sdm_as_X functions to transform caretSDM data into other classes.

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 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?

Value

The output is the desired class.

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, crs = 6933)
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio12"))
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include scenarios:
sa <- add_scenarios(sa)

# Create occurrences:
oc <- occurrences_sdm(occ, crs = 6933) |> join_area(sa)
#> Warning: Some records from `occ` do not fall in `pred`.
#> ℹ 2 elements from `occ` were excluded.
#> ℹ If this seems too much, check how `occ` and `pred` intersect.

# 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)
#> [1] "Projecting: 1/1"
#> [1] "Ensembling..."
#> [1] "current"
#> [1] "Araucaria angustifolia"

# Transform in stars:
sdm_as_stars(i)
#> stars object with 1 dimensions and 2 attributes
#> attribute(s):
#>                        Min.    1st Qu.     Median       Mean    3rd Qu.
#> cell_id        2.0000000000 10.5000000 18.0000000 18.1111111 25.5000000
#> mean_occ_prob  0.0001950093  0.3517835  0.8884193  0.6700133  0.9488596
#>                      Max.
#> cell_id        34.0000000
#> mean_occ_prob   0.9983319
#> dimension(s):
#>          from to                       refsys point
#> geometry    1 27 WGS 84 / NSIDC EASE-Grid ... FALSE
#>                                                                 values
#> geometry POLYGON ((-5201744 -27950...,...,POLYGON ((-4801744 -31950...