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MARSGWR  

A Hybrid Spatial Model for Capturing Spatially Varying Relationships Between Variables in the Data
View on CRAN: Click here


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

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

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



Attach the package and use:
library("MARSGWR")
Maintained by
Nobin Chandra Paul
[Scholar Profile | Author Map]
First Published: 2023-05-09
Latest Update: 2023-05-09
Description:
It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response variable. For method details see, Friedman, J.H. (1991). .The GWR model is then used to predict the response variable at testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations.
How to cite:
Nobin Chandra Paul (2023). MARSGWR: A Hybrid Spatial Model for Capturing Spatially Varying Relationships Between Variables in the Data. R package version 0.1.0, https://cran.r-project.org/web/packages/MARSGWR. Accessed 17 Feb. 2025.
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