plotIT

The (weighted) cdf of infection times is plotted. The infection times
jumps of the cdf are shown by the points with the
same infection times stacked vertically and respecting the weights.

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

plotIT function

<dl><dt>infection.time</dt>
<dd>vector of infection.times of the observations</dd>
<dt>weights</dt>
<dd>vector of (survey) weights of the observations</dd>
<dt>cutpoint</dt>
<dd>a cutpoint to for declaring outliers</dd></dl>

Arguments

Author

Plot of  infection times of the EA algorithm — plotIT

<dl>

<dt>infection.time</dt>
<dd>vector of infection.times of the observations</dd>


<dt>weights</dt>
<dd>vector of (survey) weights of the observations</dd>


<dt>cutpoint</dt>
<dd>a cutpoint to for declaring outliers</dd>

</dl>

plotIT: Plot of infection times of the EA algorithm

Description

Usage

Arguments

Author

Details

Examples