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.


News

2016-08-10 geoGAM Version 0.1-0 released 2017-07-23 improved bootstrap implementation and backtransformation for log and sqrt transformed responses, updated man pages with much improvment on methods descriptions.

Reference manual

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install.packages("geoGAM")

0.1-2 by Madlene Nussbaum, 2 years ago


Browse source code at https://github.com/cran/geoGAM


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports mboost, mgcv, grpreg, MASS


See at CRAN