procrustes.mds.ct

Matches classical principal coordinates
to continuous-time principal coordinates produced by <code>mds.ct</code>,
via Procrustes transformation.

Implements a basis function or functional data analysis framework
for several techniques of multivariate analysis in continuous-time
setting. Specifically, we introduced continuous-time analogues of
several classical techniques of multivariate analysis, such as
principal component analysis, canonical correlation analysis,
Fisher linear discriminant analysis, K-means clustering, and so
on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025)
"Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

Biplab Paul

ctmva

Continuous-Time Multivariate Analysis

Philip Tzvi Reiss

Noemi Foa

Dror Arbiv

procrustes.mds.ct function

<dl><dt>obj</dt>
<dd>an object of class "<code>mds.ct</code>"</dd>
<dt>points</dt>
<dd>matrix of classical principal coordinates, 
e.g. as produced by <code><a href="/link/cmdscale?package=ctmva&version=1.6.0" data-mini-rdoc="ctmva::cmdscale">cmdscale</a></code></dd>
<dt>coord</dt>
<dd>which coordinates to transform.</dd></dl>

Arguments

Philip Tzvi Reiss &lt;reiss@stat.haifa.ac.il&gt;

Author

Procrustes transformation for continuous-time multidimensional scaling — procrustes.mds.ct

<dl>

<dt>obj</dt>
<dd>an object of class "<code>mds.ct</code>"</dd>


<dt>points</dt>
<dd>matrix of classical principal coordinates, 
e.g. as produced by <code><a href='https://rdrr.io/r/stats/cmdscale.html'>cmdscale</a></code></dd>


<dt>coord</dt>
<dd>which coordinates to transform.</dd>

</dl>

procrustes.mds.ct: Procrustes transformation for continuous-time multidimensional scaling

Description

Usage

Value

Arguments

Author

See Also

Examples