[R-sig-ME] GlmmADMB: random slopes and fixed effects

[Ken Beath]

A random effect for a slope means that the slopes have a random
distribution about the fixed effect. For example if the fixed effect is 5
then the slopes for each species will be distributed around this value. So
one species may have a slope of 4 and another 5.6. Why it is sensible to
have a fixed effect is that it is usually not realistic for the mean slope
to be zero, that is to have the random slopes distributed around zero. That
would imply that some slopes are negative.

On 29 April 2015 at 23:56, Genevieve Perkins <genevieve.c.perkins at gmail.com>
wrote:

> Hi Thierry,
> Thanks for the response and great advice.
>
> I have 25 species, 59 sites and total of 1475 observations (including
> absences).
> I didn't mention in the post, but I centered all my predictor variables
> prior to fitting the model (except trait,which is coded as -1 or +1 for
> ease of interpretation).
>
> I am able to run the model both with and without fixed affects :
>
>  fitn <- glmmadmb(bird.abund ~ Cats + trait + Cat:trait + Veg + Pop + (1 +
> Veg + Pop + Cats|Species), data = bdata, family= "nbinom").
>
> The parameters for the random slope do not have normal distributions, so I
> will take your advice and also include these as fixed effects.
>
> Could you suggest any references which explain how random slopes are
> treated. I have mainly been using Zurr, Gelman and Hill, chapters from
> Ecological Statistics (eds. Fox et al.) and online postings.
>
> Thanks again!
>
>
>
>
>
> On 29 April 2015 at 03:51, Thierry Onkelinx <thierry.onkelinx at inbo.be>
> wrote:
>
> > Dear Genevieve,
> >
> > An observation level random effect (OLRE) is used in a poisson or
> binomial
> > glmm to model the overdispersion. The negative binomial distribution has
> a
> > parameter that handles the overdispersion. So you don't need the ORLE.
> >
> > Note that the as.formula() is not required.
> >
> > Random slopes assume that the parameters follow a normal distribution
> with
> > zero mean. When the overall slope is not zero, this assumption is
> violated
> > when the variable is not used as a fixed effect.
> >
> > Note that you better center random slopes to get more stable estimates.
> Do
> > you have enough data to fit such a complex model? The variance covariance
> > matrix of the Species random effect requires 10 parameters. I would
> strive
> > for >100 observations per species and >10 species.
> >
> > Best regards,
> >
> > ir. Thierry Onkelinx
> > Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and
> > Forest
> > team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> > Kliniekstraat 25
> > 1070 Anderlecht
> > Belgium
> >
> > To call in the statistician after the experiment is done may be no more
> > than asking him to perform a post-mortem examination: he may be able to
> say
> > what the experiment died of. ~ Sir Ronald Aylmer Fisher
> > The plural of anecdote is not data. ~ Roger Brinner
> > The combination of some data and an aching desire for an answer does not
> > ensure that a reasonable answer can be extracted from a given body of
> data.
> > ~ John Tukey
> >
> > 2015-04-28 22:41 GMT+02:00 Genevieve Perkins <
> > genevieve.c.perkins at gmail.com>:
> >
> >> Hello,
> >>
> >> I am a masters student new to the world of GLMMs. I have developed a
> mixed
> >> model using the glmmADMB package and I have been scouring the literature
> >> and help files, and trying to find an answer to my questions with no
> >> success.
> >>
> >> I want to estimate the effect of cats on bird abundance for birds with
> >> particular traits (all traits are binary coded (0,1);
> >> Specifically I am looking at the interaction estimate.
> >>
> >> I included species as a random effect, and I wanted the species response
> >> to
> >> vary with Vegetation (Veg) and Population (Pop). I also added a random
> >> level observation term.
> >>
> >>       Model 1: fitn <- glmmadmb(as.formula(bird.abund ~ Cat + trait +
> >> Cat:trait
> >> + (1 + Veg + Pop + Cat|Species) + (1|ID)), data = bdata,family=
> "nbinom")
> >>
> >>
> >> I noticed however that if I include Veg and Pop as fixed effects (model
> 2)
> >> my model estimate for cats at the fixed effect level and species level
> >> also
> >> change.
> >>
> >>       Model 2: fitn <- glmmadmb(as.formula(bird.abund ~ Cats + trait +
> >> Cat:trait
> >> + Veg + Pop + (1 + Veg + Pop + Cats|Species) + (1|ID)), data = bdata,
> >> family= "nbinom")
> >>
> >>
> >> My questions are:
> >> 1)  Is it possible to include varying slope coefficients (ie: Veg and
> Pop)
> >> in a GLMM model without including them as fixed effects? (I couldn't
> find
> >> any examples of this format)
> >>
> >> 2) How are the estimates for the random effects treated without a
> >> corresponding
> >> fixed effect in Glmmadmb. I was guessing they may be pooled to a group
> >> mean
> >> of zero, but I was not able to find this information in the glmmadmb
> >> literature.
> >>
> >> All suggestions greatly appreciated!
> >> Thanks
> >>
> >>         [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
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> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >
> >
>
>         [[alternative HTML version deleted]]
>
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-- 

*Ken Beath*
Lecturer
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