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>.
Version: |
0.1-3 |
Depends: |
R (≥ 2.14.0) |
Imports: |
mboost, mgcv, grpreg, MASS |
Suggests: |
raster, sp |
Published: |
2023-11-14 |
Author: |
Madlene Nussbaum [cre, aut],
Andreas Papritz [ths] |
Maintainer: |
Madlene Nussbaum <m.nussbaum at uu.nl> |
License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: |
no |
CRAN checks: |
geoGAM results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=geoGAM
to link to this page.