GWRLASSO
0.1.0A Hybrid Model for Spatial Prediction Through Local Regression
Overview
It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).DOI:10.1068/a40256. The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex spatial patterns and large datasets with multiple predictor variables.
Install
Health
- OK2026-07-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-2812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-06-2113 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-1412 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-06-0313 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 6 earlier snapshots
- ERROR2026-05-3012 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-05-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-05-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-2013 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 100%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 7.0 median / 7 max
- Test cases
- 1 / 0.08 per code line
Test coverage
Line coverage
0%
Expression
0.0%
Tests / Examples
0.0% / 100% ex
Functions
2 2 exported
Complexity
7 avg / 7 max
Call network
2 nodes / 0 edges
Call graph
Open call graph →Lowest coverage
2 functions| Function | Cyclo | Coverage |
|---|---|---|
| GWRLASSO_exponential exp | 7 | 0% |
| GWRLASSO_gaussian exp | 7 | 0% |
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2023-08-28
- Total releases
- 1 / 3 yrs
- License
- GPL (>= 2.0)
- Minimum R
- ≥ 2.10
- Download size
- 11 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet