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 -- Jetzt kostenlos herunterladen: Internet Explorer 8 und Mozilla Firefox 3.5 - sicherer, schneller und einfacher! http://portal.gmx.net/de/go/atbrowser _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
