Prediction for Penalized Partial Least Squares

Description

Given a penalized.pls. object, and new data, this function predicts the response for all components.

Usage

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new.penalized.pls(ppls, Xtest, ytest = NULL)

Arguments

ppls

Object returned from penalized.pls

Xtest

matrix of new input data

ytest

vector of new response data, optional

Details

penalized.pls returns the intercepts and regression coefficients for all penalized PLS components up to ncomp as specified in the function penalized.pls. new.penalized.pls then computes the estimated response based on these regression vectors. If ytest is given, the mean squared error for all components are computed as well.

Value

ypred

matrix of responses

mse

vector of mean squared errors, if ytest is provided.

Author(s)

Nicole Kraemer

References

N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data. Chemometrics and Intelligent Laboratory Systems 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009

See Also

penalized.pls, penalized.pls.cv, ppls.splines.cv

Examples

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# see also the example for penalised.pls 
X<-matrix(rnorm(50*200),ncol=50)
y<-rnorm(200)
Xtrain<-X[1:100,]
Xtest<-X[101:200,]
ytrain<-y[1:100]
ytest<-X[101:200]
pen.pls<-penalized.pls(Xtrain,ytrain,ncomp=10)
test.error<-new.penalized.pls(pen.pls,Xtest,ytest)$mse