06-29-2017 11:12 AM
This paper https://support.sas.com/resources/papers/proceedings13/438-2013.pdf indicates a link to source code. I can't confirm that as the site the link in the paper uses is blocked by my organization.
08-10-2017 03:19 PM - edited 08-10-2017 03:27 PM
Sorry about the website confusion. I took down the website when I stopped offering consulting services. As for the macro, I no longer recommend using MMI_IMPUTE. MMI_IMPUTE uses an imputation algorithm called PAN, which was developed a while back by Joseph Schafer. Unfortunately, PAN is a bit outdated, and is less flexible than newer algorithms (e.g., the algorithm can't incorporate random effects between incomplete variables; all incomplete variables are required to be normally distributed). I recommend that you instead use a standalone software package called Blimp. Blimp was written by Brian Keller and Craig Enders at UCLA. Unlike MMI_IMPUTE, Blimp can handle random effects between incomplete variables and can also handle some non-normal incomplete variables (e.g., binary variables). The software and associated documentation are available at http://www.appliedmissingdata.com/multilevel-imputation.html. Blimp is free, and the website contains scripts for using it from SAS.