Hi Samantha,

R2 is not a good measure for directly comparing alternative models because,
among other things, it is also affected by the number of predictors in the
model. Rather, you could use a measure such as the Akaike Information
Criterion (AIC), which has been specifically designed for such comparisons
and is relatively simple to interpret (e.g. Burnham, Anderson and Huyvaert
2011).

Best regards,
Pedro Pequeno.


2014-07-21 13:52 GMT-04:00 Samantha PameLa <manthasa_26@hotmail.com>:

> Good day everybody,
>
> I'm a marine biologist student, working on my bachelor thesis and I'm
> stucked with a statistical doubt in the process, I hope someone here could
> help me. My thesis aims to understand which biological and environmental
> factors influences the male aggressive rate of male California sea lions.
> For that purpose I'm using GLM's where the response variable is the male
> aggressive rate. Right now I am testing the goodness of fit of the global
> models and for that I'm using the deviances as a goodness of fit test. I
> calculated pseudoR2 (Zuur, 2009) in order to know the percentage of
> explanation of each candidate model. However I'm not sure how to choose the
> "good models"; since I am not sure over which percent of explanation
> indicates a "model with good fit". For my data I am working with three
> different scenarios, and it seems that 20%, is a good value to could
> indicate the best models, but I'm not sure how to choose the value.
>
> I thank you in advance for your time and the help you can give me.
>
> Best regards,
>
> Samantha.
>
>
>
>
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