sprm: Sparse and Non-Sparse Partial Robust M Regression and
Classification
Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For binary classification related discriminant methods are PRM-DA and SPRM-DA.
| Version: |
1.2.2 |
| Depends: |
ggplot2 (≥ 2.0.0) |
| Imports: |
cvTools, graphics, grDevices, grid, pcaPP, reshape2, robustbase, stats |
| Published: |
2016-02-22 |
| Author: |
Sven Serneels (BASF Corp) and Irene Hoffmann |
| Maintainer: |
Irene Hoffmann <irene.hoffmann at tuwien.ac.at> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
sprm results |
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