Hello R users,


During My PhD I collected count data which I and seems as zero inflated. 
I have run a statistical model with lmer with family=quasipoisson and 
got summary(model)@sigma~10 so I believe there is overdispertion but it 
does not high enough to use negative binomial or zero inflated models. 
Am I right?


I thought maybe using the fmr function in the 'gnlm' library but it 
seems it cannot handle random effects:


I have these variables in the model:
  count: the number of logs in a foraging tray (this is the response 
variable).
  ta: the ambient temperature at the foraging tray.
  habitat: the habitat type of the foraging tray.
  season: the season in which the experiment session took place (summer 
or winter).
  moon: the moon phase (new or full).
  position: a random factor (I had 4 foraging stations)
  individual_id: a random factor indicating the individual foraged in 
the tray.
 
This is the lmer parameters I have used:
model<-lmer(count~ta*habitat*season*moon + (1|individual_id) + 
(1|position), data=countdata, family=poisson)


Is it possible to run the same model with fmr?


I would really appreciate the help. I love working with R and it really 
changed the way I work with my data.
Thanks again,
Ofir.




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