This function finds the cross-validation score for a specified bandwidth for basic or robust GWPCA. It can be used to construct the bandwidth function across all possible bandwidths and compared to that found automatically.
gwpca.cv(bw,x,loc,k=2,robust=FALSE,kernel="bisquare",adaptive=FALSE,p=2,
theta=0, longlat=F,dMat)
cross-validation score
bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
the variable matrix
a two-column numeric array of observation coordinates
the number of retained components; k must be less than the number of variables
if TRUE, robust GWPCA will be applied; otherwise basic GWPCA will be applied
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)
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