Group V-Fold Cross-Validation

Group V-fold cross-validation creates splits of the data based on some grouping variable (which may have more than a single row associated with it). The function can create as many splits as there are unique values of the grouping variable or it can create a smaller set of splits where more than one group is left out at a time. A common use of this kind of resampling is when you have repeated measures of the same subject.

Usage

group_vfold_cv(
  data,
  group = NULL,
  v = NULL,
  repeats = 1,
  balance = c("groups", "observations"),
  ...,
  strata = NULL,
  pool = 0.1
)

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.

v

The number of partitions of the data set. If left as NULL (the default), v will be set to the number of unique values in the grouping variable, creating "leave-one-group-out" splits.

repeats

The number of times to repeat the V-fold partitioning.

balance

If v is less than the number of unique groups, how should groups be combined into folds? Should be one of "groups", which will assign roughly the same number of groups to each fold, or "observations", which will assign roughly the same number of observations to each fold.

...

These dots are for future extensions and must be empty.

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.

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.

Value

A tibble with classes group_vfold_cv, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and an identification variable.

Examples

data(ames, package = "modeldata")

set.seed(123)
group_vfold_cv(ames, group = Neighborhood, v = 5)
#> # Group 5-fold cross-validation 
#> # A tibble: 5 × 2
#>   splits             id       
#>   <list>             <chr>    
#> 1 <split [2449/481]> Resample1
#> 2 <split [2642/288]> Resample2
#> 3 <split [2218/712]> Resample3
#> 4 <split [2367/563]> Resample4
#> 5 <split [2044/886]> Resample5
group_vfold_cv(
  ames,
  group = Neighborhood,
  v = 5,
  balance = "observations"
)
#> # Group 5-fold cross-validation 
#> # A tibble: 5 × 2
#>   splits             id       
#>   <list>             <chr>    
#> 1 <split [2366/564]> Resample1
#> 2 <split [2279/651]> Resample2
#> 3 <split [2361/569]> Resample3
#> 4 <split [2361/569]> Resample4
#> 5 <split [2353/577]> Resample5
group_vfold_cv(ames, group = Neighborhood, v = 5, repeats = 2)
#> # Group 5-fold cross-validation 
#> # A tibble: 10 × 3
#>    splits             id      id2      
#>    <list>             <chr>   <chr>    
#>  1 <split [2077/853]> Repeat1 Resample1
#>  2 <split [2215/715]> Repeat1 Resample2
#>  3 <split [2392/538]> Repeat1 Resample3
#>  4 <split [2574/356]> Repeat1 Resample4
#>  5 <split [2462/468]> Repeat1 Resample5
#>  6 <split [2269/661]> Repeat2 Resample1
#>  7 <split [2426/504]> Repeat2 Resample2
#>  8 <split [2354/576]> Repeat2 Resample3
#>  9 <split [2547/383]> Repeat2 Resample4
#> 10 <split [2124/806]> Repeat2 Resample5

# Leave-one-group-out CV
group_vfold_cv(ames, group = Neighborhood)
#> # Group 28-fold cross-validation 
#> # A tibble: 28 × 2
#>    splits             id        
#>    <list>             <chr>     
#>  1 <split [2663/267]> Resample01
#>  2 <split [2779/151]> Resample02
#>  3 <split [2691/239]> Resample03
#>  4 <split [2748/182]> Resample04
#>  5 <split [2928/2]>   Resample05
#>  6 <split [2920/10]>  Resample06
#>  7 <split [2902/28]>  Resample07
#>  8 <split [2837/93]>  Resample08
#>  9 <split [2859/71]>  Resample09
#> 10 <split [2858/72]>  Resample10
#> # ℹ 18 more rows

library(dplyr)
data(Sacramento, package = "modeldata")

city_strata <- Sacramento %>%
  group_by(city) %>%
  summarize(strata = mean(price)) %>%
  summarize(city = city,
            strata = cut(strata, quantile(strata), include.lowest = TRUE))
#> Warning: Returning more (or less) than 1 row per `summarise()` group was
#> deprecated in dplyr 1.1.0.
#> ℹ Please use `reframe()` instead.
#> ℹ When switching from `summarise()` to `reframe()`, remember that
#>   `reframe()` always returns an ungrouped data frame and adjust
#>   accordingly.

sacramento_data <- Sacramento %>%
  full_join(city_strata, by = "city")

group_vfold_cv(sacramento_data, city, strata = strata)
#> Warning: Leaving `v = NULL` while using stratification will set `v` to the number
#> of groups present in the least common stratum.
#> ℹ Set `v` explicitly to override this warning.
#> # Group 14-fold cross-validation 
#> # A tibble: 14 × 2
#>    splits            id        
#>    <list>            <chr>     
#>  1 <split [881/51]>  Resample01
#>  2 <split [434/498]> Resample02
#>  3 <split [905/27]>  Resample03
#>  4 <split [913/19]>  Resample04
#>  5 <split [917/15]>  Resample05
#>  6 <split [885/47]>  Resample06
#>  7 <split [926/6]>   Resample07
#>  8 <split [793/139]> Resample08
#>  9 <split [903/29]>  Resample09
#> 10 <split [896/36]>  Resample10
#> 11 <split [904/28]>  Resample11
#> 12 <split [917/15]>  Resample12
#> 13 <split [855/77]>  Resample13
#> 14 <split [897/35]>  Resample14