GlmmADMB: random slopes and fixed effects
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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