linearized variables be returned
Should a matrix of (weighted) influence functions be returned? (for compatibility with svyby
)
Return the replicate estimates?
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)
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)
#> isq SE
#> eqincome 1.4548e+10 126027235
# 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 )
#> isq SE
#> eqincome 1.4548e+10 133254463
if (FALSE) {
# 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 )
}