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From | "Katya Mauff" <Katya.Mauff@uct.ac.za> |
To | <statalist@hsphsun2.harvard.edu> |
Subject | st: question regarding multiple imputation using ICE command |
Date | Tue, 09 Mar 2010 14:07:37 +0200 |
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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/