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geoGAM
View on CRAN: Click
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Download and install geoGAM package within the R console
Install from CRAN:
install.packages("geoGAM")
Install from Github:
library("remotes")
install_github("cran/geoGAM")
Install by package version:
library("remotes")
install_version("geoGAM", "0.1-3")
Attach the package and use:
library("geoGAM")
Maintained by
Madlene Nussbaum
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-10-29
Latest Update: 2023-11-14
Description:
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) .
How to cite:
Madlene Nussbaum (2016). geoGAM: Select Sparse Geoadditive Models for Spatial Prediction. R package version 0.1-3, https://cran.r-project.org/web/packages/geoGAM. Accessed 21 Nov. 2024.
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Complete documentation for geoGAM
Functions, R codes and Examples using
the geoGAM R package
Some associated functions: berne.grid . berne . bootstrap.geoGAM . geoGAM . methods.geoGAM . predict.geoGAM .
Some associated R codes: bootstrap.geogam.R . f.geoam.model.selection.R . methods.geogam.R . predict.geogam.R . Full geoGAM package functions and examples
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