spgwr (version 0.5-7)

gwr.sel: Crossvalidation of bandwidth for geographically weighted regression

Description

The function finds a bandwidth for a given geographically weighted regression by optimzing a selected function. For cross-validation, this scores the root mean square prediction error for the geographically weighted regressions, choosing the bandwidth minimizing this quantity.

Usage

gwr.sel(formula, data=list(), coords, adapt=FALSE, gweight=gwr.Gauss,
 method = "cv", verbose = TRUE, longlat=FALSE, RMSE=FALSE, weights)

Arguments

formula

regression model formula as in lm

data

model data frame as in lm, 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

adapt

either TRUE: find the proportion between 0 and 1 of observations to include in weighting scheme (k-nearest neighbours), or FALSE --- find global bandwidth

gweight

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

method

default "cv" for drop-1 cross-validation, or "aic" for AIC optimisation (depends on assumptions about AIC degrees of freedom)

verbose

if TRUE (default), reports the progress of search for bandwidth

longlat

if TRUE, use distances on an ellipse with WGS84 parameters

RMSE

default FALSE to correspond with CV scores in newer references (sum of squared CV errors), if TRUE the previous behaviour of scoring by LOO CV RMSE

weights

case weights used as in weighted least squares, beware of scaling issues --- only used with the cross-validation method, probably unsafe

Value

• returns the cross-validation bandwidth.

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

gwr.bisquare, gwr.gauss

Examples

data(columbus)
gwr.sel(crime ~ income + housing, data=columbus,
  coords=cbind(columbus$x, columbus$y))
gwr.sel(crime ~ income + housing, data=columbus,
  coords=cbind(columbus$x, columbus$y), gweight=gwr.bisquare)