# NOT RUN {
# 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 only presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data, test = 0.2, only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]
# Merge the training and the testing datasets together
merged <- mergeSWD(train, test, only_presence = TRUE)
# Split presence and absence locations in training (80%) and testing (20%)
datasets
datasets <- trainValTest(data, test = 0.2)
train <- datasets[[1]]
test <- datasets[[2]]
# Merge the training and the testing datasets together
merged <- mergeSWD(train, test)
# }
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