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GEE and AIC

See Pan (2001). Akaike's information criterion in generalized estimating
equations. Biometrics 57: 120-125. See also the book by Hardin and Hilbe
2003 for examples and applications.

?The only reason to jump to gee is if your dependent variable is not
Gaussian (but is an exponential family, e.g. binomial, Poisson, etc.),
and you have several covariates, some of which may be continuous. 

You should be careful with your model of evolution if your dependent
variable is non-Gaussian. Brownian motion cannot apply to discrete
variables. Instead, a discrete-state, continuous time Markov model might
be more appropriate, in which case you can't directly use the shared
branch-lengths of the phylogeny as estimates of phylogenetic covariance.
See Martins and Hansen 1997 Phylogenies and the comparative method: a
general ?approach to incorporating phylogenetic information into the
analysis of interspecific ?data. American Naturalist 149:646?667.
Erratum 153:448. Tony Ives and Ted Garland have submitted a manuscript
on this topic, but you will have to contact them for further info. The
sample size has little to do with the issue, although fitting gee models
with small sample sizes can be extremely difficult. There are also
formidable problems with bias in the parameter estimates.

I've cc'ed this to r-sig-phylo, which is probably a better forum for
discussing comparative analyses.

Cheers,

Simon.
Thu, 2008-05-29 at 15:50 -0700, BriAnne Addison wrote: