Description Usage Arguments Details Value See Also Examples
View source: R/sperrorest_resampling.R
partition_cv
creates a represampling object for
length(repetition)
repeated nfold
fold crossvalidation.
1 2  partition_cv(data, coords = c("x", "y"), nfold = 10, repetition = 1,
seed1 = NULL, return_factor = FALSE)

data 

coords 
(ignored by 
nfold 
number of partitions (folds) in 
repetition 
numeric vector: crossvalidation repetitions
to be generated. Note that this is not the number of repetitions,
but the indices of these repetitions. E.g., use 
seed1 

return_factor 
if 
This function does not actually perform a crossvalidation or partition the data set itself; it simply creates a data structure containing the indices of training and test samples.
If return_factor = FALSE
(the default), a
represampling object. Specifically, this is a (named) list of
length(repetition)
resampling
objects.
Each of these resampling objects is a list of length
nfold
corresponding to the folds.
Each fold is represented by a list of containing the components train
and test
, specifying the indices of training and test samples
(row indices for data
).
If return_factor = TRUE
(mainly used internally), a (named) list of
length length(repetition)
.
Each component of this list is a vector of length nrow(data)
of type
factor
, specifying for each sample the fold to which it belongs.
The factor levels are factor(1:nfold)
.
sperrorest, represampling
1 2 3 4 5 6 7 8  data(ecuador)
## nonspatial crossvalidation:
resamp < partition_cv(ecuador, nfold = 5, repetition = 5)
# plot(resamp, ecuador)
# first repetition, second fold, test set indices:
idx < resamp[['1']][[2]]$test
# test sample used in this particular repetition and fold:
ecuador[idx , ]

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