[R-sig-ME] about graphical checking on glmm and contradiction between parsimony and AIC values
glenda mendieta
glendamendieta at gmail.com
Thu Dec 15 15:20:01 CET 2011
sure,
Multimodel inference in ecology and evolution: challenges and solutions.
by: C. E. Grueber, S. Nakagawa, R. J. Laws, I. G. Jamieson. Journal of
evolutionary biology, Vol. 24, No. 4. (April 2011), pp. 699-711.
doi:10.1111/j.1420-9101.2010.02210.x
enjoy
glenda
On 15/12/2011 12:06, Ricardo Solar wrote:
> 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 Viçosa
> > Viçosa, MG - Brazil 36570-000
> > www.labecol.ufv.br <http://www.labecol.ufv.br>
> <http://www.labecol.ufv.br/>
> > +55 3182388810 <tel:%2B55%203182388810>
> >
>
> [[alternative HTML version deleted]]
>
>
> _______________________________________________
> 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
>
>
>
>
> --
> ==========================================
> Ricardo Ribeiro de Castro Solar (Curriculo Lattes
> <http://lattes.cnpq.br/9924177207371692>)
> MSc. em Entomologia - Doutorando - PPG Entomologia
> Laboratório de Ecologia de Comunidades/Formigas
> <http://www.labecol.ufv.br>
> (31) 3899-4018 - Contato Skype: rrsolar
> Universidade Federal de Viçosa - MG
> ==========================================
More information about the R-sig-mixed-models
mailing list