Description Usage Arguments Value References See Also Examples

Estimates a covariance or correlation matrix assuming the data came from a multivariate t distribution: this provides some degree of robustness to outlier without giving a high breakdown point.

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`x` |
data matrix. Missing values (NAs) are not allowed. |

`wt` |
A vector of weights for each case: these are treated as if the case |

`cor` |
Flag to choose between returning the correlation ( |

`center` |
a logical value or a numeric vector providing the location about which
the covariance is to be taken. If |

`nu` |
‘degrees of freedom’ for the multivariate t distribution. Must exceed 2 (so that the covariance matrix is finite). |

`maxit` |
Maximum number of iterations in fitting. |

`tol` |
Convergence tolerance for fitting. |

A list with the following components

`cov` |
the fitted covariance matrix. |

`center` |
the estimated or specified location vector. |

`wt` |
the specified weights: only returned if the |

`n.obs` |
the number of cases used in the fitting. |

`cor` |
the fitted correlation matrix: only returned if |

`call` |
The matched call. |

`iter` |
The number of iterations used. |

J. T. Kent, D. E. Tyler and Y. Vardi (1994)
A curious likelihood identity for the multivariate t-distribution.
*Communications in Statistics—Simulation and Computation*
**23**, 441–453.

Venables, W. N. and Ripley, B. D. (1999)
*Modern Applied Statistics with S-PLUS.* Third
Edition. Springer.

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