svyqsr: Quintile Share Ratio

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

Quintile Share Ratio

Description

Estimate ratio of the total income received by the highest earners to the total income received by lowest earners, defaulting to 20

Usage

svyqsr(formula, design, ...)

## S3 method for class 'survey.design'
svyqsr(
  formula,
  design,
  alpha1 = 0.2,
  alpha2 = (1 - alpha1),
  na.rm = FALSE,
  upper_quant = FALSE,
  lower_quant = FALSE,
  upper_tot = FALSE,
  lower_tot = FALSE,
  deff = FALSE,
  linearized = FALSE,
  influence = FALSE,
  ...
)

## S3 method for class 'svyrep.design'
svyqsr(
  formula,
  design,
  alpha1 = 0.2,
  alpha2 = (1 - alpha1),
  na.rm = FALSE,
  upper_quant = FALSE,
  lower_quant = FALSE,
  upper_tot = FALSE,
  lower_tot = FALSE,
  deff = FALSE,
  linearized = FALSE,
  return.replicates = FALSE,
  ...
)

## S3 method for class 'DBIsvydesign'
svyqsr(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
alpha1	order of the lower quintile
alpha2	order of the upper quintile
na.rm	Should cases with missing values be dropped?
upper_quant	return the lower bound of highest earners
lower_quant	return the upper bound of lowest earners
upper_tot	return the highest earners total
lower_tot	return the lowest earners total
deff	Return the design effect (see survey::svymean)
linearized	Should a matrix of linearized variables be returned
influence	Should a matrix of (weighted) influence functions be returned? (for compatibility with svyby)
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)

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 )

svyqsr( ~eqincome , design = des_eusilc, upper_tot = TRUE, lower_tot = TRUE )

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

svyqsr( ~eqincome , design = des_eusilc_rep, upper_tot = TRUE, lower_tot = TRUE )

## Not run: 

# linearized design using a variable with missings
svyqsr( ~ db090 , design = des_eusilc )
svyqsr( ~ db090 , design = des_eusilc , na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyqsr( ~ db090 , design = des_eusilc_rep )
svyqsr( ~ db090 , 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 )

svyqsr( ~ eqincome , design = dbd_eusilc )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

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