convey (version 0.2.2)

svyrenyi: Renyi divergence measure (EXPERIMENTAL)

Description

Estimate the Renyi divergence measure, a measure of inequality

Usage

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

If epsilon == 1, the result matches svygei with epsilon == 1. As in the generalized entropy index, when epsilon == 1, the logarithm in the function 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.

See Also

svygei

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)

svyrenyi( ~eqincome , design = des_eusilc, epsilon = .5 )

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

svyrenyi( ~eqincome , design = des_eusilc_rep, epsilon = .5 )

# }
# NOT RUN {
# linearized design using a variable with missings
svyrenyi( ~py010n , design = des_eusilc, epsilon = .5 )
svyrenyi( ~py010n , design = des_eusilc, epsilon = .5, na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyrenyi( ~py010n , design = des_eusilc_rep, epsilon = .5 )
svyrenyi( ~py010n , design = des_eusilc_rep, epsilon = .5, 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 )

svyrenyi( ~eqincome , design = dbd_eusilc, epsilon = .5 )

# Testing if Renyi and GEI match when epsilon == 1:
svyrenyi( ~eqincome , design = subset(dbd_eusilc, eqincome > 0 ), epsilon = 1 )
svygei( ~eqincome , design = subset(dbd_eusilc, eqincome > 0 ), epsilon = 1 )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )

# }
# NOT RUN {
# }