Generalized Estimating Equations and log-likelihood calculation

I'm working with clustered data sets and trying to calculate log-likelihood 
(and/or AIC, AICc) for my models.  In using the gee and geese packages one 
gets Wald test output; but apparently there is no no applicable method 
for "logLik" (log-likelihood)calculation.

Is anyone aware of a way to calculate log-likelihood for GEE models?

No (as with GLM quasi- models, it is not defined in general).  Even if
there were, you would have find the maximized log-likelihood to find AIC,
and by definition GEE is not ML fitting except in a few special cases.

Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

Is anyone aware of a way to calculate log-likelihood for GEE models?

No. GEE fitting is based on quasi-likelihood.

However, it is possible to derive AIC-like measures based on the 
quasi-likelihood. Lebreton et al (1992) suggested a simple adjustment in 
the GLM case, i.e. when using family=quasibinomal or quasipoisson. For GEE 
models, Pan (2001) has introduced QIC. None of these measures are 
implemented in R or in any add-on package as far as I know.

Henric

For GEE models, Pan (2001) has introduced QIC. None of these measures are 
implemented in R or in any add-on package as far as I know.

Actually, take a look at 
http://hisdu.sph.uq.edu.au/lsu/SSAI%20course/course_tools.htm

Henric