Calculation of variable importance for models

This function retrieves variable importance as a function of ROC curves to each predictor.

Usage

varImp_sdm(m, id = NULL, ...)

Arguments

m

A models or input_sdm object.

id

Vector of model ids to filter varImp calculation.

...

Parameters passing to caret::varImp().

Value

A data.frame with variable importance data.

Author

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

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, output_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, occ_crs = 6933)

# Create input_sdm:
i <- input_sdm(oc, sa)

# Pseudoabsence generation:
i <- pseudoabsences(i, method = "random")

# Custom trainControl:
ctrl_sdm <- caret::trainControl(
  method = "repeatedcv",
  number = 2,
  repeats = 1,
  classProbs = TRUE,
  returnResamp = "all",
  summaryFunction = summary_sdm,
  savePredictions = "all"
)

# Train models:
i <- train_sdm(i, algo = c("naive_bayes"), ctrl = ctrl_sdm) |>
  suppressWarnings()

# Variable importance:
varImp_sdm(i)
#> $`Araucaria angustifolia`
#>       mean sd
#> bio1   100  0
#> bio12    0  0
#>