[R-sig-Geo] mixed geographically weighted regression

[Marco Helbich]

Dear list,

I am trying to fit a mixed geographically weighted regression model (with adaptive kernel) using the spgwr package, i.e. I want to hold some of the coefficients fixed at the global level. Thus, I have the following questions:

1. Which is the most efficient way to estimate such a model? 
a) I found the posting http://www.mail-archive.com/r-sig-geo@stat.math.ethz.ch/msg00984.html where Roger recommended to first fit a global model, then the GWR using the residuals. 
b) The method proposed in Mei et al. (2006,  pp. 588-589, see http://www.envplan.com/abstract.cgi?id=a3768) first computes the projection matrix of the locally varying part (called S_v) and uses this in a second step to derive the fixed coefficients (this seems to me like an application of the FWL-theorem see http://en.wikipedia.org/wiki/FWL_theorem). 

2. In order to follow this method, I first have to find the kernel weights at each point. The help-file says that these can be found in the SpatialPointsDataFrame (SDF), but I could not get it from there. Where can I extract them?

We are using such a code:
library(spgwr)
data(georgia)
g.adapt.gauss <- gwr.sel(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov + PctBlack, data=gSRDF, adapt=TRUE)
res.adpt <- gwr(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov + PctBlack, data=gSRDF, adapt=g.adapt.gauss)
res.adpt$SDF

I hope my problem is clear and appreciate every hint! Thank you!

Best regards
Marco

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