Rocke's robust multivariate location and scatter estimator
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)
X |
a data matrix with observations in rows. |
initial |
A character indicating the initial estimator. Valid options are 'K' (default) for the Pena-Prieto 'KSD' estimate, and 'mve' for the Minimum Volume Ellipsoid. |
maxsteps |
Maximum number of steps for the line search section of the algorithm. |
propmin |
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. |
qs |
Tuning paramater for Rocke's loss functions. |
maxit |
Maximum number of iterations. |
tol |
Tolerance to decide converngence. |
cor |
A logical value. If |
This function computes Rocke's robust estimator for multivariate location and scatter.
A list with class “covRob” containing the following elements:
mu |
The location estimate |
V |
The scatter (or correlation) matrix estimate, scaled for consistency at the normal distribution |
center |
The location estimate. Same as |
cov |
The scatter matrix estimate, scaled for consistency at the normal distribution. Same as |
cor |
The correlation matrix estimate, if the argument |
dista |
Robust Mahalanobis distances. |
w |
weights |
gamma |
Final value of the constant gamma that regulates the efficiency. |
Ricardo Maronna, rmaronna@retina.ar
data(bus) X0 <- as.matrix(bus) X1 <- X0[,-9] tmp <- covRobRocke(X1) round(tmp$cov[1:10, 1:10], 3) tmp$mu
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.