A function for automatic bandwidth selection for GW Discriminant Analysis using a cross-validation approach only
bw.gwda(formula, data, COV.gw = T, prior.gw = T, mean.gw = T,
prior = NULL, wqda = F, kernel = "bisquare", adaptive
= FALSE, p = 2, theta = 0, longlat = F,dMat)Returns the adaptive or fixed distance bandwidth.
Model formula of a formula object
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp, or a sf object defined in package sf
if true, localised variance-covariance matrix is used for GW discriminant analysis; otherwise, global variance-covariance matrix is used
if true, localised mean is used for GW discriminant analysis; otherwise, global mean is used
if true, localised prior probability is used for GW discriminant analysis; otherwise, fixed prior probability is used
a vector of given prior probability
if TRUE, a weighted quadratic discriminant analysis will be applied; otherwise a weighted linear discriminant analysis 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 (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)
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