get.loss

A function that calculates the loss/cost

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

get.loss function

<dl><dt>y</dt>
<dd>The response vector.</dd>
<dt>pred</dt>
<dd>The predicted values for the response.</dd>
<dt>type</dt>
<dd>A string indicating the type of regression model (linear or binomial).</dd></dl>

Arguments

A function that calculates the loss/cost — get.loss

<dl>

<dt>y</dt>
<dd>The response vector.</dd>


<dt>pred</dt>
<dd>The predicted values for the response.</dd>


<dt>type</dt>
<dd>A string indicating the type of regression model (linear or binomial).</dd>

</dl>

get.loss: A function that calculates the loss/cost

Description

Usage

Value

Arguments