SDMtune: optimizeModel – R documentation

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optimizeModel

Optimize Model

Description

The function uses a Genetic Algorithm implementation to optimize the model hyperparameter configuration according to the chosen metric.

Usage

optimizeModel(
  model,
  hypers,
  metric,
  test = NULL,
  pop = 20,
  gen = 5,
  env = NULL,
  keep_best = 0.4,
  keep_random = 0.2,
  mutation_chance = 0.4,
  seed = NULL
)

Arguments

`model`	SDMmodel or SDMmodelCV object.
`hypers`	named list containing the values of the hyperparameters that should be tuned, see details.
`metric`	character. The metric used to evaluate the models, possible values are: "auc", "tss" and "aicc".
`test`	SWD object. Testing dataset used to evaluate the model, not used with aicc and SDMmodelCV objects, default is `NULL`.
`pop`	numeric. Size of the population, default is 5.
`gen`	numeric. Number of generations, default is 20.
`env`	stack containing the environmental variables, used only with "aicc", default is `NULL`.
`keep_best`	numeric. Percentage of the best models in the population to be retained during each iteration, expressed as decimal number. Default is 0.4.
`keep_random`	numeric. Probability of retaining the excluded models during each iteration, expressed as decimal number. Default is 0.2.
`mutation_chance`	numeric. Probability of mutation of the child models, expressed as decimal number. Default is 0.4.
`seed`	numeric. The value used to set the seed to have consistent results, default is `NULL`.

Details

To know which hyperparameters can be tuned you can use the output of the function getTunableArgs. Hyperparameters not included in the hypers argument take the value that they have in the passed model.

Part of the code is inspired by this post.

Value

SDMtune object.

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 <- 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, val = 0.2, test = 0.2, only_presence = TRUE,
                         seed = 61516)
train <- datasets[[1]]
val <- datasets[[2]]

# Train a model
model <- train("Maxnet", data = train)

# Define the hyperparameters to test
h <- list(reg = seq(0.2, 5, 0.2),
          fc = c("l", "lq", "lh", "lp", "lqp", "lqph"))

# Run the function using as metric the AUC
## Not run: 
output <- optimizeModel(model, hypers = h, metric = "auc", test = val,
                        pop = 15, gen = 2, seed = 798)
output@results
output@models
output@models[[1]]  # Best model

## End(Not run)

SDMtune

Species Distribution Model Selection

v1.1.4

GPL-3

Authors

Sergio Vignali [aut, cre] (<https://orcid.org/0000-0002-3390-5442>), Arnaud Barras [aut] (<https://orcid.org/0000-0003-0850-6965>), Veronika Braunisch [aut] (<https://orcid.org/0000-0001-7035-4662>), Conservation Biology - University of Bern [fnd]

Initial release