Description
Analysis of Spatial Stratified Heterogeneity.
Description
Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.
README.md
gdverse 
Analysis of Spatial Stratified Heterogeneity
Overview
Current models and functions provided by gdverse are:
| Model | Function | Support |
|---|---|---|
| GeoDetector | geodetector() | ✔️ |
| OPGD | opgd() | ✔️ |
| GOZH | gozh() | ✔️ |
| LESH | lesh() | ✔️ |
| SPADE | spade() | ✔️ |
| IDSA | idsa() | ✔️ |
| RGD | rgd() | ✔️ |
| RID | rid() | ✔️ |
| SRSGD | srsgd() | ✔️ |
Installation
- Install from CRAN with:
install.packages("gdverse", dep = TRUE)
- Install development binary version from R-universe with:
install.packages('gdverse',
repos = c("https://stscl.r-universe.dev",
"https://cloud.r-project.org"),
dep = TRUE)
- Install development source version from GitHub with:
# install.packages("devtools")
devtools::install_github("stscl/gdverse",
build_vignettes = TRUE,
dep = TRUE)
✨ Please ensure that Rcpp is properly installed and the appropriate C++ compilation environment is configured in advance if you want to install gdverse from github.
✨ The gdverse package supports the use of robust discretization for the robust geographical detector and robust interaction detector. For details on using them, please refer to https://stscl.github.io/gdverse/articles/rgdrid.html.
Example
library(gdverse)
data("ndvi")
ndvi
## # A tibble: 713 × 7
## NDVIchange Climatezone Mining Tempchange Precipitation GDP Popdensity
## <dbl> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 0.116 Bwk low 0.256 237. 12.6 1.45
## 2 0.0178 Bwk low 0.273 214. 2.69 0.801
## 3 0.138 Bsk low 0.302 449. 20.1 11.5
## 4 0.00439 Bwk low 0.383 213. 0 0.0462
## 5 0.00316 Bwk low 0.357 205. 0 0.0748
## 6 0.00838 Bwk low 0.338 201. 0 0.549
## 7 0.0335 Bwk low 0.296 210. 11.9 1.63
## 8 0.0387 Bwk low 0.230 236. 30.2 4.99
## 9 0.0882 Bsk low 0.214 342. 241 20.0
## 10 0.0690 Bsk low 0.245 379. 42.0 7.50
## # ℹ 703 more rows
OPGD model
discvar = names(ndvi)[-1:-3]
discvar
## [1] "Tempchange" "Precipitation" "GDP" "Popdensity"
ndvi_opgd = opgd(NDVIchange ~ ., data = ndvi,
discvar = discvar, cores = 6)
ndvi_opgd
## *** Optimal Parameters-based Geographical Detector
## Factor Detector
##
## | variable | Q-statistic | P-value |
## |:-------------:|:-----------:|:--------:|
## | Precipitation | 0.8693505 | 2.58e-10 |
## | Climatezone | 0.8218335 | 7.34e-10 |
## | Tempchange | 0.3330256 | 1.89e-10 |
## | Popdensity | 0.1990773 | 6.60e-11 |
## | Mining | 0.1411154 | 6.73e-10 |
## | GDP | 0.1004568 | 3.07e-10 |
GOZH model
g = gozh(NDVIchange ~ ., data = ndvi)
g
## *** Geographically Optimal Zones-based Heterogeneity Model
## Factor Detector
##
## | variable | Q-statistic | P-value |
## |:-------------:|:-----------:|:--------:|
## | Precipitation | 0.87255056 | 4.52e-10 |
## | Climatezone | 0.82129550 | 2.50e-10 |
## | Tempchange | 0.33324945 | 1.12e-10 |
## | Popdensity | 0.22321863 | 3.00e-10 |
## | Mining | 0.13982859 | 6.00e-11 |
## | GDP | 0.09170153 | 3.96e-10 |