Apply Cross-Validation to Nested Data

The nest_cv function applies cross-validation splits to nested data frames or data tables within a data table. It uses the rsample package's vfold_cv function to create cross-validation splits for predictive modeling and analysis on nested datasets.

nest_cv(
  nest_dt,
  v = 10,
  repeats = 1,
  strata = NULL,
  breaks = 4,
  pool = 0.1,
  ...
)

Arguments

nest_dt

A data.frame or data.table containing at least one nested data.frame or data.table column.

• Supports multi-level nested structures

• Requires at least one nested data column

v

The number of partitions of the data set.

repeats

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

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.

breaks

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

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.

...

These dots are for future extensions and must be empty.

Value

A data.table containing the cross-validation splits for each nested dataset. It includes:

• Original non-nested columns from nest_dt.

• splits: The cross-validation split objects returned by rsample::vfold_cv.

• train: The training data for each split.

• validate: The validation data for each split.

Details

The function performs the following steps:

1. Checks if the input nest_dt is non-empty and contains at least one nested column of data.frames or data.tables.

2. Identifies the nested columns and non-nested columns within nest_dt.

3. Applies rsample::vfold_cv to each nested data frame in the specified nested column(s), creating the cross-validation splits.

4. Expands the cross-validation splits and associates them with the non-nested columns.

5. Extracts the training and validation data for each split and adds them to the output data table.

If the strata parameter is provided, stratified sampling is performed during the cross-validation. Additional arguments can be passed to rsample::vfold_cv via ....

Note

• The nest_dt must contain at least one nested column of data.frames or data.tables.

• The function converts nest_dt to a data.table internally to ensure efficient data manipulation.

• The strata parameter should be a column name present in the nested data frames.

• If strata is specified, ensure that the specified column exists in all nested data frames.

• The breaks and pool parameters are used when strata is a numeric variable and control how stratification is handled.

• Additional arguments passed through ... are forwarded to rsample::vfold_cv.

See also

• rsample::vfold_cv() Underlying cross-validation function

• rsample::training() Extract training set

• rsample::testing() Extract test set

Examples

# Example: Cross-validation for nested data.table demonstrations

# Setup test data
dt_nest <- w2l_nest(
  data = iris,                   # Input dataset
  cols2l = 1:2                   # Nest first 2 columns
)

# Example 1: Basic 2-fold cross-validation
nest_cv(
  nest_dt = dt_nest,             # Input nested data.table
  v = 2                          # Number of folds (2-fold CV)
)
#>            name                     splits     id              train
#>          <fctr>                     <list> <char>             <list>
#> 1: Sepal.Length <vfold_split[75x75x150x4]>  Fold1 <data.table[75x4]>
#> 2: Sepal.Length <vfold_split[75x75x150x4]>  Fold2 <data.table[75x4]>
#> 3:  Sepal.Width <vfold_split[75x75x150x4]>  Fold1 <data.table[75x4]>
#> 4:  Sepal.Width <vfold_split[75x75x150x4]>  Fold2 <data.table[75x4]>
#>              validate
#>                <list>
#> 1: <data.table[75x4]>
#> 2: <data.table[75x4]>
#> 3: <data.table[75x4]>
#> 4: <data.table[75x4]>

# Example 2: Repeated 2-fold cross-validation
nest_cv(
  nest_dt = dt_nest,             # Input nested data.table
  v = 2,                         # Number of folds (2-fold CV)
  repeats = 2                    # Number of repetitions
)
#>            name                     splits      id    id2              train
#>          <fctr>                     <list>  <char> <char>             <list>
#> 1: Sepal.Length <vfold_split[75x75x150x4]> Repeat1  Fold1 <data.table[75x4]>
#> 2: Sepal.Length <vfold_split[75x75x150x4]> Repeat1  Fold2 <data.table[75x4]>
#> 3: Sepal.Length <vfold_split[75x75x150x4]> Repeat2  Fold1 <data.table[75x4]>
#> 4: Sepal.Length <vfold_split[75x75x150x4]> Repeat2  Fold2 <data.table[75x4]>
#> 5:  Sepal.Width <vfold_split[75x75x150x4]> Repeat1  Fold1 <data.table[75x4]>
#> 6:  Sepal.Width <vfold_split[75x75x150x4]> Repeat1  Fold2 <data.table[75x4]>
#> 7:  Sepal.Width <vfold_split[75x75x150x4]> Repeat2  Fold1 <data.table[75x4]>
#> 8:  Sepal.Width <vfold_split[75x75x150x4]> Repeat2  Fold2 <data.table[75x4]>
#>              validate
#>                <list>
#> 1: <data.table[75x4]>
#> 2: <data.table[75x4]>
#> 3: <data.table[75x4]>
#> 4: <data.table[75x4]>
#> 5: <data.table[75x4]>
#> 6: <data.table[75x4]>
#> 7: <data.table[75x4]>
#> 8: <data.table[75x4]>