View source: R/sperrorest_misc.R
as.resampling | R Documentation |
Create/coerce and print resampling objects, e.g., partitionings or bootstrap samples derived from a data set.
as.resampling(object, ...) ## Default S3 method: as.resampling(object, ...) ## S3 method for class 'factor' as.resampling(object, ...) ## S3 method for class 'list' as.resampling(object, ...) validate.resampling(object) is.resampling(x, ...) ## S3 method for class 'resampling' print(x, ...)
object |
depending on the function/method, a list or a vector of type factor defining a partitioning of the dataset. |
... |
currently not used. |
x |
object of class |
A resampling
object is a list of lists defining a set of training
and test samples.
In the case of k
-fold cross-validation partitioning, for example, the
corresponding resampling
object would be of length k
, i.e. contain k
lists. Each of these k
lists defines a training set of size n(k-1)/k
(where n
is the overall sample size), and a test set of size n/k
. The
resampling
object does, however, not contain the data itself, but only
indices between 1
and n
identifying the selection (see Examples).
Another example is bootstrap resampling. represampling_bootstrap with
argument oob = TRUE
generates rep
resampling
objects with indices of a
bootstrap sample in the train
component and indices of the out-of-bag
sample in the test component (see Examples below).
as.resampling.factor
: For each factor level of the input variable,
as.resampling.factor
determines the indices of samples in this level (=
test samples) and outside this level (= training samples). Empty levels of
object
are dropped without warning.
as.resampling_list
checks if the list in object
has a valid resampling
object structure (with components train
and test
etc.) and assigns the
class attribute 'resampling'
if successful.
as.resampling
methods: An object of class resampling
.
represampling, partition_cv, partition_kmeans, represampling_bootstrap, etc.
# Muenchow et al. (2012), see ?ecuador # Partitioning by elevation classes in 200 m steps: parti <- factor(as.character(floor(ecuador$dem / 200))) smp <- as.resampling(parti) summary(smp) # Compare: summary(parti) # k-fold (non-spatial) cross-validation partitioning: parti <- partition_cv(ecuador) parti <- parti[[1]] # the first (and only) resampling object in parti # data corresponding to the test sample of the first fold: str(ecuador[parti[[1]]$test, ]) # the corresponding training sample - larger: str(ecuador[parti[[1]]$train, ]) # Bootstrap training sets, out-of-bag test sets: parti <- represampling_bootstrap(ecuador, oob = TRUE) parti <- parti[[1]] # the first (and only) resampling object in parti # out-of-bag test sample: approx. one-third of nrow(ecuador): str(ecuador[parti[[1]]$test, ]) # bootstrap training sample: same size as nrow(ecuador): str(ecuador[parti[[1]]$train, ])
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