# Imputation using MICE: Use the train data to impute the missing test data

I'm using mice in R to impute missing values. If I understand correctly, mice specifies a fully conditional model to draw new values from some posterior distribution to fill the gaps.

Since my data are split into a train and test set, I don't think I can just impute the entire data set, as this would leak information from the test set. However, it seems wasteful to start the entire imputation procedure all over again, especially since the test set is smaller.

Is there a way to re-use the learned model on the test set?

• I think you should use the imputation model of the training dataset to impute the test. – Chamberlain Foncha Mar 8 '18 at 8:01
• Thank you for your comment, but my question is how to do that. – Frans Rodenburg Mar 8 '18 at 8:07