# [R-sig-Geo] stepwise algorithm for GWR

Danlin Yu yud at mail.montclair.edu
Wed May 13 16:04:22 CEST 2009

```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
> --
>
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--
___________________________________________
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: yud at mail.montclair.edu
webpage: csam.montclair.edu/~yu

```

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