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>.
||R (≥ 2.14.0)
||mboost, mgcv, grpreg, MASS
||Madlene Nussbaum [cre, aut],
Andreas Papritz [ths]
||Madlene Nussbaum <m.nussbaum at uu.nl>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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