mlr_resamplings_repeated_spcv_env {mlr3spatiotempcv}R Documentation

(blockCV) Repeated "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

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVEnv

Active bindings

iters

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

Methods

Public methods

Inherited methods

Method new()

Create an "Environmental Block" repeated resampling instance.

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

Usage
ResamplingRepeatedSpCVEnv$new(id = "repeated_spcv_env")
Arguments
id

character(1)
Identifier for the resampling strategy.


Method folds()

Translates iteration numbers to fold number.

Usage
ResamplingRepeatedSpCVEnv$folds(iters)
Arguments
iters

integer()
Iteration number.


Method repeats()

Translates iteration numbers to repetition number.

Usage
ResamplingRepeatedSpCVEnv$repeats(iters)
Arguments
iters

integer()
Iteration number.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage
ResamplingRepeatedSpCVEnv$instantiate(task)
Arguments
task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage
ResamplingRepeatedSpCVEnv$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
  rrcv = rsmp("repeated_spcv_env", folds = 4, repeats = 2)
  rrcv$instantiate(task)

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

  # Internal storage:
  rrcv$instance
}


[Package mlr3spatiotempcv version 2.3.1 Index]