rrda.summary

The function provides a summary of the results from cross-validation for Bhat obtained using the rrda.cv function. It displays the Mean Squared Error (MSE), rank, and lambda values for different cross-validation options.

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

rrda.summary function

<dl><dt>cv_result</dt>
<dd>A result list from the function <code>rrda.cv</code>, containing a matrix of MSE values for each rank and lambda, and a vector of lambda values.</dd></dl>

Arguments

Summarize the results of cross-validation for the coefficient Bhat obtained from the rrda.cv function. — rrda.summary

<dl>

<dt>cv_result</dt>
<dd>A result list from the function <code>rrda.cv</code>, containing a matrix of MSE values for each rank and lambda, and a vector of lambda values.</dd>

</dl>

rrda.summary: Summarize the results of cross-validation for the coefficient Bhat obtained from the `rrda.cv` function.

Description

Usage

Value

Arguments