Hi all, there is tune() in the e1071 package for doing this in general, and, among others, a tune.nnet() wrapper (see ?tune):
> tmodel = tune.nnet(Species ~ ., data = iris, size = 1:5) > summary(tmodel) Parameter tuning of `nnet': - sampling method: 10-fold cross validation - best parameters: size 1 - best performance: 0.01333333 - Detailed performance results: size error dispersion 1 1 0.01333333 0.02810913 2 2 0.02666667 0.04661373 3 3 0.02666667 0.04661373 4 4 0.02000000 0.04499657 5 5 0.02666667 0.04661373 > plot(tmodel) > tmodel$best.model a 4-1-3 network with 11 weights inputs: Sepal.Length Sepal.Width Petal.Length Petal.Width output(s): Species options were - softmax modelling etc. Best David On 7/23/07, S.O. Nyangoma <[EMAIL PROTECTED]> wrote: > > Hi > > It clear that to do a classification with svm under 10-fold cross > > validation one uses > > > > svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross = > > 10) > > > > What corresponds to the nnet? > > nnet(.....,cross=10)? ______________________________________________ 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.