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Folds matched to where the map will be predicted (kNNDM).

Source: R/api.R
knndm_cv.Rd

The better-founded alternative to spatial_cv(). Blocks need a block size and a buffer width, and nothing in the data says whether the chosen ones were right. kNNDM instead shapes the folds so the distance from a validation point to its nearest training point is distributed like the distance from a PREDICTION pixel to its nearest training point.

Usage

knndm_cv(
  k = 5L,
  predpoints = NULL,
  hold_out_test = FALSE,
  crs = 4326,
  project_to = "+proj=moll +lon_0=0 +datum=WGS84 +units=m",
  seed = 42L,
  hold_out_calibration = FALSE,
  ...
)

Arguments

k

Number of folds.

predpoints

Where the map will be predicted: a data frame with x and y, or an sf object. REQUIRED unless modeldomain is passed through ... – the method has no meaning without it, so it is not defaulted.

hold_out_test

Carve a test set by running kNNDM with k + 1 folds and holding one out. FALSE by default: a test set that changes with k is not a frozen test set.

crs

CRS of meta$x/meta$y. 4326 by default.

project_to

Projection used for the distance comparison, or NULL to use the coordinates as they are (correct only if already projected).

seed

Seed, for the sampling kNNDM does internally.

hold_out_calibration

Carve a calibration set for the intervals as the test set is carved: kNNDM in k + 1 folds over what the test left, one held out. FALSE by default.

...

Passed to CAST::knndm() – maxp, clustering, samplesize, modeldomain, space.

Value

A resample_spec, as spatial_cv() returns.

Details

It therefore needs predpoints: a sample of where the map will be drawn. There is no default, because a default would turn the method into an expensive random split. prediction_sample(raster) produces one.

When the samples are well spread over the prediction area it converges by itself to ordinary random k-fold – it does not impose separation that prediction will not face. That is the property blocks cannot have.

Needs the CAST and sf packages. See R/knndm.R for the projection question, which is not optional on lon/lat data.

Examples

ex <- example_landscape()
elevation <- terra::rast(file.path(ex$raster_dir, "elevation.tif"))
knndm_cv(k = 5, predpoints = prediction_sample(elevation, size = 500))
#> <resample_spec> knndm
#>   k               5
#>   predpoints      <528 rows of x, y>
#>   hold_out_test   FALSE
#>   crs             4326
#>   project_to      +proj=moll +lon_0=0 +datum=WGS84 +units=m
#>   seed            42
#>   hold_out_calibration FALSE
#>   extra