bootstrap: Generate bootstrap replicates

In jrnold/ramsleep: Resampling, Cross-Validation, and Permutation Methods Infrastucture

Description

Generate nonparametric bootstrap samples. In addition to the the ordinary bootstrap, it supports weights, the Bayesian bootstrap, and sub- or super-sampling.

Usage

bootstrap(n, times = 1L, weights = NULL, bayes = FALSE, size = n)

Arguments

n	A positive, scalar integer representing the number of observations (items to choose from).
times	A positive, scalar integer representing the number of bootstrap samples to draw.
weights	A numeric vector with observation level weights.
bayes	A scalar logical, indicating whether to use the Bayesian bootstrap.
size	The number of observations in each output sample.

References

• Angelo Canty and Brian Ripley (2016). "boot: Bootstrap R (S-Plus) Functions." R package version 1.3-18.

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

See Also

The boot function boot::boot() which is the canonical R bootstrap implementation. The modelr function modelr::bootstrap(), rsample function rsample::bootstraps provide similar implementations specialized to data frames.

Examples

# ordinary non-parametric bootstrap
bootstrap(20, 5)

# Bayesian bootstrap
bootstrap(20, 5, bayes = TRUE)

# weighted bootstrap
w <- c(rep(5, 5), rep(1, 15))
bootstrap(20, 5, weights = w / sum(w))

jrnold/ramsleep documentation built on May 29, 2019, 11:43 a.m.