GWmodel (version 2.4-1)

gwss.montecarlo: Monte Carlo (randomisation) test for gwss

Description

This function implements Monte Carlo (randomisation) tests for the GW summary statistics found in gwss.

Usage

gwss.montecarlo(data, vars, kernel = "bisquare", 
                adaptive = FALSE, bw, p = 2, theta = 0, longlat = F, 
                dMat, quantile=FALSE,nsim=99)

Value

test

probability of the test statistics of the GW summary statistics; if p<0.025 or if p>0.975 then the true local summary statistics can be said to be significantly different (at the 0.95 level) to such a local summary statistics found by chance.

Arguments

data

a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp, or a sf object defined in package sf

vars

a vector of variable names to be summarized

bw

bandwidth used in the weighting function

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 calulate the adaptive kernel, and bw correspond to the number of nearest neighbours, default is FALSE.

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

quantile

if TRUE, median, interquartile range, quantile imbalance will be calculated

nsim

default 99, the number of randomisations

Author

Binbin Lu

References

Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.

Brunsdon C, Fotheringham AS, Charlton ME (2002) Geographically weighted summary statistics - a framework for localised exploratory data analysis. Computers, Environment and Urban Systems 26:501-524

Harris P, Brunsdon C (2010) Exploring spatial variation and spatial relationships in a freshwater acidification critical load data set for Great Britain using geographically weighted summary statistics. Computers & Geosciences 36:54-70

Examples

if (FALSE) {
data(LondonHP)
DM<-gw.dist(dp.locat=coordinates(londonhp))
test.lss<-gwss.montecarlo(data=londonhp, vars=c("PURCHASE","FLOORSZ"), bw=5000,
          kernel ="gaussian", dMat=DM,nsim=99)
test.lss
}