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.

Authors:Nobin Chandra Paul [aut, cre, cph], Moumita Baishya [aut]

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)

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2 exports 0.00 score 7 dependencies 1 scripts 141 downloads

Last updated 1 years agofrom:9a9f73b60e. Checks:OK: 2. Indexed: yes.

Exports:StepGWR_exponentialStepGWR_gaussian

Dependencies:askpasscurlMASSnumbersqpdfRcppsys