Package: mgwrsar 1.1

Ghislain Geniaux

mgwrsar: GWR, Mixed GWR and Multiscale GWR with Spatial Autocorrelation

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

Authors:Ghislain Geniaux [aut, cre], Davide Martinetti [aut], César Martinez [aut]

mgwrsar_1.1.tar.gz
mgwrsar_1.1.tar.gz(r-4.5-noble)mgwrsar_1.1.tar.gz(r-4.4-noble)
mgwrsar_1.1.tgz(r-4.4-emscripten)mgwrsar_1.1.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'))

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

mydata - Mydata is a simulated data set of a mgwrsar model
mydatasf - Mydataf is a Simple Feature object with real estate data in south of France.

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

4.08 score 7 stars 34 scripts 364 downloads 22 exports 147 dependencies

Last updated 6 hours agofrom:96043ff5a4. Checks:2 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 20 2025
R-4.5-linux-x86_64	OK	Feb 20 2025

Exports:coef find_TP golden_search_bandwidth INST_C int_prems kernel_matW MGWRSAR mgwrsar_bootstrap_test mgwrsar_bootstrap_test_all multiscale_gwr normW PhWY_C plot_effect predict Proj_C QRcpp2_C residuals simu_multiscale Sl_C summary summary_Matrix tds_mgwr

Dependencies:base64enc BH brew bslib cachem caret class classInt cli clock codetools colorspace cpp11 crosstalk data.table DBI diagram digest doParallel dplyr e1071 evaluate fansi farver fastmap fontawesome foreach Formula fs future future.apply generics geojsonsf geometries ggplot2 globals glue gower gridExtra gtable hardhat highr htmltools htmlwidgets httpuv inum ipred isoband iterators jquerylib jsonify jsonlite KernSmooth knitr labeling later lattice lava lazyeval leafem leaflet leaflet.providers leafpop libcoin lifecycle listenv lubridate magrittr mapview MASS Matrix mboost memoise mgcv microbenchmark mime ModelMetrics munsell mvtnorm nabor nlme nnet nnls numDeriv parallelly partykit pillar pkgconfig plyr png pROC prodlim progressr promises proxy purrr quadprog R6 rapidjsonr rappdirs raster RColorBrewer Rcpp RcppEigen recipes reshape2 rlang rmarkdown rpart RSpectra s2 sass satellite scales servr sf sfheaders shape SKAT SMUT sp sparsevctrs SPAtest SQUAREM stabs stringi stringr survival svglite systemfonts terra tibble tidyr tidyselect timechange timeDate tinytex tzdb units utf8 uuid vctrs viridisLite withr wk xfun yaml

Estimating GWR and Mixed GWR Models with mgwrsar package: An Introduction with House Price Data

Rendered fromIntro_french_data.html.asisusingR.rsp::asison Feb 20 2025.

Last update: 2025-02-20
Started: 2025-02-20

GWR and MGWR with Space-Time Kernels

Rendered fromGWR-with-Space-Time-Kernels.html.asisusingR.rsp::asison Feb 20 2025.

Last update: 2025-02-20
Started: 2025-02-20

GWR and Mixed GWR with spatial autocorrelation

Rendered fromGWR-and-Mixed-GWR-with-spatial-autocorrelation.html.asisusingR.rsp::asison Feb 20 2025.

Last update: 2025-02-20
Started: 2025-02-20

Multiscale GWR using top down scale approaches

Rendered fromMultiscale-GWR-using-top-down-scale-approach.html.asisusingR.rsp::asison Feb 20 2025.

Last update: 2025-02-20
Started: 2025-02-20

Speeding up GWR like models with mgwrsar package using Target Points, rough gaussian kernel and parallelisation

Rendered fromSpeeding_up_GWR_like_models.html.asisusingR.rsp::asison Feb 20 2025.

Last update: 2025-02-20
Started: 2025-02-20

Citation

Readme and manuals

Help Manual

Help page	Topics
atds_gwr Top-Down Scaling approach of GWR	atds_gwr
coef for mgwrsar model	coef,mgwrsar-method
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
fitted for mgwrsar model	fitted,mgwrsar-method
golden_search_bandwidth to be documented	golden_search_bandwidth
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
Class of mgwrsar Model.	mgwrsar-class
modc is a set of models to correct approximation of hat matrix trace	modc
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
mydata is a simulated data set of a mgwrsar model	mydata
mydataf is a Simple Feature object with real estate data in south of France.	mydatasf
normW row normalization of dgCMatrix	normW
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 method for mgwrsar model	plot,mgwrsar,missing-method plot.mgwrsar
predict method for mgwrsar model	predict,mgwrsar-method
residuals for mgwrsar model	residuals,mgwrsar-method
Estimation of linear and local linear model with spatial autocorrelation model (mgwrsar).	simu_multiscale
summary_Matrix to be documented	summary_Matrix
summary for mgwrsar model	summary,mgwrsar-method
Top-Down Scaling approach of multiscale GWR	tds_mgwr

Usage by other packages (reverse dependencies)