cov.trob
Covariance Estimation for Multivariate t Distribution
Description
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.
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 |
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. |
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 |
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. |
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)
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.