Reweight outliers in the Pareto model
Reweight observations that are flagged as outliers in a Pareto model for the upper tail of the distribution.
reweightOut(x, ...) ## S3 method for class 'paretoTail' reweightOut(x, X, w = NULL, ...)
x |
an object of class |
... |
additional arguments to be passed down. |
X |
a matrix of binary calibration variables (see
|
w |
a numeric vector of sample weights. This is only used if |
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.
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.
Andreas Alfons
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.