ggwr {spgwr}R Documentation

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”:

SDF a 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.
lhat Leung et al. L matrix
lm Ordinary least squares global regression on the same model formula.
bandwidth the bandwidth used.
this.call the function call used.

Note

The use of GWR on GLM is only at the initial proof of concept stage, nothing should be treated as an accepted method at this stage.

Author(s)

Roger Bivand Roger.Bivand@nhh.no

References

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

See Also

ggwr.sel, gwr

Examples

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
## Not run: 
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
## End(Not run)

[Package spgwr version 0.5-7 Index]