Description Usage Arguments Value

Parallel implementation of cross validation.

1 2 3 4 5 6 7 | ```
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"))
``` |

`X` |
nxp data matrix. Each row corresponds to a single observation and each column contains n observations of a single feature/variable. |

`Y` |
option to provide nxr response matrix. Each row corresponds to a single response and each column contains n response of a single feature/response. |

`A` |
option to provide user-specified matrix for penalty term. This matrix must have p columns. Defaults to identity matrix. |

`B` |
option to provide user-specified matrix for penalty term. This matrix must have p rows. Defaults to identity matrix. |

`C` |
option to provide user-specified matrix for penalty term. This matrix must have nrow(A) rows and ncol(B) columns. Defaults to identity matrix. |

`lam` |
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 |

`alpha` |
elastic net mixing parameter contained in [0, 1]. |

`tau` |
optional constant used to ensure positive definiteness in Q matrix in algorithm |

`rho` |
initial step size for ADMM algorithm. |

`mu` |
factor for primal and residual norms in the ADMM algorithm. This will be used to adjust the step size |

`tau.rho` |
factor in which to increase/decrease step size |

`iter.rho` |
step size |

`crit` |
criterion for convergence ( |

`tol.abs` |
absolute convergence tolerance. Defaults to 1e-4. |

`tol.rel` |
relative convergence tolerance. Defaults to 1e-4. |

`maxit` |
maximum number of iterations. Defaults to 1e3. |

`adjmaxit` |
adjusted maximum number of iterations. During cross validation this option allows the user to adjust the maximum number of iterations after the first |

`K` |
specify the number of folds for cross validation. |

`crit.cv` |
cross validation criterion ( |

`start` |
specify |

`cores` |
option to run CV in parallel. Defaults to |

`trace` |
option to display progress of CV. Choose one of |

returns list of returns which includes:

`lam` |
optimal tuning parameter. |

`min.error` |
minimum average cross validation error (cv.crit) for optimal parameters. |

`avg.error` |
average cross validation error (cv.crit) across all folds. |

`cv.error` |
cross validation errors (cv.crit). |

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