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

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

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

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

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.

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

Author

Djalma Pessoa and Anthony Damico

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 )
icdf(~eqincome, design=des_eusilc, 10000 )
#>          cdf     SE
#> [1,] 0.11444 0.0027
# linearized design using a variable with missings
icdf( ~ py010n , design = des_eusilc, 10000 )
#>      cdf  SE
#> [1,]  NA NaN
icdf( ~ py010n , design = des_eusilc , 10000, na.rm = TRUE )
#>          cdf     SE
#> [1,] 0.60127 0.0045