sdm_as_X functions to transform caretSDM data into other classes.

This functions transform data from a caretSDM object to be used in other packages.

Usage

sdm_as_stars(x,
             what = NULL,
             spp = NULL,
             scen = NULL,
             id = NULL,
             ens = NULL)

sdm_as_raster(x, what = NULL, spp = NULL, scen = NULL, id = NULL, ens = NULL)

sdm_as_terra(x, what = NULL, spp = NULL, scen = NULL, id = NULL, ens = NULL)

Arguments

x: A caretSDM object.
what: Sometimes multiple data inside x could be transformed. This parameter allows users to specify what needs to be converted.It can be one of: "predictors", "scenarios", "predictions" or "ensembles".
spp: character. Which species should be converted?
scen: character. Which scenario should be converted?
id: character. Which id should be converted?
ens: character. Which ensemble should be converted?

Value

The output is the desired class.

Author

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

Examples

if (interactive()) {
  # Create sdm_area object:
  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,
                                  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)

  # Transform in stars:
  sdm_as_stars(i)
}