PlotMD

QQ-plot of (squared) Mahalanobis distances vs. scaled F-distribution (or a scaled chisquare distribution).
In addition, two default cutpoints are proposed.

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

PlotMD function

<dl><dt>dist</dt>
<dd>a vector of Mahalanobis distances.</dd>
<dt>p</dt>
<dd>the number of variables involved in the Mahalanobis distances.</dd>
<dt>alpha</dt>
<dd>a probability for cut-off, usually close to 1.</dd>
<dt>chisquare</dt>
<dd>a logical indicating the the chisquare distribution should be used instead of the F-distribution.</dd></dl>

Arguments

Author

QQ-Plot of Mahalanobis distances — PlotMD

<dl>

<dt>dist</dt>
<dd>a vector of Mahalanobis distances.</dd>


<dt>p</dt>
<dd>the number of variables involved in the Mahalanobis distances.</dd>


<dt>alpha</dt>
<dd>a probability for cut-off, usually close to 1.</dd>


<dt>chisquare</dt>
<dd>a logical indicating the the chisquare distribution should be used instead of the F-distribution.</dd>

</dl>

PlotMD: QQ-Plot of Mahalanobis distances

Description

Usage

Value

Arguments

Author

Details

References

Examples