"Stella Sim" <[EMAIL PROTECTED]> writes: > I want to calculate SPE (squared prediction error) in x-space, can > someone help? > > Here are my codes: > > fit.pls<- > plsr(Y~X,data=DAT,ncomp=3,scale=T,method='oscorespls',validation="CV",x= > T) > actual<-fit.pls$model$X
(The x = TRUE is not needed as long as model = TRUE (default). x=TRUE returns the predictors as fit.pls$x, and is included for "compatibility" with lm().) > pred<-fit.pls$scores %*% t(fit.pls$loadings) > SPE.x<-rowSums((actual-pred)^2) > > Am I missing something here? You are missing the mean X spectrum. See matplot(t(pred), type = "l", lty = 1) vs. matplot(t(actual), type = "l", lty = 1) The Xmeans compontent of fit.pls contains this, so pred <- sweep(fit.pls$scores %*% t(fit.pls$loadings), 2, fit.pls$Xmeans, "+") would give you what you want. Note, however, that this will calculate the _fitted_ SPE, not the cross-validated SPE. The crossvalidation implemented in the pls package does not save the cross-validated scores/loadings -- that would consume too much memory. (Calculation of SPE withing the cross-validation routines could have been implemented, but was not.) -- Regards, Bjørn-Helge Mevik ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
