Thank you all for your very informative responses.

I will try the brms package as Jon suggested - I have read a bit about WAIC 
being more appropriate or favourable than the DIC but I was (until now) 
unfamiliar with the brms package.

We are very much working within a predictive framework where model selection is 
followed by k-fold cross validation so I would very much be curious about your 
thoughts on that type of cross-validation on a phylogenetic glmm 

Thanks again and all the best


> On 22 Jun 2018, at 1:34 am, Jarrod Hadfield <> wrote:
> Hi Liam,
> In multi-level models DIC can be 'focused' at different levels. In MCMCglmm, 
> DIC is focussed at the highest possible level because this is the only level 
> at which it can be analytically computed for non-Gaussian models. The highest 
> level is not the level at which most scientists want their information 
> criteria focussed, and so I would not recommend it. In fact I have wondered 
> about removing it completely from MCMCglmm. Cross-validation is a much better 
> approach, and in some ways is what information criteria aspire to. But its 
> more computationally demanding of course.
> Cheers,
> Jarrod
> On 21/06/2018 14:24, jonnations wrote:
>> Hi Liam,
>> I don't have the exact answer you are looking for, but I would highly
>> recommend the brms package in R. It is incredibly flexible and has
>> excellent diagnostic tools like LOO and WAIC that are easy to use and
>> interpret for model selection. I think it would work well for the models
>> you presented. There is an easy to follow tutorial on phylogenetic mixed
>> models too.
>> Also there is another list serve called "r-sig-mixed-models" that you might
>> be interested in. It's not "phylo" focused, but these sorts of questions
>> come up on there all the time.
>> Good luck!
>> Jon
>> ps- my first time responding to the list, sorry for any format errors
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