Cool- thanks for the great comments Liam and Brian, as always!
J
> On Aug 7, 2019, at 11:55 AM, Liam Revell <liam.rev...@umb.edu> wrote:
>
> Hi Jake.
>
> [Edit: I see just now that Brian has also responded to this inquiry. I
> have no doubt that his message is more insightful than mine - but I'll
> nonetheless send what I was writing anyway just in case it contributes
> anything useful to the discussion.]
>
> If you're simply interested in the states at nodes, you might consider
> just multiplying the marginal reconstructions under maximum likelihood
> by the Akaike weights of each model & summing them.
>
> The former are probabilities that the node is in each state conditioned
> on a model, and the latter are the probabilities that each of the models
> is the best of the set. If the models in the set genuinely comprise all
> possible ways in which your character could have evolved (they don't,
> but still), then the model weighted average marginal reconstructions
> should give the total probability that each node is in each state.
>
> If you really want to do stochastic mapping, you might consider some
> kind of rjMCMC in which you sample models & transition rates from their
> joint posterior distribution and then generate stochastic character maps
> based on this sample. This is not implemented in phytools::make.simmap
> (it does MCMC, but only given a model), but is not to hard to envision
> doing, so long as you are careful about designing the rjMCMC.
>
> Now to read Brian's answer....
>
> All the best, Liam
>
> Liam J. Revell
> Associate Professor, University of Massachusetts Boston
> Profesor Asistente, Universidad Católica de la Ssma Concepción
> web: http://faculty.umb.edu/liam.revell/
> <http://faculty.umb.edu/liam.revell/>, http://www.phytools.org
> <http://www.phytools.org/>
>
> Academic Director UMass Boston Chile Abroad (starting 2019):
> https://www.umb.edu/academics/caps/international/biology_chile
> <https://www.umb.edu/academics/caps/international/biology_chile>
>
> On 8/7/2019 11:32 AM, Jacob Berv wrote:
>> Dear R-sig phylo
>>
>> I’ve been running a few discrete character Mk type models using phytool's
>> SIMMAP — and I had the idea that it might be useful to try model averaging
>> across posterior probabilities for node states.
>>
>> Might this make sense to do, to avoid problems associated with model ranking
>> via AIC? Ie, average the node state probabilities based on AIC weights? Is
>> there some fundamental problem with this?
>>
>> I could imagine generating some code to generate all possible transition
>> models given a set of N states, and then rather than ranking with AIC, model
>> averaging for parameter estimates (though now that I think about it, not
>> sure how one might reasonably average a symmetrical rate and an asymmetrical
>> rate).
>>
>> Best,
>> Jake
>>
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>>
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