gene

The simulated <code>gene</code> data contains 50 observations, 200 predictors,
and an continuous outcome, bone mineral density. The external information Z is four previous study results that identifies the biological importance of genes.

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

gene function

The <code>gene</code> object is a list containing three elements:<ul>
<li>GeneExpression: Matrix of gene expression predictors.</li>
<li>bonedensity: Continuous outcome variable</li>
<li>PreviousStudy: Whether each gene is identified by previous study results.</li>
</ul>

gene: Simulated gene data to predict weight loss

Description

Usage

Arguments

Format

See Also

Examples