Parallel implementation of cross validation.
CVP_ADMM(X, Y = NULL, A = diag(ncol(X)), B = diag(ncol(X)),
C = diag(ncol(X)), lam = 10^seq(-2, 2, 0.2), alpha = 1, tau = 10,
rho = 2, mu = 10, tau.rho = 2, iter.rho = 10, crit = c("ADMM",
"loglik"), tol.abs = 1e-04, tol.rel = 1e-04, maxit = 1000,
adjmaxit = NULL, K = 5, crit.cv = c("MSE", "loglik", "penloglik", "AIC",
"BIC"), start = c("warm", "cold"), cores = 1, trace = c("progress",
"print", "none"))nxp data matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable.
option to provide nxr response matrix. Each row corresponds to a single response and each column contains n response of a single feature/response.
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. Defaults to grid of values 10^seq(-2, 2, 0.2).
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 1e3.
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 (MSE, loglik, penloglik, AIC, or BIC). Defaults to MSE.
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.
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).