SDMtune (version 1.3.2)

combineCV: Combine Cross Validation models

Description

This function combines cross-validation models by retraining a new model with all presence and absence/background locations and the same hyperparameters.

Usage

combineCV(model)

Value

An SDMmodel object.

Arguments

model

SDMmodelCV object.

Author

Sergio Vignali

Details

This is an utility function to retrain a model with all data after, for example, the hyperparameters tuning (gridSearch, randomSearch or optimizeModel) to avoid manual setting of the hyperparameters in the train function.

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")

# Create 4 random folds splitting only the presence data
folds <- randomFolds(data,
                     k = 4,
                     only_presence = TRUE)

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

# Define the hyperparameters to test
h <- list(reg = 1:2,
          fc = c("lqp", "lqph"))

# Run the function using the AUC as metric
output <- gridSearch(model,
                     hypers = h,
                     metric = "auc")
output@results
output@models

# Order results by highest test AUC
output@results[order(-output@results$test_AUC), ]

# Combine cross validation models for output with highest test AUC
idx <- which.max(output@results$test_AUC)
combined_model <- combineCV(output@models[[idx]])
combined_model