thanks for your answer!

For the butterfly counts we used butterfly bait traps. They were not visible
counts. I read several ecological papers that treat species or individuals
counts as Poisson applying GLM rather than e.g. repeated measures ANOVA. I
assumed that the monthly collection out of a species pool cannot be
independent. To choose GEE was my idea because of its advantage for repeated
measures and Poisson distribution...



2011/8/29 Prof Brian Ripley <ripley@stats.ox.ac.uk>

> You need to tell use why you want to use a GEE model.  From your use of
> corstr = "ar1" I would surmise you think the counts are serially correlated
> during a year (despite the presence of a 'month' main effect), in which case
> the id is 'site'.
>
> All 'id' does is to partition the data into clusters: counts for different
> clusters are independent, counts within a cluster are (potentially)
> dependent.
>
> The common advice applies: you should talk to a statistician conversant
> with GEE models about your model formulation.  (My field experience would
> suggest that there is no good reason to suppose that the counts are Poisson:
> visible occurrences of butterfly species do not behave independently.)
>
>
> On Mon, 29 Aug 2011, Anna Mill wrote:
>
>  Hi all,
>>
>> I am trying to do a generalized estimating equation (GEE) with the
>> "geepack"
>> package and I am not 100% sure what exactly the "id" argument means. It
>> seems to be an important argument because results differ considerably
>> defining different clusters.
>>
>> I have a data set of counts (poisson distribution): numbers of butterfly
>> species counted every month during a period of one year (12 repeated
>> measures) at seven sites, three of those being "continuous forest sites"
>> and
>> four of those being "secondary forest sites". The aim is to compare
>> continuous and secondary forests.
>>
>> Would you define the sites or the forest type as id argument:
>>
>> model1<-geeglm(formula = number ~ type + month, family = poisson, *id =
>> site
>> *, corstr = "ar1")
>>
>> model2<-geeglm(formula = number ~ type + month, family = poisson, *id =
>> type
>> *, corstr = "ar1")
>>
>> or should even almost every count have a special id (e.g. *
>> id=interaction(month,site)* or *id=interaction(month,type*))
>>
>> Thanks for your help...
>> Anna
>>
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>>
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> --
> Brian D. Ripley,                  ripley@stats.ox.ac.uk
> Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~**ripley/<http://www.stats.ox.ac.uk/%7Eripley/>
> University of Oxford,             Tel:  +44 1865 272861 (self)
> 1 South Parks Road,                     +44 1865 272866 (PA)
> Oxford OX1 3TG, UK                Fax:  +44 1865 272595
>

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