weightsum

observations

weights

value

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

Sum of weights for observations &lt; value (if lt = TRUE)
or observations=value (if lt = FALSE).

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

weightsum function

<dl><dt>observations</dt>
<dd>vector of observations.</dd>
<dt>weights</dt>
<dd>vector of weights.</dd>
<dt>value</dt>
<dd>value.</dd>
<dt>lt</dt>
<dd>either <code>TRUE</code> or <code>FALSE</code>.</dd></dl>

Arguments

Author

Utility function for TRC.R among others — weightsum

<dl>

<dt>observations</dt>
<dd>vector of observations.</dd>


<dt>weights</dt>
<dd>vector of weights.</dd>


<dt>value</dt>
<dd>value.</dd>


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

</dl>

weightsum: Utility function for TRC.R among others

Description

Usage

Arguments

Author