gwr.collin.diagno: Local collinearity diagnostics for basic GWR

In GWmodel: Geographically-Weighted Models

Local collinearity diagnostics for basic GWR

Description

This function provides a series of local collinearity diagnostics for the independent variables of a basic GWR model.

Usage

gwr.collin.diagno(formula, data, bw, 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, or a sf object defined in package sf
bw	bandwidth used in the weighting function, probably calculated by bw.gwr or bw.gwr.lcr; fixed (distance) or adaptive bandwidth (number of nearest neighbours)
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

corr.mat	Local correlation matrix
VIF	Local Variance inflation factors (VIFs) matrix
local_CN	Local condition numbers
VDP	Local variance-decomposition proportions
SDF	a SpatialPointsDataFrame (may be gridded), or SpatialPolygonsDataFrame object (see package “sp”), or sf object (see package “sf”) integrated with VIF, local_CN, VDP and corr.mat

Author(s)

Binbin Lu [e-mail]

References

Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems 7:161-187

Wheeler D (2007) Diagnostic tools and a remedial method for collinearity in geographically weighted regression. Environment and Planning A 39:2464-2481

Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2015) GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models. Journal of Statistical Software, 63(17):1-50

GWmodel documentation built on Sept. 11, 2024, 9:09 p.m.