example.multiclass

The simulated data contains 600 observations, 800 predictors, 10 covariates,
and an multiclass outcome with three categories. The external information Z contains five indicator prior covariates.

datasets

Extends standard penalized regression (Lasso, Ridge, and Elastic-net) to allow feature-specific shrinkage based on external information with the goal of achieving a better prediction accuracy and variable selection. Examples of external information include the grouping of predictors, prior knowledge of biological importance, external p-values, function annotations, etc. The choice of multiple tuning parameters is done using an Empirical Bayes approach. A majorization-minimization algorithm is employed for implementation.

Jingxuan He

xtune

Regularized Regression with Feature-Specific Penalties
Integrating External Information

example.multiclass function

The <code>example.multiclass</code> object is a list containing three elements:<ul>
<li>X: A simulated 600 by 800 matrix</li>
<li>Y: Categorical outcome with three levels</li>
<li>U: Covariates matrix with 600 by 10 dimension, will be forced in the model</li>
<li>Z: A 800 by 5 matrix with indicator entries.</li>
</ul>

example.multiclass: Simulated data with multi-categorical outcome

Description

Usage

Arguments

Format

Examples