https://github.com/giscience-fsu/sperrorest

Spatial error estimation and variable importance
https://github.com/giscience-fsu/sperrorest

cross-validation machine-learning r-package rstats spatial-statistics spatio-temporal-modeling statistical-learning

Last synced: 3 months ago
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Spatial error estimation and variable importance

• Host: GitHub
• URL: https://github.com/giscience-fsu/sperrorest
• Owner: giscience-fsu
• License: gpl-3.0
• Created: 2016-10-04T13:30:02.000Z (about 8 years ago)
• Default Branch: main
• Last Pushed: 2024-05-02T04:22:27.000Z (7 months ago)
• Last Synced: 2024-05-02T05:57:19.065Z (7 months ago)
• Topics: cross-validation, machine-learning, r-package, rstats, spatial-statistics, spatio-temporal-modeling, statistical-learning
• Language: R
• Homepage: https://giscience-fsu.github.io/sperrorest/
• Size: 117 MB
• Stars: 17
• Watchers: 4
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Codemeta: codemeta.json

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README

        
# sperrorest

[![R CMD Check via {tic}](https://github.com/giscience-fsu/sperrorest/workflows/R%20CMD%20Check%20via%20{tic}/badge.svg?branch=master)](https://github.com/giscience-fsu/sperrorest/actions)

[![CRAN](https://www.r-pkg.org/badges/version/sperrorest)](https://cran.r-project.org/package=sperrorest)

[![codecov](https://codecov.io/gh/giscience-fsu/sperrorest/branch/master/graph/badge.svg)](https://app.codecov.io/gh/giscience-fsu/sperrorest)

[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)

## Description

*Spatial Error Estimation and Variable Importance*

This package implements spatial error estimation and permutation-based spatial variable importance using different spatial cross-validation and bootstrap methods.

Supported resampling methods include various types of block resampling, leave-one-out sampling with buffer, and resampling at the level of predefined groups; users can implement their own resampling functions.

To cite {sperrorest} in publications, reference the paper by @Brenning2012. 

To see the package in action, please check the vignette ["Spatial Modeling Use Case"](https://giscience-fsu.github.io/sperrorest/articles/spatial-modeling-use-case.html).

## Installation

CRAN release version

```r

install.packages("sperrorest")

```

Development version

```r

remotes::install_github("giscience-fsu/sperrorest")

```

## References

Brenning, A. 2005. Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation. *Natural Hazards and Earth System Sciences* 5 (6). Copernicus GmbH:853–62.

https://doi.org/10.5194/nhess-5-853-2005

Brenning, A. 2012. Spatial Cross-Validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing: The R Package Sperrorest. In *2012 IEEE International Geoscience and Remote Sensing Symposium*, 5372–5.

https://doi.org/10.1109/IGARSS.2012.6352393

Russ, Georg, and A. Brenning. 2010a. Data Mining in Precision Agriculture: Management of Spatial Information. In *Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings*, edited by Eyke Hüllermeier, Rudolf Kruse, and Frank Hoffmann, 350–59. Springer.

https://doi.org/10.1007/978-3-642-14049-5_36

Russ, G., and A. Brenning. 2010b. Spatial Variable Importance Assessment for Yield Prediction

in Precision Agriculture. In *Lecture Notes in Computer Science*,

184–95. 

https://doi.org/10.1007/978-3-642-13062-5_18

Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., Brenning, A. (2019). Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. *Ecological Modelling*, 406: 109-120.

https://doi.org/10.1016/j.ecolmodel.2019.06.002