[R] Multiple imputation, multinomial response & random effects

Fri Jan 17 10:16:31 CET 2014

Dear Davina,

Unfortunately (or luckily), I have almost the exact same problem. I want to 
do a multilevel analysis with imputed data and both include mixed and 
random effects in the regression model. I have imputed my data with de 
Hmisc package (aregImpute), however, the rest of the functions does not 
support to analyse the data with random effects. I saw that your question 
is from 2012, but you still did not receive a public answer. Do you have 
one by now, or did you find a solution for this problem on your own?

Best regards,

Ruben

Op zondag 3 juni 2012 17:03:21 UTC+2 schreef Davina Hill:
>
> Dear R-group, 
>
> Could somebody recommend a package that can deal with a multinomial 
> response variable (choice of breeding tactic in mice, which has four 
> unordered levels), multiply-imputed data (generated using the Amelia 
> package) and two non-nested random effects: individual identity (133 
> individuals made up to four choices each) and year (for which there are six 
> levels and sample size varies between years)? 
>
> I've tried sabreR, drm, mixcat and mlogit but none of them seem able to 
> accommodate multiply-imputed datasets.  The most promising package I’ve 
> found so far is Zelig, which can handle multiply-imputed data and either 
> multinomial responses OR random effects (but seemingly not both).  I could 
> randomly select one case per individual and run the following model (with 
> year as a fixed rather than random effect): 
>
> z.out <- zelig(as.factor(tactic) ~ mass+age+year, model = "mlogit", data = 
> a.out$imputations) 
>
> but it probably isn't the most elegant solution.  Does anybody have any 
> suggestions? 
>
> Thanks in advance, 
> Davina 
>
> ------------------------------------------ 
> Animal, Plant and Environmental Sciences 
> University of the Witwatersrand 
> South Africa 
>
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