<code>mse</code> calculate mean square error (MSE) between prediction values and true values for linear model

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

mse function

<dl><dt>pred</dt>
<dd>Prediction values vector</dd>
<dt>true</dt>
<dd>Actual values vector</dd></dl>

Arguments

Calculate mean square error — mse

<dl>

<dt>pred</dt>
<dd>Prediction values vector</dd>


<dt>true</dt>
<dd>Actual values vector</dd>

</dl>

mse: Calculate mean square error

Description

Usage

Value

Arguments

Examples