Package: GWmodel 2.4-1

GWmodel: Geographically-Weighted Models

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)<doi:10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi:10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi:10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi:10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

Authors:Binbin Lu [aut, cre], Paul Harris [aut], Martin Charlton [aut], Chris Brunsdon [aut], Tomoki Nakaya [aut], Daisuke Murakami [ctb], Yigong Hu [ctb], Fiona H Evans [ctb], Hjalmar H<c3><b6>glund [ctb]

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GWmodel.pdf |GWmodel.html✨
GWmodel/json (API)

# Install 'GWmodel' in R:
install.packages('GWmodel', repos = c('https://lbb220.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • Dub.voter - Voter turnout data in Greater Dublin
  • Gedu.counties - Georgia counties data
  • Gedu.df - Georgia census data set
  • USelect2004 - Results of the 2004 US presidential election at the county level
  • ewhp - House price data set (DataFrame) in England and Wales
  • ewoutline - Outline of England and Wales for data EWHP
  • londonborough - London boroughs data
  • londonhp - London house price data set

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

6.33 score 18 stars 3 packages 248 scripts 2.2k downloads 8 mentions 160 exports 39 dependencies

Last updated 2 months agofrom:60dc1cee77. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-win-x86_64OKNov 07 2024
R-4.5-linux-x86_64OKNov 07 2024
R-4.4-win-x86_64OKNov 07 2024
R-4.4-mac-x86_64OKNov 07 2024
R-4.4-mac-aarch64OKNov 07 2024
R-4.3-win-x86_64OKNov 07 2024
R-4.3-mac-x86_64OKNov 07 2024
R-4.3-mac-aarch64OKNov 07 2024

Exports:AICcAICc_rssAICc_rss1AICc1bias.bsbw.ggwrbw.gtwrbw.gwdabw.gwpcabw.gwrbw.gwr.lcrbw.gwr1bw.gwr3bw.gwss.averagecheck.componentsCi_matci.bsconfusion.matrixe_vecehatextract.matF1234.testGenerate.formulagenerate.lm.dataggwr.aicggwr.basicggwr.cvggwr.cv.contribglyph.plotgoldgrouping.xygtwrgtwr.aicgw_BICgw_cv_allgw_cv_all_cudagw_cv_all_ompgw_distgw_fittedgw_local_r2gw_reggw_reg_1gw_reg_2gw_reg_allgw_reg_all_cudagw_reg_all_ompgw_weightgw_weight_matgw_weight_vecgw.average.cvgw.average.cv.contribgw.distgw.fittedgw.mean.cvgw.median.cvgw.pcplotgw.weightgwdagwpcagwpca.check.componentsgwpca.cvgwpca.cv.contribgwpca.glyph.plotgwpca.montecarlo.1gwpca.montecarlo.2gwr_diaggwr_diag1gwr_mixed_2gwr_mixed_tracegwr_qgwr.aicgwr.aic1gwr.backfitgwr.basicgwr.binomialgwr.binomial.wtgwr.bootstrapgwr.collin.diagnogwr.cvgwr.cv.contribgwr.cv1gwr.generalisedgwr.heterogwr.lcrgwr.lcr.cvgwr.lcr.cv.contribgwr.mink.approachgwr.mink.matrixviewgwr.mink.pvalgwr.mink.pval.backwardgwr.mink.pval.forwardgwr.mixedgwr.mixed.2gwr.mixed.tracegwr.mixed.trace.fastgwr.model.selectiongwr.model.sortgwr.model.viewgwr.montecarlogwr.multiscalegwr.poissongwr.poisson.wtgwr.predictgwr.qgwr.q2gwr.robustgwr.scalablegwr.scalable.loocvgwr.t.adjustgwr.writegwr.write.shpgwrt.errgwrt.laggwrt.mlrgwrt.smagwrtvargwssgwss.montecarlomink.approachmink.matrixviewmodel.selection.gwrmodel.sort.gwrmodel.view.gwrmontecarlo.gwpca.1montecarlo.gwpca.2montecarlo.gwrmontecarlo.gwssnew_multiscaleparametric.bsparametric.bs.localplot.mcsimsplot.pvlasprint.gwdaprint.mgwrpval.bsridge.lmrobustSvdrssrwpcascgwr_loocvscgwr_prescgwr_regse.bssplitxst.distti.distti.distmti.distvtrhat2wldawlda.crwmeanwpcawpriorwqdawqda.crwriteGWRwriteGWR.shpwt.medianwvarcov

Dependencies:bootclassclassIntcodacodetoolsDBIdeldirDEoptimRe1071FNNintervalsKernSmoothlatticeLearnBayesmagrittrMASSMatrixmultcompmvtnormnlmeproxyRcppRcppArmadilloRcppEigenrobustbases2sandwichsfspspacetimespatialregspDataspdepsurvivalTH.dataunitswkxtszoo

Readme and manuals

Help Manual

Help pageTopics
Geographically-Weighted ModelsGWmodel-package GWmodel
Bandwidth selection for generalised geographically weighted regression (GWR)bw.ggwr ggwr.aic
Bandwidth selection for GTWRbw.gtwr gtwr.aic gtwr.cv
Bandwidth selection for GW Discriminant Analysisbw.gwda wlda.cr wqda.cr
Bandwidth selection for Geographically Weighted Principal Components Analysis (GWPCA)bw.gwpca
Bandwidth selection for basic GWRAICc_rss1 bw.gwr e_vec fitted gold gwr.aic gw_BIC gw_cv_all gw_cv_all_cuda
Bandwidth selection for locally compensated ridge GWR (GWR-LCR)bw.gwr.lcr
Bandwidth selection for GW summary averagesbw.gwss.average gw.average.cv gw.average.cv.contrib gw.mean.cv gw.median.cv
Voter turnout data in Greater Dublin(SpatialPolygonsDataFrame)Dub.voter DubVoter
House price data set (DataFrame) in England and WalesEWHP ewhp
Outline of England and Wales for data EWHPewoutline
Georgia census data set (csv file)Gedu.df Georgia
Georgia counties data (SpatialPolygonsDataFrame)Gedu.counties
Generalised GWR models with Poisson and Binomial optionsggwr.basic gwr.binomial gwr.binomial.wt gwr.fitted gwr.generalised gwr.poisson gwr.poisson.wt print.ggwrm
Cross-validation score for a specified bandwidth for generalised GWRggwr.cv
Cross-validation data at each observation location for a generalised GWR modelggwr.cv.contrib
Geographically and Temporally Weighted Regressiongtwr print.gtwrm ti.dist ti.distm ti.distv
Distance matrix calculationgw.dist gw_dist
Geographically weighted parallel coordinate plot for investigating multivariate data setsgw.pcplot
Weight matrix calculationgw.weight gw_weight gw_weight_mat gw_weight_vec
GW Discriminant Analysisconfusion.matrix grouping.xy gwda print.gwda splitx wlda wmean wprior wqda wvarcov
GWPCAgwpca print.gwpca robustSvd rwpca wpca wt.median
Interaction tool with the GWPCA glyph mapcheck.components gwpca.check.components
Cross-validation score for a specified bandwidth for GWPCAgwpca.cv
Cross-validation data at each observation location for a GWPCAgwpca.cv.contrib
Multivariate glyph plots of GWPCA loadingsglyph.plot gwpca.glyph.plot
Monte Carlo (randomisation) test for significance of GWPCA eigenvalue variability for the first component only - option 1gwpca.montecarlo.1 montecarlo.gwpca.1 plot.mcsims
Monte Carlo (randomisation) test for significance of GWPCA eigenvalue variability for the first component only - option 2gwpca.montecarlo.2 montecarlo.gwpca.2
Basic GWR modelCi_mat F1234.test gwr.basic gwr_diag gw_cv_all_omp gw_local_r2 gw_reg gw_reg_1 gw_reg_2 gw_reg_all gw_reg_all_cuda gw_reg_all_omp print.gwrm trhat2
Bootstrap GWRbias.bs bw.gwr3 ci.bs generate.lm.data gwr.bootstrap gwrt.err gwrt.lag gwrt.mlr gwrt.sma gwrtvar parametric.bs parametric.bs.local print.gwrbsm pval.bs se.bs
Local collinearity diagnostics for basic GWRgwr.collin.diagno
Cross-validation score for a specified bandwidth for basic GWRgwr.cv
Cross-validation data at each observation location for a basic GWR modelgwr.cv.contrib
Heteroskedastic GWRgwr.hetero
GWR with a locally-compensated ridge termgwr.lcr print.gwrlcr ridge.lm
Cross-validation score for a specified bandwidth for GWR-LCR modelgwr.lcr.cv
Cross-validation data at each observation location for the GWR-LCR modelgwr.lcr.cv.contrib
Minkovski approach for GWRbw.gwr1 gwr.aic1 gwr.cv1 gwr.mink.approach mink.approach
Visualisation of the results from 'gwr.mink.approach'gwr.mink.matrixview mink.matrixview
Select the values of p for the Minkowski approach for GWRgwr.mink.pval gwr.mink.pval.backward gwr.mink.pval.forward plot.pvlas
Mixed GWRgwr.mixed gwr.mixed.2 gwr.mixed.2.fast gwr.mixed.trace gwr.mixed.trace.fast gwr.q gwr_mixed_2 gwr_mixed_trace gwr_q print.mgwr
Model selection for GWR with a given set of independent variablesAICc AICc_rss ehat extract.mat Generate.formula gw.fitted gwr.model.selection model.selection.gwr rss
Sort the results of the GWR model selection function 'gwr.model.selection'.gwr.model.sort model.sort.gwr
Visualise the GWR models from 'gwr.model.selection'gwr.model.view model.view.gwr
Monte Carlo (randomisation) test for significance of GWR parameter variabilitygwr.montecarlo montecarlo.gwr
Multiscale GWRgwr.backfit gwr.multiscale gwr.q2 gw_fitted new_multiscale print.multiscalegwr
GWR used as a spatial predictorgw.reg1 gwr.predict print.gwrm.pred
Robust GWR modelgwr.robust
Scalable GWRAICc1 gwr.scalable gwr.scalable.loocv gwr_diag1 print.scgwrm scgwr_loocv scgwr_pre scgwr_reg
Adjust p-values for multiple hypothesis tests in basic GWRgwr.t.adjust
Write the GWR results into filesgwr.write gwr.write.shp writeGWR writeGWR.shp
Geographically weighted summary statistics (GWSS)gwss local.corr print.gwss
Monte Carlo (randomisation) test for gwssgwss.montecarlo montecarlo.gwss
London boroughs dataLondonBorough londonborough
London house price data set (SpatialPointsDataFrame)LondonHP londonhp
Spatio-temporal distance matrix calculationst.dist
Results of the 2004 US presidential election at the county level (SpatialPolygonsDataFrame)USelect2004