This function finds the individual cross-validation score at each observation location, for a GWPCA model, for a specified bandwidth. These data can be mapped to detect unusually high or low cross-validations scores.
gwpca.cv.contrib(x,loc,bw, k=2,robust=FALSE,kernel="bisquare",adaptive=FALSE,
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 (bw) and component (k).
the variable matrix
a two-column numeric array of observation coordinates
bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
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