checkArgs: Check Arguments Passed to cvCovEst
In cvCovEst: Cross-Validated Covariance Matrix Estimation

checkArgs

R Documentation

Check Arguments Passed to cvCovEst

Description

checkArgs() verifies that all arguments passed to cvCovEst() function meet its specifications.

Usage

checkArgs(
  dat,
  estimators,
  estimator_params,
  cv_loss,
  cv_scheme,
  mc_split,
  v_folds,
  parallel
)

Arguments

`dat`	A numeric `data.frame`, `matrix`, or similar object.
`estimators`	A `list` of estimator functions to be considered in the cross-validated estimator selection procedure.
`estimator_params`	A named `list` of arguments corresponding to the hyperparameters of covariance matrix estimators in `estimators`. The name of each list element should match the name of an estimator passed to `estimators`. Each element of the `estimator_params` is itself a named `list`, with the names corresponding to a given estimator's hyperparameter(s). These hyperparameters may be in the form of a single `numeric` or a `numeric` vector. If no hyperparameter is needed for a given estimator, then the estimator need not be listed.
`cv_loss`	A `function` indicating the loss function to be used. This defaults to the Frobenius loss, `cvMatrixFrobeniusLoss()`. An observation-based version, `cvFrobeniusLoss()`, is also made available. Additionally, the `cvScaledMatrixFrobeniusLoss(())` is included for situations in which `dat`'s variables are of different scales.
`cv_scheme`	A `character` indicating the cross-validation scheme to be employed. There are two options: (1) V-fold cross-validation, via `"v_folds"`; and (2) Monte Carlo cross-validation, via `"mc"`. Defaults to Monte Carlo cross-validation.
`mc_split`	A `numeric` between 0 and 1 indicating the proportion of observations to be included in the validation set of each Monte Carlo cross-validation fold.
`v_folds`	An `integer` larger than or equal to 1 indicating the number of folds to use for cross-validation. The default is 10, regardless of the choice of cross-validation scheme.
`parallel`	A `logical` option indicating whether to run the main cross-validation loop with `future_lapply()`. This is passed directly to `cross_validate()`.