Miguel Angel Rodríguez-Gironés Arbolí <rgirones <at> eeza.csic.es> writes:
> > Dear Colleagues, > > We are using the phylog.gls.fit() function from the R package > "PHYLOGR" (Diaz-Uriarte R, Garland T: > PHYLOGR: Functions for phylogenetically based statistical > analyses. 2007. [snip] > ... we would want to > use AIC values for model selection. We can obtain > these values directly with the function AIC(), or from > the log-likelihood obtained with the logLik() function. > (The two methods give the same results.) > > We have two questions concerning this procedure: > > 1. Does anybody know whether the output we > obtain from the AIC() and logLik() functions is reliable? Does > the algorithm used for calculating the model > likelihood and AIC take into account the non-independence > of data points? Or are the log-likelihood and > AIC calculated using some standard R algorithm that is not > valid for phylog.gls models or gls? Looking at the guts of phylog.gls.fit, I see that it is using lm() on transformed data (haven't read the paper referenced in the documentation, or at least not recently), so I doubt it is OK. > 2. Is the logLik extracted from phylog.gls > suitable for a type-III log-likelhood ratio test? What is a type III log-likelihood ratio test? I suggest that you send this query to r-sig-ph...@r-project.org instead; that is the special-interest list for phylogenetic and comparative methods ... Ted Garland even reads that list sometimes. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
