Hello Fernando,
Thank you very much for the hint, I did read the paper you indicated but 
surprisingly to me they did not report estimated values whatsoever, only 
the AIC ratios.
So, I went on looking and found Grueber's review (2011). It is a nice 
walk-through to mixed models, specially generalized models and model 
selection. They mention the function in the second step, as the easiest 
way to generate a model set.

Thanks again,

Glenda


On 13/12/2011 18:22, Fernando Schmidt wrote:
>
> Dear Glenda,
>
> Recently, I ran a model selection using the dredge function in MuMin 
> package, which ranks the models according with AIC, AICc. I used this 
> for count data using mixed effect models.
>
> This analyses follows the Information-theoretic approach (Burnham & 
> Anderson 2002), which don’t have a significance value “p” associated. 
> However, based on “delta” between the models and model “weight” you 
> could determine which model is the best approximated model for your data.
>
> I am starting to use this approach and I am not so familiar with all 
> the terms and concepts. Maybe the information-theoretic approach could 
> be useful for your aims.
>
> I attached some a paper that used it for count data. I hope this could 
> help you.
>
> All the best,
>
> Fernando
>
>
>
> 2011/12/13 glenda mendieta <glendamendieta@gmail.com 
> <mailto:glendamendieta@gmail.com>>
>
>     Dear list members,
>     I am running GLMMs with count data, Laplace approximation, poisson
>     family, using glmer {lme4}, the last version of R and R studio in
>     windows platform.
>     When fitting my final models, I run an anova to look at the
>     "significance" of a term inclusion (random effect term), of
>     course, this does not apply for random effects, but at least it
>     gives me differences in DFs and whether the models are
>     significantly different or not.
>     It basically tells me that the least parsimonious model is the one
>     with the lowest AIC value. As you can see below the difference in
>     AIC values is pretty big.
>
>     > anova(g,gwi)
>     Data: db.e
>     Models:
>     gwi: abundance ~ census * avail.surface + (1 | tree) + (1 | spp)
>     g: abundance ~ census * avail.surface + (1 | tree) + (1 | spp) +  
>     (1 | spp:tree)
>            Df     AIC     BIC   logLik Chisq Chi Df Pr(>Chisq)
>     gwi  12 22342.0 22442.9 -11159.0
>     g      13  9702.5  9811.8  -4838.3 12641      1 < 2.2e-16 ***
>
>     Then I run a qqplot on residuals vs. fitted values for each model
>     and I can see that the more parsimonious model (*gwi*) is the one
>     with the better fit (the points are pretty well alined across a
>     straight line); whereas in the other model (*g*) the points are in
>     a curved line.
>     Would this be because random crossed effects should not be
>     included as an interaction term (like in the last model [g])
>     (Johnson & Omland, 2004)? or I am overfiting?
>
>     My intuition tells me I should go for the most parsimonious model,
>     since the graphical checking works. But, I wonder if there any
>     advise you can give me to improve the fit of this model?. There is
>     still a lot of variance unexplained there, the model with the
>     random effect interaction term "spp:tree" has a variance of 59.87
>     sd. 7.73, in comparison to the model with only tree and spp (2.45
>     sd. 1.55 & 3.5 sd. 1.90).
>
>     Greetings and thanks in advance for your time,
>
>     Glenda Mendieta-Leiva
>     PhD candidate
>     University of Oldenburg
>
>     PS.  Johnson, J. B. and Omland, K. S. 2004. Model selection in
>     ecology and evolution. Trends Ecol. Evol. 19: 101-108.
>
>     _______________________________________________
>     R-sig-mixed-models@r-project.org
>     <mailto:R-sig-mixed-models@r-project.org> mailing list
>     https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
>
>
> -- 
> Fernando Augusto Schmidt
> Lab. de Ecologia de Comunidades
> PPG - Entomologia. Universidade Federal de Viçosa
> Viçosa, MG - Brazil 36570-000
> www.labecol.ufv.br <http://www.labecol.ufv.br/>
> +55 3182388810
>

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