Control Parameters for MM Robust Regression

DESCRIPTION:

Allows the users to set values affecting the estimation procedure for robust regression in lmRobMM.

USAGE:

lmRobMM.robust.control(tlo=0.0001, tua=1.5e-06, mxr=50, mxf=50,  
     mxs=50, ntm=50, tl=1e-06, estim="Auto", seed=1313, level=0.1,  
     efficiency=0.85, sampling="Random", weight=c("Optimal","Optimal")) 

OPTIONAL ARGUMENTS:

tlo
the relative tolerance in the iterative algorithms.
tua
the tolerance used for the determination of pseudo-rank.
mxr
the maximum number of iterations in the refinement step.
mxf
the maximum number of iterations in computing the final coefficient estimates.
mxs
the maximum number of iterations for computing the final scale estimate.
ntm
the parameter used in iteration monitoring. When the number of iterations reaches ntm, the current parameter values are printed.
tl
the tolerance for scale denominators. If a scale estimate becomes less than tl, the scale estimate is set equal to tl.
estim
parameter that determines the type of estimator to be computed. If estim="S", only the initial S-estimates are computed; if estim="M", the final M-estimates are returned. If estim="Auto" (default) the two types of estimators are computed.
sampling
parameter that determines the resampling algorithm. Valid choices are "Random" for random resampling, "Exhaustive" for exhaustive resampling and "Genetic" for genetic algorithm.
seed
seed parameter used in the random sampling algorithm for the computation of the initial S-estimates.
weight
a character vector that determines the type of loss functions to use. The first determines the loss function used for the initial S-estimates, and the second determines the loss function used for the final M-estimates. Valid choices are "Optimal" and "Bisquare".
level
the level of significance of the test for bias of the final M-estimates.
efficiency
the asymptotic efficiency of the final M-estimates.

VALUE:

a list containing the values used for each of the control parameters.

SEE ALSO:

.

EXAMPLES:

control.bs <- lmRobMM.robust.control(weight=c("Bisquare","Optimal"),
  level=0.05)