Create random folds for cross validation.
randomFolds(data, k, only_presence = FALSE, seed = NULL)
SWD object that will be used to train the model.
integer. Number of fold used to create the partition.
integer. The value used to set the seed for the fold partition,
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.
only_presence = FALSE, the proportion of presence and absence
# 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)