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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=sprm
to link to this page.