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://www.phytools.org Academic Director UMass Boston Chile Abroad (starting 2019): 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 > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-phylo mailing list - R-sig-phylo@r-project.org > https://nam01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-phylo&data=02%7C01%7Cliam.revell%40umb.edu%7Ce85015cb8c314c684bdc08d71b4c8fee%7Cb97188711ee94425953c1ace1373eb38%7C0%7C0%7C637007887976647805&sdata=F8qdVoT17J%2BuC6wgvFktdI1id%2Bf37hDLe1W17z6OhTw%3D&reserved=0 > Searchable archive at > https://nam01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.mail-archive.com%2Fr-sig-phylo%40r-project.org%2F&data=02%7C01%7Cliam.revell%40umb.edu%7Ce85015cb8c314c684bdc08d71b4c8fee%7Cb97188711ee94425953c1ace1373eb38%7C0%7C0%7C637007887976657803&sdata=ogCaep2DN92bk6%2FZwOOcpmHaCPQwX7BzNgKo2UMcWtw%3D&reserved=0 > _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
