SDMtune (version 0.1.0)

auc: AUC

Description

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

Usage

auc(model, test = NULL, a = NULL)

Arguments

model

'>SDMmodel or '>SDMmodelCV object.

test

'>SWD test locations for '>SDMmodel objects or logical. for '>SDMmodelCV objects, if not provided it computes the train AUC, default is NULL.

a

'>SWD absence or background locations used to compute the AUC by the permutation importance function, default is NULL.

Value

The value of the AUC.

Details

If the model is a '>SDMmodelCV object, the function computes the mean of the training or testing AUC values of the different replicates.

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.

Examples

# NOT RUN {
# 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 locations
p_coords <- condor[, 1:2]

# Prepare background locations
bg_coords <- dismo::randomPoints(predictors, 5000)

# Create SWD object
presence <- prepareSWD(species = "Vultur gryphus", coords = p_coords,
                       env = predictors, categorical = "biome")
bg <- prepareSWD(species = "Vultur gryphus", coords = bg_coords,
                 env = predictors, categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(presence, test = 0.2)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet", p = train, a = bg, fc = "l")

# Compute the training AUC
auc(model)

# Compute the testing AUC
auc(model, test)

# }
# NOT RUN {
# Same example but using cross validation instead of training and testing
# datasets
model <- train(method = "Maxnet", p = presence, a = bg, fc = "l", rep = 4,
               seed = 25)

# Compute the training AUC
auc(model)

# Compute the testing AUC
auc(model, test = TRUE)
# }