laeken (version 0.3.2)

reweightOut: 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, \dots)

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.

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.

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.

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)