est.M.ols

Estimate mixing matrix from estimates of components

Provides an implementation of the Sparse ICA method in Wang et al. (2024) <doi:10.1080/01621459.2024.2370593> for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency.

Zihang Wang

SparseICA

Sparse Independent Component Analysis

Irina Gaynanova

Aleksandr Aravkin

Benjamin Risk

est.M.ols function

<dl><dt>sData</dt>
<dd>S Dimension: P x Q</dd>
<dt>xData</dt>
<dd>X Dimension: P x T</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 Dimension: P x Q</dd>


<dt>xData</dt>
<dd>X Dimension: P x T</dd>


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

</dl>

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

Description

Usage

Value

Arguments