dat |
A numeric data.frame , matrix , or similar object.
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estimators |
A list of estimator functions to be considered in
the cross-validated estimator selection procedure.
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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.
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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.
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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.
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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.
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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.
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parallel |
A logical option indicating whether to run the main
cross-validation loop with future_lapply() . This
is passed directly to cross_validate() .
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