CRAN/E | StepGWR

StepGWR

A Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data

Installation

About

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.

Key Metrics

Version 0.1.0
R ≥ 2.10
Published 2023-05-15 358 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks StepGWR results

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Maintainer

Maintainer

Nobin Chandra Paul

nobin.paul@icar.gov.in

Authors

Nobin Chandra Paul

aut / cre / cph

Moumita Baishya

aut

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 2.10

Imports

stats
qpdf
numbers
MASS

Suggests

knitr
rmarkdown
testthat ≥ 3.0.0