svyjdivdec {convey} | R Documentation |
J-Divergence Decomposition
Description
Estimates the group decomposition of the generalized entropy index
Usage
svyjdivdec(formula, subgroup, design, ...)
## S3 method for class 'survey.design'
svyjdivdec(
formula,
subgroup,
design,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
## S3 method for class 'svyrep.design'
svyjdivdec(
formula,
subgroup,
design,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
## S3 method for class 'DBIsvydesign'
svyjdivdec(formula, subgroup, design, ...)
Arguments
formula |
a formula specifying the income variable |
subgroup |
a formula specifying the group variable |
design |
a design object of class |
... |
future expansion |
na.rm |
Should cases with missing values be dropped? Observations containing missing values in income or group variables will be dropped. |
deff |
Return the design effect (see |
linearized |
Should a matrix of linearized variables be returned |
influence |
Should a matrix of (weighted) influence functions be returned? (for compatibility with |
return.replicates |
Return the replicate estimates? |
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 "cvydstat
", which are vectors with a "var
" attribute giving the variance-covariance matrix and a "statistic
" attribute giving the name of the statistic.
Author(s)
Guilherme Jacob, Djalma Pessoa, and Anthony Damico
References
Anthony F. Shorrocks (1984). Inequality decomposition by population subgroups. Econometrica, v. 52, n. 6, 1984, pp. 1369-1385. DOI doi:10.2307/1913511.
Nicholas Rohde (2016). J-divergence measurements of economic inequality. J. R. Statist. Soc. A, v. 179, Part 3 (2016), pp. 847-870. DOI doi: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
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
svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc, eqincome > 0) )
# replicate-weighted design
svyjdivdec( ~eqincome , ~rb090 , subset(des_eusilc_rep, eqincome > 0) )
## Not run:
# 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 )
## End(Not run)