These functions implement heuristics to select the values of p from two intervals: (0, 2] in a 'backward' direction and (2, Inf) in a 'forward' direction.
gwr.mink.pval(formula, data, criterion="AIC", bw, bw.sel.approach = "AIC",
adaptive=F, kernel="bisquare", left.interval=0.25,
right.interval=0.5,drop.tol=3, theta0=0,verbose=F,nlower = 10)
gwr.mink.pval.forward(formula, data, bw, bw.sel.approach = "AIC",
adaptive=F, kernel="bisquare", p.max=Inf,p.min=2,
interval=0.5,drop.tol=3, theta0=0,verbose=F,nlower = 10)
gwr.mink.pval.backward(formula, data, bw, bw.sel.approach = "AIC",
adaptive=F, kernel="bisquare", p.max=2,p.min=0.1,
interval=0.5,drop.tol=3, theta0=0,verbose=F,nlower = 10)
# S3 method for pvlas
plot(x, ...)
A list of:
a vector of tried values of p
a vector of criterion values (AICc or CV) for tried values of p
a vector of boolean to label whether a value of p to be dropped or not: TRUE means to be dropped and FALSE means to be used for the Minkowski approach
Regression model formula of a formula object
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
the criterion used for distance metric selection, AICc ("AICc") or cross-validation ("CV") score; default is "AICc"
bandwidth used in the weighting function, possibly calculated by bw.gwr;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
approach used to seclect an optimum bandwidth for each calibration if no bandwidth (bw) is given; specified by CV for cross-validation approach or by AIC corrected (AICc) approach
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)
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
the step-size for searching the left interval (0, 2] in a 'backward' direction
the step-size for searching the right interval (2, Inf) in a 'forward' direction
the maximum value of p
the minimum value of p
the step-size for searching the given interval in a 'backward' or 'forward' direction
an AICc difference threshold to define whether the values of p to be dropped or not
a fixed rotation angle in radians
if TRUE and bandwidth selection is undertaken, the bandwidth searches are reported
the minmum number of nearest neighbours if an adaptive kernel is used
an object of class “pvlas”, returned by these functions
arguments passed through (unused)
Binbin Lu binbinlu@whu.edu.cn
Lu, B, Charlton, M, Brunsdon, C & Harris, P(2016). The Minkowski approach for choosing the distance metric in Geographically Weighted Regression. International Journal of Geographical Information Science, 30(2): 351-368.