auc {SDMtune} | R Documentation |
AUC
Description
Compute the AUC using the Man-Whitney U Test formula.
Usage
auc(model, test = NULL)
Arguments
model |
An SDMmodel or SDMmodelCV object. |
test |
SWD object when |
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.
Value
The value of the AUC.
Author(s)
Sergio Vignali
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
Examples
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
pattern = "grd",
full.names = TRUE)
predictors <- terra::rast(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")
# 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)
# Compute the testing AUC
auc(model,
test = test)
# Same example but using cross validation instead of training and testing
# datasets
folds <- randomFolds(data,
k = 4,
only_presence = TRUE)
model <- train(method = "Maxnet",
data = data,
fc = "l",
folds = folds)
# Compute the training AUC
auc(model)
# Compute the testing AUC
auc(model,
test = TRUE)
# Compute the AUC for the held apart testing dataset
auc(model,
test = test)