geoGAM: Select Sparse Geoadditive Models for Spatial Prediction
Version 0.1-1

A model building procedure to select a sparse 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 and smoothing splines. The resulting covariate set after gradient boosting is further reduced through cross validated backward selection 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 is provided.

Getting started

Package details

AuthorMadlene Nussbaum [cre, aut], Andreas Papritz [ths]
Date of publication2016-10-29 10:48:22
MaintainerMadlene Nussbaum <madlene.nussbaum@env.ethz.ch>
LicenseGPL (>= 2)
Version0.1-1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("geoGAM")

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geoGAM documentation built on May 30, 2017, 5:51 a.m.