Hi all, I have two corHMM models within 2 AICc units of each other. They differ in the number of hidden states (2 versus 3). I'm trying to decide how to proceed from here and wondering if model-averaging would be the way to go. I'm interested in drawing biological conclusions based on the parameter estimates, and in doing ancestral character estimation (strictly for visualization purposes only--I'm well aware of the caveats of over-interpreting ACE states).
corHMM doesn't seem to have an in-built model averaging utility. But by analogy with the one in hisse (modelAveRates), it seems as if it's just a matter of averaging the parameter estimates based on model AIC weights. Is it really that simple? How would I deal with the param present in one model and not the other (related to the additional hidden state)? And then would it be appropriate to run ACE using the averaged rate parameters? Or would it be more appropriate to perform ACE separately for each model and then average the node likelihoods instead? Thanks, -Rafa *--* *Rafael S. Marcondes, Ph.D. (he/him)* (The R in Rafael is pronounced like the h in "hat") *https://www.rafaelmarcondes.com/ <https://www.rafaelmarcondes.com/>* Faculty Fellow in EEB Department of BioSciences Rice University Houston TX 77005 *"Eu quase que nada não sei. Mas desconfio de muita coisa"* *"I almost don't know nothing. But I suspect many things"* -João Guimarães Rosa, Brazilian novelist (Portuguese original and free English translation by me) [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list - [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/[email protected]/
