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I need to fit a linear mixed model but my dependent variable is has some outliers that I can't discard. Then I used the rlmer() function (robustlmm package).

All this works fine. Nevertheless, I'm trying to compare several models and I need to get the p-values. AICc, BIC and the F-values. Is there any function that does this?

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In robust linear mixed models, there is no AIC and BIC given that no likelihood exists for this kind of model. Regarding p-values, they seem bad if you try to calculate them using bootstrapping methods. It's better to use confidence intervals instead. These can be calculated using wald-type tests.

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  • $\begingroup$ Hi @gung, this is not a proper answer to the question since the question is about a function but the answer counts the + and - and in the end it suggests sung CI. -1 $\endgroup$ – TPArrow Apr 26 '17 at 9:31
  • $\begingroup$ @TPArrow, this isn't my answer, I only edited it. It's true that the question asked for a function, but IMHO, sometimes the answer that will most benefit an OP is to unpack the assumptions made in the question & point out that they aren't really appropriate. $\endgroup$ – gung - Reinstate Monica Apr 26 '17 at 12:18
  • $\begingroup$ @gung oh right! However, the credit should be made for a proper answer, IMHO. $\endgroup$ – TPArrow Apr 26 '17 at 16:03
  • $\begingroup$ @Wagliss, Could you provide any reference about confidence intervals are better than p-values when performing robust linear mixed models? Thanks!! $\endgroup$ – Dekike May 17 at 8:53

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