Dear all,
I have the following data. A group identifier for the region (region) a person lives in with a few levels and a continuous variable that gives the number of free days in a region (days). So days depends on the region apparently, but two different regions can have the same number of free days. Both vars have missing values (so when region is missing, days is always missing as well). There are some more variables that need to be imputed. When I impute this dataset, how can I achieve that both variables are used as predictors for the overall imputation process but days is imputed depending on the region?

To give a concrete example:
Region A has 10 free days.
Case X has a missing on region. The algorithm decides that region A is the most probable and assigns this level. Automatically, days must be set to 10 for this case.

I would like to use PMM as imputation algorithm for all variables. Sorry if passive is the wrong term used here.