Regularized Regression with Feature-Specific Penalties Integrating External Information
Extract model coefficients from fitted xtune object
Estimate noise variance given predictor X and continuous outcome Y.
Calculate misclassification error
Calculate mean square error
Model predictions based on fitted xtune object
Control function for xtune fitting
Regularized regression incorporating external information
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.