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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