Usage
lmrob.control(seed = NULL, nResample = 500,
tuning.chi = 1.54764, bb = 0.5, tuning.psi = 4.685061,
max.it = 50, groups = 5, n.group = 400,
k.fast.s = 1, best.r.s = 2, k.max = 200,
refine.tol = 1e-7, rel.tol = 1e-7,
trace.lev = 0, compute.rd = FALSE)Arguments
seed
an integer vector, the seed to be used for random
re-sampling used in obtaining candidates for the initial
S-estimator; see .Random.seed. The current value of
.Random.seed wil nResample
number of re-sampling candidates to be
used to find the initial S-estimator. Currently defaults to 500
which works well in most situations (see references).
tuning.chi
tuning constant for the S-estimator.
The default, 1.54764, yields a 50% breakdown estimator.
bb
expected value under the normal model of the
chi (rather $\rho (rho)$) function with tuning
constant equal to tuning.chi. This is used to compute the
S-estimator.
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.
max.it
integer specifying the maximum number of IRWLS iterations.
groups
(for the fast-S algorithm): Number of
random subsets to use when the data set is large.
n.group
(for the fast-S algorithm): Size of each of the
groups above. Note that this must be at least $p$.
k.fast.s
(for the fast-S algorithm): Number of
local improvement steps (I-steps) for each
re-sampling candidate.
best.r.s
(for the fast-S algorithm): Number of
of best candidates to be iterated further (i.e.,
refined); is denoted $t$ in
Salibian-Barrera & Yohai(2006).
k.max
(for the fast-S algorithm): maximal number of
refinement steps for the fully iterated best candidates.
refine.tol
(for the fast-S algorithm): relative convergence
tolerance for the fully iterated best candidates.
rel.tol
(for the RWLS iterations of the MM algorithm): relative
convergence tolerance for the parameter vector.
trace.lev
integer indicating if the progress of the MM-algorithm
should be traced (increasingly); default trace.lev = 0 does
no tracing.
compute.rd
logical indicating if robust distances (based on
the MCD robust covariance estimator covMcd) are to be
computed for the robust diagnostic plots. This may take some
time to finish, particularly f