For-loop cross validation R - r

Question

I'm trying to create a loop (performed 10 times) to get repeated cross-validation to evaluate the predictive performance of 4 models, and then I have to compute an average of my performances. I must admit that I am new to R and I am struggling with this simple task. I started by creating my formula to cross-validate my models.
loss.mse <- function(fit, df, y, transf){
y_pred <- transf(predict(fit, df))
out <- (y - y_pred)^2
return(mean(out))
}
loss.mae <- function(fit, df, y, transf){
y_pred <- transf(predict(fit, df))
out <- abs(y - y_pred)
return(mean(out))
}
validate.cv <- function(data, folds, model_fn, y_var,
transf = identity, seed)
{
set.seed(seed)
fold_id <- sample(rep(1:folds, length.out = nrow(data)))
out.mse <- out.mae <- numeric(folds)
for(test in 1:folds){
data_test <- subset(data, fold_id == test)
data_train <- subset(data, fold_id != test)
fit <- model_fn(data_train)
y_test <- y_var[fold_id == test]
out.mse[test] <- loss.mse(fit, data_test, y_test, transf)
out.mae[test] <- loss.mae(fit, data_test, y_test, transf)
}
return(list(MAE = mean(out.mae), MSE = out.mse,
RMSE = sqrt(mean(out.mse))))
Then I named my models and I cross-validated them but I can't figure out how to get my 10 times loop !
model_lm <- function(data) lm(StockPrice ~., data)
model_step <- function(data) step(lm(StockPrice ~., data), trace = 0)
model_rpart <- function(data)
{
set.seed(1234)
mod.rpart <- rpart(StockPrice ~ ., data, cp = 0.0001, model = TRUE)
cp.select <- function(big.tree) {
min.x <- which.min(big.tree$cptable[, 4])
for(i in 1:nrow(big.tree$cptable)) {
if(big.tree$cptable[i, 4] <
(big.tree$cptable[min.x, 4] + big.tree$cptable[min.x, 5]))
return(big.tree$cptable[i, 1])
}
}
mod.rpart.prune <- prune(mod.rpart, cp = cp.select(mod.rpart))
return(mod.rpart.prune)
}
model_step_gam <- function(data)
{
mod <- model_step(data)
predictors <- all.vars(terms(mod))[-1]
f <- as.formula(
paste("StockPrice",
paste(paste("s(",predictors, ")"), collapse = " + "),
sep = " ~ "))
mod_gam <- gam(f, data = data)
seed<-1234
m.log.full <-validate.cv(log.Finance, 10, model_lm, Finance$StockPrice,exp, seed)
m.log.step <-validate.cv(log.Finance, 10, model_step, Finance$StockPrice,exp, seed)
m.log.rpart <-validate.cv(log.Finance, 10, model_rpart, Finance$StockPrice,exp, seed)
m.log.gam <-validate.cv(log.Finance, 10, model_step_gam, Finance$StockPrice,exp, seed)
mat.test <-data.frame(Model =c("Full (log)","Step (log)","CART (log)", "Step GAM (log)"),
RMSE =c(m.log.full$RMSE, m.log.step$RMSE,m.log.rpart$RMSE, m.log.gam$RMSE),
MAE =c(m.log.full$MAE, m.log.step$MAE, m.log.rpart$MAE,m.log.gam$MAE))
print(mat.test)
If you have any ideas I would gladly try them. Thank you in advance for your help :)