| 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 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 Kr"amer
N. Kr"amer, 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.
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