Predict species presence probability or counts using H3 hexagons

Uses a fitted tidymodels workflow (from h3sdm_fit_model or a standalone workflow) to predict species presence probabilities or counts on a new spatial H3 grid. Automatically generates centroid coordinates (x and y) if missing. The new_data must contain the same predictor variables as used in model training. Model mode (classification or regression) is detected automatically.

Usage

h3sdm_predict(fit_object, new_data)

Arguments

fit_object

A fitted tidymodels workflow or the output list from h3sdm_fit_model.

new_data

An sf object containing the spatial grid and the same predictor variables used for model training.

Value

An sf object with the original geometry and a new column prediction containing the predicted probability of presence (classification) or predicted count (regression) for each hexagon.

See also

h3sdm_fit_model(), h3sdm_aoa()

Examples

if (FALSE) { # \dontrun{
# Predict presence probabilities on a new hex grid
predictions_sf <- h3sdm_predict(
  fit_object = fitted_model,
  new_data   = grid_sf
)
} # }