rda_high_dim_cv: Helper function to optimize the HDRDA classifier via...
In sparsediscrim: Sparse and Regularized Discriminant Analysis

For a given data set, we apply cross-validation (cv) to select the optimal HDRDA tuning parameters.

rda_high_dim_cv(
  x,
  y,
  num_folds = 10,
  num_lambda = 21,
  num_gamma = 8,
  shrinkage_type = c("ridge", "convex"),
  verbose = FALSE,
  ...
)

`x`	Matrix or data frame containing the training data. The rows are the sample observations, and the columns are the features. Only complete data are retained.
`y`	vector of class labels for each training observation
`num_folds`	the number of cross-validation folds.
`num_lambda`	The number of values of `lambda` to consider
`num_gamma`	The number of values of `gamma` to consider
`shrinkage_type`	the type of covariance-matrix shrinkage to apply. By default, a ridge-like shrinkage is applied. If `convex` is given, then shrinkage similar to Friedman (1989) is applied. See Ramey et al. (2017) for details.
`verbose`	If set to `TRUE`, summary information will be outputted as the optimal model is being determined.
`...`	Options passed to `rda_high_dim()`.