SDMtune (version 0.1.0)

tss: True Skill Statistics

Description

Compute the max TSS of a given model.

Usage

tss(model, test = 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 training AUC, default is NULL.

Value

The value of the TSS of the given model.

Details

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

References

Allouche O., Tsoar A., Kadmon R., (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223<U+2013>1232.

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 TSS
tss(model)

# Compute the testing TSS
tss(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 TSS
TSS(model)

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