- setting
a string specifying alternative default values. Leave
empty for the defaults or use "KS2011" or "KS2014"
for the defaults suggested by Koller and Stahel (2011, 2017).
See Details.
- seed
NULL or an integer vector compatible with
.Random.seed: the seed to be used for random
re-sampling used in obtaining candidates for the initial
S-estimator. The current value of .Random.seed will be
preserved if seed is set, i.e. non-NULL;
otherwise, as by default, .Random.seed will be used and
modified as usual from calls to runif() etc.
- 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 vector for the S-estimator. If
NULL, as by default, sensible defaults are set (depending on
psi) to yield a 50% breakdown estimator. See Details.
- 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 vector for the redescending
M-estimator. If NULL, as by default, this is set (depending
on psi) to yield an estimator with asymptotic efficiency of
95% for normal errors. See Details.
- 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.
- k.m_s
(for the M-S algorithm): specifies after how many
unsuccessful refinement steps the algorithm stops.
- 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.
- maxit.scale
integer specifying the maximum number of C level
find_scale() iterations (in fast-S and M-S algorithms).
- 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.
- scale.tol
(for the scale estimation iterations of the S algorithm): relative
convergence tolerance for the scale \(\sigma(.)\).
- solve.tol
(for the S algorithm): relative
tolerance for inversion. Hence, this corresponds to
solve.default()'s tol.
- zero.tol
for checking 0-residuals in the S algorithm, non-negative number
\(\epsilon_z\) such that
\(\{i; \left|\tilde{R}_i\right| \le \epsilon_z\}\)
correspond to \(0\)-residuals, where \(\tilde{R}_i\) are standardized residuals,
\(\tilde{R}_i = R_i/s_y\) and
\(s_y = \frac{1}{n} \sum_{i=1}^n \left|y_i\right|\).
- trace.lev
integer indicating if the progress of the MM-algorithm
and the fast-S algorithms, see lmrob.S,
should be traced (increasingly); default trace.lev = 0 does
no tracing.
- mts
maximum number of samples to try in subsampling
algorithm.
- subsampling
type of subsampling to be used, a string:
"simple" for simple subsampling (default prior to version 0.9),
"nonsingular" for nonsingular subsampling. See also
lmrob.S.
- 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 for large data sets, and can lead to
singularity problems when there are factor explanatory
variables (with many levels, or levels with “few”
observations). Hence, is FALSE by default.
- method
string specifying the estimator-chain. MM
is interpreted as SM. See Details of
lmrob for a description of the possible values.
- psi
string specifying the type \(\psi\)-function
used. See Details of lmrob. Defaults to
"bisquare" for S and MM-estimates, otherwise "lqq".
- numpoints
number of points used in Gauss quadrature.
- cov
function or string with function name to be used to
calculate covariance matrix estimate. The default is
if(method %in% c('SM', 'MM')) ".vcov.avar1" else ".vcov.w".
See Details of lmrob.
- split.type
determines how categorical and continuous variables
are split. See splitFrame.
- fast.s.large.n
minimum number of observations required to
switch from ordinary “fast S” algorithm to an efficient
“large n” strategy.
- eps.outlier
limit on the robustness weight below which an observation
is considered to be an outlier.
Either a numeric(1) or a function that takes the number of observations as
an argument. Used only in summary.lmrob and
outlierStats.
- eps.x
limit on the absolute value of the elements of the design matrix
below which an element is considered zero.
Either a numeric(1) or a function that takes the maximum absolute value in
the design matrix as an argument.
- compute.outlier.stats
vector of character
strings, each valid to be used as method argument. Used to
specify for which estimators outlier statistics (and warnings)
should be produced. Set to empty (NULL or character(0))
if none are required.
Note that the default is method which by default is either
"MM", "SM", or "SMDM"; hence using
compute.outlier.stats = "S" provides outlierStats()
to a lmrob.S() result.
- warn.limit.reject
limit of ratio
\(\#\mbox{rejected} / \#\mbox{obs in level}\)
above (\(\geq\)) which a warning is produced.
Set to NULL to disable warning.
- warn.limit.meanrw
limit of the mean robustness per factor level
below which (\(\leq\)) a warning is produced.
Set to NULL to disable warning.
- object
an "lmrobCtrl" object, as resulting from a
lmrob.control(*) or an update(<lmrobCtrl>, *) call.
- ...
for
lmrob.control():further arguments to be added as
list components to the result, e.g., those to be used in
.vcov.w().
update(object, *):(named) components from
object, to be modified, not setting = *.