spgwr: ggwr – R documentation

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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 = NULL, 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`	TRUE if point coordinates are longitude-latitude decimal degrees, in which case distances are measured in kilometers; if x is a SpatialPoints object, the value is taken from the object itself
`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, dispersion if a "quasi"-family is used, and the residuals of type "type" in its "data" slot.
`lhat`	Leung et al. L matrix, here set to NA
`lm`	GLM 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://gwr.nuim.ie/

Examples

if (require(rgdal)) {
xx <- readOGR(system.file("shapes/sids.shp", package="spData")[1])
bw <- 144.4813
## Not run: 
bw <- ggwr.sel(SID74 ~ I(NWBIR74/BIR74) + offset(log(BIR74)), data=xx,
  family=poisson(), longlat=TRUE)

## End(Not run)
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)
}

spgwr

Geographically Weighted Regression

v0.6-34

GPL (>= 2)

Authors

Roger Bivand [cre, aut] (<https://orcid.org/0000-0003-2392-6140>), Danlin Yu [aut], Tomoki Nakaya [ctb], Miquel-Angel Garcia-Lopez [ctb]

Initial release

2020-08-25