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.
| Version: |
0.1-2 |
| Depends: |
R (≥ 2.14.0) |
| Imports: |
mboost, mgcv, grpreg, MASS |
| Published: |
2017-07-23 |
| Author: |
Madlene Nussbaum [cre, aut],
Andreas Papritz [ths] |
| Maintainer: |
Madlene Nussbaum <madlene.nussbaum at env.ethz.ch> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
geoGAM results |
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