weighted.quantile

A weighted cdf is calculated and quantiles are evaluated. Missing values are discarded.

Algorithms for multivariate outlier detection when missing values
occur. Algorithms are based on Mahalanobis distance or data depth.
Imputation is based on the multivariate normal model or uses nearest
neighbour donors. The algorithms take sample designs, in particular
weighting, into account. The methods are described in Bill and Hulliger
(2016) <doi:10.17713/ajs.v45i1.86>.

Beat Hulliger

modi

Multivariate Outlier Detection and Imputation for Incomplete
Survey Data

Martin Sterchi

Tobias Schoch

weighted.quantile function

<dl><dt>x</dt>
<dd>a vector of data.</dd>
<dt>w</dt>
<dd>a vector of (sampling) weights.</dd>
<dt>prob</dt>
<dd>the probability for the quantile.</dd>
<dt>plot</dt>
<dd>if <code>TRUE</code>, the weighted cdf is plotted.</dd></dl>

Arguments

Author

Quantiles of a weighted cdf — weighted.quantile

<dl>

<dt>x</dt>
<dd>a vector of data.</dd>


<dt>w</dt>
<dd>a vector of (sampling) weights.</dd>


<dt>prob</dt>
<dd>the probability for the quantile.</dd>


<dt>plot</dt>
<dd>if <code>TRUE</code>, the weighted cdf is plotted.</dd>

</dl>

weighted.quantile: Quantiles of a weighted cdf

Description

Usage

Value

Arguments

Author

Details

See Also

Examples