Description Usage Arguments Value

Cross validation function for ADMMsigma.

1 2 3 4 |

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

`S` |
option to provide a pxp sample covariance matrix (denominator n). If argument is |

`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. |

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

`path` |
option to return the regularization path. This option should be used with extreme care if the dimension is large. If set to TRUE, cores will be set to 1 and errors and optimal tuning parameters will based on the full sample. Defaults to FALSE. |

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

`maxit` |
maximum number of iterations. Defaults to 1e4. |

`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 |

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

list of returns includes:

`lam` |
optimal tuning parameter. |

`path` |
array containing the solution path. Solutions will be ordered in ascending lambda values. |

`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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