convey (version 0.2.2)

svygei: Generalized entropy index

Description

Estimate the generalized entropy index, a measure of inequality

Usage

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

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.

This measure only allows for strictly positive variables.

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

# 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)

# 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 )

# }
# NOT RUN {
# }

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