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.

 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, 

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

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