Error "variable lengths differ" when assigning weights parameter in caret (R) - r

Question

I want to apply weighted observation in caret using the code below:
model_weights <- ifelse(train$y == 0,
(1/table(train$y)[1]) * 0.5,
(1/table(train$y)[2]) * 0.5)
xgbT <- train(x = as.matrix(train[,-21]), y = make.names(as.factor(train$y)),
method = "xgbTree",
trControl = cctrl1,
metric = "MCC",
maximize = TRUE,
weights = model_weights,
preProc = c("center", "scale"),
tuneGrid = expand.grid(nrounds = c(150), #number of trees
max_depth = c(7), #max tree depth
eta = c(0.03), #learning rate
gamma = c(0.3), #min split loss
colsample_bytree = c(0.7),
min_child_weight = c(10, 1, 5), #min number of instances in the leaf
subsample = c(0.6)), #subsample ratio of the training instance
early_stop_round = c(3), #if no improvements over specified rounds
objective = c("binary:logistic"),
silent = 0)
However, it gives me this error: Error in model.frame.default(formula = .outcome ~ ., data = dat, weights = wts) :
variable lengths differ (found for '(weights)')
Though I have checked that their lengths are the same with code below:
> table(model_weights)
model_weights
0.0000277654375832963 0.000231481481481481
18008 2160
> table(train$y)
0 1
18008 2160
Any idea how to fix this?
NOTE: I can run the train function without weights parameter.

Answer 1

After further debugging, I found the problem is because I apply sampling in the cctrl1. Thus, the length of weights differ, since I generate it before they apply re-sampling.
So, you can fix this by simply removing sampling from your trControl. If you still want to apply re-sampling, then you have to re-sample the data before running below code:
model_weights <- ifelse(train$y == 0,
(1/table(train$y)[1]) * 0.5,
(1/table(train$y)[2]) * 0.5)