Description Usage Arguments Details Value See Also Examples
get_folds
returns the different pre-generated folds information. To
get the fold data for a species see also get_fold_data
.
1 |
type |
character. The type of partitioning you want to load. |
The different supported type
are:
"disc"
: 5-fold disc partitioning of occurrences with pairwise
distance sampled and buffer filtered random background points, equivalent
to calling kfold_occurrence_background
with
occurrence_fold_type = "disc", k = 5, pwd_sample = TRUE,
background_buffer = 200*1000
"grid_4"
and "grid_9"
: 4-fold and 9-fold grid partitioning of
occurrences with pairwise distance sampled and buffer filtered random
background points, equivalent to calling
kfold_occurrence_background
with occurrence_fold_type =
"grid", k = 4, pwd_sample = TRUE, background_buffer = 200*1000
"random"
: 5-fold random partitioning of occurrences and random
background points, equivalent to calling
kfold_occurrence_background
with occurrence_fold_type =
"random", k = 5, pwd_sample = FALSE, background_buffer = 0
"targetgroup"
: same way of partitioning as the "random"
folds
but instead of random background points, a random subset of all occurrences
points was used creating a targetgroup background points set which has the
same sampling bias as the entire dataset.
A list with two entries "background"
and "species"
,
each entry is a dataframe with species name column and 5 fold columns as
created by kfold_occurrence_background
lapply_kfold_species
get_fold_data
get_occurrences
get_background
kfold_data
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
folds <- get_folds("random")
abalistes <- "Abalistes stellatus"
occ <- get_occurrences(abalistes)
bg <- get_background("random")
occ_train <- kfold_data(abalistes, occ, folds$species, k=1, training=TRUE)
occ_test <- kfold_data(abalistes, occ, folds$species, k=1, training=FALSE)
bg_train <- kfold_data(abalistes, bg, folds$background, k=1, training=TRUE)
bg_test <- kfold_data(abalistes, bg, folds$background, k=1, training=FALSE)
## End(Not run)
|
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