| hgwrr-package {hgwrr} | R Documentation |
HGWR: Hierarchical and Geographically Weighted Regression
Description
An R and C++ implementation of Hierarchical and Geographically Weighted Regression (HGWR) model is provided in this package. This model divides coefficients into three types: local fixed effects, global fixed effects, and random effects. If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.
Details
| Package: | hgwrr |
| Type: | Package |
| Title: | Hierarchical and Geographically Weighted Regression |
| Version: | 0.5-0 |
| Date: | 2024-07-04 |
| Author: | Yigong Hu, Richard Harris, Richard Timmerman |
| Maintainer: | Yigong Hu <yigong.hu@bristol.ac.uk> |
| Description: | This model divides coefficients into three types, i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022)<doi:10.1177/23998083211063885>. If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness. |
| License: | GPL (>= 2) |
| URL: | https://github.com/HPDell/hgwrr/, https://hpdell.github.io/hgwrr/ |
| Imports: | Rcpp (>= 1.0.8) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Depends: | R (>= 3.5.0), sf, stats, utils |
| NeedsCompilation: | yes |
| Suggests: | knitr, rmarkdown, testthat (>= 3.0.0), |
| SystemRequirements: | GNU make |
| Roxygen: | list(markdown = TRUE) |
| RoxygenNote: | 7.2.3 |
| VignetteBuilder: | knitr |
| Config/Needs/website: | tidyverse, ggplot2, tmap, lme4, spdep, GWmodel |
Note
Acknowledgement: We gratefully acknowledge support from China Scholarship Council.
Author(s)
Yigong Hu, Richard Harris, Richard Timmerman
References
Hu, Y., Lu, B., Ge, Y., Dong, G., 2022. Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression. Environment and Planning B: Urban Analytics and City Science. doi:10.1177/23998083211063885