R help - Apr 2009 - doing zero inflated glmm for count data with fmr

Hello R users,
Doing My PhD I collected count data which I believe is zero inflated. I have
run a statistical model with lmer and family=poisson and got
summary(model)@sigma=1 so I believe there is no overdispertion.  I would
like to use the fmr function from the 'gnlm' library but I just cannot
figure out from the examples in the help page and some forums out there how
to convert the lmer parameters to the one used in fmr...

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) 

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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levyofi wrote:
> 
> Hello R users,
> Doing My PhD I collected count data which I believe is zero inflated. I
> have run a statistical model with lmer and family=poisson and got
> summary(model)@sigma=1 so I believe there is no overdispertion.  
> 
You have been misled.  sigma is set to 1 by definition for the Poisson and
binomial families.
Try with family="quasipoisson" and see what you get.


levyofi wrote:
> 
> I would like to use the fmr function from the 'gnlm' library but I
just
> cannot figure out from the examples in the help page and some forums out
> there how to convert the lmer parameters to the one used in fmr...
> 
> 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) 
> 
I think (but am not sure) that "fmr" won't do what you want; it
will fit
zero-inflated
neg binom, but not mixed-effect models.  "gnlm" in Lindsey's
repeated
package does
mixed-effect models with neg binom, but not zero-inflation.  Are you sure
you need
zero-inflation after accounting for random effects?

glmm.admb in the glmmADMB package will do *most* of what you want, but ...
not crossed random effects as you have specified above (it only allows for a
single
grouping factor, as far as I can see).

If you really want all of this (zero-inflated negative binomial, crossed
random
effects) your choices would seem to be (a) the full version of AD Model
Builder
(maybe?) or (b) WinBUGS ...

  I would strongly recommend that you forward further queries on this
to the r-sig-mixed-models specialty list ...

  cheers
    Ben Bolker


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