Hi everyone,
I'm trying to model a binary response using logistic regression for a large data set with spatial autocorrelation issues. The mixed models in SAS and R that can include spatially correlated errors cannot handle the large NxN matrix needed for their methods. Past around 700 meters, spatial autocorrelation is negligible. So, it seems that a sparse matrix with zeros for pairs of observations separated by > 700 meters would help. I've been searching and not found a way to implement this in R using established packages. Any advice on methods to try for a large data set with spatial autocorrelation is welcome. I'm currently reading to see if generalized estimating equations or some of the detrended krigging fuctions may help. If I sample systematically with a 700 m distance, I will lose substantial information and so would like to avoid this. Thanks, Seth _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
