A bootstrap sample is a sample that is the same size as the original data set that is made using replacement. This results in analysis samples that have multiple replicates of some of the original rows of the data. The assessment set is defined as the rows of the original data that were not included in the bootstrap sample. This is often referred to as the "out-of-bag" (OOB) sample.

## Usage

bootstraps(
data,
times = 25,
strata = NULL,
breaks = 4,
pool = 0.1,
apparent = FALSE,
...
)

## Arguments

data

A data frame.

times

The number of bootstrap samples.

strata

A variable in data (single character or name) used to conduct stratified sampling. When not NULL, each resample is created within the stratification variable. Numeric strata are binned into quartiles.

breaks

A single number giving the number of bins desired to stratify a numeric stratification variable.

pool

A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small.

apparent

A logical. Should an extra resample be added where the analysis and holdout subset are the entire data set. This is required for some estimators used by the summary function that require the apparent error rate.

...

Not currently used.

## Value

A tibble with classes bootstraps, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and a column called id that has a character string with the resample identifier.

## Details

The argument apparent enables the option of an additional "resample" where the analysis and assessment data sets are the same as the original data set. This can be required for some types of analysis of the bootstrap results.

With a strata argument, the random sampling is conducted within the stratification variable. This can help ensure that the resamples have equivalent proportions as the original data set. For a categorical variable, sampling is conducted separately within each class. For a numeric stratification variable, strata is binned into quartiles, which are then used to stratify. Strata below 10% of the total are pooled together; see make_strata() for more details.

## Examples

bootstraps(mtcars, times = 2)
#> # Bootstrap sampling
#> # A tibble: 2 × 2
#>   splits          id
#>   <list>          <chr>
#> 1 <split [32/10]> Bootstrap1
#> 2 <split [32/15]> Bootstrap2
bootstraps(mtcars, times = 2, apparent = TRUE)
#> # Bootstrap sampling with apparent sample
#> # A tibble: 3 × 2
#>   splits          id
#>   <list>          <chr>
#> 1 <split [32/11]> Bootstrap1
#> 2 <split [32/13]> Bootstrap2
#> 3 <split [32/32]> Apparent

library(purrr)
library(modeldata)
data(wa_churn)

set.seed(13)
resample1 <- bootstraps(wa_churn, times = 3)
map_dbl(
resample1$splits, function(x) { dat <- as.data.frame(x)$churn
mean(dat == "Yes")
}
)
#> [1] 0.2798523 0.2639500 0.2648019

set.seed(13)
resample2 <- bootstraps(wa_churn, strata = churn, times = 3)
map_dbl(
resample2$splits, function(x) { dat <- as.data.frame(x)$churn
mean(dat == "Yes")
}
)
#> [1] 0.2653699 0.2653699 0.2653699

set.seed(13)
resample3 <- bootstraps(wa_churn, strata = tenure, breaks = 6, times = 3)
map_dbl(
resample3$splits, function(x) { dat <- as.data.frame(x)$churn
mean(dat == "Yes")
}
)
#> [1] 0.2625302 0.2659378 0.2696294