mlr3spatiotempcv (version 1.0.0)

ResamplingSpCVTiles: (sperrorest) Spatial "Tiles" resampling

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 -> ResamplingSpCVTiles

Active bindings

iters

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

Methods

Public methods

• ResamplingSpCVTiles$new()

• ResamplingSpCVTiles$instantiate()

• ResamplingSpCVTiles$clone()

• mlr3::Resampling$format()

• mlr3::Resampling$help()

• mlr3::Resampling$print()

• mlr3::Resampling$test_set()

• mlr3::Resampling$train_set()

Method new()

Create a "Spatial 'Tiles' resampling" resampling instance.

Usage

ResamplingSpCVTiles$new(id = "spcv_Tiles")

Arguments

id

character(1) Identifier for the resampling strategy. For a list of available arguments, please see sperrorest::partition_tiles.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVTiles$instantiate(task)

Arguments

task

Task A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVTiles$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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. 10.1109/igarss.2012.6352393.

See Also

ResamplingSpCVBlock

Examples

# NOT RUN {
if (mlr3misc::require_namespaces("sperrorest", quietly = TRUE)) {
  library(mlr3)
  task = tsk("ecuador")

  # Instantiate Resampling
  rcv = rsmp("spcv_tiles", nsplit = c(4L, 3L), reassign = FALSE)
  rcv$instantiate(task)

  # Individual sets:
  rcv$train_set(1)
  rcv$test_set(1)
  # check that no obs are in both sets
  intersect(rcv$train_set(1), rcv$test_set(1)) # good!

  # Internal storage:
  rcv$instance # table
}
# }