gwer (version 3.0)

gwer.montecarlo: Monte Carlo (randomisation) Test for Significance of GWER Parameter Variability

Description

This function implements a Monte Carlo (randomisation) test to test for significant (spatial) variability of a geographically weighted elliptical regression model's parameters or coefficients.

Usage

gwer.montecarlo(
  formula,
  family = Normal,
  data = list(),
  nsims = 99,
  kernel = "bisquare",
  adaptive = F,
  bw,
  p = 2,
  theta = 0,
  dispersion = NULL,
  longlat = F,
  dMat,
  control = glm.control(epsilon = 1e-04, maxit = 100, trace = F)
)

Arguments

formula

regression model formula of a formula object.

family

a description of the error distribution to be used in the model (see family.elliptical for details of elliptical distribution).

data

an optional data frame, list or environment containing the variables in the model.

nsims

the number of randomisations.

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).

bw

value of the selected bandwidth used in the weighting function (see bw.gwer for bandwidth optimization).

p

the power of the Minkowski distance, default is 2 (Euclidean distance).

theta

an angle in radians to rotate the coordinate system, default is 0

dispersion

an optional fixed value for dispersion parameter.

longlat

if TRUE, great circle distances will be calculated.

dMat

a pre-specified distance matrix, it can be calculated by the function gw.dist.

control

a list of parameters for controlling the fitting process. This is passed by glm.control.

Value

A vector containing p-values for all parameters spatial variability tests

References

Brunsdon C, Fotheringham AS, Charlton ME (1998) Geographically weighted regression - modelling spatial non-stationarity. Journal of the Royal Statistical Society, Series D-The Statistician 47(3):431-443

See Also

bw.gwer, elliptical, family.elliptical

Examples

# NOT RUN {
data(georgia, package = "spgwr")
fit.formula <- PctBach ~ TotPop90 + PctRural + PctFB + PctPov
gwer.bw.t <- bw.gwer(fit.formula, data = gSRDF, family = Student(4), adapt = TRUE)
gwer.fit.t <- gwer(fit.formula, data = gSRDF, family = Student(4), bandwidth = gwer.bw.t, 
                   adapt = TRUE, parplot = FALSE, hatmatrix = TRUE, spdisp = TRUE, 
                   method = "gwer.fit")
gwer.montecarlo(fit.formula, data = gSRDF, family = Student(3), bw = gwer.bw.t, adaptive = TRUE) 
# }