reweightOut {laeken} | R Documentation |
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. doi:10.18637/jss.v054.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.
paretoTail
, shrinkOut
,
replaceOut
, replaceTail
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)