Hello, Thank you for your reply but I'm not sure your code answers my needs, from what I read it creates a 10-fold partition and then extracts the kth partition for future processing.
My question was rather: once I have a 10-fold partition of my data, how to supply it to the "train" function of the caret package. Here's some sample code : folds <- createFolds(my_dataset_classes, 10) # I can't use index=folds on this one, it will train on the 1/k and test on k-1 t_control <- trainControl(method="cv", number=10) # here I would like train to take account of my predefined folds model <- train(my_dataset_predictors, my_dataset_classes, method="svmLinear", trControl = t_control) Cheers, Fabon. On Fri, May 6, 2011 at 10:59 AM, neetika nath <nikkiha...@gmail.com> wrote: > Hi, > I did the similar experiment with my data. may be following code will give > you some idea. It might not be the best solution but for me it worked. > please do share if you get other idea. > Thank you > #### CODE### > > library(dismo) > > set.seed(111) > > dd<-read.delim("yourfile.csv",sep=",",header=T) > > # To keep a check on error > > options(error=utils::recover) > > # dd- data to be split for 10 Fold CV, this will split complete data into 10 > fold > > number<-kfold(dd, k=10) > > case 1: if k ==1 > > x<-NULL; > > #retrieve all the index (from your data) for 1st fold in x, such that you > can use it as a test set and remaining can be used as train set for #1st > iteration. > > x<-which(number==k) > > On Thu, May 5, 2011 at 11:43 PM, Fabon Dzogang <fabon.dzog...@lip6.fr> > wrote: >> >> Hi all, >> >> I run R 2.11.1 under ubuntu 10.10 and caret version 2.88. >> >> I use the caret package to compare different models on a dataset. In >> order to compare their different performances I would like to use the >> same data partitions for every models. I understand that using a LGOCV >> or a boot type re-sampling method along with the "index" argument of >> the trainControl function, one is able to supply a training partition >> to the train function. >> >> However, I would like to apply a 10-fold cross validation to validate >> the models and I did not find any way to supply some predefined >> partition (created with createFolds) in this setting. Any help ? >> >> Thank you and great package by the way ! >> >> Fabon Dzogang. >> >> ______________________________________________ >> R-help@r-project.org 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. > > -- Fabon Dzogang ______________________________________________ R-help@r-project.org 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.