# Mean comparisons following multiple imputation

I need to do some simple mean comparisons between groups (basic ANOVA F-tests) on data with missing values. I use the mice package in R for multiple imputation, but I can only pool results for the linear model coefficients, or the $R^2$.

Does anyone know how to combine to pool multiple F-statistics from each linear model fit? Or, how can I compute the standard errors for the F-test?

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Welcome to the site @Brian. Is this question only about how to get something done in R, or also about the statistical issues? If so, it really belongs on Stack Overflow, rather than here (that doesn't make it a bad question, though). Please don't cross-post (SE strongly discourages this), if it's better there, just say so, & after a bit, the moderators will migrate it for you. –  gung Aug 10 '12 at 16:14
Thanks. Well, it's kind of a bit of both. The statistical issue is "How to do significance tests for comparing sample means with multiple imputation? In particular, how do you compute the variance of the estimate (F-statistic)?". Now, I'm using R/MICE for my multiple imputation needs, so I thought someone would know of a function for it. Alternatively I'd be more than happy with a statistical explanation on how to do it so I can just write the function myself from scratch. –  Brian Aug 10 '12 at 17:16
It sounds like we can keep it here for now then. But if it doesn't get a satisfactory answer here after a while, you can also ask the moderators to migrate it to SO for you. GL –  gung Aug 10 '12 at 22:23
This paper might answer it for you: www-personal.umich.edu/~teraghu/Raghunathan-Dong.pdf –  Jeremy Miles Feb 28 at 18:18
I think this is very appropriate here. I've had this same question myself. @JeremyMiles is referring to the only paper I found on the subject, and unfortunately I didn't find it as convenient as I was hoping it would be. Perhaps this is still a domain that requires research? –  Patrick Coulombe Sep 4 at 2:13