AI-GEOSTATS: Summary of Autologistic Regression for post-fire forest study
- Dear all,
Thanks to everybody for your input. Here is a summary of these suggestions:
Carlos Carrol and Nicholas Kewin-Koh both suggested:
1) an Splus function by Fred Huffer (Fred was very helpful and was able to
tweak the program to run in R)
2) an Splus function by Jennifer Hoeting
Both of these programs can be found at their websites:
Carlos also suggested GeoBUGS software.
Brian Gray suggested several references and an excerpt from his reply
Gotway, C.A. and W.W. Stroup. 1997. A generalized linear model approach to
spatial data analysis and prediction. Journal of Agricultural,
Biological, and Environmental Statistics 2: 157-178.
Gumpertz, M.L., C. Wu and J.M. Pye. 2000. Logistic regression for Southern
Pine Beetle outbreaks with spatial and temporal correlation. Forest Science
Wolfinger, R. 1993. Covariance structure selection in general mixed models.
Communications in Statistics–Simulations 22: 1079-1106.
Wolfinger, R. and M. O'Connell. 1993. Generalized Linear Mixed Models: A
Pseudo- Likelihood Approach. Journal of Statistical Computation and
Simulation 48: 233- 243.
Hoeting, Leecaster and Bowden 2000 (JABES)
Gumpertz et al. 1997 (JABES)
"Without lat/long info, you could try using a robust variance estimate such
as is offered by SAS's PROC GENMOD to model your data. I suspect that this
will be reasonable but somewhat inefficient.
If you have spatial coordinates or can make some inferences on those
coordinates, then you can use SAS's GLIMMIX macro (I posted on this topic
earlier). This macro repeatedly calls PROC MIXED and can use all or
virtually all of the options/statements available under that proc. These
statements include the ability to treat transect as a random variable (also
possible in GENMOD) and to explicitly model spatial correlation (not
possible in GENMOD). However, GLIMMIX does so within a generalized linear
model framework--in other words, members of the exponential family of
distributions, including the binomial, may be modeled."
Thanks again for the input,
Environmental Biology and Ecology
Dept. Biological Sciences
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