Dear Ivy,

In gee there is no quasipossion, because gee is in a way already quasi.

With GEE we do not fit a poisson glm, but use in the construction of the 
sandwich covariance matrix the variance function of the poisson family. In Gee 
always an 'overdispersion' is estimated.

Regards
Søren

________________________________________
Fra: r-help-boun...@r-project.org [r-help-boun...@r-project.org] På vegne 
af JANSEN, Ivy [ivy.jan...@inbo.be]
Sendt: 7. april 2011 13:32
Til: r-help@r-project.org
Emne: [R] Quasipoisson with geeglm

Dear all,

I am trying to use the GEE methodology to fit a trend for the number of 
butterflies observed at several sites. In total, there are 66 sites, and 19 
years for which observations might be available. However, only 326 observations 
are available (instead of 1254). For the time being, I ignore the large number 
of missing values, and the fact that GEE is only valid under MCAR. When I run 
the following code

geeglm(SumOfButterflies ~ RES_YEAR, family = poisson, data = ManijurtNoNA, id = 
RES_ROTE_ID, corstr = "ar1")

I obtain "normal" output. Not surprisingly, overdispersion is present 
(Estimated Scale Parameters:  [1] 185.8571), so changing to quasipoisson is 
needed. However, the code below

geeglm(SumOfButterflies ~ RES_YEAR, family = quasipoisson, data = ManijurtNoNA, 
id = RES_ROTE_ID, corstr = "ar1")

produces the following error

Error in geese.fit(xx, yy, id, offset, soffset, w, waves = waves, zsca,  :   
variance invalid.

Other correlation structures encounter the same problem. I also tried adding 
"waves = RES_YEAR" (although I am not sure how waves should be used correctly), 
but it does not change anything.

Any suggestions what might be wrong?

Regards,
Ivy



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