convey (version 0.2.2)

svyzenga: Zenga index (EXPERIMENTAL)

Description

Estimate the Zenga index, a measure of inequality

Usage

svyzenga(formula, design, ...)
# S3 method for survey.design
svyzenga(formula, design, na.rm = FALSE, ...)
# S3 method for svyrep.design
svyzenga(formula, design, na.rm = FALSE, ...)
# S3 method for DBIsvydesign
svyzenga(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.

...

future expansion

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

Lucio Barabesi, Giancarlo Diana and Pier Francesco Perri (2016). Linearization of inequality indexes in the design-based framework. Statistics. URL http://www.tandfonline.com/doi/pdf/10.1080/02331888.2015.1135924.

Matti Langel (2012). Measuring inequality in finite population sampling. PhD thesis: Universite de Neuchatel, URL https://doc.rero.ch/record/29204/files/00002252.pdf.

See Also

svygini

Examples

# 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)

# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep(des_eusilc_rep)


# variable without missing values
svyzenga(~eqincome, des_eusilc)
svyzenga(~eqincome, des_eusilc_rep)

# subsetting:
svyzenga(~eqincome, subset( des_eusilc, db040 == "Styria"))
svyzenga(~eqincome, subset( des_eusilc_rep, db040 == "Styria"))

# }
# NOT RUN {
# variable with with missings
svyzenga(~py010n, des_eusilc )
svyzenga(~py010n, des_eusilc_rep )

svyzenga(~py010n, des_eusilc, na.rm = TRUE )
svyzenga(~py010n, 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 )


# variable without missing values
svyzenga(~eqincome, dbd_eusilc)

# subsetting:
svyzenga(~eqincome, subset( dbd_eusilc, db040 == "Styria"))

# variable with with missings
svyzenga(~py010n, dbd_eusilc )

svyzenga(~py010n, dbd_eusilc, na.rm = TRUE )


dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

# }
# NOT RUN {
# }