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Create a spatial-aware cross-validation split for H3 data

Source: R/h3sdm_spatial_cv.R
h3sdm_spatial_cv.Rd

Generates a spatially aware cross-validation split for species distribution modeling using H3 hexagonal grids. This helps avoid inflated model performance estimates caused by spatial autocorrelation, producing more robust model evaluation.

Usage

h3sdm_spatial_cv(data, method = "block", v = 5, ...)

Arguments

data

An sf object, typically the output of h3sdm_data().

method

Character. The spatial resampling method to use:

"block"

Use spatialsample::spatial_block_cv() for block-based spatial CV.

"cluster"

Use spatialsample::spatial_clustering_cv() for cluster-based spatial CV.

v

Integer. Number of folds (default = 5).

...

Additional arguments passed to the underlying spatialsample function.

Value

An rsplit object (from rsample) representing the spatial CV folds.

Details

Spatial cross-validation avoids overly optimistic performance estimates by ensuring that training and testing data are spatially separated.

  • "block": Divides the spatial domain into contiguous blocks.

  • "cluster": Groups locations into spatial clusters before splitting.

Examples

if (FALSE) { # \dontrun{
# Example: Create spatial cross-validation splits for H3 data

# Block spatial CV with default folds
spatial_cv_block <- h3sdm_spatial_cv(combined_data, method = "block")

# Cluster spatial CV with 10 folds
spatial_cv_cluster <- h3sdm_spatial_cv(combined_data, method = "cluster", v = 10)
} # }