caretSDM (version 1.1.0.1)

varImp_sdm: Calculation of variable importance for models

Description

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

Usage

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

Value

A data.frame with variable importance data.

Arguments

m

A models or input_sdm object.

id

Vector of model ids to filter varImp calculation.

...

Parameters passing to caret::varImp().

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="bioclim")

# 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)