R caret extractPrediction with random forest model: Error: $ operator is invalid for atomic vectors

Question

I want to extract the predictions for new unseen data using the function caret::extractPrediction with a random forest model but I cannot figure out, why my code throws the error Error: $ operator is invalid for atomic vectors. How should the input parameters be structured, to use this function?

Here is my reproducible code:

library(caret)

dat <- as.data.frame(ChickWeight)
# create column set
dat$set <- rep("train", nrow(dat))
# split into train and validation set
set.seed(1)
dat[sample(nrow(dat), 50), which(colnames(dat) == "set")] <- "validation"

# predictors and response
all_preds <- dat[which(dat$set == "train"), which(names(dat) %in% c("Time", "Diet"))]
response <- dat[which(dat$set == "train"), which(names(dat) == "weight")]

# set train control parameters
contr <- caret::trainControl(method="repeatedcv", number=3, repeats=5)

# recursive feature elimination caret 
set.seed(1)
model <- caret::train(x = all_preds, 
                      y = response,
                      method ="rf",
                      ntree = 250, 
                      metric = "RMSE", 
                      trControl = contr)

# validation set
vali <- dat[which(dat$set == "validation"), ]

# not working
caret::extractPrediction(models = model, testX = vali[,-c(3,5,1)], testY = vali[,1])
caret::extractPrediction(models = model, testX = vali, testY = vali)

# works without problems
caret::predict.train(model, newdata = vali)

Answer 1

I found a solution by looking at the documentation of extractPrediction. Basically, the argument models doesn't want a single model instance, but a list of models. So I just inserted list(my_rf = model) and not just model.

caret::extractPrediction(models = list(my_rf = model), testX = vali[,-c(3,5,1)], testY = vali[,1])