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Partial ROC calculation for multiple candidate models

partial_roc.Rd

Computes partial ROC tests for multiple candidate models.

Usage

partial_roc(formula_grid, data, omission_rate = 10,
            addsamplestobackground = TRUE, weights = NULL,
            algorithm = "maxnet", parallel = FALSE, ncores = NULL,
            progress_bar = TRUE)

Arguments

formula_grid

a data.frame with the grid of formulas defining the candidate models to test.

data

an object of class prepared_data returned by the prepare_data() function or an object of class calibration_results returned by the calibration() function. It contains the calibration data and k-folds.

omission_rate

(numeric) values from 0 to 100 representing the percentage of potential error due to any source of uncertainty. This value is used to calculate the omission rate. Default is 10. See details.

addsamplestobackground

(logical) whether to add to the background any presence sample that is not already there. Default is TRUE.

weights

(numeric) a numeric vector specifying weights for the occurrence records. Default is NULL.

algorithm

(character) type algorithm, either "glm" or "maxnet". Default is "maxnet".

parallel

(logical) whether to fit the candidate models in parallel. Default is FALSE.

ncores

(numeric) number of cores to use for parallel processing. Default is NULL and uses available cores - 1. This is only applicable if parallel = TRUE.

progress_bar

(logical) whether to display a progress bar during processing. Default is TRUE.

Value

A data frame with summary statistics of the and AUC ratios and significance calculated from the replicates of each candidate model. Specifically, it includes the mean and standard deviation of these metrics for each model.

Details

Partial ROC is calculated following Peterson et al. (2008) doi:10.1016/j.ecolmodel.2007.11.008.

Examples

# Import prepared data to get model formulas
data(sp_swd, package = "kuenm2")

# Calculate proc for the first 5 candidate models
res_proc <- partial_roc(formula_grid = sp_swd$formula_grid[1:2,],
                        data = sp_swd, omission_rate = 10,
                        algorithm = "maxnet")
#> 
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