Ensemble GCMs into one scenario

An ensembling method to group different GCMs into one SSP scenario

Usage

gcms_ensembles(i, gcms = NULL)

Arguments

i

A input_sdm object.

gcms

GCM codes in scenarios_names(i) to group scenarios.

Value

A input_sdm object with grouped GCMs.

See also

GBIF_data occurrences_sdm sdm_area input_sdm get_predictor_names

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

if (interactive()) {
  # Create sdm_area object:
  set.seed(1)
  sa <- sdm_area(parana, cell_size = 100000, output_crs = 6933)

  # Include predictors:
  sa <- add_predictors(sa, bioc)

  # Include scenarios:
  sa <- add_scenarios(sa, scen) |> select_predictors(c("bio1", "bio12"))

  # Create occurrences:
  oc <- occurrences_sdm(occ, occ_crs = 6933)

  # Create input_sdm:
  i <- input_sdm(oc, sa)

  # Pseudoabsence generation:
  i <- pseudoabsences(i, method = "random", n_set = 2)

  # Custom trainControl:
  ctrl_sdm <- caret::trainControl(
    method = "boot",
    number = 1,
    classProbs = TRUE,
    returnResamp = "all",
    summaryFunction = summary_sdm,
    savePredictions = "all"
  )

  # Train models:
  i <- train_sdm(i,
    algo = c("naive_bayes"),
    ctrl = ctrl_sdm,
    variables_selected = c("bio1", "bio12")
  ) |>
    suppressWarnings()

  # Predict models:
  i <- predict_sdm(i, th = 0.8)

  # Ensemble:
  i <- ensemble_sdm(i, method = "average")
  i

  # Ensemble GCMs:
  i <- gcms_ensembles(i, gcms = c("ca", "mi"))
  i
}