unscale_nested_matrices_map

Apply unscaling to a nested list of matrices using specified mean and standard deviation values.

Efficient framework for ridge redundancy analysis (rrda),
tailored for high-dimensional omics datasets where the number of
predictors exceeds the number of samples. The method leverages
Singular Value Decomposition (SVD) to avoid direct inversion of the
covariance matrix, enhancing scalability and performance. It also
introduces a memory-efficient storage strategy for coefficient
matrices, enabling practical use in large-scale applications. The
package supports cross-validation for selecting regularization
parameters and reduced-rank dimensions, making it a robust and
flexible tool for multivariate analysis in omics research. Please
refer to our article (Yoshioka et al., 2025) for more details.

Julie Aubert

rrda

Ridge Redundancy Analysis for High-Dimensional Omics Data

Hayato Yoshioka

Tristan Mary-Huard

unscale_nested_matrices_map function

<dl><dt>mat</dt>
<dd>A nested list of matrices.</dd>
<dt>means</dt>
<dd>A numeric vector of means for each matrix.</dd>
<dt>std</dt>
<dd>A numeric vector of standard deviations for each matrix (optional).</dd></dl>

Arguments

Apply unscaling to a nested list of matrices using specified mean and standard deviation values. — unscale_nested_matrices_map

<dl>

<dt>mat</dt>
<dd>A nested list of matrices.</dd>


<dt>means</dt>
<dd>A numeric vector of means for each matrix.</dd>


<dt>std</dt>
<dd>A numeric vector of standard deviations for each matrix (optional).</dd>

</dl>

unscale_nested_matrices_map: Apply unscaling to a nested list of matrices using specified mean and standard deviation values.

Description

Usage

Value

Arguments