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Cross-validation Framework

cv.Rd

Generic function for performing cross-validation on various objects or data. Specific methods should be implemented for different data types or model types.

Usage

cv(x, folds, ...)

Arguments

x

The object to perform cross-validation on (e.g., data matrix, formula, model object).

folds

A list defining the cross-validation folds, typically containing train and test indices for each fold.

...

Additional arguments passed to specific methods.

Value

The structure of the return value depends on the specific S3 method. Typically, it will be an object containing the results of the cross-validation, such as performance metrics per fold or aggregated metrics.

Details

The specific implementation details, default functions, and relevant arguments vary by method.

Bi-Projector Method (cv.bi_projector): Relevant arguments: x, folds, max_comp, fit_fun, measure, measure_fun, return_models, ....

This method performs cross-validation specifically for bi_projector models (or models intended to be used like them, typically from unsupervised methods like PCA or SVD). For each fold, it fits a single model using the training data with the maximum number of components specified (max_comp). It then iterates from 1 to max_comp components:

  1. It truncates the full model to k components using truncate(). (Requires a truncate method for the fitted model class).

  2. It reconstructs the held-out test data using the k-component truncated model via reconstruct_new().

  3. It calculates reconstruction performance metrics (e.g., MSE, R2) by comparing the original test data to the reconstruction using the measure argument or a custom measure_fun.

The fit_fun must accept an argument ncomp. Additional arguments in ... are passed to fit_fun and measure_fun.

The return value is a cv_fit object (a list with class cv_fit), where the $results element is a tibble. Each row corresponds to a fold, containing the fold index (fold) and a nested tibble (component_metrics). The component_metrics tibble has rows for each component evaluated (1 to max_comp) and columns for the component index (comp) plus all calculated metrics (e.g., mse, r2, mae) or error messages (comp_error). If return_models=TRUE, the full model fitted on the training data for each fold is included in a list column model_full.

See also

cv_generic