gwpca.cv {GWmodel}

Cross-validation score for a specified bandwidth for GWPCA

Description

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.

Usage

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

Arguments

bw

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

x

the variable matrix

loc

a two-column numeric array of observation coordinates

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

Value

CV.score

cross-validation score

Author(s)

Binbin Lu [e-mail]