doJk(
  model,
  metric,
  variables = NULL,
  test = NULL,
  with_only = TRUE,
  env = NULL,
  return_models = FALSE
)

# 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 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 = "lq")

# Execute the Jackknife test only for the environmental variables "bio1" and
# "bio12", using the metric AUC
doJk(model, metric = "auc", variables = c("bio1", "bio12"), test = test)

# Execute the Jackknife test only for the environmental variables "bio1" and
# "bio12", using the metric TSS but without running the test for one single
# variable
doJk(model, metric = "tss", variables = c("bio1", "bio12"), test = test,
     with_only = FALSE)

# Execute the Jackknife test only for the environmental variables "bio1" and
# "bio12", using the metric AICc but without running the test for one single
# variable
doJk(model, metric = "aicc", variables = c("bio1", "bio12"),
     with_only = FALSE, env = predictors)

# Execute the Jackknife test for all the environmental variables using the
# metric AUC and returning all the trained models
jk <- doJk(model, metric = "auc", test = test, return_models = TRUE)
jk$results
jk$models_without
jk$models_withonly