ensemble learners, bagging and adaboosting - r

Question

i have a implemented two ensemble techniques, i.e., bagging and adaboosting in r that should work with any learner.
my grid:
grids <- list(
"knn" = expand.grid(k = c(3, 5, 7, 9, 11, 13, 15))
)
my variables:
n <- c(2, 4)
boots <- createResample(trainData$BAD, times = 50, list = TRUE)
my bagging:
for(i in seq_along(grids)) {
method <- names(grids[i])
for(j in 1:nrow(grids[[i]])) {
grid <- data.frame(grids[[i]][j, ])
colnames(grid) <- names(grids[[i]])
# start bagging
bagging <- foreach(k = 1:length(n)) %do% {
predictions <- foreach(m = 1:n[k], .combine = cbind) %do% {
tune <- train(BAD ~ ., data = trainData, method = method, trControl = ctrl, tuneGrid = grid,
metric = "ROC")
pred <- c(predict(tune, newdata = trainData, type = "prob")$BAD,
predict(tune, newdata = testData, type = "prob")$BAD)
}
pred_means <- rowMeans(predictions)
}
resu_bag <- c(resu_bag, unlist(bagging))
}
}
my adaboosting:
for(i in seq_along(grids)) {
method <- names(grids[i])
for(j in 1:nrow(grids[[i]])) {
grid <- data.frame(grids[[i]][j, ])
colnames(grid) <- names(grids[[i]])
# start boosting
boosting <- foreach(k = 1:length(n)) %do% {
predictions <- foreach(m = 1:n[k], .combine = cbind) %do% {
train_boo <- trainData[boots[[m]], ]
tune <- train(BAD ~ ., data = train_boo, method = method, trControl = ctrl, tuneGrid = grid,
metric = "ROC")
pred <- c(predict(tune, newdata = trainData, type = "prob")$BAD,
predict(tune, newdata = testData, type = "prob")$BAD)
}
pred_means <- rowMeans(predictions)
}
resu_boo <- c(resu_boo, unlist(boosting))
}
}
my questions:
could you please give an advice whether the implementation are correct?
the performance of the model is the same as of a single learner or even worse. why it happens? what do i do wrong?
thank you very much!