Dear all, I am using the PLS package for PLSR analysis. And I have a basic question about the standardize procedure, which I feel the PLS manual does not explain clearly. I am hoping that I could get some help from the list.
>From the example in the "Standardization of Data Matrices" section, I can standardize X matrix and make prediction by using: mod=plsr(y~stdize(X),ncomp=6,data=NIR[NIR$train,]) pred=predict(mod,newdata=NIR[!NIR$train,]) In the manual, it is commented that the prediction is "automatically standardized". So I guess I won't need to standardize X matrix of the test set for the prediction. However, what if I do not want a standardize model from the beginning? Then my code would be like: mod=plsr(y~X,ncomp=6,data=NIR[NIR$train,]) But the R code for the prediction should still be the same (please correct me if any code is wrong): pred=predict(mod,newdata=NIR[!NIR$train,]) Would this time the X matrix of the newdata be automatical standardized or not? I am so confused about the "automatically standardization". Please share some experience. Really appreciate your kind help! Sincerely, Jeny ______________________________________________ R-help@stat.math.ethz.ch 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.