Description Usage Arguments Details Value Author(s) References See Also Examples
Tuning parameters for multivariate S, MM and GS estimates as used in FRB functions for multivariate regression, PCA and Hotelling tests. Mainly regarding the fast-(G)S algorithm.
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nsamp |
number of random subsamples to be used in the fast-(G)S algorithm |
k |
number of initial concentration steps performed on each subsample candidate |
bestr |
number of best candidates to keep for full iteration (i.e. concentration steps until convergence) |
convTol |
relative convergence tolerance for estimates used in (G)S-concentration iteration |
maxIt |
maximal number of steps in (G)S-concentration iteration |
bdp |
breakdown point of the MM-estimates; usually equals 0.5 |
eff |
Gaussian efficiency of the MM-estimates; usually set at 0.95 |
shapeEff |
logical; if |
convTol.MM |
relative convergence tolerance for estimates used in MM-iteration |
maxIt.MM |
maximal number of steps in MM-iteration |
fastScontrols |
the tuning parameters of the initial S-estimate |
... |
allows for any individual parameter from |
The default number of random samples is lower for GS-estimates than for S-estimates, because computations regarding the former are more demanding.
A list with the tuning parameters as set by the arguments.
Gert Willems and Ella Roelant
S. Van Aelst and G. Willems (2013). Fast and robust bootstrap for multivariate inference: The R package FRB. Journal of Statistical Software, 53(3), 1–32. URL: http://www.jstatsoft.org/v53/i03/.
GSest_multireg
, Sest_multireg
,
MMest_multireg
, Sest_twosample
, MMest_twosample
, FRBpcaS
, ...
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