gcms_ensembles {caretSDM} | R Documentation |
Ensemble GCMs into one scenario
Description
An ensembling method to group different GCMs into one SSP scenario
Usage
gcms_ensembles(i, gcms = NULL)
Arguments
i |
A |
gcms |
GCM codes in |
Value
A input_sdm
object with grouped GCMs.
Author(s)
Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com
See Also
GBIF_data occurrences_sdm sdm_area input_sdm
predictors
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
[Package caretSDM version 1.1.0.1 Index]