gwrr (version 0.2-2)

gwr.vdp: Collinearity diagnostics for geographically weighted regression

Description

Uses the collinearity diagnostic tools of variance-decomposition proportions and condition indexes for geographically weighted regression (GWR) models.

Usage

gwr.vdp(form, locs, data, phi, kernel = "exp", sel.ci = 30, sel.vdp = 0.5)

Arguments

form

A regression model forumula, as in the functions lm and glm

locs

A matrix of spatial coordinates of data points, where the x coordinate is first, then the y coordinate; coordinates are assumed to not be latitude and longitude, as Euclidean distance is calculated from coordinates

data

A data frame with data to fit model

phi

The kernel bandwidth used in the GWR model

kernel

The kernel weighting function used in the GWR model, either exp or gauss; exp is the default

sel.ci

The threshold value to use for the condition index to indicate observations with a collinearity issue; indexes above this value will be flagged; the default is 30

sel.vdp

The threshold value to use for the variance-decomposition proportion to indicate observations with a collinearity issue; proportions above this value will be flagged; the default is 0.5

Value

A list with the following items:

condition

Largest condition index for each observation

vdp

Variance-decomposition proportions for the largest variance component for each observation

flag.cond

True if largest condition index exceeds threshold

flag.vdp

True if variance-decomposition proportions for more than one term exceed threshold

flag.cond.vdp

True if condition index and variance-decompostion proportions exceed thresholds

Details

This function calculates the variance-decomposition proportions and the condition indexes for the weighted design matrix used in a GWR model. The kernel function and bandwidth used to estimate the GWR model must be input to this function. Observations with a large condition index and relatively large variance-decomposition proportions for more than one regression term indicate an issue with collinearity.

References

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

See Also

gwr.bw.est

Examples

# NOT RUN {
data(columbus)
locs <- cbind(columbus$x, columbus$y)
col.bw <- gwr.bw.est(crime ~ income + houseval, locs, columbus, "exp")
col.vdp <- gwr.vdp(crime ~ income + houseval, locs, columbus, col.bw$phi, "exp")
hist(col.vdp$condition)
# }