fraction_variance_explained_perPC

Computes the fraction of variance explained (variance explained normalized by the trace of the covariance/correlation matrix) by each PC.

Implements an algorithm for computing multiple sparse principal
components of a dataset. The method is based on Cory-Wright and Pauphilet
"Sparse PCA with Multiple Principal Components" (2022)
<doi:10.48550/arXiv.2209.14790>. The algorithm uses an iterative deflation
heuristic with a truncated power method applied at each iteration to compute
sparse principal components with controlled sparsity.

Jean Pauphilet

msPCA

Sparse Principal Component Analysis with Multiple Principal
Components

Ryan Cory-Wright

fraction_variance_explained_perPC function

<dl><dt>C</dt>
<dd>A matrix. The correlation or covariance matrix (p x p).</dd>
<dt>U</dt>
<dd>A matrix. The matrix containing the r PCs (p x r).</dd></dl>

Arguments

Fraction of variance explained per PC — fraction_variance_explained_perPC

<dl>

<dt>C</dt>
<dd>A matrix. The correlation or covariance matrix (p x p).</dd>


<dt>U</dt>
<dd>A matrix. The matrix containing the r PCs (p x r).</dd>

</dl>

fraction_variance_explained_perPC: Fraction of variance explained per PC

Description

Usage

Value

Arguments