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 voil? 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 <yud at mail.montclair.edu>
An: Marco Helbich <marco.helbich at gmx.at>
CC: r-sig-geo at 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,