Description Usage Arguments Details Value Examples

Computes the Robust Regularized Estimator for location and inverse scatter.

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`data` |
Matrix or data.frame of observations |

`lambda` |
Penalty parameter which controls the sparseness of the resulting inverse scatter matrix. Default is 0.5 |

`hp` |
Robustness parameter which specifies the amount of observations to be included in the computations. Default is 0.75 |

`thresh` |
Threshold value controlling the convergence of the iterative algorithm. Default is 0.0001. In most cases this argument does not have to be supplied. |

`maxit` |
Maximum number of iterations of the algorithm. Default is 10. |

`penalty` |
Type of penalty to be applied. Possible values are "L1" and "L2". |

The Robust Regularized Estimator computes a sparse inverse covariance matrix of the given observations by maximization of a penalized likelihood function. The sparseness is controlled by a penalty parameter lambda. Possible outliers are dealt with by a robustness parameter alpha which specifies the amount of observations for which the likelihood function is maximized.

`mean` |
The resulting location estimate. |

`covi_nocons` |
The resulting inverse covariance estimate. |

`subset` |
An index vector specifying the data subset used (see robustness parameter alpha). |

`objective` |
The maximized objective value. |

`loglik` |
The maximized (log-)likelihood value. |

`niter` |
The number of iterations |

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

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