[R-sig-ME] Overdispersion and R2 in GLMM

[Teresa Oliveira]

Dear all,

I want to estimate RSF, and to obtain the relative probabilities I will use
the coefficients obtained with GLMMs. I am new in this so I hope I can
express myself well.
To construct the GLMM's, I am using glmer(). I will to use the MuMIn
package to perform model selection [using dredge()].
I have two doubts:
1) For the top models I get, in order to understand if they are really
meaningful, I need to estimate R2 [r.squaredGLMM()] and to test for
overdispersion [overdisp.glmer() with "RVAideMemoire" package], right? Is
there anything else I must consider (besides de AIC)?

2) How do I interpret the results in both tests?
For a model with all variables I wanted to include (so, before performing
model selection), I estimated R2 and overdispersion.

For overdispersion I got this:
"> overdisp.glmer(lm_set3)
Residual deviance: 8537.397 on 32658 degrees of freedom (ratio: 0.261)"
Which value for ratio is acceptable?

For R2 I got this:
"> r.squaredGLMM(lm_set3)
The result is correct only if all data used by the model has not changed
since model was fitted.
        R2m         R2c
0.006139516 0.788967246 "
Is it normal to get values so different? Should I consider both?

Thank you very much for your time and help!

Best regards,
Teresa

	[[alternative HTML version deleted]]