Thresholds

Compute three threshold values: minimum training presence, equal training sensitivity and specificity and maximum training sensitivity plus specificity together with fractional predicted area and the omission rate. If a test dataset is provided it returns also the equal test sensitivity and specificity and maximum test sensitivity plus specificity thresholds and the p-values of the one-tailed binomial exact test.

Usage

thresholds(model, type = NULL, test = NULL)

Arguments

model

SDMmodel object.

type

character. The output type used for "Maxent" and "Maxnet" methods, possible values are "cloglog" and "logistic".

test

SWD testing locations, if not provided it returns the training and test thresholds.

Value

data.frame with the thresholds.

Details

The equal training sensitivity and specificity minimizes the difference between sensitivity and specificity. The one-tailed binomial test checks that test points are predicted no better than by a random prediction with the same fractional predicted area.

Author

Sergio Vignali

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")
#> ℹ Extracting predictor information for presence locations
#> ✔ Extracting predictor information for presence locations [20ms]
#> 
#> ℹ Extracting predictor information for absence/background locations
#> ✔ Extracting predictor information for absence/background locations [47ms]
#> 

# 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")

# Get the cloglog thresholds
thresholds(model,
           type = "cloglog")
#>                                       Threshold Cloglog value
#> 1                     Minimum training presence    0.08913867
#> 2    Equal training sensitivity and specificity    0.49192908
#> 3 Maximum training sensitivity plus specificity    0.29045251
#>   Fractional predicted area Training omission rate
#> 1                    0.5332               0.000000
#> 2                    0.2376               0.237500
#> 3                    0.3412               0.059375

# Get the logistic thresholds passing the test dataset
thresholds(model,
           type = "logistic",
           test = test)
#>                                       Threshold Logistic value
#> 1                     Minimum training presence     0.08539202
#> 2    Equal training sensitivity and specificity     0.40374481
#> 3 Maximum training sensitivity plus specificity     0.25546924
#> 4        Equal test sensitivity and specificity     0.36816416
#> 5     Maximum test sensitivity plus specificity     0.21103523
#>   Fractional predicted area Training omission rate Test omission rate P-values
#> 1                    0.5332               0.000000       0.0000000000        0
#> 2                    0.2376               0.237500       0.0045275591        0
#> 3                    0.3412               0.059375       0.0019685039        0
#> 4                    0.2618               0.190625       0.0041338583        0
#> 5                    0.3736               0.043750       0.0005905512        0