sprm-package: Sparse and Non-Sparse Partial Robust M Regression and...

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

Description

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 bianry classification related discriminant methods are PRM-DA and SPRM-DA.

Details

Package:	sprm
Type:	Package
Version:	1.1
Date:	2014-12-10
License:	GPL(>=3)

The main functions in this package are prms and sprms for non-spares and sparse partial robust M regression, respectively, and prmda and sprmda for non-spares and sparse partial robust M discriminant analysis. Further cross validation procedures for tuning parameter selection are implemented in prmsCV, sprmsCV, prmdaCV and sprmdaCV. Various plot options are available to visualize the results.

Author(s)

Sven Serneels, BASF Corp and Irene Hoffmann

References

Hoffmann, I., Filzmoser, P., Serneels, S., Varmuza, K., Sparse and robust PLS for binary classification. In print.

Hoffmann, I., Serneels, S., Filzmoser, P., Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59.

Serneels, S., Croux, C., Filzmoser, P., Van Espen, P.J. (2005). Partial Robust M-Regression. Chemometrics and Intelligent Laboratory Systems, 79, 55-64.

See Also

prms, sprms, prmda, sprmda

Examples

set.seed(50235)
U1 <- c(rep(3,20), rep(4,30))
U2 <- rep(3.5,50)
X1 <- replicate(5, U1+rnorm(50))
X2 <- replicate(20, U2+rnorm(50))
X <- cbind(X1,X2)
beta <- c(rep(1, 5), rep(0,20))
e <- c(rnorm(45,0,1.5),rnorm(5,-20,1))
y <- X%*%beta + e
d <- as.data.frame(X)
d$y <- y
mod <- prms(y~., data=d, a=2, fun="Hampel")
smod <- sprms(y~., data=d, a=2, eta=0.5, fun="Hampel")

biplot(mod)
biplot(smod)

sprm documentation built on May 2, 2019, 9:57 a.m.