NG_number

find the number of non-Gaussian components in the data.

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

NG_number function

<dl><dt>data</dt>
<dd>original matrix with n x p.</dd>
<dt>type</dt>
<dd>'S1', 'S2' or 'S3'</dd></dl>

Arguments

find the number of non-Gaussian components in the data. — NG_number

<dl>

<dt>data</dt>
<dd>original matrix with n x p.</dd>


<dt>type</dt>
<dd>'S1', 'S2' or 'S3'</dd>

</dl>

NG_number: find the number of non-Gaussian components in the data.

Description

Usage

Value

Arguments

Examples