svypoormed: Relative median poverty gap

Description

Estimate the median of incomes less than the at-risk-of-poverty threshold (arpt).

Usage

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, ...)

Arguments

formula

a formula specifying the income variable

design

a design object of class survey.design or class svyrep.design from the survey library.

...

arguments passed on to `survey::svyquantile`

quantiles

income quantile, usually .5 (median)

percent

fraction of the quantile, usually .60

na.rm

Should cases with missing values be dropped?

Value

Object of class "cvystat", which are vectors with a "var" attribute giving the variance and a "statistic" attribute giving the name of the statistic.

Details

you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.

References

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 http://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 http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng&catno=12-001-X19990024882.

Examples

Run this code

# NOT RUN {
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 )

# 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 )

# }
# NOT RUN {
# 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 )

# }
# NOT RUN {
# }

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