Prediction Change Analysis

Provides an automate way for the visualization of projections gain, loss, and stability between different scenarios.

Usage

prediction_change_sdm(i, scenario = NULL, ensemble_type = NULL, species = NULL, th = 0.5)

Arguments

i

A input_sdm object with projections.

scenario

Character. One of the scenarios that were projected. Can be ensembles as well.

ensemble_type

Character. Type of ensemble to be used. Standard is NULL, but will return the average.

species

Character. Species to be analyzed. Standard is NULL.

th

Numeric. Threshold to binarize the ensemble.

Value

A plot with comparison between current and other scenario.

See also

species_names scenarios_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, 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, 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,
                                  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")

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

  # Change Analysis
  prediction_change_sdm(i, scenario = "_ssp585_2090", ensemble_type = "mean_occ_prob")
}