checkArgs()
verifies that all arguments
passed to cvCovEst()
function meet its specifications.
checkArgs(
dat,
estimators,
estimator_params,
cv_loss,
cv_scheme,
mc_split,
v_folds,
center,
scale,
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. |
center |
A logical indicating whether to center the columns of
dat to have mean zero. |
scale |
A logical indicating whether to scale the columns of
dat to have unit variance. |
parallel |
A logical option indicating whether to run the main
cross-validation loop with future_lapply() . This
is passed directly to cross_validate() . |
Value
Whether all argument conditions are satisfied