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StepGWR  

A Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data
View on CRAN: Click here


Download and install StepGWR package within the R console
Install from CRAN:
install.packages("StepGWR")

Install from Github:
library("remotes")
install_github("cran/StepGWR")

Install by package version:
library("remotes")
install_version("StepGWR", "0.1.0")



Attach the package and use:
library("StepGWR")
Maintained by
Nobin Chandra Paul
[Scholar Profile | Author Map]
First Published: 2023-05-15
Latest Update: 2023-05-15
Description:
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.
How to cite:
Nobin Chandra Paul (2023). StepGWR: A Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data. R package version 0.1.0, https://cran.r-project.org/web/packages/StepGWR. Accessed 21 Feb. 2025.
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