Description Usage Arguments Value

Cross validation (no folds) function for shrink. This function is to be used with CVP_ADMM.

1 2 3 4 5 |

`X_train` |
nxp training data matrix. |

`X_valid` |
(n - q)xp validation data matrix matrix. |

`Y_train` |
nxr training response matrix. |

`Y_valid` |
(n - q)xr validation response matrix. |

`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]. |

`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 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 |

`crit_cv` |
cross validation criterion ( |

`start` |
specify |

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

cross validation errors (cv_crit)

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