cvCovEst {cvCovEst}  R Documentation 
CrossValidated Covariance Matrix Estimator Selector
Description
cvCovEst()
identifies the optimal covariance matrix
estimator from among a set of candidate estimators.
Usage
cvCovEst(
dat,
estimators = c(linearShrinkEst, thresholdingEst, sampleCovEst),
estimator_params = list(linearShrinkEst = list(alpha = 0), thresholdingEst =
list(gamma = 0)),
cv_loss = cvMatrixFrobeniusLoss,
cv_scheme = "v_fold",
mc_split = 0.5,
v_folds = 10L,
center = TRUE,
scale = FALSE,
parallel = FALSE
)
Arguments
dat 
A numeric data.frame , matrix , or similar object.

estimators 
A list of estimator functions to be considered in
the crossvalidated 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). The hyperparameter(s) 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 observationbased 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 crossvalidation scheme
to be employed. There are two options: (1) Vfold crossvalidation, via
"v_folds" ; and (2) Monte Carlo crossvalidation, via "mc" .
Defaults to Monte Carlo crossvalidation.

mc_split 
A numeric between 0 and 1 indicating the proportion
of observations to be included in the validation set of each Monte Carlo
crossvalidation fold.

v_folds 
An integer larger than or equal to 1 indicating the
number of folds to use for crossvalidation. The default is 10, regardless
of the choice of crossvalidation 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
crossvalidation loop with future_lapply() . This
is passed directly to cross_validate() .

Value
A list
of results containing the following elements:

estimate
 A matrix
corresponding to the estimate of
the optimal covariance matrix estimator.

estimator
 A character
indicating the optimal
estimator and corresponding hyperparameters, if any.

risk_df
 A tibble
providing the
crossvalidated risk estimates of each estimator.

cv_df
 A tibble
providing each
estimators' loss over the folds of the crossvalidated procedure.

args
 A named list
containing arguments passed to
cvCovEst
.
Examples
cvCovEst(
dat = mtcars,
estimators = c(
linearShrinkLWEst, thresholdingEst, sampleCovEst
),
estimator_params = list(
thresholdingEst = list(gamma = seq(0.1, 0.3, 0.1))
),
center = TRUE,
scale = TRUE
)
[Package
cvCovEst version 1.1.0
Index]