Create random folds for cross validation.
randomFolds(data, k, only_presence = FALSE, seed = NULL)
data | SWD object that will be used to train the model. |
---|---|
k | integer. Number of fold used to create the partition. |
only_presence | logical, if |
seed | integer. The value used to set the seed for the fold partition,
default is |
list with two matrices, the first for the training and the second for
the testing dataset. Each column of one matrix represents a fold with
TRUE
for the locations included in and FALSE
excluded from the partition.
When only_presence = FALSE
, the proportion of presence and absence
is preserved.
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 data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords, env = predictors, categorical = "biome")#>#># Create 4 random folds splitting presence and absence locations folds <- randomFolds(data, k = 4) # Create 4 random folds splitting only the presence locations folds <- randomFolds(data, k = 4, only_presence = TRUE)