mlr_resamplings_spcv_env {mlr3spatiotempcv}

(blockCV) "Environmental blocking" resampling

Description

Splits data by clustering in the feature space. See the upstream implementation at blockCV::cv_cluster() and Valavi et al. (2018) for further information.

Details

Useful when the dataset is supposed to be split on environmental information which is present in features. The method allows for a combination of multiple features for clustering.

The input of raster images directly as in blockCV::cv_cluster() is not supported. See mlr3spatial and its raster DataBackends for such support in mlr3.

Parameters

• folds (integer(1))
  Number of folds.

• features (character())
  The features to use for clustering.

Super class

mlr3::Resampling -> ResamplingSpCVEnv

Active bindings

iters

integer(1)
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

• ResamplingSpCVEnv$new()

• ResamplingSpCVEnv$instantiate()

• ResamplingSpCVEnv$clone()

Inherited methods

• mlr3::Resampling$format()
• mlr3::Resampling$help()
• mlr3::Resampling$print()
• mlr3::Resampling$test_set()
• mlr3::Resampling$train_set()

Method new()

Create an "Environmental Block" resampling instance.

For a list of available arguments, please see blockCV::cv_cluster.

Usage

ResamplingSpCVEnv$new(id = "spcv_env")

Arguments

id

character(1)
Identifier for the resampling strategy.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVEnv$instantiate(task)

Arguments

task

Task
A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVEnv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi:10.1101/357798.

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_env", folds = 4)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  intersect(rcv$train_set(1), rcv$test_set(1))

  # Internal storage:
  rcv$instance
}