ggwr: Generalised geographically weighted regression

Description

The function implements generalised geographically weighted regression approach to exploring spatial non-stationarity for given global bandwidth and chosen weighting scheme.

Usage

ggwr(formula, data = list(), coords, bandwidth, gweight = gwr.Gauss,
 adapt = NULL, fit.points, family = gaussian, longlat = FALSE, type = 
c("working", "deviance", "pearson", "response"))

Arguments

formula

regression model formula as in glm

data

model data frame as in glm, or may be a SpatialPointsDataFrame or SpatialPolygonsDataFrame object as defined in package sp

coords

matrix of coordinates of points representing the spatial positions of the observations

bandwidth

bandwidth used in the weighting function, possibly calculated by ggwr.sel

gweight

geographical weighting function, at present gwr.Gauss() default, or gwr.gauss(), the previous default or gwr.bisquare()

adapt

either NULL (default) or a proportion between 0 and 1 of observations to include in weighting scheme (k-nearest neighbours)

fit.points

an object containing the coordinates of fit points; often an object from package sp; if missing, the coordinates given through the data argument object, or the coords argument are used

family

a description of the error distribution and link function to be used in the model, see glm

longlat

if TRUE, use distances on an ellipse with WGS84 parameters

type

the type of residuals which should be returned. The alternatives are: "working" (default), "pearson", "deviance" and "response"

Value

A list of class gwr:
SDFa SpatialPointsDataFrame (may be gridded) or SpatialPolygonsDataFrame object (see package "sp") with fit.points, weights, GWR coefficient estimates, R-squared, and coefficient standard errors in its "data" slot.
lhatLeung et al. L matrix
lmOrdinary least squares global regression on the same model formula.
bandwidththe bandwidth used.
this.callthe function call used.

References

Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2002, Geographically Weighted Regression, Chichester: Wiley; http://www.nuim.ie/ncg/GWR/index.htm

Examples

Run this code

library(maptools)
xx <- readShapePoly(system.file("shapes/sids.shp", package="maptools")[1], 
  IDvar="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))
bw <- ggwr.sel(SID74 ~ I(NWBIR74/BIR74) + offset(log(BIR74)), data=xx,
  family=poisson(), longlat=TRUE)
nc <- ggwr(SID74 ~ I(NWBIR74/BIR74) + offset(log(BIR74)), data=xx,
  family=poisson(), longlat=TRUE, bandwidth=bw)
nc
nc <- ggwr(SID74 ~ I(NWBIR74/10000) + offset(log(BIR74)), data=xx,
  family=poisson(), longlat=TRUE, bandwidth=bw)
nc
nc <- ggwr(SID74 ~ I(NWBIR74/10000) + offset(log(BIR74)), data=xx,
  family=quasipoisson(), longlat=TRUE, bandwidth=bw)
nc