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

[Thierry Onkelinx]

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

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than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
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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 op 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
>
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>
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