lmrob.control {robustbase} | R Documentation |
Tuning parameters for, lmrob
, the MM-regression
estimator and the associated S-estimator.
lmrob.control(seed = 37, Nres = 500, tuning.chi = 1.54764, bb = 0.5, tuning.psi = 4.685061, max.it = 50, groups = 5, n.group = 400, k.fast.s = 1, compute.rd = TRUE)
seed |
random seed for the re-samples used in obtaining candiates
for the initial S-estimator. The default, 37 used to be
frozen in the underlying C code. |
Nres |
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 below). User-choice capability will be added in future releases. |
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" 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 |
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. |
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. |
Matias Salibian-Barrera
lmrob
, also for references and examples.