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!

ps- my first time responding to the list, sorry for any format errors

Jonathan A. Nations
PhD Candidate
Esselstyn Lab <http://www.museum.lsu.edu/esselstyn>
Museum of Natural Sciences <http://sites01.lsu.edu/wp/mns>
Louisiana State University

> Message: 2
> Date: Wed, 20 Jun 2018 19:13:28 +1000
> From: Liam Kendall <liam.k.kend...@gmail.com>
> To: r-sig-phylo@r-project.org
> Subject: [R-sig-phylo] Comparing DIC of phylogenetic and
>         non-phylogenetic GLMM run with MCMC (MCMCglmm)
> Message-ID: <cc5cf66d-88ad-4fb0-aac0-556a7d46d...@gmail.com>
> Content-Type: text/plain; charset="utf-8"
> Dear all,
> I am conducting an analysis predicting insect body sizes using a
> co-varying trait and their biogeographic region within two model
> formulations using MCMCglmm.
> The first model has the structure: log(Weight) ~ log(Trait)+ Biogeography
> + Family (i.e. Taxonomic family of species)
> The second model has the structure: log(Weight) ~ log(Trait)+ Biogeography
> + (1|Species/Animal), pedigree = phylogeny, i.e. variance between species
> is constrained by the branch lengths between the species.
> The aim of running these two models is compare which is more predictive
> and to increase usability: Including family is user-friendly (and easy for
> the end user, especially if they’re not a taxonomist) whereas the
> phylogenetic model is more attractive theoretically however from a
> predictive sense requires your species of interest to be contained within
> the phylogeny used to fit the model,
> Therefore, my question is how best can I compare these two models in model
> selection? Can I compare them directly by their DIC weighting if the only
> difference is the phylogenetic random term? Or is there be a better way to
> compare them? So far, we are also comparing their performance based off
> k-fold cross validation and RMSE but in the ‘age of AIC’, DIC appears a
> good place to start for model selection.
> Any advice would be much appreciated.
> Best,
> Liam
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