weighted.var

a vector of positive weights (may not have missings where x is observed).

if <code>TRUE</code> remove missing values.

na.rm

This function is analogous to weighted.mean.

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

weighted.var function

<dl><dt>x</dt>
<dd>a vector of data.</dd>
<dt>w</dt>
<dd>a vector of positive weights (may not have missings where x is observed).</dd>
<dt>na.rm</dt>
<dd>if <code>TRUE</code> remove missing values.</dd></dl>

Arguments

Author

Weighted univariate variance coping with missing values — weighted.var

<dl>

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


<dt>w</dt>
<dd>a vector of positive weights (may not have missings where x is observed).</dd>


<dt>na.rm</dt>
<dd>if <code>TRUE</code> remove missing values.</dd>

</dl>

weighted.var: Weighted univariate variance coping with missing values

Description

Usage

Value

Arguments

Author

Details

See Also

Examples