StepGWR
A Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data
It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000).<DOI:10.1068/a3162>.This hybrid spatial model aims to improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly useful for modeling spatially varying relationships and improving the accuracy of spatial predictions.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | StepGWR_0.1.0.tar.gz |
25.3 KiB |
0.1.0 |
rolling linux/noble R-4.5 | StepGWR_0.1.0.tar.gz |
25.2 KiB |
0.1.0 |
rolling source/ R- | StepGWR_0.1.0.tar.gz |
4.0 KiB |
0.1.0 |
latest linux/jammy R-4.5 | StepGWR_0.1.0.tar.gz |
25.3 KiB |
0.1.0 |
latest linux/noble R-4.5 | StepGWR_0.1.0.tar.gz |
25.2 KiB |
0.1.0 |
latest source/ R- | StepGWR_0.1.0.tar.gz |
4.0 KiB |
0.1.0 |
2026-04-26 source/ R- | StepGWR_0.1.0.tar.gz |
4.0 KiB |
0.1.0 |
2026-04-23 source/ R- | StepGWR_0.1.0.tar.gz |
4.0 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | StepGWR_0.1.0.zip |
27.9 KiB |
0.1.0 |
2025-04-20 source/ R- | StepGWR_0.1.0.tar.gz |
4.0 KiB |