internal_Cniperplots: Internal / Helper functions of package ROptEst for cniper...
In ROptEst: Optimally Robust Estimation

internals_for_ROptEst_Cniperplots

R Documentation

Internal / Helper functions of package ROptEst for cniper plot functions

Description

These functions are internally used helper function for cniperCont and cniperPointPlot.

Usage

.plotData(data, dots, fun, L2Fam,  IC, jit.fac, jit.tol, plotInfo )
.getFunCnip(IC1,IC2, risk, L2Fam, r, b20=NULL)

Arguments

`data`	data to be plot in
`dots`	list; argument `...` from the calling function
`fun`	function from data to reals; function according to which the data is ordered
`L2Fam`	object of class `L2ParamFamily`
`IC`	object of class `IC`: To determine the size of the observation symbols, we compute the norm of the IC evaluated at this point.
`IC1`	object of class `IC`: one of the ICs from which to take the risk difference.
`IC2`	object of class `IC`: one of the ICs from which to take the risk difference.
`risk`	object of class `asGRisk`: the risk to take the fifference from
`r`	positive numeric of length 1: the neighborhood radius
`b20`	positive numeric of length 1: the maximal bias of `IC2`.
`jit.fac`	jittering factor used in case of a `DiscreteDistribution` for plotting points of the second argument in a jittered fashion.
`jit.tol`	jittering tolerance used in case of a `DiscreteDistribution` for plotting points of the second argument in a jittered fashion.
`plotInfo`	stored info from the plot

Details

.plotData takes argument data and plots it into the cniper graph.

.getFunCnip produces a function to compute the risk difference. If argument b20 is not NULL, in the risk difference, for IC2 uses the least favorable contamination situation ('over all real Dirac contamination points'), i.e. leading to a bias of b20. Otherwise it uses the bias obtaine from a contamination in the evaluation point.

Value

`.plotData`	If argument `dots` contains an element `return.Order` and if this equals `TRUE`, the order of the (remaining) observations given by their original index is returned (remaining means: after a possible reduction by argument `which.lbs`, and ordering is according `fun`; otherwise we return `invisible()` as usual.
`.getFunCnip`	a vectorized function to compute the risk difference.