CLPpred

Predicts category levels from an MCA based biplot using the distances between coordinates

Aligning multiple visualisations by utilising generalised orthogonal Procrustes analysis (GPA) before combining coordinates into a single biplot display as described in Nienkemper-Swanepoel, le Roux and Lubbe (2023)<doi:10.1080/03610918.2021.1914089>. This is mainly suitable to combine visualisations constructed from multiple imputations, however, it can be generalised to combine variations of visualisations from the same datasets (i.e. resamples).

Johané Nienkemper-Swanepoel

GPAbin

Unifying Multiple Biplot Visualisations into a Single Display

CLPpred function

<dl><dt>CLPs</dt>
<dd>Category level point coordinates</dd>
<dt>Zs</dt>
<dd>Sample coordinates</dd>
<dt>p</dt>
<dd>Number of variables</dd>
<dt>n</dt>
<dd>Number of samples</dd>
<dt>lvls</dt>
<dd>Names of category levels</dd>
<dt>datIN</dt>
<dd>Input data from which <code>CLPs</code> and <code>Zs</code> are obtained</dd></dl>

CLPpred: Category level prediction

Description

Usage

Value

Arguments