This class extends the CovControlMcd class
and contains control parameters for the robust estimation of parametric interval data models.
Objects can be created by calls of the form new("RobEstControl", ...)
or by calling the constructor-function RobEstControl.
alpha:Inherited from class "CovControlMcd". Numeric parameter controlling the size of the subsets over which the trimmed likelihood is maximized; roughly alpha*Idt@NIVar observations are used for computing the trimmed likelihood. Allowed values are between 0.5 and 1.
nsamp:Inherited from class "CovControlMcd". Number of subsets used for initial estimates. Note that when argument ‘getalpha’ is set to “TwoStep” the final value of ‘alpha’ is estimated by a two-step procedure and the value of argument ‘alpha’ is only used to specify the size of the samples used in the first step.
scalefn:Inherited from class "CovControlMcd" and not used in the package \sQuoteMaint.Data.
maxcsteps:Inherited from class "CovControlMcd" and not used in the package \sQuoteMaint.Data.
seed:Inherited from class "CovControlMcd". Starting value for random generator. Default is seed = NULL.
use.correction:Inherited from class "CovControlMcd". Whether to use finite sample correction factors.
Default is use.correction=TRUE.
trace, tolSolve:Inherited from class "CovControl".
ncsteps:The maximum number of concentration steps used each iteration of the fasttle algorithm.
getalpha:Argument specifying if the ‘alpha’ parameter (roughly the percentage of the sample used for computing the trimmed likelihood) should be estimadted from the data, or if the value of the argument ‘alpha’ should be used instead. When set to “TwoStep”, ‘alpha’ is estimated by a two-step procedure with the value of argument ‘alpha’ specifying the size of the samples used in the first step. Otherwise, with the value of argument ‘alpha’ is used directly.
getkdblstar:Argument specifying the size of the initial small (in order to minimize the probability of outliers) subsets. If set to the string “Twopplusone” (default) the initial sets have twice the number of interval-value variables plus one (i.e., they are the smaller samples that lead to a non-singular covaraince estimate). Otherwise, an integer with the size of the initial sets.
outlin:The type of outliers to be consideres. “MidPandLogR” if outliers may be present in both MidPpoints and LogRanges, “MidP” if outliers are only present in MidPpoints, or “LogR” if outliers are only present in LogRanges.
trialmethod:The method to find a trial subset used to initialize each replication of the fasttle algorithm. The current options are “simple” (default) that simply selects ‘kdblstar’ observations at random, and “Poolm” that divides the original sample into ‘m’ non-overlaping subsets, applies the ‘simple trial’ and the refinement methods to each one of them, and merges the results into a trial subset.
m:Number of non-overlaping subsets used by the trial method when the argument of ‘trialmethod’ is set to 'Poolm'.
reweighted:Should a (Re)weighted estimate of the covariance matrix be used in the computation of the trimmed likelihood or just a “raw” covariance estimate; default is (Re)weighting.
otpType:The amount of output returned by fasttle. Current options are “OnlyEst” (default) where only an ‘IdtE’ object with the fasttle estimates is returned, “SetMD2andEst” which returns a list with an ‘IdtE’ object of fasttle estimates, a vector with the final trimmed subset elements used to compute these estimates and the corresponding robust squared Mahalanobis distances, and “SetMD2EstandPrfSt” wich returns a list with the previous three components plust a list of some performance statistics concerning the algorithm execution.
No methods defined with class "RobEstControl" in the signature.
Hadi, A. S. and Luceno, A. (1997), Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Computational Statistics and Data Analysis 25(3), 251--272.
Todorov V. and Filzmoser P. (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software 32(3), 1--47.