ggwr.cv | R Documentation |
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.
ggwr.cv(bw, X, Y,family="poisson", kernel="bisquare",adaptive=F, dp.locat,
p=2, theta=0, longlat=F,dMat)
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 |
CV.score |
cross-validation score |
Binbin Lu binbinlu@whu.edu.cn
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.