Regression of multiply imputed dataset (mitml)

I have completed a hierarchical multiple imputation of my data using the hmi package and have transformed the 10 imputed datasets it into a mitml.list. I am able to fit frequentists regression models to it using the with() function.

e.g., fittedn.model <- with(data, lmer(score ~ 1 + gender + (1|course_id:student)))

I can separate out one of the 10 imputed datasets and perform a bayesian regression. I could perform 10 independent regressions and then combine them and pool their variances using Rubins Rules, but it seems like there has to be a package that allows for the analysis of MI datasets. Can anyone point me in the right direction? Thanks!

Have you considered doing the missing data imputation in the model itself? In fact, depending on what’s missing I find that for hierarchical models the missing data is handled automatically


Did you see this vignette? I think you may find what you need in it!


It was on our agenda to move in this direction, but I was trying to do it piecemeal. This seems like the right call though. I’ll just do it bayesian starting with the imputation. Thanks!

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This looks like a very useful vignette, thank you!