spgwr (version 0.6-31)

LMZ.F3GWR.test: Global tests of geographical weighted regressions

Description

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).

Usage

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 gwr
anova(object, …, approx=FALSE)

Arguments

go, x, object

a gwr object returned by gwr()

…

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

Value

BFC99.GWR.test, BFC02.gwr.test, LMZ.F1GWR.test and LMZ.F2GWR.test return "htest" objects, LMZ.F3GWR.test a matrix of test results.

Details

The papers in the references give the background for the analyses of variance presented.

References

Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2002, Geographically Weighted Regression, Chichester: Wiley; http://gwr.nuim.ie/

See Also

gwr

Examples

# NOT RUN {
data(columbus)
col.bw <- gwr.sel(crime ~ income + housing, data=columbus,
  coords=cbind(columbus$x, columbus$y))
col.gauss <- gwr(crime ~ income + housing, 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 ~ income + housing, data=columbus,
  coords=cbind(columbus$x, columbus$y), gweight=gwr.bisquare)
col.bisq <- gwr(crime ~ income + housing, data=columbus,
  coords=cbind(columbus$x, columbus$y), bandwidth=col.d, 
  gweight=gwr.bisquare, hatmatrix=TRUE)
BFC99.gwr.test(col.bisq)
# }