sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification

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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.

Author
Sven Serneels (BASF Corp) and Irene Hoffmann
Date of publication
2016-02-22 14:33:44
Maintainer
Irene Hoffmann <irene.hoffmann@tuwien.ac.at>
License
GPL (>= 3)
Version
1.2.2

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Man pages

biplot.prm
Biplot for prm objects
biplot.prmda
Biplot for prmda objects of PRM discriminant analysis
biplot.sprm
Biplot for sprm objects
biplot.sprmda
Biplot for sprmda objects of Sparse PRM discriminant analysis
plot.prm
Plots for prm objects
plot.sprm
Plots for sprm objects
predict.prm
Predict method for models of class prm
predict.prmda
Predict method for models of class prmda
predict.sprm
Predict method for models of class sprm
predict.sprmda
Predict method for models of class sprmda
prmda
Robust PLS for binary classification
prmdaCV
Cross validation method for PRM classification models.
prms
Partial robust M regression
prmsCV
Cross validation method for PRM regression models.
sprmda
Sparse and robust PLS for binary classification
sprmdaCV
Cross validation method for sparse PRM classification models.
sprm-package
Sparse and Non-Sparse Partial Robust M Regression and...
sprms
Sparse partial robust M regression
sprmsCV
Cross validation method for SPRM regression models.
summary.prm
Summary of a prm model
summary.prmda
Summary of a prmda model
summary.sprm
Summary of a sprm model
summary.sprmda
Summary of a sprmda model

Files in this package

sprm
sprm/NAMESPACE
sprm/R
sprm/R/sprms.R
sprm/R/int_weight.R
sprm/R/summary.prmda.R
sprm/R/brokenstick.R
sprm/R/biplot.prm.R
sprm/R/print.sprmda.R
sprm/R/balancedfolds.R
sprm/R/snipls.R
sprm/R/sprmsCV.R
sprm/R/dbshdy.R
sprm/R/predict.sprm.R
sprm/R/nipls.R
sprm/R/biplot.prmda.R
sprm/R/print.sprm.R
sprm/R/print.prm.R
sprm/R/prmsCV.R
sprm/R/daprpr.R
sprm/R/intervals.prm.R
sprm/R/biweight.R
sprm/R/sprmdaCV.R
sprm/R/weig.R
sprm/R/sprmda.R
sprm/R/predict.sprmda.R
sprm/R/biplot.sprmda.R
sprm/R/prmda.R
sprm/R/plot.sprm.R
sprm/R/plot.prm.R
sprm/R/prms.R
sprm/R/print.prmda.R
sprm/R/intervals.sprm.R
sprm/R/prmdaCV.R
sprm/R/predict.prmda.R
sprm/R/ldafitfun.R
sprm/R/summary.sprm.R
sprm/R/predict.prm.R
sprm/R/summary.sprmda.R
sprm/R/summary.prm.R
sprm/R/biplot.sprm.R
sprm/MD5
sprm/DESCRIPTION
sprm/man
sprm/man/summary.prm.Rd
sprm/man/predict.sprmda.Rd
sprm/man/biplot.sprm.Rd
sprm/man/summary.prmda.Rd
sprm/man/sprmda.Rd
sprm/man/summary.sprm.Rd
sprm/man/plot.sprm.Rd
sprm/man/sprmsCV.Rd
sprm/man/predict.prmda.Rd
sprm/man/biplot.sprmda.Rd
sprm/man/prmdaCV.Rd
sprm/man/summary.sprmda.Rd
sprm/man/biplot.prmda.Rd
sprm/man/prmda.Rd
sprm/man/sprm-package.Rd
sprm/man/sprmdaCV.Rd
sprm/man/biplot.prm.Rd
sprm/man/predict.prm.Rd
sprm/man/plot.prm.Rd
sprm/man/prms.Rd
sprm/man/predict.sprm.Rd
sprm/man/sprms.Rd
sprm/man/prmsCV.Rd