For a given data set, we apply cross-validation (cv) to select the optimal HDRDA tuning parameters.
hdrda_cv(x, y, num_folds = 10, num_lambda = 21, num_gamma = 8,
shrinkage_type = c("ridge", "convex"), verbose = FALSE, ...)matrix containing the training data. The rows are the sample observations, and the columns are the features.
vector of class labels for each training observation
the number of cross-validation folds.
The number of values of lambda to consider
The number of values of gamma to consider
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.
If set to TRUE, summary information will be outputted
as the optimal model is being determined.
Additional arguments passed to hdrda.
list containing the HDRDA model that minimizes cross-validation as
well as a data.frame that summarizes the cross-validation results.
The number of cross-validation folds is given in num_folds.