convey (version 0.2.5)

svygeidec: Generalized entropy index Decomposition

Description

Estimates the group decomposition of the generalized entropy index

Usage

svygeidec(formula, subgroup, design, ...)
# S3 method for survey.design
svygeidec(formula, subgroup, design, epsilon = 1, na.rm = FALSE, ...)
# S3 method for svyrep.design
svygeidec(formula, subgroup, design, epsilon = 1, na.rm = FALSE, ...)
# S3 method for DBIsvydesign
svygeidec(formula, subgroup, design, ...)

Value

Object of class "cvydstat", which are vectors with a "var" attribute giving the variance-covariance matrix and a "statistic" attribute giving the name of the statistic.

Arguments

formula

a formula specifying the income variable

subgroup

a formula specifying the group 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? Observations containing missing values in income or group variables will be dropped.

Author

Guilherme Jacob, Djalma Pessoa and Anthony Damico

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

Anthony F. Shorrocks (1984). Inequality decomposition groups population subgroups. Econometrica, v. 52, n. 6, 1984, pp. 1369-1385. URL https://www.jstor.org/stable/1913511.

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

svygei

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
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 0 )
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = .5 )
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 1 )
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc, eqincome > 0 ) , epsilon = 2 )

# replicate-weighted design
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 0 )
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = .5 )
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 1 )
svygeidec( ~eqincome , ~rb090 , subset( des_eusilc_rep, eqincome > 0 ) , epsilon = 2 )

if (FALSE) {

# linearized design using a variable with missings
sub_des_eusilc <- subset(des_eusilc, py010n > 0 | is.na(py010n) )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 0 )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 0, na.rm = TRUE )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 1 )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc , epsilon = 1, na.rm = TRUE )

# replicate-weighted design using a variable with missings
sub_des_eusilc_rep <- subset(des_eusilc_rep, py010n > 0 | is.na(py010n) )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 0 )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 0, na.rm = TRUE )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 1 )
svygeidec( ~py010n , ~rb090 , sub_des_eusilc_rep , epsilon = 1, 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
svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 0 )
svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = .5 )
svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 1 )
svygeidec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) , epsilon = 2 )

# database-backed linearized design using a variable with missings
sub_dbd_eusilc <- subset(dbd_eusilc, py010n > 0 | is.na(py010n) )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 0 )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 0, na.rm = TRUE )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = .5 )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = .5, na.rm = TRUE )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 1 )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 1, na.rm = TRUE )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 2 )
svygeidec( ~py010n , ~rb090 , sub_dbd_eusilc , epsilon = 2, na.rm = TRUE )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

}