mlr_resamplings_spcv_tiles {mlr3spatiotempcv}

(sperrorest) Spatial "Tiles" resampling

Description

Spatial partitioning using rectangular tiles. Small partitions can optionally be merged into adjacent ones to avoid partitions with too few observations. This method is similar to ResamplingSpCVBlock by making use of rectangular zones in the coordinate space. See the upstream implementation at sperrorest::partition_disc() and Brenning (2012) for further information.

Parameters

• dsplit (integer(2))
  Equidistance of splits in (possibly rotated) x direction (dsplit[1]) and y direction (dsplit[2]) used to define tiles. If dsplit is of length 1, its value is recycled. Either dsplit or nsplit must be specified.

• nsplit (integer(2))
  Number of splits in (possibly rotated) x direction (nsplit[1]) and y direction (nsplit[2]) used to define tiles. If nsplit is of length 1, its value is recycled.

• rotation (character(1))
  Whether and how the rectangular grid should be rotated; random rotation is only possible between -45 and +45 degrees. Accepted values: One of c("none", "random", "user").

• user_rotation (character(1))
  Only used when rotation = "user". Angle(s) (in degrees) by which the rectangular grid is to be rotated in each repetition. Either a vector of same length as repeats, or a single number that will be replicated length(repeats) times.

• offset (logical(1))
  Whether and how the rectangular grid should be shifted by an offset. Accepted values: One of c("none", "random", "user").

• user_offset (logical(1))
  Only used when offset = "user". A list (or vector) of two components specifying a shift of the rectangular grid in (possibly rotated) x and y direction. The offset values are relative values, a value of 0.5 resulting in a one-half tile shift towards the left, or upward. If this is a list, its first (second) component refers to the rotated x (y) direction, and both components must have same length as repeats (or length 1). If a vector of length 2 (or list components have length 1), the two values will be interpreted as relative shifts in (rotated) x and y direction, respectively, and will therefore be recycled as needed (length(repeats) times each).

• reassign (logical(1))
  If TRUE, 'small' tiles (as per min_frac and min_n) are merged with (smallest) adjacent tiles. If FALSE, small tiles are 'eliminated', i.e., set to NA.

• min_frac (numeric(1))
  Value must be >=0, <1. Minimum relative size of partition as percentage of sample.

• min_n (integer(1))
  Minimum number of samples per partition.

• iterate (integer(1))
  Passed down to sperrorest::tile_neighbors().

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()

Inherited methods

• 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. doi:10.1109/igarss.2012.6352393.

See Also

ResamplingSpCVBlock

Examples

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
}