| new.penalized.pls {ppls} | R Documentation |
Given a penalized.pls. object, and new data, this function predicts the response for all components.
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 simply 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.
ypres |
matrix of responses |
mse |
vector of mean squared errors, if ytest is provided. |
Nicole Kraemer
N. Kraemer, A.-L. Boulesteix, G. Tutz (2007) "Penalized Partial Least Squares with Applications to B-Splines Transformations and Functional Data", preprint
available at http://ml.cs.tu-berlin.de/~nkraemer/publications.html
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