Thank you all for your responses, they have been of great use.
First, I have not rescaled the tree, thus alpha values are really rare
and they indicate a rate adaption very fast.
For the selection of models I have used AICc and then I have analysed the
reliability of the parameters with a
In many situations, the OU model parameter alpha is not well identified.
This has been pointed out before, but it's quite common for the parameter
values (and not just alpha, though that's typically the worst) to be poorly
identified, even if the model selection is unambiguous. OU model
Thank you for your answer Aaron.
Clear to me that this situation is common in the OU models, but I don't
know what criteria I should use when select between different OU models,
AICc or reliability of parameters?
Thanks in advance
Regards
Diego Salazar
2015-03-27 11:08 GMT+00:00 Aaron King
You can select between models using AICc, but then you can look at the
reliability, more robustly than using eigenvalues, by using OUwie.boot() to
do parametric bootstrapping. Another very useful thing done by
Hansen, Bartoszek, and colleagues is to do a contour plot around the
maximum likelihood
