ggwr.cv: Cross-validation score for a specified bandwidth for...

In GWmodel: Geographically-Weighted Models

Cross-validation score for a specified bandwidth for generalised GWR

Description

This function finds the cross-validation score for a specified bandwidth for generalised GWR. It can be used to construct the bandwidth function across all possible bandwidths and compared to that found automatically.

Usage

ggwr.cv(bw, X, Y,family="poisson", kernel="bisquare",adaptive=F, dp.locat,  
        p=2, theta=0, longlat=F,dMat)

Arguments

bw	bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
X	a numeric matrix of the independent data with an extra column of “ones” for the 1st column
Y	a column vector of the dependent data
family	a description of the error distribution and link function to be used in the model, which can be specified by “poisson” or “binomial”
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)
dp.locat	a two-column numeric array of observation coordinates
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
dMat	a pre-specified distance matrix, it can be calculated by the function gw.dist

Value

CV.score

cross-validation score

Author(s)

Binbin Lu [e-mail]

GWmodel documentation built on July 9, 2023, 5:52 p.m.