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AI-GEOSTATS: Summary of Autologistic Regression for post-fire forest study

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  • Agnes Wong
    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
    Message 1 of 1 , May 21, 2001
      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:
      http://stat.fsu.edu/~huffer/
      www.stat.colostate.edu/~jah

      Carlos also suggested GeoBUGS software.


      Brian Gray suggested several references and an excerpt from his reply
      follows:

      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
      46: 95-107.
      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,

      Agnes


      ====================================
      Agnes Wong
      M.Sc. Candidate
      Environmental Biology and Ecology
      Dept. Biological Sciences
      Edmonton, AB
      agnesw@...
      ====================================


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