permTestJointRank

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

permTestJointRank function

<dl><dt>MatchedMx</dt>
<dd>matrix with nsubject x n.comp.X, comes from greedymatch</dd>
<dt>MatchedMy</dt>
<dd>matrix with nsubject2 x n.comp.Y, comes from greedymatch</dd>
<dt>nperm</dt>
<dd>default value = 1000</dd>
<dt>alpha</dt>
<dd>default value = 0.01</dd>
<dt>multicore</dt>
<dd>default value = 0</dd></dl>

Arguments

Permutation test with Greedymatch — permTestJointRank

<dl>

<dt>MatchedMx</dt>
<dd>matrix with nsubject x n.comp.X, comes from greedymatch</dd>


<dt>MatchedMy</dt>
<dd>matrix with nsubject2 x n.comp.Y, comes from greedymatch</dd>


<dt>nperm</dt>
<dd>default value = 1000</dd>


<dt>alpha</dt>
<dd>default value = 0.01</dd>


<dt>multicore</dt>
<dd>default value = 0</dd>

</dl>

permTestJointRank: Permutation test with Greedymatch

Description

Usage

Value

Arguments