diet

The simulated <code>diet</code> data contains 100 observations, 14 predictors,
and an binary outcome, weightloss. The external information Z is the nutrition fact about the dietary items.
Z contains three external information variables: Calories, protein and carbohydrates.

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

diet function

The <code>diet</code> object is a list containing three elements:<ul>
<li>DietItems: Matrix of predictors.</li>
<li>weightloss: 0: no weight loss; 1: weight loss</li>
<li>nutritionFact: External information of the predictors</li>
</ul>

diet: Simulated diet data to predict weight loss

Description

Usage

Arguments

Format

References

See Also

Examples