ResamplingRepeatedSpCVTiles: (sperrorest) Repeated spatial "tiles" resampling

Description mlr3spatiotempcv notes Super class Active bindings Methods Note References See Also Examples

Description

partition_tiles divides the study area into a specified number of rectangular tiles. Optionally small partitions can be merged with adjacent tiles to achieve a minimum number or percentage of samples in each tile.

mlr3spatiotempcv notes

The 'Description' and 'Note' fields are inherited from the respective upstream function.

For a list of available arguments, please see sperrorest::partition_tiles.

This method is similar to ResamplingSpCVBlock.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVTiles

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 a "Spatial 'Tiles' resampling" resampling instance.

For a list of available arguments, please see sperrorest::partition_tiles.

Usage
ResamplingRepeatedSpCVTiles$new(id = "repeated_spcv_tiles")
Arguments
id

character(1)
Identifier for the resampling strategy.


Method folds()

Translates iteration numbers to fold number.

Usage
ResamplingRepeatedSpCVTiles$folds(iters)
Arguments
iters

integer()
Iteration number.


Method repeats()

Translates iteration numbers to repetition number.

Usage
ResamplingRepeatedSpCVTiles$repeats(iters)
Arguments
iters

integer()
Iteration number.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage
ResamplingRepeatedSpCVTiles$instantiate(task)
Arguments
task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage
ResamplingRepeatedSpCVTiles$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Note

Default parameter settings may change in future releases. This function, especially the rotation and shifting part of it and the algorithm for cleaning up small tiles is still a bit experimental. Use with caution. For non-zero offsets (offset!='none')), the number of tiles may actually be greater than nsplit[1]*nsplit[2] because of fractional tiles lurking into the study region. reassign=TRUE with suitable thresholds is therefore recommended for non-zero (including random) offsets.

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. doi: 10.1109/igarss.2012.6352393.

See Also

ResamplingSpCVBlock

Examples

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

  # Instantiate Resampling
  rrcv = rsmp("repeated_spcv_tiles",
    repeats = 2,
    nsplit = c(4L, 3L), reassign = FALSE)
  rrcv$instantiate(task)

  # Individual sets:
  rrcv$iters
  rrcv$folds(10:12)
  rrcv$repeats(10:12)

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

  # Internal storage:
  rrcv$instance # table
}

mlr-org/mlr3spatiotempcv documentation built on May 4, 2021, 9:44 a.m.