Group bootstrapping creates splits of the data based on some grouping variable (which may have more than a single row associated with it). A common use of this kind of resampling is when you have repeated measures of the same subject. 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

group_bootstraps(data, group, times = 25, apparent = FALSE, ...)

## Arguments

data

A data frame.

group

A variable in data (single character or name) used for grouping observations with the same value to either the analysis or assessment set within a fold.

times

The number of bootstrap samples.

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

An tibble with classes group_bootstraps

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.

## Examples

data(ames, package = "modeldata")

set.seed(13)
group_bootstraps(ames, Neighborhood, times = 3)
#> # Group bootstrap sampling
#> # A tibble: 3 × 2
#>   splits              id
#>   <list>              <chr>
#> 1 <split [2959/1072]> Bootstrap1
#> 2 <split [2899/1334]> Bootstrap2
#> 3 <split [2937/1203]> Bootstrap3
group_bootstraps(ames, Neighborhood, times = 3, apparent = TRUE)
#> # Group bootstrap sampling with apparent sample
#> # A tibble: 4 × 2
#>   splits              id
#>   <list>              <chr>
#> 1 <split [2969/1196]> Bootstrap1
#> 2 <split [2931/983]>  Bootstrap2
#> 3 <split [2896/1208]> Bootstrap3
#> 4 <split [2930/2930]> Apparent