Charlotte Bell <charlotte.bell <at> sheffield.ac.uk> writes: > > Hi, > > I have spatially autocorrelated data (with a binary response variable and > continuous predictor variables). I believe I need to do an autologistic > model, does anyone know a method for doing this in R?
There are several approaches that you could try. One direct spatial approach is the off-CRAN Rcitrus package: http://www.leg.ufpr.br/Rcitrus/ which although the documentation is in Portuguese, should get you most of the way there. You could also look at geoRglm on CRAN, which handles a similar setting in a geostatistical way. You may also find it helpful to look at the handling of spatial autocorrelation in the nlme package in a GLMM context, using the CorSpatial approach. If you like, you could also look at a GAMM approach in mgcv. The glmmBUGS package can be used for preparing a GLMM for running in *BUGS if the spatial autocorrelation is expressed through a spatial weights matrix rather than as a function of distance. Hope this helps, Roger Bivand. PS. RSiteSearch on autologistic does find: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/147538.html which is a posting by Elias Krainski on R-sig-geo, where a further link is given for a forthcoming stLattice package. > > Many thanks > > C Bell > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
