print.cvo2m

Cross-validate procedure for O2PLS

Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset.
The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
It provides a rigorous and a faster alternative cross-validation method to select the number of components,
as well as functions to report proportions of explained variation and to construct plots of the results.
See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>,
and Trygg and Wold (2003) <doi:10.1002/cem.775>.
It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.

Said Bouhaddani

OmicsPLS

Data Integration with Two-Way Orthogonal Partial Least Squares

Said el Bouhaddani

Zander Gu

Jeanine Houwing-Duistermaat

Geurt Jongbloed

Szymon Kielbasa

Hae-Won Uh

print.cvo2m function

<dl><dt>x</dt>
<dd>List of class <code>"cvo2m"</code>, produced by <code>crossval_o2m</code>.</dd>
<dt>include_matrix</dt>
<dd>Logical. Should the 3-d array with Prediction errors also be printed.</dd>
<dt>...</dt>
<dd>For consistency.</dd></dl>

Arguments

Cross-validate procedure for O2PLS — print.cvo2m

<dl>

<dt>x</dt>
<dd>List of class <code>"cvo2m"</code>, produced by <code>crossval_o2m</code>.</dd>


<dt>include_matrix</dt>
<dd>Logical. Should the 3-d array with Prediction errors also be printed.</dd>


<dt>...</dt>
<dd>For consistency.</dd>

</dl>

print.cvo2m: Cross-validate procedure for O2PLS

Description

Usage

Arguments