cov_Huber {robmed}

Huber M-estimator of location and scatter

Description

Compute a Huber M-estimator of location and scatter, which is reasonably robust for a small number of variables.

Usage

cov_Huber(x, control = cov_control(...), ...)

Arguments

x

a numeric matrix or data frame.

control

a list of tuning parameters as generated by cov_control().

...

additional arguments can be used to specify tuning parameters directly instead of via control.

Details

An iterative reweighting algorithm is used to compute the Huber M-estimator. The Huber weight function thereby corresponds to a convex optimization problem, resulting in a unique solution.

Value

An object of class "cov_Huber" with the following components:

center

a numeric vector containing the location vector estimate.

cov

a numeric matrix containing the scatter matrix estimate.

prob

numeric; probability for the quantile of the \chi^{2} distribution used as cutoff point in the Huber weight function.

weights

a numeric vector containing the relative robustness weights for the observations.

tau

numeric; correction for Fisher consistency under multivariate normal distributions.

converged

a logical indicating whether the iterative reweighting algorithm converged.

iterations

an integer giving the number of iterations required to obtain the solution.

Author(s)

Andreas Alfons

References

Huber, P.J. (1981) Robust Statistics. John Wiley & Sons.

Zu, J. and Yuan, K.-H. (2010) Local Influence and Robust Procedures for Mediation Analysis. Multivariate Behavioral Research, 45(1), 1–44. doi:10.1080/00273170903504695.

See Also

cov_control(), test_mediation(), fit_mediation()

Examples

data("BSG2014")

# define variables
x <- "ValueDiversity"
y <- "TeamCommitment"
m <- "TaskConflict"

# compute Huber M-estimator
cov_Huber(BSG2014[, c(x, y, m)])