# cov.trob

From MASS v7.3-19
by Brian Ripley

##### Covariance Estimation for Multivariate t Distribution

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.

- Keywords
- multivariate

##### Usage

```
cov.trob(x, wt = rep(1, n), cor = FALSE, center = TRUE, nu = 5,
maxit = 25, tol = 0.01)
```

##### Arguments

- x
- data matrix. Missing values (NAs) are not allowed.
- wt
- A vector of weights for each case: these are treated as if the case
`i`

actually occurred`wt[i]`

times. - cor
- Flag to choose between returning the correlation (
`cor = TRUE`

) or covariance (`cor = FALSE`

) matrix. - center
- a logical value or a numeric vector providing the location about which
the covariance is to be taken. If
`center = FALSE`

, no centering is done; if`center = TRUE`

the MLE of the location vector is used. - 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.

##### Value

- 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 `wt`

argument was given.n.obs the number of cases used in the fitting. cor the fitted correlation matrix: only returned if `cor = TRUE`

.call The matched call. iter The number of iterations used.

##### References

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.

##### See Also

##### Examples

`cov.trob(stackloss)`

*Documentation reproduced from package MASS, version 7.3-19, License: GPL-2 | GPL-3*

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