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blockCV R Documentation
blockCV: Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
Description
Simple random selection of training and testing folds in the structured environment leads to
an underestimation of error in the evaluation of spatial
predictions and may result in inappropriate model selection (Telford and Birks, 2009; Roberts et al., 2017). The use of spatial and
environmental blocks to separate training and testing sets has been suggested as a good strategy for realistic error estimation in datasets
with dependence structures, and more generally as a robust method for estimating the predictive performance of models used to predict mapped
distributions (Roberts et al., 2017). The package blockCV offers
a range of functions for generating train and test folds
for k-fold and leave-one-out (LOO) cross-validation (CV). It allows for separation
of data spatially and environmentally, with various options for block construction.
Additionally, it includes a function for assessing the level of spatial autocorrelation
in response or raster covariates, to aid in selecting an appropriate distance band for
data separation. The blockCV package is suitable for the evaluation of a variety of
spatial modelling applications, including classification of remote sensing imagery,
soil mapping, and species distribution modelling (SDM). It also provides support for
different SDM scenarios, including presence-absence and presence-background species
data, rare and common species, and raster data for predictor variables.
Author(s)
Roozbeh Valavi, Jane Elith, José Lahoz-Monfort, Ian Flint, and Gurutzeta Guillera-Arroita
References
Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225-232. doi:10.1111/2041-210X.13107.
See Also
cv_spatial, cv_cluster, cv_buffer, and cv_nndm for blocking strategies.
blockCV documentation built on Nov. 1, 2024, 9:09 a.m.
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| blockCV | R Documentation |
blockCV: Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation
Description
Simple random selection of training and testing folds in the structured environment leads to
an underestimation of error in the evaluation of spatial
predictions and may result in inappropriate model selection (Telford and Birks, 2009; Roberts et al., 2017). The use of spatial and
environmental blocks to separate training and testing sets has been suggested as a good strategy for realistic error estimation in datasets
with dependence structures, and more generally as a robust method for estimating the predictive performance of models used to predict mapped
distributions (Roberts et al., 2017). The package blockCV offers
a range of functions for generating train and test folds
for k-fold and leave-one-out (LOO) cross-validation (CV). It allows for separation
of data spatially and environmentally, with various options for block construction.
Additionally, it includes a function for assessing the level of spatial autocorrelation
in response or raster covariates, to aid in selecting an appropriate distance band for
data separation. The blockCV package is suitable for the evaluation of a variety of
spatial modelling applications, including classification of remote sensing imagery,
soil mapping, and species distribution modelling (SDM). It also provides support for
different SDM scenarios, including presence-absence and presence-background species
data, rare and common species, and raster data for predictor variables.
Author(s)
Roozbeh Valavi, Jane Elith, José Lahoz-Monfort, Ian Flint, and Gurutzeta Guillera-Arroita
References
Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). blockCV: An R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225-232. doi:10.1111/2041-210X.13107.
See Also
cv_spatial, cv_cluster, cv_buffer, and cv_nndm for blocking strategies.
R Package Documentation
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