Hi Ben,
Just one more place to look is R (or Gnu Splus). There is a module on cran
for generalized linear mixed models, which is actually a port of the
software (beam) that was used by Clayton and Kaldor 1987 JASA. They use a
hierarchical modeling approach and get the randome effects distributions
using mcmc. You can also use bugs to do this as was mentioned in your
summary.
In your question I was not sure if you wanted to model the functional
relationship between your response and predictors or to predict unobserved
locations. If the former then the glm approach might be the best, if the
latter than the Gottaway and stroup approach might be better. I recall
that articale dealt more with prediction. Another article to look at is
diggle, tawn and moyeed (or some permutation of the names) I think the
article is called Model Based Geostatistics and is in JRSS A or C,
whichever is applied statistics. I don't know if they ever distributed
software for the application, I think the MCMC procedure they used was not
very stable.
So the question is what are the goals of this analysis and the methods
will follow.
Nicholas
CH3
|
N Nicholas Lewin-Koh
/ \ Dept of Statistics
N----C C==O Program in Ecology and Evolutionary Biology
|| || | Iowa State University
|| || | Ames, IA 50011
CH C N--CH3 http://www.public.iastate.edu/~nlewin
\ / \ / [EMAIL PROTECTED]
N C
| || Currently
CH3 O Graphics Lab
School of Computing
National University of Singapore
The Real Part of Coffee [EMAIL PROTECTED]
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