svygei | R Documentation |
Estimate the generalized entropy index, a measure of inequality
svygei(formula, design, ...) ## S3 method for class 'survey.design' svygei(formula, design, epsilon = 1, na.rm = FALSE, ...) ## S3 method for class 'svyrep.design' svygei(formula, design, epsilon = 1, na.rm = FALSE, ...) ## S3 method for class 'DBIsvydesign' svygei(formula, design, ...)
formula |
a formula specifying the income variable |
design |
a design object of class |
... |
future expansion |
epsilon |
a parameter that determines the sensivity towards inequality in the top of the distribution. Defaults to epsilon = 1. |
na.rm |
Should cases with missing values be dropped? |
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.
Object of class "cvystat
", which are vectors with a "var
" attribute giving the variance and a "statistic
" attribute giving the name of the statistic.
Guilherme Jacob, Djalma Pessoa and Anthony Damico
Matti Langel (2012). Measuring inequality in finite population sampling. PhD thesis: Universite de Neuchatel, URL https://doc.rero.ch/record/29204/files/00002252.pdf.
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.
svyatk
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 svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = .5 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , subset(des_eusilc, eqincome > 0), epsilon = 2 ) # replicate-weighted design svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = .5 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , subset(des_eusilc_rep, eqincome > 0), epsilon = 2 ) ## Not run: # linearized design using a variable with missings svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2 ) svygei( ~py010n , subset(des_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 2, na.rm = TRUE ) # replicate-weighted design using a variable with missings svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2 ) svygei( ~py010n , subset(des_eusilc_rep, py010n > 0 | is.na(py010n) ), epsilon = 2, 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 svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 0 ) svygei( ~eqincome , dbd_eusilc, epsilon = .5 ) svygei( ~eqincome , subset(dbd_eusilc, eqincome > 0), epsilon = 1 ) svygei( ~eqincome , dbd_eusilc, epsilon = 2 ) # database-backed linearized design using a variable with missings svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0 ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 0, na.rm = TRUE ) svygei( ~py010n , dbd_eusilc, epsilon = .5 ) svygei( ~py010n , dbd_eusilc, epsilon = .5, na.rm = TRUE ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1 ) svygei( ~py010n , subset(dbd_eusilc, py010n > 0 | is.na(py010n) ), epsilon = 1, na.rm = TRUE ) svygei( ~py010n , dbd_eusilc, epsilon = 2 ) svygei( ~py010n , dbd_eusilc, epsilon = 2, na.rm = TRUE ) dbRemoveTable( conn , 'eusilc' ) dbDisconnect( conn , shutdown = TRUE ) ## End(Not run)
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