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TotalVarIC

Generating function for TotalVarIC-class

Description

Generates an object of class "TotalVarIC"; i.e., an influence curves eta of the form

eta = max(c, min(A Lambda, d))

with lower clipping bound c, upper clipping bound d and standardizing matrix A. Lambda stands for the L2 derivative of the corresponding L2 differentiable parametric family which can be created via CallL2Fam.

Usage

TotalVarIC(name, CallL2Fam = call("L2ParamFamily"), 
           Curve = EuclRandVarList(RealRandVariable(Map = c(function(x) {x}), 
                                                    Domain = Reals())), 
           Risks, Infos, clipLo = -Inf, clipUp = Inf, stand = as.matrix(1), 
           lowerCase = NULL, neighborRadius = 0, w = new("BdStWeight"),
           normtype = NormType(), biastype = symmetricBias(),
           modifyIC = NULL)

Arguments

name

object of class "character".

CallL2Fam

object of class "call": creates an object of the underlying L2-differentiable parametric family.

Curve

object of class "EuclRandVarList".

Risks

object of class "list": list of risks; cf. RiskType-class.

Infos

matrix of characters with two columns named method and message: additional informations.

clipLo

negative real: lower clipping bound.

clipUp

positive real: lower clipping bound.

stand

matrix: standardizing matrix

w

BdStWeight: weight object
lowerCase

optional constant for lower case solution.

neighborRadius

radius of the corresponding (unconditional) contamination neighborhood.

biastype

BiasType: type of the bias
normtype

NormType: type of the norm
modifyIC

object of class "OptionalFunction": function of four arguments: (1) L2Fam an L2 parametric family (2) IC an optional influence curve, (3) withMakeIC a logical argument whether to enforce the IC side conditions by makeIC, and (4) ... for arguments to be passed to calls to E in makeIC. Returns an object of class "IC". This function is mainly used for internal computations!

Value

Object of class "TotalVarIC"

Author(s)

References

Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.

Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.

See Also

Examples

IC1 <- TotalVarIC()
plot(IC1)
RobAStBase

Robust Asymptotic Statistics

v1.2.1
LGPL-3
Authors
Matthias Kohl [cre, cph, aut], Peter Ruckdeschel [aut, cph], Mykhailo Pupashenko [ctb] (contributed wrapper functions for diagnostic plots), Gerald Kroisandt [ctb] (contributed testing routines), R Core Team [ctb, cph] (for source file 'format.perc')
Initial release
2019-04-07

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