This makes sense to me too… thanks for your thoughts!
Jake
> On Jan 26, 2017, at 7:55 PM, Donald Miles <urosau...@gmail.com> wrote:
>
> I agree with Ted. The comparison should be how the transformation affects the
> distribution of the response variable. As Ted mentioned, you should examine
> the behavior of the residuals to determine whether they conform to the
> assumptions of the statistical analysis after transformation.
>
> Donald Miles
>
> On Thu, Jan 26, 2017 at 7:49 PM, Theodore Garland <theodore.garl...@ucr.edu
> <mailto:theodore.garl...@ucr.edu>> wrote:
> 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
> <mailto: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
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