example

The simulated <code>example</code> data contains 100 observations, 200 predictors, and an continuous outcome. Z contains 3 columns, each column is
indicator variable (can be viewed as the grouping of predictors).

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 function

The <code>example</code> object is a list containing three elements:<ul>
<li>X: A simulated 100 by 200 matrix</li>
<li>Y: Continuous response vector of length 100</li>
<li>Z: A 200 by 3 matrix. Z_jk indicates whether predictor X_j has external variable Z_k or not.</li>
</ul>

example: An simulated example dataset

Description

Usage

Arguments

Format

Examples