caretSDM (version 1.2.3)

prediction_change_sdm: Prediction Change Analysis

Description

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)

Value

A plot with comparison between current and other scenario.

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 mean_occ_prob

species

Character. Species to be analyzed. Standard is NULL.

th

Numeric. Threshold to binarize the ensemble.

Author

Luíz Fernando Esser ([e-mail]) https://luizfesser.wordpress.com

See Also

species_names scenarios_names

Examples

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

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