reg_control {robmed}

Tuning parameters for MM-regression

Description

Obtain a list with tuning paramters for lmrob().

Usage

reg_control(efficiency = 0.85, max_iterations = 200, tol = 1e-07, seed = NULL)

Arguments

efficiency

a numeric value giving the desired efficiency (defaults to 0.85 for 85% efficiency).

max_iterations

an integer giving the maximum number of iterations in various parts of the algorithm.

tol

a small positive numeric value to be used to determine convergence in various parts of the algorithm.

seed

optional initial seed for the random number generator (see .Random.seed).

Value

A list of tuning parameters as returned by lmrob.control().

Note

This is a simplified wrapper function for lmrob.control(), as the latter requires detailed knowledge of the MM-type regression algorithm. Currently only 95%, 90%, 85% (the default) and 80% efficiency are supported. For other values, please specify the corresponding tuning parameters in lmrob.control() directly.

Author(s)

Andreas Alfons

References

Salibian-Barrera, M. and Yohai, V.J. (2006) A Fast Algorithm for S-regression Estimates. Journal of Computational and Graphical Statistics, 15(2), 414–427. doi:10.1198/106186006x113629.

Yohai, V.J. (1987) High Breakdown-Point and High Efficiency Estimates for Regression. The Annals of Statistics, 15(20), 642–656. doi:10.1214/aos/1176350366.

See Also

lmrob(), lmrob.control()

Examples

data("BSG2014")

# run fast-and-robust bootstrap test
ctrl <- reg_control(efficiency = 0.95)
boot <- test_mediation(BSG2014,
                       x = "ValueDiversity",
                       y = "TeamCommitment",
                       m = "TaskConflict",
                       control = ctrl)
summary(boot)