Package: geoGAM 0.1-3

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. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.

Authors:Madlene Nussbaum [cre, aut], Andreas Papritz [ths]

geoGAM_0.1-3.tar.gz
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geoGAM.pdf |geoGAM.html✨
geoGAM/json (API)

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2 exports 0.09 score 17 dependencies 20 scripts 213 downloads

Last updated 10 months agofrom:d7eb363e97. Checks:OK: 5 NOTE: 2. Indexed: yes.

Exports:bootstrapgeoGAM

Dependencies:FormulagrpreginumlatticelibcoinMASSMatrixmboostmgcvmvtnormnlmennlspartykitquadprogrpartstabssurvival