sdm_as_stars {caretSDM} | R Documentation |
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)
Arguments
x |
A |
what |
Sometimes multiple data inside |
spp |
|
scen |
|
id |
|
ens |
|
Value
The output is the desired class.
Author(s)
Luíz Fernando Esser (luizesser@gmail.com) 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)
[Package caretSDM version 1.1.0.1 Index]