Description Usage Arguments Details Value Author(s) References See Also Examples
Compute a Huber Mestimator of location and scatter, which is reasonably robust for a small number of variables.
1  cov_Huber(x, control = cov_control(...), ...)

x 
a numeric matrix or data frame. 
control 
a list of tuning parameters as generated by

... 
additional arguments can be used to specify tuning parameters
directly instead of via 
An iterative reweighting algorithm is used to compute the Huber Mestimator. The Huber weight function thereby corresponds to a convex optimization problem, resulting in a unique solution.
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 chisquared 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. 
Andreas Alfons
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.
cov_control
, test_mediation
,
fit_mediation
1 2 3 4 5 6 7 8 9 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.