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(blockCV) Repeated spatial block resampling

Source: R/ResamplingRepeatedSpCVBlock.R
mlr_resamplings_repeated_spcv_block.Rd

This function creates spatially separated folds based on a distance to number of row and/or column. It assigns blocks to the training and testing folds randomly, systematically or in a checkerboard pattern. The distance (size) should be in metres, regardless of the unit of the reference system of the input data (for more information see the details section). By default, the function creates blocks according to the extent and shape of the spatial sample data (x e.g. the species occurrence), Alternatively, blocks can be created based on r assuming that the user has considered the landscape for the given species and case study. Blocks can also be offset so the origin is not at the outer corner of the rasters. Instead of providing a distance, the blocks can also be created by specifying a number of rows and/or columns and divide the study area into vertical or horizontal bins, as presented in Wenger & Olden (2012) and Bahn & McGill (2012). Finally, the blocks can be specified by a user-defined spatial polygon layer.

Details

To maintain consistency, all functions in this package use meters as their unit of measurement. However, when the input map has a geographic coordinate system (in decimal degrees), the block size is calculated by dividing the size parameter by deg_to_metre (which defaults to 111325 meters, the standard distance of one degree of latitude on the Equator). In reality, this value varies by a factor of the cosine of the latitude. So, an alternative sensible value could be cos(mean(sf::st_bbox(x)[c(2,4)]) * pi/180) * 111325.

The offset can be used to change the spatial position of the blocks. It can also be used to assess the sensitivity of analysis results to shifting in the blocking arrangements. These options are available when size is defined. By default the region is located in the middle of the blocks and by setting the offsets, the blocks will shift.

Roberts et. al. (2017) suggest that blocks should be substantially bigger than the range of spatial autocorrelation (in model residual) to obtain realistic error estimates, while a buffer with the size of the spatial autocorrelation range would result in a good estimation of error. This is because of the so-called edge effect (O'Sullivan & Unwin, 2014), whereby points located on the edges of the blocks of opposite sets are not separated spatially. Blocking with a buffering strategy overcomes this issue (see cv_buffer).

mlr3spatiotempcv notes

By default blockCV::cv_spatial() does not allow the creation of multiple repetitions. mlr3spatiotempcv adds support for this when using the size argument for fold creation. When supplying a vector of length(repeats) for argument size, these different settings will be used to create folds which differ among the repetitions.

Multiple repetitions are not possible when using the "row & cols" approach because the created folds will always be the same.

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

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

blockCV >= 3.0.0 changed the argument names of the implementation. For backward compatibility, mlr3spatiotempcv is still using the old ones. Here's a list which shows the mapping between blockCV < 3.0.0 and blockCV >= 3.0.0:

  • range -> size

  • rasterLayer -> r

  • speciesData -> points

  • showBlocks -> plot

  • cols and rows -> rows_cols

The default of argument hexagon is different in mlr3spatiotempcv (FALSE instead of TRUE) to create square blocks instead of hexagonal blocks by default.

Parameters

  • repeats (integer(1))
    Number of repeats.

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 .

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVBlock

Public fields

blocks

sf | list of sf objects
Polygons (sf objects) as returned by blockCV which grouped observations into partitions.

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 "spatial block" repeated resampling instance.

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

Usage

ResamplingRepeatedSpCVBlock$new(id = "repeated_spcv_block")

Arguments

id

character(1)
Identifier for the resampling strategy.

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVBlock$folds(iters)

Arguments

iters

integer()
Iteration number.

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVBlock$repeats(iters)

Arguments

iters

integer()
Iteration number.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVBlock$instantiate(task)

Arguments

task

Task
A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVBlock$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) {
if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  task = tsk("diplodia")

  # Instantiate Resampling
  rrcv = rsmp("repeated_spcv_block",
    folds = 3, repeats = 2,
    range = c(5000L, 10000L))
  rrcv$instantiate(task)

  # Individual sets:
  rrcv$iters
  rrcv$folds(1:6)
  rrcv$repeats(1:6)

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

  # Internal storage:
  rrcv$instance # table
}
}