Hello all, I am doing ancestral state estimation analyses with binary traits (presence or absence of a behavior), and while doing so I ran into something that is puzzling me.
For one particular behavior, the results look wrong to me, although R doesn't report any errors during the analyses. Assuming Equal rates is the best model (which I previously assessed with *fitDiscrete*), no matter if I use function *ace*, *make.simmap*, or *ancr* after *fitMk*, this behavior shows the same values of scaled likelihoods for all ancestral states (0.5, figure attached). Setting marginal = T does not change the results. When looking at this information plotted on my tree that just doesn't feel right. I would expect likelihood values to be greater in some ancestral nodes (those immediately before groups of species where all of them have the behavior) and smaller in others (those before groups without the behavior), not the same likelihood for all of them. Now, if I choose to run analyses with model = ARD, for *ace *and *ancr* I get results that look similar to what I would expect (although *make.simmap* yields the same result as model = ER; figure attached). However, as I have previously run *fitDiscrete* to determine what is the best model for this behavior, and it was ER, I don't think it makes sense to just choose ARD instead because 'it looks better' (AIC weight ER = 0.74 *vs.* AIC weight ARD = 0.25). As I have congruent results no matter which functions I use, I wonder if this could be influenced by the way that this behavior is distributed across the tree (too dispersed? - but not a 'problem' that I could fix, it's just how my data is). Does anyone have any thoughts on whether my results are correct or if something might be off? I appreciate your time and effort. Best, Laura -- Laura Maria Schaedler Doutoranda em Ecologia Laboratório de Biologia Evolutiva e Comportamento Animal Instituto Nacional de Pesquisas da Amazônia http://lattes.cnpq.br/5795430755021849 *PhD student in EcologyEvolutionary Biology and Animal Behavior LabNational Institute for Amazon ResearchManaus, AM, Brazil*
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