Description Usage Arguments Value Author(s) Examples

Apply a random cross-validation (CV) to select tuning parameters for regualrized covariance matrix with banding, tapering, soft-thresholding or hard-thresholding method under the Frobenius norm or the operator norm. The random CV randomly splits the data set to two parts, a training set and a validation set with user-specifed sizes, and repeats the process for multiple times.

1 2 |

`X` |
input data matrix with dimension |

`k.grid` |
the default value is 0.5. |

`method` |
the regularized method, which can be "Banding", "Tapering", "HardThresholding" or "SoftThresholding". the default value is "Tapering". |

`test.size` |
the size of the validation set, which should be |

`norm` |
the norms which can be used to measure the estimation accuracy, which can be the Frobenius norm "F" or the operator norm "L2". |

`boot.num` |
the number of random split. The default value is 50. |

`seed` |
the default value is 10323. |

A list including elements:

`CV.k ` |
the optimal tuning parameter selected by the random CV. |

`k.grid ` |
the vector of tuning parameters |

`CV.pre.error` |
a vector denoting predicting errors by random CV at each element of tuning parameters based on the selected norm. |

Binhuan Wang

1 2 3 4 5 6 7 8 9 |

CVTuningCov documentation built on May 29, 2017, 9:07 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.