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

In GWmodel: Geographically-Weighted Models

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) 

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

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.

Note

The function “montecarlo.gwss” (in the early versions of GWmodel) has been renamed as “gwss.montecarlo”, while the old name is still kept valid.

Author(s)

Binbin Lu [e-mail]

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

## Not run: 
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

## End(Not run)

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