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). <doi:10.1214/aos/1176347963>.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.
Version: 0.1.0 Depends: R (≥ 2.10) Imports: stats, qpdf, numbers, earth Suggests: knitr, rmarkdown, testthat (≥ 3.0.0) Published: 2023-05-09 DOI: 10.32614/CRAN.package.MARSGWR Author: Nobin Chandra Paul [aut, cre, cph], Anil Rai [aut], Ankur Biswas [aut], Tauqueer Ahmad [aut], Dhananjay D. Nangare [aut], Bhaskar B. Gaikwad [aut] Maintainer: Nobin Chandra Paul <nobin.paul at icar.gov.in> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] NeedsCompilation: no CRAN checks: MARSGWR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=MARSGWR to link to this page.
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