new.penalized.pls: Prediction for Penalized Partial Least Squares
In ppls: Penalized Partial Least Squares

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`ppls`	Object returned from penalized.pls
`Xtest`	matrix of new input data
`ytest`	vector of new response data, optional

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.

`ypred`	matrix of responses
`mse`	vector of mean squared errors, if ytest is provided.

Nicole Kraemer

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

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

# 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