reduceVar: Reduce Variables

In SDMtune: Species Distribution Model Selection

Reduce Variables

Description

Remove variables whose importance is less than the given threshold. The function removes one variable at time and after trains a new model to get the new variable contribution rank. If use_jk is TRUE the function checks if after removing the variable the model performance decreases (according to the given metric and based on the starting model). In this case the function stops removing the variable even if the contribution is lower than the given threshold.

Usage

reduceVar(
  model,
  th,
  metric,
  test = NULL,
  env = NULL,
  use_jk = FALSE,
  permut = 10,
  use_pc = FALSE,
  interactive = TRUE,
  verbose = TRUE
)

Arguments

model	SDMmodel or SDMmodelCV object.
th	numeric. The contribution threshold used to remove variables.
metric	character. The metric used to evaluate the models, possible values are: "auc", "tss" and "aicc", used only if use_jk is TRUE.
test	SWD object containing the test dataset used to evaluate the model, not used with aicc, and if use_jk = FALSE.
env	rast containing the environmental variables, used only with "aicc".
use_jk	Flag to use the Jackknife AUC test during the variable selection, if FALSE the function uses the percent variable contribution.
permut	integer. Number of permutations, used if use_pc = FALSE.
use_pc	logical. If TRUE and the model is trained using the Maxent method, the algorithm uses the percent contribution computed by Maxent software to score the variable importance.
interactive	logical. If FALSE the interactive chart is not created.
verbose	logical. If TRUE prints informative messages.

Details

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

Value

The model trained using the selected variables.

Author(s)

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 Maxnet model
model <- train(method = "Maxnet",
               data = train,
               fc = "lq")

# Remove all variables with permuation importance lower than 2%
output <- reduceVar(model,
                    th = 2,
                    metric = "auc",
                    test = test,
                    permut = 1)

# Remove variables with permuation importance lower than 3% only if testing
# TSS doesn't decrease
## Not run: 
output <- reduceVar(model,
                    th = 3,
                    metric = "tss",
                    test = test,
                    permut = 1,
                    use_jk = TRUE)

# Remove variables with permuation importance lower than 2% only if AICc
# doesn't increase
output <- reduceVar(model,
                    th = 2,
                    metric = "aicc",
                    permut = 1,
                    use_jk = TRUE,
                    env = predictors)

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

# Remove all variables with percent contribution lower than 2%
output <- reduceVar(model,
                    th = 2,
                    metric = "auc",
                    test = test,
                    use_pc = TRUE)
## End(Not run)

SDMtune documentation built on July 9, 2023, 6:03 p.m.