est.M.ols

Estimate mixing matrix from estimates of components

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

est.M.ols function

<dl><dt>sData</dt>
<dd>S rx x px</dd>
<dt>xData</dt>
<dd>dX n x px</dd>
<dt>intercept</dt>
<dd>default = TRUE</dd></dl>

Arguments

Estimate mixing matrix from estimates of components — est.M.ols

<dl>

<dt>sData</dt>
<dd>S rx x px</dd>


<dt>xData</dt>
<dd>dX n x px</dd>


<dt>intercept</dt>
<dd>default = TRUE</dd>

</dl>

est.M.ols: Estimate mixing matrix from estimates of components

Description

Usage

Value

Arguments