sweep.operator

either <code>TRUE</code> or <code>FALSE</code>.

reverse

Definition of the sweep and reverse-sweep operator (Schafer pp 159-160)

internal

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>.

Martin Sterchi

modi

Multivariate Outlier Detection and Imputation for Incomplete
Survey Data

Beat Hulliger

sweep.operator function

<dl><dt>M</dt>
<dd>a matrix.</dd>
<dt>k</dt>
<dd>column.</dd>
<dt>reverse</dt>
<dd>either <code>TRUE</code> or <code>FALSE</code>.</dd></dl>

Arguments

Author

Sweep operator — sweep.operator

<dl>

<dt>M</dt>
<dd>a matrix.</dd>


<dt>k</dt>
<dd>column.</dd>


<dt>reverse</dt>
<dd>either <code>TRUE</code> or <code>FALSE</code>.</dd>

</dl>

sweep.operator: Sweep operator

Description

Usage

Arguments

Author