Creating an LOOCV loop using the randomForest package. I have adapted the following code from this link (https://stats.stackexchange.com/questions/459293/loocv-in-caret-package-randomforest-example-not-unique-results) however I am unable to reproduce a successful code.
Here is the code that I am running but on the iris dataset.
irisdata <- iris[1:150,]
predictionsiris <- 1:150
for (k in 1:150){
set.seed(123)
predictioniris[k] <- predict(randomForest(Petal.Width ~ Sepal.Length, data = irisdata[-k], ntree = 10), newdata = irisdata[k,,drop=F])[2]
}
What I would expect to happen is for it to run the random forest model on all but one row and then use that one row to test the model.
However, when I run this code, I get the following error:
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'object' in selecting a method for function 'predict': object 'Sepal.Length' not found
Any suggestions? I have been messing around with LOOCV code for the past two days including messing with code in this page (Compute Random Forest with a leave one ID out cross validation in R) and running the following:
iris %>%
mutate(ID = 1:516)
loocv <- NULL
for(i in iris$ID){
test[[i]] <- slice(iris, i)
train[[i]] <- slice(iris, i+1:516)
rf <- randomForest(Sepal.Length ~., data = train, ntree = 10, importance = TRUE)
loocv[[i]] <- predict(rf, newdata = test)
}
but I have had no success. Any help would be appreciated.
