coef_xtune

<code>coef_xtune</code> extracts model coefficients from objects returned by <code>xtune</code> object.

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

coef_xtune function

<dl><dt>object</dt>
<dd>Fitted 'xtune' model object.</dd>
<dt>...</dt>
<dd>Not used</dd></dl>

Arguments

Extract model coefficients from fitted xtune object — coef_xtune

<dl>

<dt>object</dt>
<dd>Fitted 'xtune' model object.</dd>


<dt>...</dt>
<dd>Not used</dd>

</dl>

coef_xtune: Extract model coefficients from fitted `xtune` object

Description

Usage

Value

Arguments

Details

See Also

Examples