svyamato: Amato index (EXPERIMENTAL)

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/svyamato.R

Description

Estimate the Amato index, a measure of inequality.

Usage

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svyamato(formula, design, ...)

## S3 method for class 'survey.design'
svyamato(formula, design, standardized = FALSE, na.rm = FALSE, ...)

## S3 method for class 'svyrep.design'
svyamato(formula, design, standardized = FALSE, na.rm = FALSE, ...)

## S3 method for class 'DBIsvydesign'
svyamato(formula, design, ...)

Arguments

formula

a formula specifying the income variable.

design

a design object of class survey.design or class svyrep.design from the survey library.

...

future expansion

standardized

If standardized = TRUE, returns the standardized Amato index, i.e., a linear tranformation of the amato index.

na.rm

Should cases with missing values be dropped?

Details

you must run the convey_prep function on your survey design object immediately after creating it with the svydesign or svrepdesign function.

The Amato index is the length of the Lorenz curve.

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.

Note

This function is experimental and is subject to change in later versions.

Author(s)

Guilherme Jacob, Djalma Pessoa and Anthony Damico

References

Lucio Barabesi, Giancarlo Diana and Pier Francesco Perri (2016). Linearization of inequality indexes in the design-based framework. Statistics. URL http://www.tandfonline.com/doi/pdf/10.1080/02331888.2015.1135924.

Barry C. Arnold (2012). On the Amato inequality index. Statistics & Probability Letters, v. 82, n. 8, August 2012, pp. 1504-1506, ISSN 0167-7152. URL http://dx.doi.org/10.1016/j.spl.2012.04.020.

See Also

svygini

Examples

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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)


# variable without missing values
svyamato(~eqincome, des_eusilc)
svyamato(~eqincome, des_eusilc_rep)

# subsetting:
svyamato(~eqincome, subset( des_eusilc, db040 == "Styria"))
svyamato(~eqincome, subset( des_eusilc_rep, db040 == "Styria"))

## Not run: 

# variable with with missings
svyamato(~py010n, des_eusilc )
svyamato(~py010n, des_eusilc_rep )

svyamato(~py010n, des_eusilc, na.rm = TRUE )
svyamato(~py010n, 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 )


# variable without missing values
svyamato(~eqincome, dbd_eusilc)

# subsetting:
svyamato(~eqincome, subset( dbd_eusilc, db040 == "Styria"))

# variable with with missings
svyamato(~py010n, dbd_eusilc )

svyamato(~py010n, dbd_eusilc, na.rm = TRUE )


dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

convey documentation built on July 1, 2020, 11:44 p.m.