Estimate the J-divergence measure, an entropy-based measure of inequality
svyjdiv(formula, design, ...)
# S3 method for survey.design
svyjdiv(
formula,
design,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
# S3 method for svyrep.design
svyjdiv(
formula,
design,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
# S3 method for DBIsvydesign
svyjdiv(formula, design, ...)
a formula specifying the income variable
a design object of class survey.design
or class svyrep.design
from the survey
library.
future expansion
Should cases with missing values be dropped?
Return the design effect (see survey::svymean
)
Should a matrix of linearized variables be returned
Should a matrix of (weighted) influence functions be returned? (for compatibility with svyby
)
Return the replicate estimates?
Object of class "cvystat
", which are vectors with a "var
" attribute giving the variance and a "statistic
" attribute giving the name of the statistic.
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.
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.
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 ) )
#> j-divergence SE
#> eqincome 0.2519 0.0043
# 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 ) )
#> j-divergence SE
#> eqincome 0.2519 0.0044
if (FALSE) {
# 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 )
}