Parallel implementation of cross validation.
CV(X = NULL, S = NULL, lam = 10^seq(-2, 2, 0.2), diagonal = FALSE,
path = FALSE, tol = 1e-04, maxit = 10000, adjmaxit = NULL, K = 5,
crit.cv = c("loglik", "AIC", "BIC"), start = c("warm", "cold"),
cores = 1, trace = c("progress", "print", "none"), ...)option to provide a nxp data matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
option to provide a pxp sample covariance matrix (denominator n). If argument is NULL and X is provided instead then S will be computed automatically.
positive tuning parameters for elastic net penalty. If a vector of parameters is provided, they should be in increasing order. Defaults to grid of values 10^seq(-2, 2, 0.2).
option to penalize the diagonal elements of the estimated precision matrix (\(\Omega\)). Defaults to FALSE.
option to return the regularization path. This option should be used with extreme care if the dimension is large. If set to TRUE, cores must be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE.
convergence tolerance. Iterations will stop when the average absolute difference in parameter estimates in less than tol times multiple. Defaults to 1e-4.
maximum number of iterations. Defaults to 1e4.
adjusted maximum number of iterations. During cross validation this option allows the user to adjust the maximum number of iterations after the first lam tuning parameter has converged. This option is intended to be paired with warm starts and allows for 'one-step' estimators. Defaults to NULL.
specify the number of folds for cross validation.
cross validation criterion (loglik, AIC, or BIC). Defaults to loglik.
specify warm or cold start for cross validation. Default is warm.
option to run CV in parallel. Defaults to cores = 1.
option to display progress of CV. Choose one of progress to print a progress bar, print to print completed tuning parameters, or none.
additional arguments to pass to glasso.
returns list of returns which includes:
optimal tuning parameter.
minimum average cross validation error (cv.crit) for optimal parameters.
average cross validation error (cv.crit) across all folds.
cross validation errors (cv.crit).