angleMatchICA

<code>angleMatchICA</code> match the colums of Mx and My, using the n x p parameterization of the JIN decomposition assumes

Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

Liangkang Wang

singR

Simultaneous Non-Gaussian Component Analysis

Irina Gaynanova

Benjamin Risk

angleMatchICA function

<dl><dt>Mx</dt>
<dd>Subject score for X matrix of n x n.comp</dd>
<dt>My</dt>
<dd>Subject score for Y matrix of n x n.comp</dd>
<dt>Sx</dt>
<dd>Variable loadings for X matrix of n.comp x px</dd>
<dt>Sy</dt>
<dd>Variable loadings for Y matrix of n.comp x py</dd></dl>

Arguments

Match the colums of Mx and My — angleMatchICA

<dl>

<dt>Mx</dt>
<dd>Subject score for X matrix of n x n.comp</dd>


<dt>My</dt>
<dd>Subject score for Y matrix of n x n.comp</dd>


<dt>Sx</dt>
<dd>Variable loadings for X matrix of n.comp x px</dd>


<dt>Sy</dt>
<dd>Variable loadings for Y matrix of n.comp x py</dd>

</dl>

angleMatchICA: Match the colums of Mx and My

Description

Usage

Value

Arguments