ggwr.cv.contrib: Cross-validation data at each observation location for a...
In GWmodel: Geographically-Weighted Models

ggwr.cv.contrib

R Documentation

Cross-validation data at each observation location for a generalised GWR model

Description

This function finds the individual cross-validation score at each observation location, for a generalised GWR model, for a specified bandwidth. These data can be mapped to detect unusually high or low cross-validations scores.

Usage

ggwr.cv.contrib(bw, X, Y,family="poisson", kernel="bisquare",adaptive=F, 
                dp.locat, 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`	a numeric matrix of the independent data with an extra column of “ones” for the 1st column
`Y`	a column vector of the dependent data
`family`	a description of the error distribution and link function to be used in the model, which can be specified by “poisson” or “binomial”
`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)
`dp.locat`	a two-column numeric array of observation coordinates
`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`