Dear list! I am doing some geographically weighted regression and I am intersted in the most suitable model (the one with the lowest AIC). Because there is no stepwise algorithm, I am trying to write a "brute force" function, which uses all possible variable combination, applies the gwr and returns the AIC value with the used variable combination in a dataframe. For instance the model below: gwr1: crime ~ income, gwr2: crime ~ housing, gwr3: crime ~ var1, gwr4: crime ~ income + housing, ...
I hope my problem is clear and appreciate every hint! Thank you! All the best Marco library(spgwr) data(columbus) columbus[,"var1"] <- rnorm(length(columbus[,1])) col.bw <- gwr.sel(crime ~ income + housing + var1, data=columbus, coords=cbind(columbus$x, columbus$y)) col.gauss <- gwr(crime ~ income + housing + var1, data=columbus, coords=cbind(columbus$x, columbus$y), bandwidth=col.bw, hatmatrix=TRUE) col.gauss -- _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo