Hi Balázs,

The difference between your largest and smallest AIC values here is only
0.78 meaning that there is very little support for your best model over any
of the others. This does not mean that your best model is poor, they could
be all good, it just means no one model is particularly better than the
others, at least according to AIC.

It's important to carefully consider the set of models that you compare
because AIC only tells you how good a model is relative to the others in the
set. Are these just your 4 best models after discarding more obviously
inferior ones?


This paper here by Burnham and Anderson might help you. Definitely read the
section on deltaAIC values which starts at the bottom of the 10th page (p270
in the journal's numbering)

http://www.uvm.edu/~bmitchel/NR385/Burnham_Multimodel_inference.pdf



Andy.


andydolman@gmail.com


2009/8/12 Balázs Lestár <ebszolocsucsor@freemail.hu>

>  Dear All!
>
> This is not a real mixed models related question, but I really would be
> gratefull if somebody could help me.
>
> 1) I'm always getting confused using AIC for model selection. Mainly I have
> 4 models with quite similar AICs (i.e. -5.88, -5.43, -5.14, -5.10). Is the
> AIC reliable in this case?
>
> 2) What to do when my model contains a variable with significant effect,
> but the AIC decreases when I get rid of it?  Keep it or leave it out of the
> model?
>
> Best regards,
> Balazs
>
>
>
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>
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