| Scontrol {FRB} | R Documentation |
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.
Scontrol(nsamp = 500, k = 3, bestr = 5, convTol = 1e-10, maxIt = 50)
MMcontrol(bdp = 0.5, eff = 0.95, shapeEff = FALSE, convTol.MM = 1e-07,
maxIt.MM = 50, fastScontrols = Scontrol(...), ...)
GScontrol(nsamp = 100, k = 3, bestr = 5, convTol = 1e-10, maxIt = 50)
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 TRUE, eff is with regard to shape-efficiency,
otherwise location-efficiency |
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 Scontrol to be set directly |
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
Sest_loccov, MMest_loccov, GSest_multireg, Sest_multireg,
MMest_multireg, Sest_twosample, MMest_twosample, FRBpcaS, ...
## Show the default settings: str(Scontrol()) str(MMcontrol()) str(GScontrol())