Hello I try to fit a LDA and RDA model to the same data, which has two classes. The problem now is that the training errors of the LDA model and the training error of the RDA model with alpha=0 are not the same. In my understanding this should be the case. Am I wrong? Can someone explain what the reason for this difference could be?
Here my code: LDA model: =========== % x is a dataframe tmp = lda(response ~ ., data=x) tmp.hat = predict(tmp) tab = table(x$response, tmp.hat$class) lda.training.err = 1 - sum(tab[row(tab)==col(tab)])/sum(tab) RDA model: =========== % x is converted into a matrix without the response % variable. This matrix is then transposed tmp = rda(x, y, alpha=0, delta=0) rda.training.err = tmp$error / dim(x)[2] % The training error provided by rda.cv() is also different % from the training errors provided by lda() or rda() tmp.cv = rda.cv(tmp, x=x, y=y, nfold=10) tmp.cv$err / dim(x)[2] / 10 Thanks a lot! ______________________________________________ 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.
