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GWmodel (version 2.2-9)

Geographically-Weighted Models

Description

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms.

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Install

install.packages('GWmodel')

Monthly Downloads

1,962

Version

2.2-9

License

GPL (>= 2)

Maintainer

Last Published

June 17th, 2022

Functions in GWmodel (2.2-9)

Georgia

Georgia census data set (csv file)
USelect

Results of the 2004 US presidential election at the county level (SpatialPolygonsDataFrame)
GWmodel-package

Geographically-Weighted Models
GeorgiaCounties

Georgia counties data (SpatialPolygonsDataFrame)
bw.ggwr

Bandwidth selection for generalised geographically weighted regression (GWR)
LondonBorough

London boroughs data
LondonHP

London house price data set (SpatialPointsDataFrame)
EWHP

House price data set (DataFrame) in England and Wales
EWOutline

Outline of England and Wales for data EWHP
DubVoter

Voter turnout data in Greater Dublin(SpatialPolygonsDataFrame)
bw.gwss.average

Bandwidth selection for GW summary averages
bw.gtwr

Bandwidth selection for GTWR
bw.gwr.lcr

Bandwidth selection for locally compensated ridge GWR (GWR-LCR)
ggwr.basic

Generalised GWR models with Poisson and Binomial options
ggwr.cv

Cross-validation score for a specified bandwidth for generalised GWR
gwda

GW Discriminant Analysis
gw.weight

Weight matrix calculation
gwpca.cv

Cross-validation score for a specified bandwidth for GWPCA
ggwr.cv.contrib

Cross-validation data at each observation location for a generalised GWR model
gwpca.glyph.plot

Multivariate glyph plots of GWPCA loadings
gwpca.montecarlo.1

Monte Carlo (randomisation) test for significance of GWPCA eigenvalue variability for the first component only - option 1
bw.gwr

Bandwidth selection for basic GWR
gwpca.cv.contrib

Cross-validation data at each observation location for a GWPCA
gtwr

Geographically and Temporally Weighted Regression
bw.gwpca

Bandwidth selection for Geographically Weighted Principal Components Analysis (GWPCA)
gwr.hetero

Heteroskedastic GWR
bw.gwda

Bandwidth selection for GW Discriminant Analysis
gwpca.montecarlo.2

Monte Carlo (randomisation) test for significance of GWPCA eigenvalue variability for the first component only - option 2
gwr.lcr.cv

Cross-validation score for a specified bandwidth for GWR-LCR model
gw.dist

Distance matrix calculation
gwr.mink.pval

Select the values of p for the Minkowski approach for GWR
gwr.lcr.cv.contrib

Cross-validation data at each observation location for the GWR-LCR model
gwr.mixed

Mixed GWR
gwss

Geographically weighted summary statistics (GWSS)
gwr.mink.approach

Minkovski approach for GWR
gwr.lcr

GWR with a locally-compensated ridge term
gwpca.check.components

Interaction tool with the GWPCA glyph map
gwr.mink.matrixview

Visualisation of the results from gwr.mink.approach
gwpca

GWPCA
gwr.multiscale

Multiscale GWR
gwr.basic

Basic GWR model
gwr.predict

GWR used as a spatial predictor
gwss.montecarlo

Monte Carlo (randomisation) test for gwss
gwr.model.view

Visualise the GWR models from gwr.model.selection
gwr.cv

Cross-validation score for a specified bandwidth for basic GWR
gwr.cv.contrib

Cross-validation data at each observation location for a basic GWR model
gw.pcplot

Geographically weighted parallel coordinate plot for investigating multivariate data sets
gwr.robust

Robust GWR model
st.dist

Spatio-temporal distance matrix calculation
gwr.model.sort

Sort the results of the GWR model selection function gwr.model.selection.
gwr.montecarlo

Monte Carlo (randomisation) test for significance of GWR parameter variability
gwr.scalable

Scalable GWR
gwr.model.selection

Model selection for GWR with a given set of independent variables
gwr.bootstrap

Bootstrap GWR
gwr.collin.diagno

Local collinearity diagnostics for basic GWR
gwr.write

Write the GWR results into files
gwr.t.adjust

Adjust p-values for multiple hypothesis tests in basic GWR