bootSamples: Bootstrap samples

In perry: Resampling-Based Prediction Error Estimation for Regression Models

Description

Draw bootstrap samples of observations or groups of observations and specify which bootstrap estimator of prediction error to compute.

Usage

bootSamples(n, R = 1, type = c("0.632", "out-of-bag"), grouping = NULL)

Arguments

n	an integer giving the number of observations for which to draw bootstrap samples. This is ignored if grouping is supplied in order to respect the group structure of the data in the bootstrap samples.
R	an integer giving the number of bootstrap samples.
type	a character string specifying a bootstrap estimator. Possible values are "0.632" (the default), or "out-of-bag".
grouping	a factor specifying groups of observations. If supplied, the groups are resampled rather than individual observations such that all observations within a group belong either to the bootstrap sample or the test data.

Value

An object of class "bootSamples" with the following components:

n

an integer giving the number of observations or groups.

R

an integer giving the number of bootstrap samples.

subsets

an integer matrix in which each column contains the indices of the observations or groups in the corresponding bootstrap sample.

grouping

a list giving the indices of the observations belonging to each group. This is only returned if a grouping factor has been supplied.

Note

This is a simple wrapper function for perrySplits with a control object generated by bootControl.

Author(s)

Andreas Alfons

References

Efron, B. (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. Journal of the American Statistical Association, 78(382), 316–331.

See Also

perrySplits, bootControl, cvFolds, randomSplits

Examples

set.seed(1234)  # set seed for reproducibility
bootSamples(20)
bootSamples(20, R = 10)

perry documentation built on Nov. 3, 2021, 5:08 p.m.