Hi Chysanthi,

check out the randomForest package, with the function randomForest. It has a CV option. Sorry for not providing you with a lengthier response at the moment but I'm rather busy on a project. Let me know if you need more help.

Also, to split your data into two parts- the training and the test set you can do (n the number of data points):
n<-length(data[,1])
indices<-sample(rep(c(TRUE,FALSE),each=n/2),round(n/2),replace=TRUE)
training_indices<-(1:n)[indices]
test_indices<-(1:n)[!indices]

Then, data[train,] is the training set and data[test,] is the test set.

Best,
Pierre


________________________________
De : Chrysanthi A. <chrysain@gmail.com>
À : r-help@r-project.org
Envoyé le : Dimanche, 12 Avril 2009, 17h26mn 59s
Objet : [R] Running random forest using different training and testing schemes

Hi,

I would like to run random Forest classification algorithm and check the
accuracy of the prediction according to different training and testing
schemes. For example, extracting 70% of the samples for training and the
rest for testing, or using 10-fold cross validation scheme.
How can I do that? Is there a function?

Thanks a lot,

Chrysanthi.

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