From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Thu 08 Jun 2006 - 14:01:08 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Jun 08 14:07:32 2006

Date: Thu 08 Jun 2006 - 14:01:08 EST

Have you tried RSiteSearch("spatial ecology")? I just got 47 hits from that. Some of them might be relevant to your question.

If that fails, you might consider providing this group with the math behind the automata models you are considering. I might expect them to be expressed in terms of Markov chain (or Markov random field) probability models with parameters to be estimated. The standard statistical approach is to consider a sequence of different models, with at least some of them nested, with increasing numbers of parameters and levels of complexity. We then estimate the parameters to maximize the likelihood (= probability of what was observed given the data). Testing typically assumes that 2*log(likelihood ratio) is approximately chi-square, with additional precision given by simulation if desired.

Hope this helps. Spencer Graves

Ronaldo Reis-Jr. wrote:

*> Hi,
**>
*

> I try to make an analyses to discover what is the time that an area begin to

*> have spacial autocorrelation. And after, what is the number of individuals
**> responsible for this autocorrelation.
**>
**> The main idea is to discover if exist a contamination of a quadrat from others
**> quadrats and how is the population needed to make this contamination.
**>
**> This is very common to use automata to simulate this situation. But I try to
**> make a more statistical approach. I'm studing about, but I dont know the tool
**> for testing examples.
**>
**> I make an example just for tests:
**>
**> Geodata <- data.frame(X=rep(rep(c(1:10),
**> (rep(10,10))),5),Y=rep(c(1:10),50),Abund=c(1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
**> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
**> 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 3, 0, 0, 2, 0, 0, 1, 2, 0, 0,
**> 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0,
**> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 2, 0, 0,
**> 0, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 5,
**> 0, 2, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 2, 3, 0, 0, 3, 0, 0, 3,
**> 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 4, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0,
**> 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 10, 0, 0, 3, 0, 0, 0, 0, 3,
**> 3, 4, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 3, 0,
**> 0, 0, 0, 4, 0, 2, 1, 0, 0, 3, 0, 0, 0, 2, 0, 1, 4, 0, 0, 4, 0, 0, 4, 0, 0, 0,
**> 0, 0, 4, 0, 0, 0, 0, 0, 3, 0, 0, 5, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
**> 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 10, 15, 0, 0, 4, 0, 0, 0, 0, 8, 11, 9, 0,
**> 0, 0, 0, 0, 0, 0, 1, 5, 3, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0,
**> 5, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 1, 0, 5, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0,
**> 0, 0, 0, 0, 0, 4, 0, 0, 6, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
**> 0, 0, 0, 0, 0, 0, 0, 3, 10, 15, 20, 0, 0, 0, 0, 0, 0, 4, 13, 16, 13, 0, 0, 0,
**> 0, 0, 0, 5, 8, 8, 10, 0, 0, 0, 0, 0, 0, 1, 2, 3, 5, 0, 0, 0, 0, 0, 0, 0, 0,
**> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
**> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
**> 0),Time=rep(c(1:5),rep(100,5)))
**>
**> X and Y are coordinates, Abund is the number of individuals and Time is the
**> date of observation. In this example the population grows from an vertice,
**> and after 10 individuals it contaminates your neighbors. I need ideas about
**> the best approach and R's tools for this problem.
**>
**> I'm studing this question in these books:
**>
**> W.N. Venables, B.D. Ripley. 2003. Modern Applied Statistics with S. Springer;
**> 4 edition (September 2, 2003). 512 pages.
**>
**> Crawley, M. J. 2002. Statistical Computing: An Introduction to Data Analysis
**> using S-Plus. John Wiley & Sons; 1st edition (May 15, 2002). 772 pages.
**>
**> Diggle, Peter J. 2003. Statistical Analysis of Spatial Point Patterns (2nd
**> ed.), Arnold, London.
**>
**> Ripley, B.D. Spatial Statistics
**>
**> Spatial Ecology
**>
**> Thanks for all
**>
*

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