svyrich: Richness measures

In DjalmaPessoa/convey: Income Concentration Analysis with Complex Survey Samples

Richness measures

Description

Estimate Peichl, Schaefer and Scheicher (2010) richness measures.

Usage

svyrich(formula, design, ...)

## S3 method for class 'survey.design'
svyrich(
  formula,
  design,
  type_measure,
  g,
  type_thresh = "abs",
  abs_thresh = NULL,
  percent = 1.5,
  quantiles = 0.5,
  thresh = FALSE,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  ...
)

## S3 method for class 'svyrep.design'
svyrich(
  formula,
  design,
  type_measure,
  g,
  type_thresh = "abs",
  abs_thresh = NULL,
  percent = 1.5,
  quantiles = 0.5,
  thresh = FALSE,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  return.replicates = FALSE,
  ...
)

## S3 method for class 'DBIsvydesign'
svyrich(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.
...	passed to svyarpt
type_measure	A string "Cha", "FGTT1" or "FGTT2" defining the richness measure.
g	Richness preference parameter.
type_thresh	type of richness threshold. If "abs" the threshold is fixed and given the value of abs_thresh; if "relq" it is given by percent times the quantile; if "relm" it is percent times the mean.
abs_thresh	richness threshold value if type_thresh is "abs"
percent	the multiple of the quantile or mean used in the richness threshold definition. Defaults to percent = 1.5; i.e., 1.5 times the quantile or mean.
quantiles	the quantile used used in the richness threshold definition. Defaults to quantiles = .5, the median.
thresh	return the richness threshold value
na.rm	Should cases with missing values be dropped?
deff	Return the design effect (see survey::svymean)
linearized	Should a matrix of linearized variables be returned
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.

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

Michal Brzezinski (2014). Statistical Inference for Richness Measures. Applied Economics, Vol. 46, No. 14, pp. 1599-1608, DOI \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00036846.2014.880106")}.

Andreas Peichl, Thilo Schaefer, and Christoph Scheicher (2010). Measuring richness and poverty: A micro data application to Europe and Germany. Review of Income and Wealth, Vol. 56, No.3, pp. 597-619.

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.

See Also

svyfgt

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 )

# concave Chakravarty richness measure
# higher g= parameters tend toward headcount ratio, richness threshold fixed
svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3,  abs_thresh=30000)
# g=1 parameter computes the richness gap index, richness threshold fixed
svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1,  abs_thresh=30000)
# higher g= parameters tend toward headcount ratio, richness threshold equal to the median
svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, type_thresh= "relq" )
# g=1 parameter computes the richness gap index, richness threshold equal to the median
svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, type_thresh= "relq" )
# higher g= parameters tend toward headcount ratio, richness threshold equal to the mean
svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=3, type_thresh= "relm" )
# g=1 parameter computes the richness gap index, richness threshold equal to the mean
svyrich(~eqincome, des_eusilc, type_measure = "Cha" , g=1, type_thresh= "relm" )

#  using svrep.design:
# higher g= parameters tend toward headcount ratio, richness threshold fixed
svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, abs_thresh=30000 )
# g=1 parameter computes the richness gap index, richness threshold fixed
svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, abs_thresh=30000 )
# higher g= parameters tend toward headcount ratio, richness threshold equal to the median
svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, type_thresh= "relq" )
# g=1 parameter computes the richness gap index, richness threshold equal to the median
svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, type_thresh= "relq" )
# higher g= parameters tend toward headcount ratio, richness threshold equal to the mean
svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=3, type_thresh= "relm" )
# g=1 parameter computes the richness gap index, richness threshold equal to the mean
svyrich(~eqincome, des_eusilc_rep, type_measure = "Cha" , g=1, type_thresh= "relm" )

## Not run: 

# 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 )

# higher g= parameters tend toward headcount ratio, richness threshold fixed
svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, abs_thresh=30000 )
# g=1 parameter computes the richness gap index, richness threshold fixed
svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, abs_thresh=30000 )
# higher g= parameters tend toward headcount ratio, richness threshold equal to the median
svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, type_thresh= "relq" )
# g=1 parameter computes the richness gap index, richness threshold equal to the median
svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, type_thresh= "relq" )
# higher g= parameters tend toward headcount ratio, richness threshold equal to the mean
svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=3, type_thresh= "relm" )
# g=1 parameter computes the richness gap index, richness threshold equal to the mean
svyrich(~eqincome, dbd_eusilc, type_measure = "Cha" , g=1, type_thresh= "relm" )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

DjalmaPessoa/convey documentation built on Jan. 31, 2024, 4:16 a.m.