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
glmm to model the overdispersion. The negative binomial distribution has
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
zero mean. When the overall slope is not zero, this assumption is
when the variable is not used as a fixed effect.
Note that you better center random slopes to get more stable estimates.
you have enough data to fit such a complex model? The variance covariance
matrix of the Species random effect requires 10 parameters. I would
for >100 observations per species and >10 species.
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
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
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
~ 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
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=
I noticed however that if I include Veg and Pop as fixed effects (model
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
in a GLMM model without including them as fixed effects? (I couldn't
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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