Predict SDM models in new data

This function projects SDM models to new scenarios

Usage

predict_sdm(m,
            scen = NULL,
            metric = "ROC",
            th = 0.9,
            tp = "prob",
            file = NULL,
            add.current = TRUE)

get_predictions(i)

add_predictions(p1, p2)

Arguments

m: A input_sdm or a models object.
scen: A scenarios object or NULL. If NULL and m is a input_sdm with a scenarios slot, it will be used.
metric: A character containing the metric in which the th will be calculated/applied. Default is ROC. See ?mean_validation_metrics for the metrics available.
th: Thresholds for metrics. Can be numeric or a function.
tp: Type of output to be retrieved. See details.
file: File to sabe predictions.
add.current: If current scenario is not available, predictors will be used as the current scenario.
i: A input_sdm or a predictions object.
p1: A predictions object.
p2: A predictions object.

Value

A input_sdm or a predictions object.

Details

tp is a parameter to be passed on caret to retrieve either the probabilities of classes (tp="prob") or the raw output (tp="raw"), which could vary depending on the algorithm used, but usually would be on of the classes (factor vector with presences and pseudoabsences).

get_predictions returns the list of all predictions to all scenarios, all species, all algorithms and all repetitions. Useful for those who wish to implement their own ensemble methods.

scenarios_names returns the scenarios names in a sdm_area or input_sdm object.

get_scenarios_data returns the data from scenarios in a sdm_area or input_sdm object.

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, 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, crs = 6933) |> join_area(sa)

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

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

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