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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.


  times = 25,
  apparent = FALSE,
  strata = NULL,
  pool = 0.1



A data frame.


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.


The number of bootstrap samples.


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.


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.


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.


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.


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.


data(ames, package = "modeldata")

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