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mlr3spatiotempcv (version 1.0.1)

ResamplingSpCVBuffer: (blockCV) Spatial buffering resampling

Description

(blockCV) Spatial buffering resampling

(blockCV) Spatial buffering resampling

Arguments

mlr3spatiotempcv notes

The 'Description' and 'Details' fields are inherited from the respective upstream function. For a list of available arguments, please see blockCV::buffering.

Super class

mlr3::Resampling -> ResamplingSpCVBuffer

Active bindings

iters

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

Methods

Public methods

Method new()

Create an "Environmental Block" resampling instance.

For a list of available arguments, please see blockCV::buffering().

Usage

ResamplingSpCVBuffer$new(id = "spcv_buffer")

Arguments

id

character(1) Identifier for the resampling strategy.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVBuffer$instantiate(task)

Arguments

task

Task A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVBuffer$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. 10.1101/357798.

See Also

ResamplingSpCVDisc

Examples

Run this code
# NOT RUN {
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_buffer", theRange = 10000)
  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
}
# }

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