Group Monte Carlo cross-validation creates splits of the data based on some grouping variable (which may have more than a single row associated with it). One resample of Monte Carlo cross-validation takes a random sample (without replacement) of groups in the original data set to be used for analysis. All other data points are added to the assessment set. A common use of this kind of resampling is when you have repeated measures of the same subject.

## Usage

group_mc_cv(
data,
group,
prop = 3/4,
times = 25,
...,
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.

prop

The proportion of data to be retained for modeling/analysis.

times

The number of times to repeat the sampling.

...

Not currently used.

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_mc_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_mc_cv(ames, group = Neighborhood, times = 5)
#> # Group Monte Carlo cross-validation (0.75/0.25) with 5 resamples
#> # A tibble: 5 × 2
#>   splits             id
#>   <list>             <chr>
#> 1 <split [2395/535]> Resample1
#> 2 <split [2236/694]> Resample2
#> 3 <split [2168/762]> Resample3
#> 4 <split [2331/599]> Resample4
#> 5 <split [2115/815]> Resample5