get_Bhat_comp

Compute the components of Bhat using SVD. In our formula, Bhat is stored as LeftBhatlambda_k (p times r matrix) and RightBhatlambda_k (q times r). Here, n is the number of samples, p is the number of variables of X, q is the number of variables of Y, and r is the specified rank in the Ridge Redundancy Analysis.

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

get_Bhat_comp function

<dl><dt>rsid2_1L</dt>
<dd>A numeric vector used for each lambda value.</dd>
<dt>DUtY</dt>
<dd>A numeric matrix (n times q).</dd>
<dt>nrank</dt>
<dd>A numeric vector indicating the rank(s) of Bhat.</dd>
<dt>tV</dt>
<dd>A numeric matrix.</dd></dl>

Arguments

Compute the components of the coefficient Bhat using SVD. — get_Bhat_comp

<dl>

<dt>rsid2_1L</dt>
<dd>A numeric vector used for each lambda value.</dd>


<dt>DUtY</dt>
<dd>A numeric matrix (n times q).</dd>


<dt>nrank</dt>
<dd>A numeric vector indicating the rank(s) of Bhat.</dd>


<dt>tV</dt>
<dd>A numeric matrix.</dd>

</dl>

get_Bhat_comp: Compute the components of the coefficient Bhat using SVD.

Description

Usage

Value

Arguments