Package: mgwrsar 1.0.5

Ghislain Geniaux

mgwrsar: GWR and MGWR with Spatial Autocorrelation

Functions for computing (Mixed) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.

Authors:Ghislain Geniaux and Davide Martinetti

mgwrsar_1.0.5.tar.gz
mgwrsar_1.0.5.tar.gz(r-4.5-noble)mgwrsar_1.0.5.tar.gz(r-4.4-noble)
mgwrsar_1.0.5.tgz(r-4.4-emscripten)mgwrsar_1.0.5.tgz(r-4.3-emscripten)
mgwrsar.pdf |mgwrsar.html✨
mgwrsar/json (API)
NEWS

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • mydata - Mydata is a simulated data set of a mgwrsar model

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

3.74 score 6 stars 46 scripts 332 downloads 20 exports 144 dependencies

Last updated 12 months agofrom:8b2be22f40. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-linux-x86_64OKOct 26 2024

Exports:bandwidths_mgwrsarfind_TPINST_Cint_premskernel_matWMGWRSARmgwrsar_bootstrap_testmgwrsar_bootstrap_test_allmultiscale_gwrnormWPhWY_Cplot_effectplot_mgwrsarpredict_mgwrsarProj_CQRcpp2_Csimu_multiscaleSl_Csummary_Matrixsummary_mgwrsar

Dependencies:base64encBHbrewbslibcachemcaretclassclassIntcliclockcodetoolscolorspacecpp11crosstalkdata.tableDBIdiagramdigestdoParalleldplyre1071evaluatefansifarverfastmapfontawesomeforeachFormulafsfuturefuture.applygenericsgeojsonsfgeometriesggplot2globalsgluegowergridExtragtablehardhathighrhtmltoolshtmlwidgetshttpuvinumipredisobanditeratorsjquerylibjsonifyjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevalleafemleafletleaflet.providersleafpoplibcoinlifecyclelistenvlubridatemagrittrmapviewMASSMatrixmboostmemoisemgcvmicrobenchmarkmimeModelMetricsmunsellmvtnormnabornlmennetnnlsnumDerivparallellypartykitpillarpkgconfigplyrpngpROCprodlimprogressrpromisesproxypurrrquadprogR6rapidjsonrrappdirsrasterRColorBrewerRcppRcppEigenrecipesreshape2rlangrmarkdownrparts2sasssatellitescalesservrsfsfheadersshapespspDataspgwrSQUAREMstabsstringistringrsurvivalsvglitesystemfontsterratibbletidyrtidyselecttimechangetimeDatetinytextzdbunitsutf8uuidvctrsviridisLitewithrwkxfunyaml

Examples of basic uses of mgwrsar package

Ghislain Geniaux

Rendered frommgwrsar-basic_examples.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2023-12-01
Started: 2018-05-11

Speeding up GWR like models with mgwrsar package

Ghislain Geniaux

Rendered fromSpeeding_up_GWR_like_models_with_mgwrsar_package.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2023-12-01
Started: 2022-05-05

Readme and manuals

Help Manual

Help pageTopics
bandwidths_mgwrsarbandwidths_mgwrsar
Search of a suitable set of target points. find_TP is a wrapper function that identifies a set of target points based on spatial smoothed OLS residuals.find_TP
kernel_matW A function that returns a sparse weight matrix based computed with a specified kernel (gauss,bisq,tcub,epane,rectangle,triangle) considering coordinates provides in S and a given bandwidth. If NN<nrow(S) only NN firts neighbours are considered. If Type!='GD' then S should have additional columns and several kernels and bandwidths should be be specified by the user.kernel_matW
Estimation of linear and local linear model with spatial autocorrelation model (mgwrsar).MGWRSAR
A bootstrap test for Betas for mgwrsar class model.mgwrsar_bootstrap_test
A bootstrap test for testing nullity of all Betas for mgwrsar class model,mgwrsar_bootstrap_test_all
multiscale_gwr This function adapts the multiscale Geographically Weighted Regression (GWR) methodology proposed by Fotheringam et al. in 2017, employing a backward fitting procedure within the MGWRSAR subroutines. The consecutive bandwidth optimizations are performed by minimizing the corrected Akaike criteria.multiscale_gwr
multiscale_gwr.cv to be documented (experimental)multiscale_gwr.cv
mydata is a simulated data set of a mgwrsar modelmydata
normW row normalization of dgCMatrixnormW
plot_effect plot_effect is a function that plots the effect of a variable X_k with spatially varying coefficient, i.e X_k * Beta_k(u_i,v_i) for comparing the magnitude of effects of between variables.plot_effect
plot_mgwrsar plots the value of local paramaters of a mgwrsar models using a leaflet map.plot_mgwrsar
mgwrsar Model Predictions predict_mgwrsar is a function for computing predictions of a mgwrsar models. It uses Best Linear Unbiased Predictor for mgwrsar models with spatial autocorrelation.predict_mgwrsar
Estimation of linear and local linear model with spatial autocorrelation model (mgwrsar).simu_multiscale
summary_Matrix to be documentedsummary_Matrix
Print a summary of mgwrsar modelssummary_mgwrsar