thresh_n_gr

Soft threshholding a vector with respect to a number of groups

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

thresh_n_gr function

<dl><dt>w</dt>
<dd>Numerical loading vector</dd>
<dt>keep_gr</dt>
<dd>How many groups to retain</dd>
<dt>index_gr</dt>
<dd>List of index and size. index are the index of variables belongs to the group in the original vector, size is the group size</dd></dl>

Arguments

Soft threshholding a vector with respect to a number of groups — thresh_n_gr

<dl>

<dt>w</dt>
<dd>Numerical loading vector</dd>


<dt>keep_gr</dt>
<dd>How many groups to retain</dd>


<dt>index_gr</dt>
<dd>List of index and size. index are the index of variables belongs to the group in the original vector, size is the group size</dd>

</dl>

thresh_n_gr: Soft threshholding a vector with respect to a number of groups

Description

Usage

Value

Arguments