cv_lambda_checker

Check if sparsity parameters satisfy input conditions in cross-validation functions

internal

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

cv_lambda_checker function

<dl><dt>x</dt>
<dd>Should be numeric matrix.</dd>
<dt>y</dt>
<dd>Should be numeric matrix.</dd>
<dt>keepx_seq</dt>
<dd>Input of <code>crossval_sparsity</code> function.</dd>
<dt>keepy_seq</dt>
<dd>Input of <code>crossval_sparsity</code> function.</dd></dl>

Arguments

Check if sparsity parameters satisfy input conditions in cross-validation functions — cv_lambda_checker

<dl>

<dt>x</dt>
<dd>Should be numeric matrix.</dd>


<dt>y</dt>
<dd>Should be numeric matrix.</dd>


<dt>keepx_seq</dt>
<dd>Input of <code>crossval_sparsity</code> function.</dd>


<dt>keepy_seq</dt>
<dd>Input of <code>crossval_sparsity</code> function.</dd>

</dl>

cv_lambda_checker: Check if sparsity parameters satisfy input conditions in cross-validation functions

Description

Usage

Arguments

Details