svyzenga: Zenga index (EXPERIMENTAL)

In convey: Income Concentration Analysis with Complex Survey Samples

Description

Estimate the Zenga index, a measure of inequality

Usage

svyzenga(formula, design, ...)

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

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

## S3 method for class '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?

Details

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

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.

Note

This function is experimental and is subject to changes in later versions.

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

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

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 )


## End(Not run)

convey documentation built on July 1, 2020, 11:44 p.m.