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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.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | stats, qpdf, numbers, MASS |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2023-05-15 |
| Author: | Nobin Chandra Paul [aut, cre, cph], Moumita Baishya [aut] |
| Maintainer: | Nobin Chandra Paul <nobin.paul at icar.gov.in> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: | no |
| CRAN checks: | StepGWR results |
| Reference manual: | StepGWR.pdf |
| Package source: | StepGWR_0.1.0.tar.gz |
| Windows binaries: | r-devel: StepGWR_0.1.0.zip, r-release: StepGWR_0.1.0.zip, r-oldrel: StepGWR_0.1.0.zip |
| macOS binaries: | r-release (arm64): StepGWR_0.1.0.tgz, r-oldrel (arm64): StepGWR_0.1.0.tgz, r-release (x86_64): StepGWR_0.1.0.tgz, r-oldrel (x86_64): StepGWR_0.1.0.tgz |
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