geoGAM: Select Sparse Geoadditive Models for Spatial Prediction

A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided.

Getting started

Package details

AuthorMadlene Nussbaum [cre, aut], Andreas Papritz [ths]
MaintainerMadlene Nussbaum <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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geoGAM documentation built on July 23, 2017, 5:02 p.m.