search for: createresampl

...ything is quite clear (the examples on the website help a lot!), but I am still a confused about the relationship between two arguments to trainControl: "method" "index" and the interplay between trainControl and the data splitting functions in caret (e.g. createDataPartition, createResample, createFolds and createMultiFolds) To better frame my questions, let me use the following example from the documentation: ************************************* data(BloodBrain) set.seed(1) tmp <- createDataPartition(logBBB,p = .8, times = 100) trControl = trainControl(method = "LGOCV&qu...

...set into 4 parts with the 4th one as the test dataset, and the other three to fit a model. I've been searching for the difference between these 2 functions in Caret package, but the most I can get is this-- A series of test/training partitions are created using createDataPartition while createResample creates one or more bootstrap samples. createFolds splits the data into k groups. I'm missing something here? What is the difference btw createPartition and createFold? I guess they wouldn't be equivalent. Thank you. Bonnie Yuan

> > Estimados muy buenas quería hacerles unas consulta: Estoy trabajando en mi tesis sobre mejoramiento animal y mi objetivo es evaluar la habilidad predictiva de modelos estadísticos mediante validación cruzada. Pero antes la intención es dividir mi base de datos en 3 partes y quisiera que todos los efectos incluidos en el estudio y cada uno de sus niveles, estén lo más equitativamente

I looked at the documentation of source() and summary(), and I could not find the reason why calling something like: > summary(resamps) from the command line, works (it prints the summary) whereas calling summary(resampls) from a file that I source with source("my_file.r") does not print anything. How can I get summary(resamps) to print when I source a file with this command?

...ate(5, rnorm(2000))) #Construct Y using X Y <- runif(1)*X[,1]*X[,2]^2+runif(1)*X[,3]/X[,4] #Convert Y to binary Y <- as.factor(ifelse(sign(Y)>0,'X1','X0')) #Create bootstrap samples for fitting models library(caret) print("Creating bootstrap samples") tmp <- createResample(Y,times = 25) myCtrl <- trainControl(method = "boot", index = tmp, timingSamps = 10) RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) NNmodel <- train(X,Y,method='nnet',trControl=myCtrl,tuneLength=3, trace = FALSE) ## GLMnet = GL...