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 |
| Reference manual: | sprm.pdf |
| Package source: | sprm_1.2.2.tar.gz |
| Windows binaries: | r-devel: sprm_1.2.2.zip, r-release: sprm_1.2.2.zip, r-oldrel: sprm_1.2.2.zip |
| macOS binaries: | r-release: sprm_1.2.2.tgz, r-oldrel: sprm_1.2.2.tgz |
| Old sources: | sprm archive |
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