gwr.hetero | R Documentation |
This function implements a heteroskedastic GWR model
gwr.hetero(formula, data, regression.points, bw, kernel="bisquare",
adaptive=FALSE, tol=0.0001,maxiter=50,verbose=T,
p=2, theta=0, longlat=F,dMat)
formula |
Regression model formula of a formula object |
data |
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp, or a sf object defined in package sf |
regression.points |
a Spatial*DataFrame object, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp |
bw |
bandwidth used in the weighting function, possibly calculated by bw.gwr;fixed (distance) or adaptive bandwidth(number of nearest neighbours) |
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) |
tol |
the threshold that determines the convergence of the iterative procedure |
maxiter |
the maximum number of times to try the iterative procedure |
verbose |
logical, if TRUE verbose output will be made from the iterative procedure |
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 |
SDF |
a SpatialPointsDataFrame (may be gridded), or SpatialPolygonsDataFrame object (see package “sp”), or sf object (see package “sf”) integrated with coefficient estimates in its "data" slot. |
Binbin Lu binbinlu@whu.edu.cn
Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.
Harris P, Fotheringham AS, Juggins S (2010) Robust geographically weighed regression: a technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes. Annals of the Association of American Geographers 100(2): 286-306
Harris P, Brunsdon C, Fotheringham AS (2011) Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic Environmental Research and Risk Assessment 25:123-138
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