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.
|Author||Madlene Nussbaum [cre, aut], Andreas Papritz [ths]|
|Date of publication||2017-07-23 16:25:17 UTC|
|Maintainer||Madlene Nussbaum <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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