I don't think you can compare models like this for different transforms of the dependent variable. The likelihood, etc., values are not comparable, as your results suggest. But I am sure someone will correct me if I am wrong!
Assuming this is some sort of regression model (i.e., you have one or more independent variables), then you CAN look at residuals to see if they are better/worse behaved (e.g., approximately normal, no nasty outliers, homoscedasticity). Cheers, Ted On Thu, Jan 26, 2017 at 2:25 PM, Jacob Berv <jakeberv.r.sig.ph...@gmail.com> wrote: > Dear R-sig-phylo, > > When analyzing comparative data in a PCM framework, is it be appropriate > to use an AIC score to advocate for log transforming input data? ie > model(data) vs model(log(data)) > > I’m running a few OUwie models and it’s not entirely clear from the > biology whether or not the ‘standard’ log transformation makes sense. In my > case, the model(log(data)) results have much lower AIC values relative to > the model(data) results. > > Cheers, > Jake Berv > _______________________________________________ > 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-ph...@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/
