View source: R/GeneralizedGWR.r
ggwr.basic | R Documentation |
This function implements generalised GWR
ggwr.basic(formula, data, regression.points, bw, family =
"poisson", kernel = "bisquare", adaptive = FALSE, cv =
T, tol = 1e-05, maxiter = 20, p = 2, theta = 0,
longlat = F, dMat, dMat1)
## S3 method for class 'ggwrm'
print(x, ...)
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.ggwr();fixed (distance) or adaptive bandwidth(number of nearest neighbours) |
family |
a description of the error distribution and link function to be used in the model, which can be specified by “poisson” or “binomial” |
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 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) |
cv |
if TRUE, cross-validation data will be calculated |
tol |
the threshold that determines the convergence of the IRLS procedure |
maxiter |
the maximum number of times to try the IRLS 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 between regression points and observations, it can be calculated by the function |
dMat1 |
a square distance matrix between each pair of observations, it can be calculated by the function |
x |
an object of class “ggwrm”, returned by the function gwr.generalised |
... |
arguments passed through (unused) |
A list of class “ggwrm”:
GW.arguments |
a list class object including the model fitting parameters for generating the report file |
GW.diagnostic |
a list class object including the diagnostic information of the model fitting |
glm.res |
an object of class inheriting from “glm” which inherits from the class “lm”, see glm. |
SDF |
a SpatialPointsDataFrame (may be gridded), or SpatialPolygonsDataFrame object (see package “sp”), or sf object (see package “sf”) integrated with regression.points, GWR coefficient estimates, y value,predicted values, coefficient standard errors and t-values in its "data" slot. |
CV |
a data vector consisting of the cross-validation data |
Note that this function calibrates a Generalised GWR model via an approximating algorithm, which is different from the back-fitting algorithm used in the GWR4 software by Tomoki Nakaya.
Binbin Lu binbinlu@whu.edu.cn
Nakaya, T., A. S. Fotheringham, C. Brunsdon & M. Charlton (2005) Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine, 24, 2695-2717.
Nakaya, T., M. Charlton, S. Fotheringham & C. Brunsdon. 2009. How to use SGWRWIN (GWR4.0). Maynooth, Ireland: National Centre for Geocomputation.
Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.
data(LondonHP)
## Not run:
DM<-gw.dist(dp.locat=coordinates(londonhp))
bw.f1 <- bw.ggwr(BATH2~FLOORSZ,data=londonhp, dMat=DM)
res.poisson<-ggwr.basic(BATH2~FLOORSZ, bw=bw.f1,data=londonhp, dMat=DM)
bw.f2 <- bw.ggwr(BATH2~FLOORSZ,data=londonhp, dMat=DM,family ="binomial")
res.binomial<-ggwr.basic(BATH2~FLOORSZ, bw=bw.f2,data=londonhp, dMat=DM,
family ="binomial")
## End(Not run)
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