It seems that MICE does not have a "predict" function which allows to use a fitted mids object to predict the missing values in test data set. I can certainly combine the training and test set together, do an imputation, and then split them as before. But I think in this way the imputation model also uses information from test set, which forfeited the purpose of setting up a test set in the first place.

I wonder if there is a more elegant way of doing imputation for test set using MICE (or other packages). I see that the method described in here https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#missing1 are promising. But does it mean that I have to embed a Random Forrest in my model only for imputation?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.