Dear R-sig-phylo list,
I am fitting hidden-rate discrete trait models using corHMM (v2.8) across a
posterior tree sample (200 trees of the same set of species), and I would
appreciate guidance on how to interpret frequent boundary-hitting ML estimates.
In particular, I observe two patterns:
Lower-bound hits (rates �� 0):
Some transition rates repeatedly hit the lower bound across many trees. In a
hidden-state setting, this can effectively shut off transitions or simplify the
state graph. Is it reasonable to treat such fits as degenerate or unreliable
for process-level interpretation, and to exclude models that frequently exhibit
this behavior?
Upper-bound hits (rates �� large):
Other rates��primarily within the fast regime��often hit the upper bound.
Increasing the bound (e.g. 20 �� 30 �� 100) typically causes the same or
another within-regime rate to peg the new limit, while regime decoding and
downstream summaries remain stable. Is it reasonable to interpret this as weak
estimability of rate magnitude (i.e. ��very fast�� but not precisely
estimable), and to focus inference on derived quantities rather than the rate
values themselves?
More generally, is boundary behavior across tree uncertainty a reasonable
criterion for model reliability, alongside likelihood or AIC, when choosing
among hidden-state models?
Any advice or relevant references would be greatly appreciated. I am happy to
provide a minimal example if useful.
Best regards,
Emma
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