Package: StepGWR 0.1.0
Nobin Chandra Paul
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.
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StepGWR_0.1.0.tar.gz
StepGWR_0.1.0.tar.gz(r-4.5-noble)StepGWR_0.1.0.tar.gz(r-4.4-noble)
StepGWR_0.1.0.tgz(r-4.4-emscripten)StepGWR_0.1.0.tgz(r-4.3-emscripten)
StepGWR.pdf |StepGWR.html✨
StepGWR/json (API)
# Install 'StepGWR' in R: |
install.packages('StepGWR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:9a9f73b60e. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 29 2024 |
R-4.5-linux | OK | Oct 29 2024 |