convey (version 1.0.1)

svyisq: 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 survey.design
svyisq(
  formula,
  design,
  alpha,
  quantile = FALSE,
  upper = FALSE,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  influence = FALSE,
  ...
)
# S3 method for svyrep.design
svyisq(
  formula,
  design,
  alpha,
  quantile = FALSE,
  upper = FALSE,
  na.rm = FALSE,
  deff = FALSE,
  linearized = FALSE,
  return.replicates = FALSE,
  ...
)
# S3 method for DBIsvydesign
svyisq(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.

...

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?

Author

Djalma Pessoa, Guilherme Jacob, 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

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 )

if (FALSE) {

# 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 )

}