Decision tree model evaluation for "training set " vs "testing set " in R So I got my training set with 70% of my data called "train" / 30% "test"
I use ctree to get my decision tree model with something like this code below : 
model_ctree <- ctree(response ~ x1 + .. xn , data = train) 

How can I apply this model to "test" and evaluate the model, use something like lift or gain chart or ROC; something that I would normally get from SAS miner? 
I am new to R.
 A: Try this for class predictions:
pred <- predict(model_ctree, newdata=test)
library(caret)
confusionMatrix(pred, test$response)

Try this for class probabilities:
probs <- treeresponse(model_ctree, newdata=test)
pred <- do.call(rbind, pred)
summary(pred)

Try this for a roc curve:
library(ROCR)
roc_pred <- prediction(pred[,1], test$response)
plot(performance(roc_pred, measure="tpr", x.measure="fpr"), colorize=TRUE)

Try this for a lift curve:
plot(performance(roc_pred, measure="lift", x.measure="rpp"), colorize=TRUE)

Sensitivity/specificity curve and precision/recall curve:
plot(performance(roc_pred, measure="sens", x.measure="spec"), colorize=TRUE)
plot(performance(roc_pred, measure="prec", x.measure="rec"), colorize=TRUE)

More info:
?ctree
?confusionMatrix
?performance

Also, you should check out the caret package if you're building predictive models in R.  It implements a number of out-of-sample evaluation schemes, including bootstrap sampling, cross-validation, and multiple train/test splits.  caret is really nice because it provides a unified interface to all the models, so you don't have to remember, e.g., that treeresponse is the function to get class probabilities from a ctree model.  Here's an example of using 10-fold cross-validation to evaluation your model, which is much better than a single train/test split:
model <- train(response ~ x1 + .. xn , data = train, method='ctree', tuneLength=10,
               trControl=trainControl(
                 method='cv', number=10, classProbs=TRUE, summaryFunction=twoClassSummary))
model
plot(model)

