ggwr {spgwr} | R Documentation |
The function implements generalised geographically weighted regression approach to exploring spatial non-stationarity for given global bandwidth and chosen weighting scheme.
ggwr(formula, data = list(), coords, bandwidth, gweight = gwr.Gauss, adapt = NULL, fit.points, family = gaussian, longlat = FALSE, type = c("working", "deviance", "pearson", "response"))
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" |
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. |
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.
Roger Bivand Roger.Bivand@nhh.no
Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2002, Geographically Weighted Regression, Chichester: Wiley; http://www.nuim.ie/ncg/GWR/index.htm
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)