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

In GWmodel: Geographically-Weighted Models

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)

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

Value

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

Author(s)

Binbin Lu [e-mail]

GWmodel documentation built on July 9, 2023, 5:52 p.m.