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.

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



A data frame.


The number of bootstrap samples.


A variable that is used to conduct stratified sampling. When not NULL, each bootstrap sample is created within the stratification variable. This could be a single character value or a variable name that corresponds to a variable that exists in the data frame.


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


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.


An 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.


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. The strata argument is based on a similar argument in the random forest package were the bootstrap samples are conducted within the stratification variable. This can help ensure that the number of data points in the bootstrap sample is equivalent to the proportions in the original data set. (Strata below 10% of the total are pooled together.)


bootstraps(mtcars, times = 2)
#> # Bootstrap sampling #> # A tibble: 2 x 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 x 2 #> splits id #> <list> <chr> #> 1 <split [32/11]> Bootstrap1 #> 2 <split [32/13]> Bootstrap2 #> 3 <split [32/32]> Apparent
library(purrr) iris2 <- iris[1:130, ] set.seed(13) resample1 <- bootstraps(iris2, times = 3) map_dbl(resample1$splits, function(x) { dat <- as.data.frame(x)$Species mean(dat == "virginica") })
#> [1] 0.2615385 0.2769231 0.2076923
set.seed(13) resample2 <- bootstraps(iris2, strata = "Species", times = 3) map_dbl(resample2$splits, function(x) { dat <- as.data.frame(x)$Species mean(dat == "virginica") })
#> [1] 0.2307692 0.2307692 0.2307692
set.seed(13) resample3 <- bootstraps(iris2, strata = "Sepal.Length", breaks = 6, times = 3) map_dbl(resample3$splits, function(x) { dat <- as.data.frame(x)$Species mean(dat == "virginica") })
#> [1] 0.2230769 0.2076923 0.2384615