[R-sig-Geo] stepwise algorithm for GWR

[Marco Helbich]

Dear Joshua and Danlin,

your remarks are right, but I am not fully convinced. What are the differences between using it in an ols framwork and using it in a GWR one under the same conditions (same bandwith, amount of neighbors...)?
Any further hints?

Thank you and best regards
Marco


-------- Original-Nachricht --------
> Datum: Wed, 13 May 2009 14:32:35 -0400
> Von: "Myers, Joshua" <Joshua.Myers at norfolk.gov>
> An: "Danlin Yu" <yud at mail.montclair.edu>, "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

> Marco,
> 	 I agree with Danlin.  You can use AIC to compare models of the same type
> (i.e. OLS) with different model specifications, or, you can use AIC to
> compare models of different types (SAR, OLS, GWR) but with the same model
> specification.  Alternatively, RMSE can be used to compare models of any type
> together no matter what specification, but there is no penalization for
> number of parameters used.  Oftentimes, we either have a fixed number of
> parameters or we have a good idea which parameters are best or interesting, so we
> are able to cut down on some of the many possible specification options.  
> 
> -Josh
> 
> -----Original Message-----
> From: Danlin Yu [mailto:yud at mail.montclair.edu] 
> Sent: Wednesday, May 13, 2009 2:12 PM
> To: Marco Helbich
> Cc: r-sig-geo at stat.math.ethz.ch; Myers, Joshua
> Subject: Re: [R-sig-Geo] stepwise algorithm for GWR
> 
> Marco:
> 
> That's the point - I don't think such comparison is quite appropriate (I 
> might be wrong) since the model specifications are not the same. You can 
> compare AICs across OLS, SAR, and GWR with the same specification (same 
> set of dependent and independent variables), but it's quite doubtful to 
> compare AICs across any of these with different specifications.
> 
> It really depends upon what's the purpose of your analysis. I assume you 
> were trying to find the best model to fit your data. Maybe using all the 
> models to do a prediction and calculate the RMSE could give you some
> hints?
> 
> Hope this helps.
> 
> Danlin
> 
> Marco Helbich ??:
> > 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 voilà 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,
> >>>       
> >> hatmatrix=TRUE)
> >>     
> >>> col.gauss
> >>> --
> >>>
> >>> _______________________________________________
> >>> R-sig-Geo mailing list
> >>> R-sig-Geo at 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: yud at mail.montclair.edu
> >> webpage: csam.montclair.edu/~yu
> >>     
> >
> >   
> 
> -- 
> ___________________________________________
> 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

--