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I have completed a binomial GLM in R (details attached (finalModel.docx)) and I am trying to create a graph of observed and fitted values using the following commands: > MyData<-data.frame(time=seq(from=0,to=1323,by=1)) > Pred<-predict(M2,newdata=MyData,type="response") > plot(x=turtle$time,y=turtle$success) > lines(MyData$time,Pred)...

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]]

...,sizes=subsets,rfeControl=rfectrl,method="rf",tuneGrid=rfGrid) Loading required package: class Attaching package: 'class' The following object(s) are masked from package:reshape : condense Fitting: mtry=1 Fitting: mtry=2 Error in varImp.randomForest(object$finalModel, ...) : subscript out of bounds In addition: Warning message: package 'e1071' was built under R version 2.10.1 At the same time, If I want to use RFE+SVM, RFE+nnet, and so on ,how can I do? I have try RFE+SVM, also wrong message:> set.seed(1) > svmProfile<-rfe(trx,try,sizes...

Hi all I am using the caret package and having difficulty in obtaining the results using regression, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE);

...response and design matrix X<-matrix(rnorm(50*100),nrow=50) y<-rnorm(50*1) # Applying caret package con<-trainControl(method="cv",number=10) data<-NULL data<- train(X,y, "lasso", metric="RMSE",tuneLength = 10, trControl = con) coefs<-predict(data$finalModel,s=data$bestTune$.fraction, type ="coefficients", mode ="fraction")$coef coefs *This is the output which I got :* you can see some of the predictors are missing like V4, V6, V7 V1 V2 V3 V5 V8 V9 V10 V11 V13...

Using caret on the Titanic data from Kaggle, I tried various models, including rfRules which produces a model, partly described as such: > caret.rfRules.cv$finalModel $model len freq err [1,] "2" "0.0368" "0" [2,] "2" "0.032" "0.05" [3,] "2" "0.1824" "0.0526315789473685" [4,] "4" "0.0656" &qu...

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),

...regression . I can get the final fiited vs observed values. How can I get > the coefficients? Any ideas? You didn't say what version of caret and R you are using, what kernel or what type of coefficients. If you tune a model using train: fit <- train(x, y, "enet") then fit$finalModel contains an enet object with the final parameters. 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 m...

...rfGrid) > Loading required package: class > > Attaching package: 'class' > > > ? ? ? ? The following object(s) are > masked from package:reshape : > > ? ? ? ???condense > > Fitting: mtry=1 > Fitting: mtry=2 > Error in varImp.randomForest(object$finalModel, ...) : > ? subscript out of bounds > In addition: Warning message: > package 'e1071' was built under R version 2.10.1 > > > At the same time, If I want to? use RFE+SVM,? > RFE+nnet, and so on ,how can I > do? I have try RFE+SVM, also wrong message:> > set....