balanced_bootstrap: Generate balanced bootstrap replicates
In jrnold/resamplr: Resampling methods

Description Usage Arguments Value Methods (by class) References See Also Examples

A balanced bootstrap generates R replicates in which each element appears R times.

balanced_bootstrap(data, ...)

## S3 method for class 'data.frame'
balanced_bootstrap(data, R = 1L, ...)

## S3 method for class 'grouped_df'
balanced_bootstrap(data, R = 1L, stratify = FALSE, ...)

`data`	A data frame
`...`	Arguments passed to methods
`R`	Number of replicates
`stratify`	If `TRUE`, resample within groups, otherwise, resample by groups instead of rows.

A data frame with R 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

data.frame: Balanced bootstrap of rows in a data frame.
grouped_df: Balanced bootstrap either by group, or within groups (if stratify = TRUE).

Gleason, John 1988. "Algorithms for Balanced Bootstrap Simulations." The American Statistician doi:10.2307/2685134.
Davison, A.C., Hinkley, D. V., and Schechtman, E. 1986. "Efficient Bootstrap Simulation." Biometrika.
Angelo Canty and Brian Ripley (2016). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-18.

The boot function boot which is the canonical R bootstrap implementation.

# example from modelr::bootstrap
library("purrr")
library("dplyr")
boot <- balanced_bootstrap(mtcars, 100)

models <- map(boot$sample, ~ lm(mpg ~ wt, data = .))
tidied <- map_df(models, broom::tidy, .id = "id")

hist(subset(tidied, term == "wt")$estimate)
hist(subset(tidied, term == "(Intercept)")$estimate)