Train, Validation and Test datasets
Split a dataset randomly in training and testing datasets or training, validation and testing datasets.
trainValTest(x, test, val = 0, only_presence = FALSE, seed = NULL)
x |
SWD object containing the data that have to be split in training, validation and testing datasets. |
test |
numeric. The percentage of data withhold for testing. |
val |
numeric. The percentage of data withhold for validation, default
is |
only_presence |
logical, if |
seed |
numeric. The value used to set the seed in order to have
consistent results, default is |
When only_presence = FALSE, the proportion of presence and
absence is preserved.
A list with the training, validation and testing or training and testing SWD objects accordingly.
Sergio Vignali
# 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
# and splitting only the presence locations
datasets <- trainValTest(data, test = 0.2, only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]
# Split presence locations in training (60%), validation (20%) and testing
# (20%) datasets and splitting the presence and the absence locations
datasets <- trainValTest(data, val = 0.2, test = 0.2)
train <- datasets[[1]]
val <- datasets[[2]]
test <- datasets[[3]]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.