Spatially blocked k-fold: whole blocks of ground go to one fold.

Usage

spatial_cv(
  k = 5L,
  block_size = "auto",
  buffer = "auto",
  test_frac = 0.15,
  max_share = 0.1,
  buffer_metric = c("chebyshev", "euclidean"),
  seed = 42L,
  calibration_frac = 0
)

Arguments

k: Folds.
block_size: "auto" measures it from the points (see suggest_block_size); a number is used as given, in x/y units.
buffer: "auto" is max(window) * cell_size, which is the exact distance at which two patches stop sharing a pixel under the Chebyshev metric; a number is used as given; NULL applies none.
test_frac: Share held out entirely, carved by the same criterion.
max_share: Balance constraint for "auto": the largest share of the points one block may hold.
buffer_metric: "chebyshev" (the default) or "euclidean". Chebyshev is exact for square patches: two patches share a pixel when their centres are within the window in both axes.
seed: Seed for this split only; the training seeds do not move it.
calibration_frac: Share of ALL the points held out as a calibration set for the intervals, carved after the test set by the same criterion; 0 for none. It trains nothing and chooses nothing: dsm_final() predicts it and calibrates its "split" interval there.

Value

A resample_spec. resolve_resampling() turns it into folds against the points, and dsm_train() does that itself.

Examples

spatial_cv(k = 5, block_size = "auto", buffer = "auto")
#> <resample_spec> spatial
#>   k               5
#>   block_size      auto
#>   buffer          auto
#>   test_frac       0.15
#>   max_share       0.1
#>   buffer_metric   chebyshev
#>   seed            42
#>   calibration_frac 0
# 15% of the points held out to calibrate split conformal intervals
spatial_cv(k = 5, calibration_frac = 0.15)
#> <resample_spec> spatial
#>   k               5
#>   block_size      auto
#>   buffer          auto
#>   test_frac       0.15
#>   max_share       0.1
#>   buffer_metric   chebyshev
#>   seed            42
#>   calibration_frac 0.15