Predict SDM models in new data

This function projects SDM models to new scenarios

Usage

ensemble_sdm(m,
            scen = NULL,
            method = "average",
            metric = NULL,
            fun = NULL
            )

get_ensembles(i)

add_ensembles(e1, e2)

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.
method: Character or a function. Which ensembles should be calculated? See details.
metric: Character. Used with method = "weighted_average": Which metric should be used to weight predictions? If NULL
fun: Function. If method = "committee_average", the function will be used to binarize the data. It will receive caret's train object and must return a numeric value (the threshold, see details).
i: A input_sdm or a predictions object.
e1: A ensembles object.
e2: A ensembles object.

Value

A input_sdm or a predictions object.

Details

ensembles could be set to three different strategies OR a custom function. The three implemented strategies are: average is the mean occurrence probability, which is a simple mean of predictions; weighted_average is the same average, but weighted by a metric, which needs to be set using argument metric (see mean_validation_metrics for the metrics available). committee_average is the committee average, as known as majority rule, where predictions are binarized and then a mean is obtained. To binarize predictions, user can set a custom function in the fun argument to calculate a threshold for each model. Standardly, the committee average uses the caret::thresholder function to find the threshold that maximizes the sum of sensitivity and specificity (through caretSDM:::.MaxSeSp). The custom function must use the argument mod, which is the model output from caret package (see get_models) and must return a numeric value (see example).

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.

get_ensembles returns a matrix of data.frames, where each column is a scenario and each row is a species.

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)

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

# Example from a custom function to obtain the threshold that maximizes
# the sensitivity plus specificity:
MaxSeSp <- function(mod) {
   th <- caret::thresholder(mod,
                            threshold = seq(0, 1, by = 0.001),
                            final = TRUE,
                            statistics = c("Sensitivity", "Specificity")
                            )
   th <- th$prob_threshold[which.max(th$Sensitivity + th$Specificity)]
   if (length(th) > 1) mean(th) else th
}