bw.ggwr: Bandwidth selection for generalised geographically weighted...

In GWmodel: Geographically-Weighted Models

Bandwidth selection for generalised geographically weighted regression (GWR)

Description

A function for automatic bandwidth selection to calibrate a generalised GWR model

Usage

bw.ggwr(formula, data, family ="poisson", approach="CV",
kernel="bisquare",adaptive=FALSE, p=2, theta=0, longlat=F,dMat)

Arguments

formula	Regression model formula of a formula object
data	a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
family	a description of the error distribution and link function to be used in the model, which can be specified by “poisson” or “binomial”
approach	specified by CV for cross-validation approach or by AIC corrected (AICc) approach
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 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

Returns the adaptive or fixed distance bandwidth

Note

For a discontinuous kernel function, a bandwidth can be specified either as a fixed (constant) distance or as a fixed (constant) number of local data (i.e. an adaptive distance). For a continuous kernel function, a bandwidth can be specified either as a fixed distance or as a 'fixed quantity that reflects local sample size' (i.e. still an 'adaptive' distance but the actual local sample size will be the sample size as functions are continuous). In practise a fixed bandwidth suits fairly regular sample configurations whilst an adaptive bandwidth suits highly irregular sample configurations. Adaptive bandwidths ensure sufficient (and constant) local information for each local calibration. This note is applicable to all GW models

Author(s)

Binbin Lu [e-mail]

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