gwpca.cv: Cross-validation score for a specified bandwidth for GWPCA
In GWmodel: Geographically-Weighted Models

gwpca.cv

R Documentation

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`