sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification
Version 1.2.2

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.

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
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("sprm")

Getting started

Package overview

Popular man pages

biplot.prm: Biplot for prm objects
predict.prm: Predict method for models of class prm
predict.sprm: Predict method for models of class sprm
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
See all...

All man pages Function index File listing

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

Functions

balancedfolds Source code
biplot.prm Man page Source code
biplot.prmda Man page Source code
biplot.sprm Man page Source code
biplot.sprmda Man page Source code
biweight Source code
brokenstick Source code
daprpr Source code
dbshdy Source code
int_weight Source code
intervals.prm Source code
intervals.sprm Source code
ldafitfun Source code
nipls Source code
plot.prm Man page Source code
plot.sprm Man page Source code
predict.prm Man page Source code
predict.prmda Man page Source code
predict.sprm Man page Source code
predict.sprmda Man page Source code
print.prm Man page Source code
print.prmda Man page Source code
print.sprm Man page Source code
print.sprmda Man page Source code
prmda Man page Source code
prmdaCV Man page Source code
prms Man page Source code
prmsCV Man page Source code
snipls Source code
sprm Man page
sprm-package Man page
sprmda Man page Source code
sprmdaCV Man page Source code
sprms Man page Source code
sprmsCV Man page Source code
summary.prm Man page Source code
summary.prmda Man page Source code
summary.sprm Man page Source code
summary.sprmda Man page Source code
weig Source code

Files

NAMESPACE
R
R/sprms.R
R/int_weight.R
R/summary.prmda.R
R/brokenstick.R
R/biplot.prm.R
R/print.sprmda.R
R/balancedfolds.R
R/snipls.R
R/sprmsCV.R
R/dbshdy.R
R/predict.sprm.R
R/nipls.R
R/biplot.prmda.R
R/print.sprm.R
R/print.prm.R
R/prmsCV.R
R/daprpr.R
R/intervals.prm.R
R/biweight.R
R/sprmdaCV.R
R/weig.R
R/sprmda.R
R/predict.sprmda.R
R/biplot.sprmda.R
R/prmda.R
R/plot.sprm.R
R/plot.prm.R
R/prms.R
R/print.prmda.R
R/intervals.sprm.R
R/prmdaCV.R
R/predict.prmda.R
R/ldafitfun.R
R/summary.sprm.R
R/predict.prm.R
R/summary.sprmda.R
R/summary.prm.R
R/biplot.sprm.R
MD5
DESCRIPTION
man
man/summary.prm.Rd
man/predict.sprmda.Rd
man/biplot.sprm.Rd
man/summary.prmda.Rd
man/sprmda.Rd
man/summary.sprm.Rd
man/plot.sprm.Rd
man/sprmsCV.Rd
man/predict.prmda.Rd
man/biplot.sprmda.Rd
man/prmdaCV.Rd
man/summary.sprmda.Rd
man/biplot.prmda.Rd
man/prmda.Rd
man/sprm-package.Rd
man/sprmdaCV.Rd
man/biplot.prm.Rd
man/predict.prm.Rd
man/plot.prm.Rd
man/prms.Rd
man/predict.sprm.Rd
man/sprms.Rd
man/prmsCV.Rd
sprm documentation built on May 19, 2017, 11:30 p.m.

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