unscale_matrices

Unscale a matrix based on provided 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_matrices function

<dl><dt>mat</dt>
<dd>A numeric matrix to be unscaled.</dd>
<dt>means</dt>
<dd>A numeric vector of means for each column.</dd>
<dt>std</dt>
<dd>A numeric vector of standard deviations for each column (optional).</dd></dl>

Arguments

Unscale a matrix based on provided mean and standard deviation values. — unscale_matrices

<dl>

<dt>mat</dt>
<dd>A numeric matrix to be unscaled.</dd>


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


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

</dl>

unscale_matrices: Unscale a matrix based on provided mean and standard deviation values.

Description

Usage

Value

Arguments