caretSDM (version 1.1.0.1)

sdm_as_stars: sdm_as_X functions to transform caretSDM data into other classes.

Description

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)

Value

The output is the desired class.

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?

Author

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

Examples

# 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)