sodavis: SODA: Main and Interaction Effects Selection for Logistic
Regression, Quadratic Discriminant and General Index Models
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.
| Version: |
1.2 |
| Depends: |
R (≥ 3.0.0), nnet, MASS, mvtnorm |
| Published: |
2018-05-13 |
| Author: |
Yang Li, Jun S. Liu |
| Maintainer: |
Yang Li <yangli.stat at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
sodavis results |
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