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.

Install the latest version of this package by entering the following in R:
install.packages("sprm")
AuthorSven Serneels (BASF Corp) and Irene Hoffmann
Date of publication2016-02-22 14:33:44
MaintainerIrene Hoffmann <irene.hoffmann@tuwien.ac.at>
LicenseGPL (>= 3)
Version1.2.2

View on CRAN

Functions

biplot.prm Man page
biplot.prmda Man page
biplot.sprm Man page
biplot.sprmda Man page
plot.prm Man page
plot.sprm Man page
predict.prm Man page
predict.prmda Man page
predict.sprm Man page
predict.sprmda Man page
print.prm Man page
print.prmda Man page
print.sprm Man page
print.sprmda Man page
prmda Man page
prmdaCV Man page
prms Man page
prmsCV Man page
sprm Man page
sprmda Man page
sprmdaCV Man page
sprm-package Man page
sprms Man page
sprmsCV Man page
summary.prm Man page
summary.prmda Man page
summary.sprm Man page
summary.sprmda Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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