Compute the AUC using the Man-Whitney U Test formula.

auc(model, test = NULL)

Arguments

model

An SDMmodel or SDMmodelCV object.

test

SWD object when model is an SDMmodel object; logical or SWD object when model is an SDMmodelCV object. If not provided it computes the training AUC, see details. Default is NULL.

Value

The value of the AUC.

Details

For SDMmodelCV objects, the function computes the mean of the training AUC values of the k-folds. If test = TRUE it computes the mean of the testing AUC values for the k-folds. If test is an SWD object, it computes the mean AUC values for the provided testing dataset.

References

Mason, S. J. and Graham, N. E. (2002), Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Q.J.R. Meteorol. Soc., 128: 2145-2166.

See also

aicc and tss.

Author

Sergio Vignali

Examples

# Acquire environmental variables files <- list.files(path = file.path(system.file(package = "dismo"), "ex"), pattern = "grd", full.names = TRUE) predictors <- raster::stack(files) # Prepare presence and background locations p_coords <- virtualSp$presence bg_coords <- virtualSp$background # Create SWD object data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords, env = predictors, categorical = "biome")
#> Extracting predictor information for presence locations...
#> Extracting predictor information for absence/background locations...
# Split presence locations in training (80%) and testing (20%) datasets datasets <- trainValTest(data, test = 0.2, only_presence = TRUE) train <- datasets[[1]] test <- datasets[[2]] # Train a model model <- train(method = "Maxnet", data = train, fc = "l") # Compute the training AUC auc(model)
#> [1] 0.8411522
# Compute the testing AUC auc(model, test = test)
#> [1] 0.8352
# \donttest{ # Same example but using cross validation instead of training and testing # datasets # Create the folds folds <- randomFolds(data, k = 4, only_presence = TRUE) model <- train(method = "Maxnet", data = data, fc = "l", folds = folds) # Compute the training AUC auc(model)
#> [1] 0.8415831
# Compute the testing AUC auc(model, test = TRUE)
#> [1] 0.8344168
# Compute the AUC for the held apart testing dataset auc(model, test = test)
#> [1] 0.8393469
# }