gwr.mink.pval | R Documentation |
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 class 'pvlas'
plot(x, ...)
formula |
Regression model formula of a formula object |
data |
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp |
criterion |
the criterion used for distance metric selection, AICc ("AICc") or cross-validation ("CV") score; default is "AICc" |
bw |
bandwidth used in the weighting function, possibly calculated by bw.gwr;fixed (distance) or adaptive bandwidth(number of nearest neighbours) |
bw.sel.approach |
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 |
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) |
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 |
left.interval |
the step-size for searching the left interval (0, 2] in a 'backward' direction |
right.interval |
the step-size for searching the right interval (2, Inf) in a 'forward' direction |
p.max |
the maximum value of p |
p.min |
the minimum value of p |
interval |
the step-size for searching the given interval in a 'backward' or 'forward' direction |
drop.tol |
an AICc difference threshold to define whether the values of p to be dropped or not |
theta0 |
a fixed rotation angle in radians |
verbose |
if TRUE and bandwidth selection is undertaken, the bandwidth searches are reported |
nlower |
the minmum number of nearest neighbours if an adaptive kernel is used |
x |
an object of class “pvlas”, returned by these functions |
... |
arguments passed through (unused) |
A list of:
p.vals |
a vector of tried values of p |
cretion.vals |
a vector of criterion values (AICc or CV) for tried values of p |
p.dropped |
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 |
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.
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