icdf: Linearization of the cumulative distribution function (cdf) of a variable

Description

Computes the linearized variable of the cdf function in a point.

Usage

icdf(formula, design, x, na.rm = FALSE, ...)

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.
x: the point where the cdf is calculated
na.rm: Should cases with missing values be dropped?
...: future expansion

Author

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.

Examples

Run this code

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 )
icdf(~eqincome, design=des_eusilc, 10000 )
# linearized design using a variable with missings
icdf( ~ py010n , design = des_eusilc, 10000 )
icdf( ~ py010n , design = des_eusilc , 10000, na.rm = TRUE )

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning