CRAN

geoGAM 0.1-2

Select Sparse Geoadditive Models for Spatial Prediction

Released Jul 23, 2017 by Madlene Nussbaum

This package can be loaded by Renjin but 3 out 4 tests failed.

Dependencies

MASS 7.3-50 mgcv 1.8-23 mboost 2.8-1 grpreg 3.1-3

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.

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>geoGAM</artifactId>
    <version>0.1-2-b12</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:geoGAM')

Test Results

This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.

Source

R

View GitHub Mirror

Release History