gwr.scalable: Scalable GWR

In GWmodel: Geographically-Weighted Models

Scalable GWR

Description

This function implements Scalable GWR for large dataset

Usage

gwr.scalable(formula, data, bw.adapt=100, kernel = "gaussian", polynomial = 4, 
             p = 2, theta = 0, longlat = F, dMat)
## S3 method for class 'scgwrm'
print(x, ...)

Arguments

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
bw.adapt	adaptive bandwidth (i.e. number of nearest neighbours) used for geographically weighting
kernel	Kernel function to calculate the spatial weights, but note only two continuous functions available: gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw);
polynomial	Degree of the polyunomial to approximate the kernel function, and default is 4.
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 gw.dist
x	an object of class “scgwrm”, returned by the function gwr.scalable
...	arguments passed through (unused)

Value

A list of class “scgwrm”:

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
lm	an object of class inheriting from “lm”, see lm.
SDF	a SpatialPointsDataFrame (may be gridded), or SpatialPolygonsDataFrame object (see package “sp”), or sf object (see package “sf”) integrated with fit.points,GWR coefficient estimates, y value,predicted values, coefficient standard errors and t-values in its "data" slot.
timings	starting and ending time.

Author(s)

Binbin Lu [e-mail]

References

Murakami, D., N. Tsutsumida, T. Yoshida, T. Nakaya & B. Lu (2019) Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels. arXiv:1905.00266.

Examples

## Not run: 
require(spData)
data(boston)
boston <- boston.c
coordinates(boston) <- ~ LON + LAT
res <- gwr.scalable(formula = MEDV ~ CRIM + ZN + INDUS + CHAS + AGE, data = boston, bw.adapt = 100)
res

## End(Not run)

GWmodel documentation built on Sept. 11, 2024, 9:09 p.m.