[R-sig-Geo] stepwise algorithm for GWR

[Myers, Joshua]

Dear Marco,
	I think Danlin is more experienced with this than myself, but in
my experience I have found that best global OLS model is usually at
least somewhat different than the best GWR model.   I have found that
there is usually a slightly different variable set (at least in the two
datasets that I have been working with).  In my datasets I have also
found that it yields better results to not use a log or square root (or
any other like variable transformation) in the local model, whereas it
might make a difference in a global model.  I am not saying it will be
the same for you, but I am cautioning you to not just take what you see
the global case and apply it blindly to the local GWR case.  

	I have actually thought a lot about what you are suggesting, a
selection algorithm for gwr, but I haven't had the time to play with it
yet.  It can be noted, however, that any such search algorithm will take
a lonnnnggggg time.  It will probably need to be run overnight, unless
you have some kind supercomputing cluster.

-Josh

-----Original Message-----
From: r-sig-geo-bounces at stat.math.ethz.ch
[mailto:r-sig-geo-bounces at stat.math.ethz.ch] On Behalf Of Danlin Yu
Sent: Wednesday, May 13, 2009 10:04 AM
To: Marco Helbich
Cc: r-sig-geo at stat.math.ethz.ch
Subject: 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
> --
>
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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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