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...istribution=list(name="quantile",alpha=0.5), verbose=FALSE, trControl=trainControl(method="cv",number=5), tuneGrid=gbmGrid ) Model 1: interaction.depth=1, shrinkage=0.1, n.trees=300 collapsing over other values of n.trees Error in gbm.fit(trainX, modY, interaction.depth = tuneValue$.interaction.depth, : formal argument "distribution" matched by multiple actual arguments The same error occured with distribution="laplace". I also tried the following without and success : gbm.test <- train(x.enet, y.matrix[,7], method="gbm", list(dis...

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

Dear All, I am using "Caret"package for SVM regression and elastic net regression . I can get the final fiited vs observed values. How can I get the coefficients? Any ideas? Thanks Alex [[alternative HTML version deleted]]

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +

...ameters. You can use coef() or any other appropriate function on this object. In the case of the elastic net, see ?predict.enet, specifically the type argument to get the regression coefficients. If you need to get to the final tuning parameters for the model, you can access them via x$finalModel$tuneValue. There are a few exceptions to this: models using S4 classes won't allow adding new objects. For SVMs, you haven't said what kernel type you used. caret uses kernel methods from kernlab (for no other reason that I like the package). The only package that I know gives the *regression* coef...