Hello mixed modelers,

I am having problems with some GEE models I am trying to run using geepack.

I have species abundance data for 52 different species in 154 sites over 47
years, and I am trying to extract slope parameter estimates so that I can
look at whether these species have increased or decreased in abundance over
time, while taking into account the repeated measurements at each site over
time.  I originally started doing this with mixed models, but have been
advised that GEE would be more appropriate for my data as it gives
population-averaged responses.

However, when I try to run GEE's on my data I get really bizarre parameter
estimates for some of my species.  As my dataset is huge I unfortunately
cannot provide the whole thing, but I have uploaded a subset of the data
for one species with a particularly bizarre slope parameter estimate here:
http://dl.dropbox.com/u/4481861/Example_for_GEE_one_species.csv

The data look like this:

  Site Year Species Value_Pres Value_Abs
1    1 1961       1          0            2089
2    1 1962       1          0            2120
3    1 1963       1          0            2089
4    1 1964       1          0            2225
5    1 1965       1          0            2197
6    1 1966       1          0            2208

I have been using the following model specification (I have been running a
loop to calculate estimates for all 52 species separately, but this is for
just one species):


speciesA<-orderBy(~Site+Year,data=speciesA) #using the doBy package to
order by subject then time


speciesA.mod<-geeglm(cbind(Value_Pres,Value_Abs)~I(Year-1961),data=speciesA,
family=binomial,id=Site,corstr="ar1")


Call:

geeglm(formula = cbind(Value_Pres, Value_Abs) ~ I(Year - 1961),

    family = binomial, data = speciesA, id = Site, corstr = "ar1")


 Coefficients:

                Estimate   Std.err    Wald Pr(>|W|)

(Intercept)    -2.99e+14  9.10e+11  107705   <2e-16 ***

I(Year - 1961) -9.62e+13  3.88e+10 6155147   <2e-16 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Estimated Scale Parameters:

            Estimate  Std.err

(Intercept) 6.57e+10 5.62e+30


Correlation: Structure = ar1  Link = identity


Estimated Correlation Parameters:

      Estimate Std.err

alpha     0.98 4.4e+18

Number of clusters:   154   Maximum cluster size: 47


I suspect the problem might have something to do with the correlation
structure, as species abundances in subsequent years are often very highly
correlated, even if there is substantial change over the 47 years overall.
If I use the corstr="independence" command I get parameter estimate that
are very similar to those I got using mixed effects models (at least, the
slopes for species responses relative to each other are similar).
 Furthermore, if I use corstr="ar1" but subset my data to every 5 years
instead of every year, I get much more reasonable slope estimates for this
particular species as well as most of the other species (slope values are
very similar to the corstr="independence" value), but a few different
species' slopes then get very weird. (By get weird I mean that they have
abnormally large positive or negative slopes that don't reflect what's
happening in the raw data at all).


I would really appreciate some insight into what the problem with my data
could be, or, more particularly, how to fix it! My head and my wall would
be very grateful! Perhaps I should just give up on GEE's and go back to
mixed models??


Thanks very much,

Anne

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