st: question regarding multiple imputation using ICE command

["Katya Mauff" <Katya.Mauff@uct.ac.za>]

Dear all
 
I am attempting to impute several variables (all MAR as far as I can tell) using the ICE command in Stata, specifically, weight (continuous) and number of mutations (categorical 4 levels).
 
The data I have on weight is bimodal, and the ICE command requires a normality assumption. I've attempted to determine why the split in the weight data occurs with respect to other available information, and have run a regression of weight on several possible culprit variables (e.g age and pregnancy status). When I run the regression, my residuals are approximately normal. 
 
My first question is thus: if I run the ICE command including all the variables in the earlier regression command, do I still have to normalize weight? (and if so-possible suggestions on how to do this (?) seeing as the split is not determined by any single variable...)
 
My second question is with regards to the perfect prediction message I get when running the ICE command for the variable indicating mutation numbers- Do I have to use (e.g.) pred_eq or check_eq? Or will the use of augmlogit correct for the perfect prediction in my equation?
 
Kind Regards
Katya Mauff

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