Hi Paulo For extracting model parameters from mvgls, unfortunately, mvgls does not provide a straightforward way to extract all model parameters as mvBM, mvEB, and mvOU do. If you are specifically looking to retrieve sigma, beta, r, and alfa parameters, you might need to use custom functions or consult the package documentation to see if they’re accessible via model$par or similar components of the model object. Alternatively, you may continue using mvBM, mvEB, and mvOU for a more direct approach, as you’ve started to do.
In terms of differences between mvgls and mvOU, you're right that mvgls incorporates penalized likelihood, which can influence model selection outcomes. Penalized likelihood methods, like those used in mvgls, typically account for model complexity differently from the AIC used in mvOU. This may contribute to the discrepancy in model performance (OU1 vs. OUM) between the two methods. The choice of approach depends on your specific research question and how you want to handle model complexity. For comparative purposes across models, mvOU might be more consistent if AIC is the preferred metric. As for convergence issues with mvOU (OU1), the “Likelihood at a saddle point” warning suggests that the optimization process may have reached a point that is not a true maximum, making the solution potentially unstable or unreliable. This can happen due to flat likelihood surfaces or local optima in complex models. To address this: consider trying different starting values or optimization methods, if available, within the mvMORPH functions; reducing the parameter space or simplifying the model might help achieve a more stable solution; and/or exploring alternative packages or methods for fitting OU models if this issue persists. Hope this helps! Best wishes Michael Zyphur Director Institute for Statistical and Data Science *instats.org* <http://instats.org> On Fri, 8 Nov 2024 at 08:37, Paulo Mateus Martins <paulomateu...@gmail.com> wrote: > Dear all, > > I'm using the mvMORPH package to fit the BM, EB, OU1, and OUM > macroevolutionary models to the PCA axes of the log-shape ratios of > whip-spiders. The simmap tree contains 69 species and 4 habitat states > (cave = 24, cave/forest = 6, city = 5, forest = 34). > > At first, I used mvgls but couldn't find a way of extracting model > parameters (e.g., sigma, beta, r, and alfa), so I moved to the separate > functions for each model (mvBM, mvEB, and mvOU). > > The two approaches produced different results, with the best mvgls model > (according to GIC) being OU1, and the best mvOU model (according to AIC) > being OUM. > > Could someone please help me with the following questions? > > 1) How to extract model parameters from models fitted using mvgls? > 2) I understand that mvgls incorporates penalized likelihood, whereas mvOU > does not. Does that explain the discrepancy in the results? Which approach > would be more appropriate in my case? > 3) When I fit the mvOU (OU1) model, it says it has converged, but it also > says "Unreliable solution (Likelihood at a saddle point)". Does anyone know > how serious this is and how to deal with it? > > Thank you! > > Paulo > > [[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/
