bootstrap_plsc

Provides bootstrap ratios (mean / sd) for X and Y loadings to assess stability,
mirroring common practice in Behavior PLSC.

Provides a set of basic and extensible data structures and functions for multivariate analysis, including dimensionality reduction techniques, projection methods, and preprocessing functions. The aim of this package is to offer a flexible and user-friendly framework for multivariate analysis that can be easily extended for custom requirements and specific data analysis tasks.

Bradley Buchsbaum

multivarious

Extensible Data Structures for Multivariate Analysis

bootstrap_plsc function

<dl><dt>x</dt>
<dd>A fitted <code>plsc</code> object.</dd>
<dt>X</dt>
<dd>Original X block.</dd>
<dt>Y</dt>
<dd>Original Y block.</dd>
<dt>nboot</dt>
<dd>Number of bootstrap samples (default 500).</dd>
<dt>comps</dt>
<dd>Number of components to bootstrap (default: <code>ncomp(x)</code>).</dd>
<dt>seed</dt>
<dd>Optional integer seed for reproducibility.</dd>
<dt>parallel</dt>
<dd>Use future.apply for parallelization (default FALSE).</dd>
<dt>epsilon</dt>
<dd>Small positive constant to stabilize division for ratios.</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused).</dd></dl>

Arguments

Bootstrap inference for PLSC loadings — bootstrap_plsc

<dl>

<dt>x</dt>
<dd>A fitted <code>plsc</code> object.</dd>


<dt>X</dt>
<dd>Original X block.</dd>


<dt>Y</dt>
<dd>Original Y block.</dd>


<dt>nboot</dt>
<dd>Number of bootstrap samples (default 500).</dd>


<dt>comps</dt>
<dd>Number of components to bootstrap (default: <code>ncomp(x)</code>).</dd>


<dt>seed</dt>
<dd>Optional integer seed for reproducibility.</dd>


<dt>parallel</dt>
<dd>Use future.apply for parallelization (default FALSE).</dd>


<dt>epsilon</dt>
<dd>Small positive constant to stabilize division for ratios.</dd>


<dt>...</dt>
<dd>Additional arguments (currently unused).</dd>

</dl>

bootstrap_plsc: Bootstrap inference for PLSC loadings

Description

Usage

Arguments