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Last data update: 2014.03.03

R: GWR used as a spatial predictor

gwr.predict

R Documentation

GWR used as a spatial predictor

Description

This function implements basic GWR as a spatial predictor. The GWR prediction function is able to do leave-out-one predictions (when the observation locations are used for prediction) and predictions at a set-aside data set(when the new locations are used for prediction). It is also able to reproduce the global OLS regression prediction results.

Usage

gwr.predict(formula, data, predictdata, bw, kernel="bisquare",adaptive=FALSE, p=2,
           theta=0, longlat=F,dMat1, dMat2)

Arguments

`formula`	Regression model formula of a formula object
`data`	a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
`predictdata`	a Spatial*DataFrame object to provide prediction locations, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
`bw`	bandwidth used in the weighting function, possibly calculated by bw.gwr;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
`kernel`	function chosen as follows: gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw); bisquare: wgt = (1-(vdist/bw)^2)^2 if vdist < bw, wgt=0 otherwise; tricube: wgt = (1-(vdist/bw)^3)^3 if vdist < bw, wgt=0 otherwise; boxcar: wgt=1 if dist < bw, wgt=0 otherwise
`adaptive`	if TRUE calculate an adaptive kernel where the bandwidth (bw) corresponds to the number of nearest neighbours (i.e. adaptive distance); default is FALSE, where a fixed kernel is found (bandwidth is a fixed distance)
`p`	the power of the Minkowski distance, default is 2, i.e. the Euclidean distance
`theta`	an angle in radians to rotate the coordinate system, default is 0
`longlat`	if TRUE, great circle distances will be calculated
`dMat1`	a pre-specified distance matrix between data points and prediction locations; if not given, it will be calculated by the given parameters
`dMat2`	a pre-specified sysmetric distance matrix between data points; if not given, it will be calculated by the given parameters

Value

A list of class “gwrm.pred”:

`GW.arguments`	a list of geographically weighted arguments
`SDF`	a SpatialPointsDataFrame (may be gridded) or SpatialPolygonsDataFrame object (see package "sp") with GWR coefficients, predictions and prediction variances in its "data" slot.
`this.call`	the function call used.

Author(s)

Binbin Lu binbinlu@whu.edu.cn

References

Harris P, Fotheringham AS, Crespo R, Charlton M (2010) The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences 42:657-680

Harris P, Juggins S (2011) Estimating freshwater critical load exceedance data for Great Britain using space-varying relationship models. Mathematical Geosciences 43: 265-292

Harris P, Brunsdon C, Fotheringham AS (2011) Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic Environmental Research and Risk Assessment 25:123-138

Examples

## Not run: 
data(LondonHP)
gwr.pred<-gwr.predict(PURCHASE~FLOORSZ, data=londonhp, bw=2000,kernel = "gaussian")
gwr.pred
#########Global regression  Compare with gstat
if(require("gstat"))
{
  mlr.g <- gstat(id = "xx1", formula = PURCHASE~FLOORSZ,data=londonhp)
  mlr.g1 <- predict(mlr.g, newdata = londonhp, BLUE = TRUE)
  mlr.g1
}
############
ols.pred<-gwr.predict(PURCHASE~FLOORSZ, data=londonhp, bw=100000000000000000000000)
ols.pred$SDF

## End(Not run)