SODA: Main and Interaction Effects Selection for Logistic Regression, Quadratic Discriminant and General Index Models
S-SODA algorithm for general index model variable selection
S-SODA model estimation.
Predict the response y using S-SODA model.
Predict the response y using S-SODA model in a 2-dimensional grid.
SODA algorithm for variable and interaction selection
Calculate a trace of cross-validation error rate for SODA forward-back...
Gene expression data for Michigan lung cancer study in Beer et al. (20...
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.