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Block cross-validation splits the area of your data into a number of grid cells, or "blocks", and then assigns all data into folds based on the blocks their centroid falls into.

Usage

spatial_block_cv(
  data,
  method = c("random", "snake", "continuous"),
  v = 10,
  relevant_only = TRUE,
  radius = NULL,
  buffer = NULL,
  ...
)

Arguments

data

An object of class sf or sfc.

method

The method used to sample blocks for cross validation folds. Currently supports "random", which randomly assigns blocks to folds, "snake", which labels the first row of blocks from left to right, then the next from right to left, and repeats from there, and "continuous", which labels each row from left to right, moving from the bottom row up.

v

The number of partitions for the resampling. Set to NULL or Inf for the maximum sensible value (for leave-one-X-out cross-validation).

relevant_only

For systematic sampling, should only blocks containing data be included in fold labeling?

radius

Numeric: points within this distance of the initially-selected test points will be assigned to the assessment set. If NULL, no radius is applied.

buffer

Numeric: points within this distance of any point in the test set (after radius is applied) will be assigned to neither the analysis or assessment set. If NULL, no buffer is applied.

...

Arguments passed to sf::st_make_grid().

Value

A tibble with classes spatial_block_cv, spatial_rset, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and an identification variable id.

Details

The grid blocks can be controlled by passing arguments to sf::st_make_grid() via .... Some particularly useful arguments include:

  • cellsize: Target cellsize, expressed as the "diameter" (shortest straight-line distance between opposing sides; two times the apothem) of each block, in map units.

  • n: The number of grid blocks in the x and y direction (columns, rows).

  • square: A logical value indicating whether to create square (TRUE) or hexagonal (FALSE) cells.

If both cellsize and n are provided, then the number of blocks requested by n of sizes specified by cellsize will be returned, likely not lining up with the bounding box of data. If only cellsize is provided, this function will return as many blocks of size cellsize as fit inside the bounding box of data. If only n is provided, then cellsize will be automatically adjusted to create the requested number of cells.

References

D. R. Roberts, V. Bahn, S. Ciuti, M. S. Boyce, J. Elith, G. Guillera-Arroita, S. Hauenstein, J. J. Lahoz-Monfort, B. Schröder, W. Thuiller, D. I. Warton, B. A. Wintle, F. Hartig, and C. F. Dormann. "Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure," 2016, Ecography 40(8), pp. 913-929, doi: 10.1111/ecog.02881.

Examples


spatial_block_cv(boston_canopy, v = 3)
#> #  3-fold spatial block cross-validation 
#> # A tibble: 3 × 2
#>   splits            id   
#>   <list>            <chr>
#> 1 <split [445/237]> Fold1
#> 2 <split [434/248]> Fold2
#> 3 <split [485/197]> Fold3