Package: mlr3spatiotempcv 2.3.2
Patrick Schratz
mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'
Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.
Authors:
mlr3spatiotempcv_2.3.2.tar.gz
mlr3spatiotempcv_2.3.2.tar.gz(r-4.5-noble)mlr3spatiotempcv_2.3.2.tar.gz(r-4.4-noble)
mlr3spatiotempcv_2.3.2.tgz(r-4.4-emscripten)mlr3spatiotempcv_2.3.2.tgz(r-4.3-emscripten)
mlr3spatiotempcv.pdf |mlr3spatiotempcv.html✨
mlr3spatiotempcv/json (API)
NEWS
| # Install 'mlr3spatiotempcv' in R: |
| install.packages('mlr3spatiotempcv', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/mlr-org/mlr3spatiotempcv/issues4 issues
Pkgdown site:https://mlr3spatiotempcv.mlr-org.com
- cookfarm_mlr3 - Cookfarm Profiles Regression Task
- diplodia - Diplodia Classification Task
- ecuador - Ecuador Classification Task
Last updated 4 months agofrom:1d3938a4a0. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 29 2025 |
| R-4.5-linux | OK | Mar 29 2025 |
| R-4.4-linux | OK | Mar 29 2025 |
Exports:as_task_classif_stas_task_classif_st.data.frameas_task_classif_st.DataBackendas_task_classif_st.sfas_task_classif_st.TaskClassifSTas_task_regr_stas_task_regr_st.data.frameas_task_regr_st.DataBackendas_task_regr_st.sfas_task_regr_st.TaskClassifSTas_task_regr_st.TaskRegrSTautoplotResamplingRepeatedSpCVBlockResamplingRepeatedSpCVCoordsResamplingRepeatedSpCVDiscResamplingRepeatedSpCVEnvResamplingRepeatedSpCVKnndmResamplingRepeatedSpCVTilesResamplingRepeatedSptCVCstfResamplingSpCVBlockResamplingSpCVBufferResamplingSpCVCoordsResamplingSpCVDiscResamplingSpCVEnvResamplingSpCVKnndmResamplingSpCVTilesResamplingSptCVCstfTaskClassifSTTaskRegrST
Dependencies:backportscheckmateclicodetoolscolorspacedata.tabledigestevaluatefansifarverfuturefuture.applyggplot2globalsgluegtableisobandlabelinglatticelgrlifecyclelistenvmagrittrMASSMatrixmgcvmlbenchmlr3mlr3measuresmlr3miscmunsellnlmepalmerpenguinsparadoxparallellypillarpkgconfigPRROCR6RColorBrewerrlangscalestibbleutf8uuidvctrsviridisLitewithr
Citation
To cite mlr3spatiotempcv in publications use:
Schratz P, Becker M, Lang M, Brenning A (2024). “mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R.” Journal of Statistical Software, 111(7), 1–36. doi:10.18637/jss.v111.i07.
Corresponding BibTeX entry:
@Article{,
title = {{mlr3spatiotempcv}: Spatiotemporal Resampling Methods for
Machine Learning in {R}},
author = {Patrick Schratz and Marc Becker and Michel Lang and
Alexander Brenning},
journal = {Journal of Statistical Software},
year = {2024},
volume = {111},
number = {7},
pages = {1--36},
doi = {10.18637/jss.v111.i07},
}
Readme and manuals
mlr3spatiotempcv
Package website: release | dev
Spatiotemporal resampling methods for mlr3.
This package extends the mlr3 package framework with spatiotemporal resampling and visualization methods.
If you prefer the tidymodels ecosystem, have a look at the {spatialsample} package for spatial sampling methods.
Installation
CRAN version
install.packages("mlr3spatiotempcv")
Development version
remotes::install_github("mlr-org/mlr3spatiotempcv")
# R Universe Repo
install.packages('mlr3spatiotempcv', mlrorg = 'https://mlr-org.r-universe.dev')
Get Started
See the "Get Started" vignette for a quick introduction.
For more detailed information including an usage example see the "Spatiotemporal Analysis" chapter in the mlr3book.
Article "Spatiotemporal Visualization" shows how 3D subplots grids can be created.
Citation
To cite the package in publications, use the output of citation("mlr3spatiotempcv").
Resources
- Recorded talk about mlr3spatiotempcv and mlr3spatial at OpenDataScience Europe Conference 2021 in Wageningen, NL
- List of scientific articles related to spatiotemporal modeling and/or spatial partitioning
Other spatiotemporal resampling packages
This list does not claim to be comprehensive.
(Disclaimer: Because CRAN does not like DOI URLs in their automated checks, direct linking to scientific articles is not possible...)
| Name | Language | Resources |
|---|---|---|
| blockCV | R | CRAN |
| CAST | R | Paper, CRAN |
| ENMeval | R | CRAN |
| spatialsample | R | CRAN |
| sperrorest | R | CRAN |
| Pyspatialml | Python | GitHub |
| spacv | Python | GitHub |
| Museo Toolbox | Python | Paper, GitHub |
| spatial-kfold | Python | GitHub |
FAQ
Which resampling method should I use?
There is no single-best resampling method. It depends on your dataset characteristics and what your model should is about to predict on. The resampling scheme should reflect the final purpose of the model - this concept is called "target-oriented" resampling. For example, if the model was trained on multiple forest plots and its purpose is to predict something on unknown forest stands, the resampling structure should reflect this.
Are there more resampling methods than the one {mlr3spatiotempcv} offers?
{mlr3spatiotempcv} aims to offer all resampling methods that exist in R. Though this does not mean that it covers all resampling methods. If there are some that you are missing, feel free to open an issue.
How can I use the "blocking" concept of the old {mlr}?
This concept is now supported via the "column roles" concept available in {mlr3} [Task](https://mlr3.mlr-org.com/reference/Task.html) objects. See [this documentation](https://mlr3.mlr-org.com/reference/Resampling.html#grouping-blocking) for more information.
For the methods that offer buffering, how can an appropriate value be chosen?
There is no easy answer to this question. Buffering train and test sets reduces the similarity between both. The degree of this reduction depends on the dataset itself and there is no general approach how to choosen an appropriate buffer size. Some studies used the distance at which the autocorrelation levels off. This buffer distance often removes quite a lot of observations and needs to be calculated first.
Help Manual
| Help page | Topics |
|---|---|
| mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3' | mlr3spatiotempcv-package mlr3spatiotempcv |
| Convert to a Spatiotemporal Classification Task | as_task_classif_st as_task_classif_st.data.frame as_task_classif_st.DataBackend as_task_classif_st.sf as_task_classif_st.TaskClassifST |
| Convert to a Spatiotemporal Regression Task | as_task_regr_st as_task_regr_st.data.frame as_task_regr_st.DataBackend as_task_regr_st.sf as_task_regr_st.TaskClassifST as_task_regr_st.TaskRegrST |
| Visualization Functions for Non-Spatial CV Methods. | autoplot.ResamplingCustomCV plot.ResamplingCustomCV |
| Visualization Functions for Non-Spatial CV Methods. | autoplot.ResamplingCV autoplot.ResamplingRepeatedCV plot.ResamplingCV plot.ResamplingRepeatedCV |
| Visualization Functions for SpCV Block Methods. | autoplot.ResamplingRepeatedSpCVBlock autoplot.ResamplingSpCVBlock plot.ResamplingRepeatedSpCVBlock plot.ResamplingSpCVBlock |
| Visualization Functions for SpCV Buffer Methods. | autoplot.ResamplingSpCVBuffer plot.ResamplingSpCVBuffer |
| Visualization Functions for SpCV Coords Methods. | autoplot.ResamplingRepeatedSpCVCoords autoplot.ResamplingSpCVCoords plot.ResamplingRepeatedSpCVCoords plot.ResamplingSpCVCoords |
| Visualization Functions for SpCV Disc Method. | autoplot.ResamplingRepeatedSpCVDisc autoplot.ResamplingSpCVDisc plot.ResamplingRepeatedSpCVDisc plot.ResamplingSpCVDisc |
| Visualization Functions for SpCV Env Methods. | autoplot.ResamplingRepeatedSpCVEnv autoplot.ResamplingSpCVEnv plot.ResamplingRepeatedSpCVEnv plot.ResamplingSpCVEnv |
| Visualization Functions for SpCV knndm Method. | autoplot.ResamplingRepeatedSpCVKnndm autoplot.ResamplingSpCVKnndm plot.ResamplingRepeatedSpCVKnndm plot.ResamplingSpCVKnndm |
| Visualization Functions for SpCV Tiles Method. | autoplot.ResamplingRepeatedSpCVTiles autoplot.ResamplingSpCVTiles plot.ResamplingRepeatedSpCVTiles plot.ResamplingSpCVTiles |
| Visualization Functions for SptCV Cstf Methods. | autoplot.ResamplingRepeatedSptCVCstf autoplot.ResamplingSptCVCstf plot.ResamplingRepeatedSptCVCstf plot.ResamplingSptCVCstf |
| (blockCV) Repeated spatial block resampling | mlr_resamplings_repeated_spcv_block ResamplingRepeatedSpCVBlock |
| (sperrorest) Repeated coordinate-based k-means clustering | mlr_resamplings_repeated_spcv_coords ResamplingRepeatedSpCVCoords |
| (sperrorest) Repeated spatial "disc" resampling | mlr_resamplings_repeated_spcv_disc ResamplingRepeatedSpCVDisc |
| (blockCV) Repeated "environmental blocking" resampling | mlr_resamplings_repeated_spcv_env ResamplingRepeatedSpCVEnv |
| (CAST) Repeated K-fold Nearest Neighbour Distance Matching | mlr_resamplings_repeated_spcv_knndm ResamplingRepeatedSpCVKnndm |
| (sperrorest) Repeated spatial "tiles" resampling | mlr_resamplings_repeated_spcv_tiles ResamplingRepeatedSpCVTiles |
| (CAST) Repeated spatiotemporal "leave-location-and-time-out" resampling | mlr_resamplings_repeated_sptcv_cstf ResamplingRepeatedSptCVCstf |
| (blockCV) Spatial block resampling | mlr_resamplings_spcv_block ResamplingSpCVBlock |
| (blockCV) Spatial buffering resampling | mlr_resamplings_spcv_buffer ResamplingSpCVBuffer |
| (sperrorest) Coordinate-based k-means clustering | mlr_resamplings_spcv_coords ResamplingSpCVCoords |
| (sperrorest) Spatial "disc" resampling | mlr_resamplings_spcv_disc ResamplingSpCVDisc |
| (blockCV) "Environmental blocking" resampling | mlr_resamplings_spcv_env ResamplingSpCVEnv |
| (CAST) K-fold Nearest Neighbour Distance Matching | mlr_resamplings_spcv_knndm ResamplingSpCVKnndm |
| (sperrorest) Spatial "Tiles" resampling | mlr_resamplings_spcv_tiles ResamplingSpCVTiles |
| (CAST) Spatiotemporal "Leave-location-and-time-out" resampling | mlr_resamplings_sptcv_cstf ResamplingSptCVCstf |
| Cookfarm Profiles Regression Task | cookfarm_mlr3 mlr_tasks_cookfarm_mlr3 |
| Diplodia Classification Task | diplodia mlr_tasks_diplodia |
| Ecuador Classification Task | ecuador mlr_tasks_ecuador |
| Create a Spatiotemporal Classification Task | TaskClassifST |
| Create a Spatiotemporal Regression Task | TaskRegrST |
