A function for automatic bandwidth selection to calibrate a basic or robust GWPCA via a cross-validation approach only
bw.gwpca(data,vars,k=2, robust=FALSE,kernel="bisquare",adaptive=FALSE,p=2,
theta=0, longlat=F,dMat)Returns the adaptive or fixed distance bandwidth
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
a vector of variable names to be evaluated
the number of retained components, and it must be less than the number of variables
if TRUE, robust GWPCA will be applied; otherwise basic GWPCA will be applied
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
if TRUE calculate an adaptive kernel where the bandwidth 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)
the power of the Minkowski distance, default is 2, i.e. the Euclidean distance
an angle in radians to rotate the coordinate system, default is 0
if TRUE, great circle distances will be calculated
a pre-specified distance matrix, it can be calculated by the function gw.dist
Binbin Lu binbinlu@whu.edu.cn
Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2015) Enhancements to a geographically weighted principal components analysis in the context of an application to an environmental data set. Geographical Analysis 47: 146-172