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