I'm writing a code which task is to grow Random Forest trees based on multiple parameters. In short:

1. Firstly, I declare a data frame in which model parameters and some stats will be saved.
2. Secondly, I declare model parameters and the loop iterator (it will be showed after every loop iteration).
3. Next, I have a nested loops with the model and prediction function.
4. Furthermore, parameters and some stats from the confusion matrix are saved to the dataframe.
5. Additionally, the number of iterations is printed and counted.
6. Last but not least, garbage collector is called.

The code looks like this:

## data frame in which model parameters and some stats will be saved

model_eff <- data.frame("ntrees" = numeric(0),
                        "zeros" = numeric(0), 
                        "mvars"= numeric(0),
                        "eff" = numeric(0),
                        "0_0" = numeric(0),
                        "0_1" = numeric(0),
                        "1_0" = numeric(0),
                        "1_1" = numeric(0),
                        "predict_sum" = numeric(0),
                        "triangle" = numeric(0))


## parameteres

ntrees <- c(300, 500)
zeros <- sum(train.target) * c(1, 2, 3, 4, 5)
mvars <- c(30, 50, 70, 90, 110, 130)

## loop counter

i = 1

## loop with model, prediction etc.

for (j in 1:length(ntrees)){
  for (k in 1:length(zeros)){
    for (l in 1:length(mvars)){

      ## i-th model

      model <- randomForest(train,
                            y = as.factor(train.target),
                            ntree = ntrees[j],
                            do.trace = T,
                            sampsize = c('0' = zeros[k], '1' = sum(train.target)),
                            mtry = mvars[l])

      ## prediction - my function, apart from a regular prediction
      ##              outputs additional info

      predict.model(model, val, val.target)

      ## inserting model parameters and stats to a data frame for further comparisons

      model_eff <- rbind(model_eff,
                         c("ntrees" = ntrees[j],
                           "zeros" = zeros[k],
                           "mvars"= mvars[l],
                           "eff" = eff_measures$eff,
                           "0_0" = eff_measures$c.m[1, 1],
                           "0_1" = eff_measures$c.m[1, 2],
                           "1_0" = eff_measures$c.m[2, 1],
                           "1_1" = eff_measures$c.m[2, 2],
                           "predict_sum" = sum(TARGET3),
                           "triangle" = eff_measures$triangle))

      ## printing the number of iteration

      cat("iteration =", i)
      i <- i+1

      ## calling garbage collector to assure free space in RAM

      gc()
    }
  }
}

I have already split the train/validation data sets and their target variables, knowing that Random Forest deals with such data mor efficiently. I also tried to use the "foreach" package for parallelizing computations, however, the growing time for only one tree was 10-15% longer than without using all the cores.

I would like to know if I can shorten the time of execution of this code, especially if there is a way to avoid multiple loops since I heard that they are not the best way of programming in R.