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Calculates performance across resamples

Source: R/summary_sdm.R
summary_sdm.Rd

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)

summary_sdm_presence_only(data, lev, threshold)

validate_on_independent_data(model, data_independent, obs_col_name)

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).

threshold

Threshold for presence-only models.

data_independent

independent data.frame to calculate metrics.

obs_col_name

The name of the column with observed values.

Value

A input_sdm or a predictions object.

Details

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

See also

train_sdm

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)

# 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()