as.resampling {sperrorest}

Resampling objects such as partitionings or bootstrap samples

Description

Create/coerce and print resampling objects, e.g., partitionings or bootstrap samples derived from a data set.

Usage

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, ...)

Arguments

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 resampling.

Details

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 represampling 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.

Value

as.resampling methods: An object of class resampling.

See Also

represampling, partition_cv, partition_kmeans, represampling_bootstrap, etc.

Examples

# 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, ])