Exported because a plan is worth looking at before spending a night on it:
plan <- resolve_resampling(spatial_cv(k = 5), data); print(plan).
Usage
resolve_resampling(
spec,
data,
test_ids = NULL,
windows = NULL,
verbose = TRUE,
calibration_ids = NULL
)Arguments
- spec
From spatial_cv() and friends, or an existing fold_plan, which is returned unchanged.
- data
From dsm_load().
- test_ids
Sample ids to force into the test set, so a frozen test set survives a change of method.
- windows
Windows the grid will use, for
buffer = "auto".- verbose
Print the block size that
block_size = "auto"chose.- calibration_ids
Sample ids to force into the calibration set, in place of the
calibration_fracofspec: several designs then calibrate their "split" intervals on the same points.
Examples
ex <- example_landscape()
store <- dsm_prepare(ex$profiles, target = "soc_stock", raster_dir = ex$raster_dir,
windows = c(3, 7), out_dir = file.path(tempdir(), "landscape"),
percentage = "^clay_pct$", transform = "log1p", n_cores = 1,
overwrite = TRUE, verbose = FALSE)
data <- dsm_load(store, windows = integer(0), verbose = FALSE)
resolve_resampling(spatial_cv(k = 3, block_size = 0.05, test_frac = 0.2), data,
windows = c(3, 7))
#> <fold_plan> spatial_folds | 3 fold(s) | 160 rows in the store
#> params: block_size=0.05 | block_size_auto=FALSE | n_blocks=15 | k=3 | test_frac=0.2 | n_test=45 | seed=42 | buffer=0.0175 | buffer_metric=chebyshev | buffer_protect=validation+test
#> # A tibble: 3 × 4
#> fold n_train n_validation n_test
#> <int> <int> <int> <int>
#> 1 1 58 37 45
#> 2 2 53 37 45
#> 3 3 43 38 45
#> buffer: 76 training point(s) dropped (33.1% per fold on average)
#> near validation: 70 | near test: 6 (a point can be both)
#> validation points dropped for being near test: 3