svyisq: Linearization of the total below a quantile

In convey: Income Concentration Analysis with Complex Survey Samples

Linearization of the total below a quantile

Description

Computes the linearized variable of the total in the lower tail of the distribution of a variable.

Usage

svyisq(formula, design, ...)

## S3 method for class 'survey.design'
svyisq(
  formula,
  design,
  alpha,
  quantile = FALSE,
  upper = FALSE,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  influence = FALSE,
  ...
)

## S3 method for class 'svyrep.design'
svyisq(
  formula,
  design,
  alpha,
  quantile = FALSE,
  upper = FALSE,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  return.replicates = FALSE,
  ...
)

## S3 method for class 'DBIsvydesign'
svyisq(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.
...	arguments passed on to 'survey::oldsvyquantile'
alpha	the order of the quantile
quantile	return the upper bound of the lower tail
upper	return the total in the total in the upper tail. Defaults to FALSE.
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
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, Guilherme Jacob, 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

svyarpr

Examples

library(laeken)
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
library(survey)
des_eusilc <- svydesign(ids = ~rb030, strata =~db040,  weights = ~rb050, data = eusilc)
des_eusilc <- convey_prep(des_eusilc)
svyisq(~eqincome, design=des_eusilc,.20 , quantile = TRUE)

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

svyisq( ~eqincome , design = des_eusilc_rep, .20 , quantile = TRUE )

## Not run: 

# linearized design using a variable with missings
svyisq( ~ py010n , design = des_eusilc, .20 )
svyisq( ~ py010n , design = des_eusilc , .20, na.rm = TRUE )
# replicate-weighted design using a variable with missings
svyisq( ~ py010n , design = des_eusilc_rep, .20 )
svyisq( ~ py010n , design = des_eusilc_rep , .20,  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 )

svyisq( ~ eqincome , design = dbd_eusilc, .20 )

dbRemoveTable( conn , 'eusilc' )

dbDisconnect( conn , shutdown = TRUE )


## End(Not run)

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