svyjdiv: J-divergence measure (EXPERIMENTAL)

In convey: Income Concentration Analysis with Complex Survey Samples

J-divergence measure (EXPERIMENTAL)

Description

Estimate the j-divergence measure, an entropy-based measure of inequality

Usage

svyjdiv(formula, design, ...)

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

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

## S3 method for class 'DBIsvydesign'
svyjdiv(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.

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.

Note

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

Author(s)

Guilherme Jacob

References

Nicholas Rohde (2016). J-divergence measurements of economic inequality. J. R. Statist. Soc. A, v. 179, Part 3 (2016), pp. 847-870. URL https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssa.12153.

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)

svyjdiv( ~eqincome , design = subset( des_eusilc , eqincome > 0 ) )

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

svyjdiv( ~eqincome , design = subset( des_eusilc_rep , eqincome > 0 ) )

## Not run: 

# linearized design using a variable with missings
svyjdiv( ~py010n , design = subset( des_eusilc , py010n > 0 | is.na( py010n ) ) )
svyjdiv( ~py010n , design = subset( des_eusilc , py010n > 0 | is.na( py010n ) ), na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyjdiv( ~py010n , design = subset( des_eusilc_rep , py010n > 0 | is.na( py010n ) ) )
svyjdiv( ~py010n , design = subset( des_eusilc_rep , py010n > 0 | is.na( py010n ) ) , 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 )

svyjdiv( ~eqincome , design = subset( dbd_eusilc , eqincome > 0 ) )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

convey documentation built on April 28, 2022, 1:06 a.m.