The main functions of
sperrorest have been integrated into mlr. Review is still ongoing. Once it is available in the CRAN version of
mlr we will let you know. There will be no active development of
sperrorest anymore. We recommend to use
mlr for all future (spatial) cross-validation work. We will provide an tutorial for spatial data in the mlr-tutorial soon.
|Platforms:||Multiple||Linux & macOS||Windows|
|R CMD check|
Spatial Error Estimation and Variable Importance
This package implements spatial error estimation and permutation-based spatial variable importance using different spatial cross-validation and spatial block bootstrap methods. To cite
sperrorest in publications, reference the paper by Brenning (2012). To see the package in action, please check the vignette.
Get the released version from CRAN:
Or the development version from Github:
Brenning, A. 2005. “Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation.” Natural Hazards and Earth System Science 5 (6). Copernicus GmbH: 853–62. doi:10.5194/nhess-5-853-2005.
———. 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. doi:10.1109/IGARSS.2012.6352393.
Russ, Georg, and Alexander Brenning. 2010a. “Data Mining in Precision Agriculture: Management of Spatial Information.” In Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processingand Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, edited by Eyke Hüllermeier, Rudolf Kruse, and Frank Hoffmann, 350–59. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-14049-5_36.
———. 2010b. “Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture.” In Lecture Notes in Computer Science, 184–95. Springer Science + Business Media. doi:10.1007/978-3-642-13062-5_18.