Hi Ben,
Thank you so much for your helpful advice! I changed the lmer model to
REML=FALSE and the best-fitting model in dredge is MUCH more similar to what I
found with MCMCglmm.
My best,
Corina
--
Dr. Corina Logan
Leverhulme Early Career Research Fellow
Department of Zoology
University of C
Hi Bert,
Thanks for the model selection philosophy! It’s definitely not a perfect world
in terms of trying to understand the “truth", but I find that there are so many
opinions about how to do model selection that I just choose one and go with it
:)
My best,
Corina
--
Dr. Corina Logan
Leverh
(slightly off topic)
One might also add that different model fitting criteria might produce
rankings that are quite different, but with model predictions that are
very similar. This reflects the inconvenient reality that empirical
models are merely data interpolators and not representations of
"tr
Corina CorinaLogan.com> writes:
>
> Hello,
> I am running my full model (fm) through lmer() and MCMCglmm() using the
> default settings:
>
> model.lmer <- lmer(fm)
> model.MCMCglmm <- MCMCglmm(fm)
>
[snip]
> However, when I run the models through dredge():
>
> dredge(model.lmer)
> dredge(
Hello,
I am running my full model (fm) through lmer() and MCMCglmm() using the
default settings:
model.lmer <- lmer(fm)
model.MCMCglmm <- MCMCglmm(fm)
And the summary outputs are almost exactly the same:
summary(model.lmer)
summary(model.MCMCglmm)
However, when I run the models through dredge()
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