This function finds the individual cross-validation score at each observation location, for a generalised GWR model, for a specified bandwidth. These data can be mapped to detect unusually high or low cross-validations scores.
ggwr.cv.contrib(bw, X, Y,family="poisson", kernel="bisquare",adaptive=F,
dp.locat, p=2, theta=0, longlat=F,dMat)
a data vector consisting of squared residuals, whose sum is the cross-validation score for the specified bandwidth
bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
a numeric matrix of the independent data with an extra column of “ones” for the 1st column
a column vector of the dependent data
a description of the error distribution and link function to be used in the model, which can be specified by “poisson” or “binomial”
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
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)
a two-column numeric array of observation coordinates
the power of the Minkowski distance, default is 2, i.e. the Euclidean distance
an angle in radians to rotate the coordinate system, default is 0
if TRUE, great circle distances will be calculated
a pre-specified distance matrix, it can be calculated by the function gw.dist
Binbin Lu binbinlu@whu.edu.cn