convey (version 1.0.1)

svyqsr: 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 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 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 DBIsvydesign
svyqsr(formula, design, ...)

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.

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?

Author

Djalma Pessoa and Anthony Damico

Details

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

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 )

if (FALSE) {

# 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 )

}