caretSDM (version 1.1.0.1)

summary_sdm: Calculates performance across resamples

Description

This function is used in caret::trainControl(summaryFunction=summary_sdm) to calculate performance metrics across resamples.

Usage

summary_sdm(data, lev = NULL, model = NULL, custom_fun=NULL)

Value

A input_sdm or a predictions object.

Arguments

data

A data.frame with observed and predicted values.

lev

A character vector of factors levels for the response.

model

Models names taken from train object.

custom_fun

A custom function to be applied in models (not yet implemented).

Author

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

Details

See ?caret::defaultSummary for more details and options to pass on caret::trainControl.

See Also

train_sdm

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()