# densfun: Estimate the derivative of the cdf function using kernel... In convey: Income Concentration Analysis with Complex Survey Samples

 densfun R Documentation

## Estimate the derivative of the cdf function using kernel estimator

### Description

computes the derivative of a function in a point using kernel estimation

### Usage

```densfun(formula, design, x, h = NULL, FUN = "F", na.rm = FALSE, ...)
```

### Arguments

 `formula` a formula specifying the income variable `design` a design object of class `survey.design` from the `survey` library. `x` the point where the derivative is calculated `h` value of the bandwidth based on the whole sample `FUN` if `F` estimates the derivative of the cdf function; if `big_s` estimates the derivative of total in the tails of the distribution `na.rm` Should cases with missing values be dropped? `...` future expansion

### Value

the value of the derivative at `x`

### Author(s)

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

```

convey documentation built on April 28, 2022, 1:06 a.m.