This function computes Rocke's robust estimator for multivariate location and scatter.
covRobRocke(X, initial = "K", maxsteps = 5, propmin = 2, qs = 2,
maxit = 50, tol = 1e-04, cor = FALSE)
a data matrix with observations in rows.
A character indicating the initial estimator. Valid options are 'K' (default) for the Pena-Prieto 'KSD' estimate, and 'mve' for the Minimum Volume Ellipsoid.
Maximum number of steps for the line search section of the algorithm.
Regulates the proportion of weights computed from the initial estimator that will be different from zero. The number of observations with initial non-zero weights will be at least p (the number of columns of X) times propmin.
Tuning paramater for Rocke's loss functions.
Maximum number of iterations.
Tolerance to decide converngence.
A logical value. If TRUE
a correlation matrix is included in the element cor
of the returned object. Defaults to FALSE
.
A list with class “covRob” containing the following elements:
The location estimate
The scatter (or correlation) matrix estimate, scaled for consistency at the normal distribution
The location estimate. Same as mu
above.
The scatter matrix estimate, scaled for consistency at the normal distribution. Same as V
above.
The correlation matrix estimate, if the argument cor
equals TRUE
. Otherwise it is set to NULL
.
Robust Mahalanobis distances.
weights
Final value of the constant gamma that regulates the efficiency.
This function computes Rocke's robust estimator for multivariate location and scatter.
# NOT RUN {
data(bus)
X0 <- as.matrix(bus)
X1 <- X0[,-9]
tmp <- covRobRocke(X1)
round(tmp$cov[1:10, 1:10], 3)
tmp$mu
# }
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