ggwr.cv {GWmodel}

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]