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> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,]

Hi, I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for

* What were your biggest misconceptions or stumbling blocks to getting up and running with R? * What documents helped you the most in this initial phase? I especially want to hear from people who are lazy and impatient. Feel free to write to me off-list. Definitely write off-list if you are just confirming what has been said on-list. -- Patrick Burns pburns at pburns.seanet.com

Hi, Anyone tried to do stemming for emails (mbox format) using the Text Mining (tm) package in R. According to the documentation the text document collection has to be converted to text and there is no method to convert Newsgroup (email) to plain text. Thanks, Saeed

Is the rmpi package (or rpvm) needed to exploit multiple cores on a single unix box using the snow package. The documentation of the package does not provide info about setting up a single machine with multiple cores. Also, if how effective is it to run a bayesian simulation on parallel (or distributed) processors using the snow package. Thanks, Saeed

Hi, I installed the snow package on a unix box that has multiple cores. To be able to exploit the multiple cores (on one pc) do I still need to install the rmpi package (or rpvm). Another question, if i run a bayesian simulation on the multiple core after setting them up correctly (using snow), would you think there will be a noticeable speedup gain. Thanks, Saeed --- linux centos 4 dual core

I have a dataset of multiple variables and a response. For example, > str(x) 'data.frame': 3557238 obs. of 44 variables: $ response : Factor w/ 2 levels $ var2: Factor w/5000 levels If var2 for example is a factor with 5000 levels, what is the best approach to determine which of these levels is the most important to include in building the model, and which ones to discard.

If we use the ROCR package to find the accuracy of a classifier pred <- prediction(svm.pred, testset[,2]) perf.acc <- performance(pred,"acc") Do we?find the maximum accuracy?as follows?(is there a simplier way?): > max(perf.acc at x.values[[1]]) Then to find the cutoff point that maximizes the accuracy?do we do the following?(is there a simpler way): > cutoff.list <-