SDMtune (version 0.1.0)

trainValTest: Train, Validation and Test datasets

Description

Split a dataset randomly in training and testing datasets or training, validation and testing datasets.

Usage

trainValTest(x, test, val = 0, seed = NULL)

Arguments

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 0.

seed

numeric. The value used to set the seed in order to have consistent results, default is NULL.

Value

A list with the training, validation and testing data sets or training and testing data sets accordingly.

Examples

# 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 locations
p_coords <- condor[, 1:2]

# Create SWD object
presence <- prepareSWD(species = "Vultur gryphus", coords = p_coords,
                       env = predictors, categorical = "biome")

# Split presence locations in training (70%) and testing (30%) datasets
datasets <- trainValTest(presence, test = 0.3)
train <- datasets[[1]]
test <- datasets[[2]]

# Split presence locations in training (60%), validation (20%) and testing
# (20%) datasets
datasets <- trainValTest(presence, val = 0.2, test = 0.2)
train <- datasets[[1]]
val <- datasets[[2]]
test <- datasets[[3]]
# }