about graphical checking on glmm and contradiction between parsimony and AIC values

Hello Glenda;

Could you please send us the complete reference for this paper? Sounds 
really interesting!

Cheers, RSolar.

On 15 December 2011 08:18, glenda mendieta <glendamendieta at gmail.com 
<mailto:glendamendieta at gmail.com>> wrote:

    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 at gmail.com

    <mailto:glendamendieta at gmail.com>

    > <mailto:glendamendieta at gmail.com <mailto:glendamendieta at 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 at r-project.org

    <mailto:R-sig-mixed-models at r-project.org>

    > <mailto:R-sig-mixed-models at r-project.org

    <mailto:R-sig-mixed-models at 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 Vi?osa
    > Vi?osa, MG - Brazil 36570-000
    > www.labecol.ufv.br <http://www.labecol.ufv.br>

    <http://www.labecol.ufv.br/>

    > +55 3182388810 <tel:%2B55%203182388810>
    >

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