gwr.bw.est: Cross-validation estimation of kernel bandwidth

In gwrr: Fits Geographically Weighted Regression Models with Diagnostic Tools

Cross-validation estimation of kernel bandwidth

Description

Estimate the kernel function bandwidth with cross-validation

Usage

gwr.bw.est(form, locs, data, kernel = "exp", cv.tol)

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
kernel	A kernel weighting function, either exp or gauss, where exponential function is default
cv.tol	A stopping tolerance in terms of cross-validation error for the bi-section search routine to estimate the kernel bandwidth using cross-validation; if missing an internally calculated value is used

Details

This function estimates the kernel bandwidth in a GWR model with leave-one-out cross-validation. It does not estimate the final regression coefficients or outcome variable.

Value

A list with the following items:

phi	Kernel bandwidth
RMSPE	Root mean squared prediction error from bandwidth estimation
cv.score	Sum of squared prediction errors from bandwidth estimation

Author(s)

David Wheeler

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.est

Examples

data(columbus)
locs <- cbind(columbus$x, columbus$y)
col.bw <- gwr.bw.est(crime ~ income + houseval, locs, columbus, "exp")
col.gwr <- gwr.est(crime ~ income + houseval, locs, columbus, "exp", bw=col.bw$phi)

gwrr documentation built on May 10, 2022, 9:05 a.m.