icdf {convey} | R Documentation |
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, ...)
Arguments
formula |
a formula specifying the income variable |
design |
a design object of class |
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.
Author(s)
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.
See Also
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 )
# linearized design using a variable with missings
icdf( ~ py010n , design = des_eusilc, 10000 )
icdf( ~ py010n , design = des_eusilc , 10000, na.rm = TRUE )