Estimates the group decomposition of the generalized entropy index
svyjdivdec(formula, subgroup, design, ...)
# S3 method for survey.design
svyjdivdec(
formula,
subgroup,
design,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
# S3 method for svyrep.design
svyjdivdec(
formula,
subgroup,
design,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
# S3 method for DBIsvydesign
svyjdivdec(formula, subgroup, design, ...)
a formula specifying the income variable
a formula specifying the group 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? Observations containing missing values in income or group variables will 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 "cvydstat
", which are vectors with a "var
" attribute giving the variance-covariance matrix 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.
Anthony F. Shorrocks (1984). Inequality decomposition by population subgroups. Econometrica, v. 52, n. 6, 1984, pp. 1369-1385. URL https://www.jstor.org/stable/1913511.
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)
# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep(des_eusilc_rep)
# linearized design
svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc, eqincome > 0) )
#> jdiv decomposition SE
#> total 0.2518962 0.0043
#> within 0.2503206 0.0043
#> between 0.0015756 0.0003
# replicate-weighted design
svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc_rep, eqincome > 0) )
#> jdiv decomposition SE
#> total 0.2518962 0.0044
#> within 0.2503206 0.0045
#> between 0.0015756 0.0004
if (FALSE) {
# linearized design using a variable with missings
sub_des_eusilc <- subset(des_eusilc, py010n > 0 | is.na(py010n) )
svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc )
svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
sub_des_eusilc_rep <- subset(des_eusilc_rep, py010n > 0 | is.na(py010n) )
svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc_rep )
svyjdivdec( ~py010n , ~rb090 , sub_des_eusilc_rep , 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
svyjdivdec( ~eqincome , ~rb090 , subset(dbd_eusilc, eqincome > 0) )
# database-backed linearized design using a variable with missings
sub_dbd_eusilc <- subset(dbd_eusilc, py010n > 0 | is.na(py010n) )
svyjdivdec( ~py010n , ~rb090 , sub_dbd_eusilc )
svyjdivdec( ~py010n , ~rb090 , sub_dbd_eusilc , na.rm = TRUE )
dbRemoveTable( conn , 'eusilc' )
dbDisconnect( conn , shutdown = TRUE )
}