GWRLASSO: A Hybrid Model for Spatial Prediction Through Local Regression
It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of
LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that
captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<doi:10.1068/a40256>.
The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables
are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations
and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data.
Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between
the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex
spatial patterns and large datasets with multiple predictor variables.
||R (≥ 2.10)
||stats, qpdf, numbers, glmnet, Matrix
||knitr, rmarkdown, testthat
||Nobin Chandra Paul [aut, cre, cph],
Anil Rai [aut],
Ankur Biswas [aut],
Tauqueer Ahmad [aut],
Bhaskar B. Gaikwad [aut],
Dhananjay D. Nangare [aut],
K. Sammi Reddy [aut]
||Nobin Chandra Paul <nobin.paul at icar.gov.in>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
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