Global tests of geographical weighted regressions
Four related test statistics for comparing OLS and GWR models based on bapers by Brunsdon, Fotheringham and Charlton (1999) and Leung et al (2000), and a development from the GWR book (2002).
LMZ.F3GWR.test(go) LMZ.F2GWR.test(x) LMZ.F1GWR.test(x) BFC99.gwr.test(x) BFC02.gwr.test(x, approx=FALSE) ## S3 method for class 'gwr' anova(object, ..., approx=FALSE)
go, x, object |
a |
... |
arguments passed through (unused) |
approx |
default FALSE, if TRUE, use only (n - tr(S)) instead of (n - 2*tr(S) - tr(S'S)) as the GWR degrees of freedom |
The papers in the references give the background for the analyses of variance presented.
BFC99.GWR.test, BFC02.gwr.test, LMZ.F1GWR.test and LMZ.F2GWR.test return "htest" objects, LMZ.F3GWR.test a matrix of test results.
Roger Bivand Roger.Bivand@nhh.no and Danlin Yu
Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2002, Geographically Weighted Regression, Chichester: Wiley; http://gwr.nuim.ie/
data(columbus, package="spData") col.bw <- gwr.sel(CRIME ~ INC + HOVAL, data=columbus, coords=cbind(columbus$X, columbus$Y)) col.gauss <- gwr(CRIME ~ INC + HOVAL, data=columbus, coords=cbind(columbus$X, columbus$Y), bandwidth=col.bw, hatmatrix=TRUE) BFC99.gwr.test(col.gauss) BFC02.gwr.test(col.gauss) BFC02.gwr.test(col.gauss, approx=TRUE) anova(col.gauss) anova(col.gauss, approx=TRUE) ## Not run: col.d <- gwr.sel(CRIME ~ INC + HOVAL, data=columbus, coords=cbind(columbus$X, columbus$Y), gweight=gwr.bisquare) col.bisq <- gwr(CRIME ~ INC + HOVAL, data=columbus, coords=cbind(columbus$X, columbus$Y), bandwidth=col.d, gweight=gwr.bisquare, hatmatrix=TRUE) BFC99.gwr.test(col.bisq) ## End(Not run)
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