Dear Danlin and Joshua, first of all thank you for your replies! Here some further notes for clarification: I have already estimated a global ols model (based on stepwise model selection) and because of some spatial effects I recalculated it as simultaneous autoregressive model. After that I tested this model for non-stationarity... and voilĂ there is one. Now I want to compare this one with the one offering the lowest aic.
All the best Marco -------- Original-Nachricht -------- > Datum: Wed, 13 May 2009 10:04:22 -0400 > Von: Danlin Yu <y...@mail.montclair.edu> > An: Marco Helbich <marco.helb...@gmx.at> > CC: r-sig-geo@stat.math.ethz.ch > Betreff: Re: [R-sig-Geo] stepwise algorithm for GWR > Dear Marco: > > Before doing so, you'll have to ask yourself that whether all those AICs > are comparable among different model specifications. As a matter of > fact, I believe it might be more plausible if you stepwise it first as a > global model (OLS, after all, global models are an "averaged" view of > the local models), and then work with the selected specification. > > Hope this helps, > > Danlin > > Marco Helbich ??: > > 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 > > > > -- > ___________________________________________ > Danlin Yu, Ph.D. > Assistant Professor of GIS and Urban Geography > Department of Earth & Environmental Studies > Montclair State University > Montclair, NJ, 07043 > Tel: 973-655-4313 > Fax: 973-655-4072 > email: y...@mail.montclair.edu > webpage: csam.montclair.edu/~yu -- _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo