Train a Stacked Ensemble for SDM

This function builds a meta-model (Layer 2) using the out-of-fold predictions from models trained in Layer 1.

Usage

stack_sdm(m, meta_algo = "glm", ctrl = NULL, ...)

Arguments

m

A models or input_sdm object.

meta_algo

A character string specifying the algorithm for the meta-learner.

ctrl

A trainControl object for the meta-learner. If NULL, a simple CV is used.

...

Additional arguments passed to caret::train.

Value

A stacked_models object.

See also

input_sdm sdm_area algorithms train_sdm

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) |> select_predictors(c("bio1", "bio12"))

  # Include scenarios:
  sa <- add_scenarios(sa)

  # 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 = "repeatedcv",
    number = 2,
    repeats = 1,
    classProbs = TRUE,
    returnResamp = "all",
    summaryFunction = summary_sdm,
    savePredictions = "all"
  )

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

  # Train stacked ensemble:
  i <- stack_sdm(i, meta_algo = "nnet", ctrl = ctrl_sdm)

  # Prediction of stacked models is still under development.
}