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MaxEnt Thresholds

Source: R/maxentTh.R
maxentTh.Rd

Returns the value of the thresholds generated by the MaxEnt software.

Usage

maxentTh(model)

Arguments

model

SDMmodel object trained using the "Maxent" method.

Value

data.frame with the thresholds.

See also

maxentVarImp.

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 [35ms]
#> 
#> ℹ Extracting predictor information for absence/background locations
#> ✔ Extracting predictor information for absence/background locations [64ms]
#> 

# Train a Maxent model
model <- train(method = "Maxent",
               data = data,
               fc = "l")

maxentTh(model)
#>                                                                       threshold
#> Fixed.cumulative.value.1.Cloglog                                         0.0320
#> Fixed.cumulative.value.5.Cloglog                                         0.1013
#> Fixed.cumulative.value.10.Cloglog                                        0.2317
#> Minimum.training.presence.Cloglog                                        0.1068
#> X10.percentile.training.presence.Cloglog                                 0.3373
#> Equal.training.sensitivity.and.specificity.Cloglog                       0.4937
#> Maximum.training.sensitivity.plus.specificity.Cloglog                    0.2880
#> Balance.training.omission..predicted.area.and.value.Cloglog              0.1068
#> Balance.training.omission..predicted.area.and.value.area                 0.5096
#> Balance.training.omission..predicted.area.and.value.training.omission    0.0000
#> Equate.entropy.ofed.and.original.distributions.Cloglog                   0.1412
#> Equate.entropy.ofed.and.original.distributions.area                      0.4506
#> Equate.entropy.ofed.and.original.distributions.training.omission         0.0200