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.
cov.trob(x, wt = rep(1, n), cor = FALSE, center = TRUE, nu = 5,
maxit = 25, tol = 0.01)A list with the following components
the fitted covariance matrix.
the estimated or specified location vector.
the specified weights: only returned if the wt argument was given.
the number of cases used in the fitting.
the fitted correlation matrix: only returned if cor = TRUE.
The matched call.
The number of iterations used.
data matrix. Missing values (NAs) are not allowed.
A vector of weights for each case: these are treated as if the case i
actually occurred wt[i] times.
Flag to choose between returning the correlation (cor = TRUE) or
covariance (cor = FALSE) matrix.
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.
‘degrees of freedom’ for the multivariate t distribution. Must exceed 2 (so that the covariance matrix is finite).
Maximum number of iterations in fitting.
Convergence tolerance for fitting.
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.