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

[Genevieve Perkins]

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
>>
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>>
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>
>

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