Using the random forest method for classification to train my model, tuning your model based on the validation data set.Not using cross validation

I separate my dataset into three sets. train set, validation set, and test set. I want to use random forest method to train the data. But, To find the best ntree, mytry, and nnodes I want to use a validation set and see which are the best parameters. Then, I want to use those parameters for my training set. I do not want to use the caret package since it used cross-validation. I am dealing with classification problem.

 a=as.numeric(2:15)
 for (i in 2:15){
 model2= randomForest(as.factor(V2)~ .,data = vset, ntree=500, mtry=i, importance=TRUE)
 predValid2 = predict(model2, newdata = test, type = "class")
a[i-1]= mean(predValid2 == test$V2)
}
n.tree=seq(from = 100, to = 5000, by = 100)
n.mtry= seq(from = 1, to = 15, by = 1)

model3= randomForest(as.factor(V2)~ .,data = vset, ntree=n.tree, mtry=n.mtry, 
importance=TRUE)

I use the above codes to write a loop but I believe they are not correct. I'd appreciate it if you could help me to find the best parameters based on validation set not cross validation

How many English words
do you know?
Test your English vocabulary size, and measure
how many words do you know
Online Test
Powered by Examplum