R: Reweight outliers in the Pareto model

reweightOut {laeken} | R Documentation

## Reweight outliers in the Pareto model

### Description

Reweight observations that are flagged as outliers in a Pareto model for the upper tail of the distribution.

### Usage

    reweightOut(x, ...)

    ## S3 method for class 'paretoTail'
    reweightOut(x, X, w = NULL, ...)

### Arguments

`x` | an object of class `"paretoTail"` (see `paretoTail`).
`X` | a matrix of binary calibration variables (see `calibVars`). This is only used if `x` contains sample weights or if `w` is supplied.
`w` | a numeric vector of sample weights. This is only used if `x` does not contain sample weights, i.e., if sample weights were not considered in estimating the shape parameter of the Pareto distribution.
`...` | additional arguments to be passed down.

### Details

If the data contain sample weights, the weights of the outlying observations are set to 1 and the weights of the remaining observations are calibrated according to auxiliary variables. Otherwise, weight 0 is assigned to outliers and weight 1 to other observations.

### Value

If the data contain sample weights, a numeric containing the recalibrated weights is returned, otherwise a numeric vector assigning weight 0 to outliers and weight 1 to other observations.

### Author(s)

Andreas Alfons

### References

A. Alfons and M. Templ (2013) Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken. Journal of Statistical Software, 54(15), 1–25. URL http://www.jstatsoft.org/v54/i15/

A. Alfons, M. Templ, P. Filzmoser (2013) Robust estimation of economic indicators from survey samples based on Pareto tail modeling. Journal of the Royal Statistical Society, Series C, 62(2), 271–286.

### See Also

`paretoTail`, `shrinkOut` , `replaceOut`, `replaceTail`

### Examples

    data(eusilc)

    ## gini coefficient without Pareto tail modeling
    gini("eqIncome", weights = "rb050", data = eusilc)

    ## gini coefficient with Pareto tail modeling
    # estimate threshold
    ts <- paretoScale(eusilc$eqIncome, w = eusilc$db090,
        groups = eusilc$db030)
    # estimate shape parameter
    fit <- paretoTail(eusilc$eqIncome, k = ts$k,
        w = eusilc$db090, groups = eusilc$db030)
    # calibration of outliers
    w <- reweightOut(fit, calibVars(eusilc$db040))
    gini(eusilc$eqIncome, w)

[Package laeken version 0.4.6 Index]