Cross validation (no folds) function for shrink. This function is to be used with CVP_ADMM.
CVP_ADMMc(X_train, X_valid, Y_train, Y_valid, A, B, C, lam, alpha = 1,
tau = 10, rho = 2, mu = 10, tau_rho = 2, iter_rho = 10L,
crit = "ADMM", tol_abs = 1e-04, tol_rel = 1e-04, maxit = 10000L,
adjmaxit = 10000L, crit_cv = "MSE", start = "warm",
trace = "progress")nxp training data matrix.
(n - q)xp validation data matrix matrix.
nxr training response matrix.
(n - q)xr validation response matrix.
option to provide user-specified matrix for penalty term. This matrix must have p columns. Defaults to identity matrix.
option to provide user-specified matrix for penalty term. This matrix must have p rows. Defaults to identity matrix.
option to provide user-specified matrix for penalty term. This matrix must have nrow(A) rows and ncol(B) columns. Defaults to identity matrix.
positive tuning parameters for elastic net penalty. If a vector of parameters is provided, they should be in increasing order.
elastic net mixing parameter contained in [0, 1]. 0 = ridge, 1 = lasso. Alpha must be a single value (cross validation across alpha not supported).
optional constant used to ensure positive definiteness in Q matrix in algorithm
initial step size for ADMM algorithm.
factor for primal and residual norms in the ADMM algorithm. This will be used to adjust the step size rho after each iteration.
factor in which to increase/decrease step size rho
step size rho will be updated every iter.rho steps
criterion for convergence (ADMM or loglik). If crit = loglik then iterations will stop when the relative change in log-likelihood is less than tol.abs. Default is ADMM and follows the procedure outlined in Boyd, et al.
absolute convergence tolerance. Defaults to 1e-4.
relative convergence tolerance. 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 1e4.
cross validation criterion (MSE, loglik, penloglik AIC, or BIC). Defaults to MSE.
specify warm or cold start for cross validation. Default is warm.
option to display progress of CV. Choose one of progress to print a progress bar, print to print completed tuning parameters, or none.
cross validation errors (cv_crit)