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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Brian D. Ripley, rip...@stats.ox.ac.uk
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