svygei {convey} R Documentation

Generalized entropy index

Description

Estimate the generalized entropy index, a measure of inequality

Usage

svygei(formula, design, ...)

## S3 method for class 'survey.design'
svygei(formula, design, epsilon = 1, na.rm = FALSE, ...)

## S3 method for class 'svyrep.design'
svygei(formula, design, epsilon = 1, na.rm = FALSE, ...)

## S3 method for class 'DBIsvydesign'
svygei(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 epsilon a parameter that determines the sensivity towards inequality in the top of the distribution. Defaults to epsilon = 1. 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.

This measure only allows for strictly positive variables.

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.

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

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

Martin Biewen and Stephen Jenkins (2002). Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data. DIW Discussion Papers, No.345, URL https://www.diw.de/documents/publikationen/73/diw_01.c.40394.de/dp345.pdf.

See Also

svyatk

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)

# linearized design
svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 0 )
svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = .5 )
svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 1 )
svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 2 )

# replicate-weighted design
svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 0 )
svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = .5 )
svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 1 )
svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 2 )

## Not run:

# linearized design using a variable with missings
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5 )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2 )
svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2, na.rm = TRUE )

# replicate-weighted design using a variable with missings
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0 )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5 )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1 )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2 )
svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2, 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 )

# database-backed linearized design
svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 0 )
svygei( ~eqincome , dbd_eusilc, epsilon = .5 )
svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 1 )
svygei( ~eqincome , dbd_eusilc, epsilon = 2 )

# database-backed linearized design using a variable with missings
svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 )
svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE )
svygei( ~py010n , dbd_eusilc, epsilon = .5 )
svygei( ~py010n , dbd_eusilc, epsilon = .5, na.rm = TRUE )
svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 )
svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE )
svygei( ~py010n , dbd_eusilc, epsilon = 2 )
svygei( ~py010n , dbd_eusilc, epsilon = 2, na.rm = TRUE )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

## End(Not run)



[Package convey version 0.2.5 Index]