gwpca.montecarlo.2 | R Documentation |
This function implements a Monte Carlo (randomisation) test for a basic or robust GW PCA with the bandwidth automatically re-selected via the cross-validation approach. The test evaluates whether the GW eigenvalues vary significantly across space for the first component only.
gwpca.montecarlo.2(data, vars, k = 2, nsims=99,robust = FALSE, scaling=T,
kernel = "bisquare", adaptive = FALSE, p = 2,
theta = 0, longlat = F, dMat)
data |
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp |
vars |
a vector of variable names to be evaluated |
k |
the number of retained components; k must be less than the number of variables |
nsims |
the number of simulations for MontCarlo test |
robust |
if TRUE, robust GWPCA will be applied; otherwise basic GWPCA will be applied |
scaling |
if TRUE, the data is scaled to have zero mean and unit variance (standardized); otherwise the data is centered but not scaled |
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) |
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 |
A list of components:
actual |
the observed standard deviations (SD) of eigenvalues |
sims |
a vector of the simulated SDs of eigenvalues |
The function “montecarlo.gwpca.2” (in the early versions of GWmodel) has been renamed as “gwpca.montecarlo.2”, while the old name is still kept valid.
Binbin Lu binbinlu@whu.edu.cn
Harris P, Brunsdon C, Charlton M (2011) Geographically weighted principal components analysis. International Journal of Geographical Information Science 25:1717-1736
## Not run:
data(DubVoter)
DM<-gw.dist(dp.locat=coordinates(Dub.voter))
gmc.res.autow<-gwpca.montecarlo.2(data=Dub.voter, vars=c("DiffAdd", "LARent",
"SC1", "Unempl", "LowEduc"), dMat=DM,adaptive=TRUE)
gmc.res.autow
plot.mcsims(gmc.res.autow)
## End(Not run)
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