svygei {convey} | R Documentation |
Generalized entropy index
Description
Estimate the generalized entropy index, a measure of inequality
Usage
svygei(formula, design, ...)
## S3 method for class 'survey.design'
svygei(
formula,
design,
epsilon = 1,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
influence = FALSE,
...
)
## S3 method for class 'svyrep.design'
svygei(
formula,
design,
epsilon = 1,
na.rm = FALSE,
deff = FALSE,
linearized = FALSE,
return.replicates = FALSE,
...
)
## S3 method for class 'DBIsvydesign'
svygei(formula, design, ...)
Arguments
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? |
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 "cvystat
", which are vectors with a "var
" attribute giving the variance and a "statistic
" attribute giving the name of the statistic.
Author(s)
Guilherme Jacob, Djalma Pessoa and Anthony Damico
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.
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
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)