Fit and evaluate multiple H3SDM species distribution models

Fits one or more species distribution models using tidymodels workflows and a specified resampling scheme, then computes standard metrics (ROC AUC, accuracy, sensitivity, specificity, F1-score, Kappa) along with TSS (True Skill Statistic) and the Boyce index for model evaluation. Returns both the fitted models and a comparative metrics table.

Usage

h3sdm_fit_models(
  workflows,
  data_split,
  presence_data = NULL,
  truth_col = "presence",
  pred_col = ".pred_1"
)

Arguments

workflows

A named list of tidymodels workflows created with h3sdm_workflow() or manually.

data_split

A resampling object (e.g., from vfold_cv() or h3sdm_spatial_cv()) for cross-validation.

presence_data

An sf object or tibble with presence locations to compute the Boyce index (optional).

truth_col

Character. Name of the column containing true presence/absence values (default "presence").

pred_col

Character. Name of the column containing predicted probabilities (default ".pred_1").

Value

A list with two elements:

models

A list of fitted models returned by h3sdm_fit_model().

metrics

A tibble with one row per model per metric, including standard yardstick metrics, TSS, and Boyce index.

Examples

if (FALSE) { # \dontrun{
# Define workflows for different models
mod_log <- logistic_reg() %>%
  set_engine("glm") %>%
  set_mode("classification")

mod_rf <- rand_forest() %>%
  set_engine("ranger") %>%
  set_mode("classification")

workflows_list <- list(
  logistic = h3sdm_workflow(mod_log, my_recipe),
  rf       = h3sdm_workflow(mod_rf, my_recipe)
)

results <- h3sdm_fit_models(
  workflows     = workflows_list,
  data_split    = my_cv_folds,
  presence_data = presence_sf
)
metrics_table <- results$metrics
} # }