Did you ever figure out a solution to this issue? I am facing the same situation. I have close to 200 variables that I need to impute, most of which are ordinal. I am using FCS since it can handle continuous, ordinal, and discrete data. However, the full imputation model takes about three days to run. OUCH! Moreover, I have too many variables for my sample size (498). Consequently, the model was not able to impute several variables; basically, it could not impute the majority of continuous variables that also happened to have the highest missing response rate. Hence, I would like to break up the imputation in two parts: use MCMC for the continuous data and FCS for the ordinal and discriminant data. Unfortunately, I cannot see how to exclude variables from being imputed but still used them as predictors. How did you resolve this problem? There is no real advantage to using the method that was suggested to your question because it would not save runtime.   Cristian ... View more