Error: The tuning parameter grid should have columns mtry - r

Question

I'm trying to train a random forest model using caret in R. I want to tune the parameters to get the best values, using the expand.grid function. However, I keep getting this error:
Error: The tuning parameter grid should have columns mtry
This is my code. The data I use here is called scoresWithResponse:
ctrlCV = trainControl(method = 'cv', number = 10 , classProbs = TRUE , savePredictions = TRUE, summaryFunction = twoClassSummary )
rfGRID = expand.grid(interaction.depth = c(2, 3, 5, 6, 7, 8, 10),
n.trees = c(50,75,100,125,150,200,250),
shrinkage = seq(.005, .2,.005),
n.minobsinnode = c(5,7,10, 12 ,15, 20),
nodesize = c(1:10),
mtry = c(1:10))
RF_loop_trn = c()
RF_loop_tst = c()
for(i in (1:5)){
print(i)
IND = createDataPartition(y = scoresWithResponse$response, p=0.75, list = FALSE)
scoresWithResponse.trn = scoresWithResponse[IND, ]
scoresWithResponse.tst = scoresWithResponse[-IND,]
rfFit = train(response~., data = scoresWithResponse.trn,
importance = TRUE,
method = "rf",
metric="ROC",
trControl = ctrlCV,
tuneGrid = rfGRID,
classProbs = TRUE,
summaryFunction = twoClassSummary
)
RF_loop_trn[i] = auc(roc(scoresWithResponse.trn$response,predict(rfFit,scoresWithResponse.trn, type='prob')[,1]))
RF_loop_tst[i] = ahaveroc(scoresWithResponse.tst$response,predict(rfFit,scoresWithResponse.tst, type='prob')[,1]))
}
After investigating for some time, there has been several suggestions like redownloading the caret package from github, adding a . before each parameter in expand.grid, adding a dot only before the mtry parameter (something like .mtry), adding the mtry to the train function instead expand.grid.. I tried all that and they all produce the same error.
Where and how should I add the mtry parameter? what is causing this error?

Answer 1

I can't comment yet so I'll reply
Have you tried to insert the mtry argument directly into the train function? for example:
rfGRID = expand.grid(interaction.depth = c(2, 3, 5, 6, 7, 8, 10),
n.trees = c(50,75,100,125,150,200,250),
shrinkage = seq(.005, .2,.005),
n.minobsinnode = c(5,7,10, 12 ,15, 20),
nodesize = c(1:10)
)
rfFit = train(response~., data = scoresWithResponse.trn,
importance = TRUE,
method = "rf",
metric="ROC",
trControl = ctrlCV,
tuneGrid = rfGRID,
classProbs = TRUE,
summaryFunction = twoClassSummary,
mtry = 1000
)