roblm.control {roblm}R Documentation

Tuning parameters for roblm

Description

Tuning parameters for the MM-regression estimator and the associated S-estimator

Usage

roblm.control(M = 2000, Nres = NA, seed = 99, fixed = FALSE,
        tuning.chi = 1.54764, tuning.psi = 4.685061, 
        compute.roboot = FALSE, 
        compute.rd = TRUE, 
        max.it = 50,
        groups = 5, n.group = 400, k.fast.s = 1)

Arguments

M Number of bootstrap samples. This is used when compute.roboot = TRUE
Nres Number of re-sampling candidates to be used to find the initial S-estimator. This parameter is currently set to 500, which works well in most situations (see References below). User-choice capability will be added in future releases
seed Random seed for the re-samples used in obtaining candiates for the initial S-estimator.
fixed If FALSE the explanatory variables are treated as random variables. Used when compute.roboot = TRUE
tuning.chi Tuning constant for the S-estimator. The choice 1.54764 yields a 50% breakdown estimator.
max.it Maximum number of IRWLS iterations
tuning.psi Tuning constant for the re-descending M-estimator. The choice 4.685061 yields an estimator with asymptotic efficiency of 95% for normal errors.
compute.roboot If TRUE standard errors are computed using the Robust Bootstrap of Salibian-Barrera and Zamar (2002).
compute.rd If TRUE robust distances (based on the MCD robust covariance matrix) are computed for the robust diagnostic plots. This may take some time to finish, specially for large data sets.
groups This parameter is for the fast-S algorithm. Number of random subsets to use when the data set is large.
n.group This parameter is for the fast-S algorithm. Size of each of the groups above.
k.fast.s This parameter is for the fast-S algorithm. Number of local improvement steps for each re-sampling candidate.

Author(s)

Matias Salibian-Barrera

References

Rousseeu and Yohai (1984); Yohai (1987); Salibian-Barrera and Zamar (2002); Salibian-Barrera and Yohai (2005)

See Also

roblm


[Package roblm version 0.5-1 Index]