I think this is ok in theory: say you have 10% of the weight on the ER model, 90% on the ARD model, so your states at the nodes are based 10% on the probabilities from ER, 90% from ARD. [Worth noting, of course, that with N tips with one character each, estimating N-1 ancestral states, plus the rates, plus the model weights, is asking a bit much of the data -- it can be worth asking if there's another way to answer the biological question]. The risk of model averaging is if some of the models are truly terrible: you might get extreme parameter values because there's not enough info to estimate them well. It won't be apparent with discrete ancestral states (since there's a finite, reasonable state space), but you could have an asymmetrical rate near infinity, for example, if you fit an ARD model with not much data, and infinity times a weight for that model still means a big number.
Model averaging for parameter estimates between the rates is ok, btw: symmetrical model: rate_0_to_1 = s rate_1_to_0 = s asymmetrical model: rate_0_to_1 = a rate_1_to_0 = b weighted params: rate_0_to_1 = s * weight_sym + a * weight_asym rate_1_to_0 = s * weight_sym + b * weight_asym Best, Brian _______________________________________________________________________ Brian O'Meara, http://www.brianomeara.info, especially Calendar <http://brianomeara.info/calendars/omeara/>, CV <http://brianomeara.info/cv/>, and Feedback <http://brianomeara.info/teaching/feedback/> Professor, Dept. of Ecology & Evolutionary Biology, UT Knoxville Associate Head, Dept. of Ecology & Evolutionary Biology, UT Knoxville On Wed, Aug 7, 2019 at 11:33 AM Jacob Berv <jakeberv.r.sig.ph...@gmail.com> 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://stat.ethz.ch/mailman/listinfo/r-sig-phylo > Searchable archive at > http://www.mail-archive.com/r-sig-phylo@r-project.org/ > [[alternative HTML version deleted]] _______________________________________________ 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/
