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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, 2017 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. $\endgroup$ Apr 26, 2017 at 12:18
  • $\begingroup$ @gung oh right! However, the credit should be made for a proper answer, IMHO. $\endgroup$
    – TPArrow
    Apr 26, 2017 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, 2020 at 8:53
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This answer comes late to the OP but I found this post trying to get the answer to the OP's question and then later found part of the answer. @Wagliss is correct that you cannot obtain an AIC or BIC for robust models because there is no likelihood estimation in these situations. The makers of the robustlmm package provide means to obtain p-values using the TestFixef function. For more info check out the github Robustlmm Github.

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