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Create random folds for cross validation.
randomFolds(data, k, only_presence = FALSE, seed = NULL)
integer. Number of fold used to create the partition.
logical, if TRUE
the random folds are created
only for the presence locations and all the background locations are included
in each fold, used manly for presence-only methods,
default is FALSE
.
integer. The value used to set the seed for the fold partition,
default is NULL
.
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.
# 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
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)
# }
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