SDMtune (version 1.3.2)

doJk: Jackknife Test

Description

Run the Jackknife test for variable importance removing one variable at time.

Usage

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

Value

A data frame with the test results. If return_model = TRUE it returns a list containing the test results together with the models.

Arguments

model

SDMmodel or SDMmodelCV object.

metric

character. The metric used to evaluate the models, possible values are: "auc", "tss" and "aicc".

variables

vector. Variables used for the test, if not provided it takes all the variables used to train the model.

test

SWD. If provided it reports the result also for the testing dataset. Not used for aicc and SDMmodelCV.

with_only

logical. If TRUE it runs the test also for each variable in isolation.

env

rast containing the environmental variables, used only with "aicc".

return_models

logical. If TRUE returns all the models together with the test result.

progress

logical If TRUE shows a progress bar.

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")

# 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)

# The same without testing dataset
doJk(model,
     metric = "auc",
     variables = c("bio1", "bio12"))

# 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