svyrmpg: Relative median poverty gap

In convey: Income Concentration Analysis with Complex Survey Samples

Relative median poverty gap

Description

Estimate the difference between the at-risk-of-poverty threshold (arpt) and the median of incomes less than the arpt relative to the arpt.

Usage

svyrmpg(formula, design, ...)

## S3 method for class 'survey.design'
svyrmpg(
  formula,
  design,
  quantiles = 0.5,
  percent = 0.6,
  na.rm = FALSE,
  thresh = FALSE,
  poor_median = FALSE,
  ...
)

## S3 method for class 'svyrep.design'
svyrmpg(
  formula,
  design,
  quantiles = 0.5,
  percent = 0.6,
  na.rm = FALSE,
  thresh = FALSE,
  poor_median = FALSE,
  ...
)

## S3 method for class 'DBIsvydesign'
svyrmpg(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
quantiles	income quantile, usually .5 (median)
percent	fraction of the quantile, usually .60
na.rm	Should cases with missing values be dropped?
thresh	return the poverty poverty threshold
poor_median	return the median income of poor people

Details

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

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)

Djalma Pessoa and Anthony Damico

References

Guillaume Osier (2009). Variance estimation for complex indicators of poverty and inequality. Journal of the European Survey Research Association, Vol.3, No.3, pp. 167-195, ISSN 1864-3361, URL https://ojs.ub.uni-konstanz.de/srm/article/view/369.

Jean-Claude Deville (1999). Variance estimation for complex statistics and estimators: linearization and residual techniques. Survey Methodology, 25, 193-203, URL https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19990024882.

See Also

svyarpt

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 )

svyrmpg( ~eqincome , design = des_eusilc, thresh = TRUE )

# replicate-weighted design
des_eusilc_rep <- as.svrepdesign( des_eusilc , type = "bootstrap" )
des_eusilc_rep <- convey_prep( des_eusilc_rep )

svyrmpg( ~eqincome , design = des_eusilc_rep, thresh = TRUE )

## Not run: 

# linearized design using a variable with missings
svyrmpg( ~ py010n , design = des_eusilc )
svyrmpg( ~ py010n , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyrmpg( ~ py010n , design = des_eusilc_rep )
svyrmpg( ~ py010n , design = 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 )

svyrmpg( ~ eqincome , design = dbd_eusilc )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

convey documentation built on Oct. 16, 2024, 5:10 p.m.