process.X

A function that checks the design matrix X for possible errors and scales it.

Fits the regularization path of regression models
(linear and logistic) with additively combined penalty terms. All
possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO),
Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and
Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL),
Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE).
For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

Gregor Buch

SGPR

Sparse Group Penalized Regression for Bi-Level Variable
Selection

Andreas Schulz

Irene Schmidtmann

Konstantin Strauch

Philipp Wild

process.X function

<dl><dt>X</dt>
<dd>The design matrix without intercept with the variables to be selected.</dd>
<dt>group</dt>
<dd>A vector that specifies the group membership of each variable in X.</dd></dl>

Arguments

Process X for a sparse group penalty — process.X

<dl>

<dt>X</dt>
<dd>The design matrix without intercept with the variables to be selected.</dd>


<dt>group</dt>
<dd>A vector that specifies the group membership of each variable in X.</dd>

</dl>

process.X: Process X for a sparse group penalty

Description

Usage

Value

Arguments