misclassification

<code>misclassification</code> calculate misclassification error between predicted class and true class

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

misclassification function

<dl><dt>pred</dt>
<dd>Predicted class</dd>
<dt>true</dt>
<dd>Actual class</dd></dl>

Arguments

Calculate misclassification error — misclassification

<dl>

<dt>pred</dt>
<dd>Predicted class</dd>


<dt>true</dt>
<dd>Actual class</dd>

</dl>

misclassification: Calculate misclassification error

Description

Usage

Value

Arguments

See Also

Examples