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GWmodel (version 2.4-1)

gwpca.cv.contrib: Cross-validation data at each observation location for a GWPCA

Description

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.

Usage

gwpca.cv.contrib(x,loc,bw, k=2,robust=FALSE,kernel="bisquare",adaptive=FALSE,
                 p=2, theta=0, longlat=F,dMat)

Value

CV

a data vector consisting of squared residuals, whose sum is the cross-validation score for the specified bandwidth (bw) and component (k).

Arguments

x

the variable matrix

loc

a two-column numeric array of observation coordinates

bw

bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours)

k

the number of retained components; k must be less than the number of variables

robust

if TRUE, robust GWPCA will be applied; otherwise basic GWPCA will be applied

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)

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

Author

Binbin Lu binbinlu@whu.edu.cn