jackknife: Generate Jackknife Subsamples

In jrnold/resamplr: Resampling methods

Description

Generate jackknife subsamples. For data with n elements, the jackknife generates n subsamples, each with one element deleted. The generalized (delete-p, block) jackknife generates subsamples by deleting p observations.

Usage

jackknife(data, p, ...)

## S3 method for class 'data.frame'
jackknife(data, p = 1L, ...)

## S3 method for class 'grouped_df'
jackknife(data, p = 1L, ...)

Arguments

data	A data frame or vector
p	The number of elements to delete. p = 1 is the ordinary (delete-one) jackknife. p > 1 is the block (delete-p) jackknife.
...	Arguments passed to methods

Value

A data frame with n rows, for p = 1, or more generally, n choose p rows, and the following columns:

train

A list of resample objects. Training sets.

test

A list of resample objects. Test sets.

.id

An integer vector of identifiers

Methods (by class)

• data.frame: Delete rows from a data frame.

• grouped_df: Delete groups from a grouped data frame.

References

• Davison, A. C. & Hinkley, D. V. (1997) Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge. ISBN 0-521-57391-2

• Tukey, J. W. (1958). "Bias and confidence in not quite large samples". The Annals of Mathematical Statistics. doi:10.1214/aoms/1177706647.

• Efron, B.; Stein, C. (May 1981). "The Jackknife Estimate of Variance". The Annals of Statistics. doi:10.1214/aos/1176345462.

jrnold/resamplr documentation built on May 20, 2019, 1:05 a.m.