[Robast-commits] r844 - branches/robast-1.0/pkg/ROptEst/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jun 16 09:43:12 CEST 2015
Author: ruckdeschel
Date: 2015-06-16 09:43:12 +0200 (Tue, 16 Jun 2015)
New Revision: 844
Modified:
branches/robast-1.0/pkg/ROptEst/man/0ROptEst-package.Rd
branches/robast-1.0/pkg/ROptEst/man/CniperPointPlotWrapper.Rd
branches/robast-1.0/pkg/ROptEst/man/asL1-class.Rd
branches/robast-1.0/pkg/ROptEst/man/asL1.Rd
branches/robast-1.0/pkg/ROptEst/man/asL4-class.Rd
branches/robast-1.0/pkg/ROptEst/man/asL4.Rd
branches/robast-1.0/pkg/ROptEst/man/cniperCont.Rd
branches/robast-1.0/pkg/ROptEst/man/getAsGRiskFct-methods.Rd
branches/robast-1.0/pkg/ROptEst/man/getBiasIC.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfCent.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfClip.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfGamma.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfRad.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfRobIC.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfStand.Rd
branches/robast-1.0/pkg/ROptEst/man/getInfV.Rd
branches/robast-1.0/pkg/ROptEst/man/getL1normL2deriv.Rd
branches/robast-1.0/pkg/ROptEst/man/getL2normL2deriv.Rd
branches/robast-1.0/pkg/ROptEst/man/getRadius.Rd
branches/robast-1.0/pkg/ROptEst/man/getRiskIC.Rd
branches/robast-1.0/pkg/ROptEst/man/getStartIC-methods.Rd
branches/robast-1.0/pkg/ROptEst/man/getinfLM.Rd
branches/robast-1.0/pkg/ROptEst/man/inputGenerator.Rd
branches/robast-1.0/pkg/ROptEst/man/internals.Rd
branches/robast-1.0/pkg/ROptEst/man/leastFavorableRadius.Rd
branches/robast-1.0/pkg/ROptEst/man/lowerCaseRadius.Rd
branches/robast-1.0/pkg/ROptEst/man/minmaxBias.Rd
branches/robast-1.0/pkg/ROptEst/man/radiusMinimaxIC.Rd
branches/robast-1.0/pkg/ROptEst/man/robest.Rd
branches/robast-1.0/pkg/ROptEst/man/roptest.Rd
branches/robast-1.0/pkg/ROptEst/man/updateNorm-methods.Rd
Log:
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Modified: branches/robast-1.0/pkg/ROptEst/man/0ROptEst-package.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/0ROptEst-package.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/0ROptEst-package.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -1,79 +1,79 @@
-\name{ROptEst-package}
-\alias{ROptEst-package}
-\alias{ROptEst}
-\docType{package}
-\title{
-Optimally robust estimation
-}
-\description{
-Optimally robust estimation in general smoothly parameterized models
-using S4 classes and methods.
-}
-\details{
-\tabular{ll}{
-Package: \tab ROptEst \cr
-Version: \tab 1.0 \cr
-Date: \tab 2015-05-03 \cr
-Depends: \tab R(>= 2.14.0), methods, distr(>= 2.5.2), distrEx(>= 2.5), distrMod(>= 2.5.2),
-RandVar(>= 0.9.2), RobAStBase(>= 0.9) \cr
-Suggests: \tab RobLox, MASS \cr
-Imports: \tab startupmsg \cr
-ByteCompile: \tab yes \cr
-License: \tab LGPL-3 \cr
-URL: \tab http://robast.r-forge.r-project.org/\cr
-SVNRevision: \tab 728 \cr
-}
-}
-\author{
-Peter Ruckdeschel \email{Peter.Ruckdeschel at itwm.fraunhofer.de},\cr%
-Matthias Kohl \email{Matthias.Kohl at stamats.de}\cr
-
-Maintainer: Matthias Kohl \email{matthias.kohl at stamats.de}}
-\references{
- M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness.
- Dissertation. University of Bayreuth.
-
- M. Kohl, P. Ruckdeschel, H. Rieder (2010). Infinitesimally Robust Estimation in
- General Smoothly Parametrized Models. Statistical Methods and Application 19(3):333–354.
-}
-\seealso{
-\code{\link[distr:0distr-package]{distr-package}},
-\code{\link[distrEx:0distrEx-package]{distrEx-package}},
-\code{\link[distrMod:0distrMod-package]{distrMod-package}},
-\code{\link[RandVar:0RandVar-package]{RandVar-package}},
-\code{\link[RobAStBase:0RobAStBase-package]{RobAStBase-package}}
-}
-\section{Package versions}{
-Note: The first two numbers of package versions do not necessarily reflect
- package-individual development, but rather are chosen for the
- RobAStXXX family as a whole in order to ease updating "depends"
- information.
-}
-\examples{
-library(ROptEst)
-
-## Example: Rutherford-Geiger (1910); cf. Feller~(1968), Section VI.7 (a)
-x <- c(rep(0, 57), rep(1, 203), rep(2, 383), rep(3, 525), rep(4, 532),
- rep(5, 408), rep(6, 273), rep(7, 139), rep(8, 45), rep(9, 27),
- rep(10, 10), rep(11, 4), rep(12, 0), rep(13, 1), rep(14, 1))
-
-## ML-estimate from package distrMod
-MLest <- MLEstimator(x, PoisFamily())
-MLest
-## confidence interval based on CLT
-confint(MLest)
-
-## compute optimally (w.r.t to MSE) robust estimator (unknown contamination)
-robEst <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
-estimate(robEst)
-## check influence curve
-pIC(robEst)
-checkIC(pIC(robEst))
-## plot influence curve
-plot(pIC(robEst))
-## confidence interval based on LAN - neglecting bias
-confint(robEst)
-## confidence interval based on LAN - including bias
-confint(robEst, method = symmetricBias())
-}
-\keyword{package}
+\name{ROptEst-package}
+\alias{ROptEst-package}
+\alias{ROptEst}
+\docType{package}
+\title{
+Optimally robust estimation
+}
+\description{
+Optimally robust estimation in general smoothly parameterized models
+using S4 classes and methods.
+}
+\details{
+\tabular{ll}{
+Package: \tab ROptEst \cr
+Version: \tab 1.0 \cr
+Date: \tab 2015-05-03 \cr
+Depends: \tab R(>= 2.14.0), methods, distr(>= 2.5.2), distrEx(>= 2.5), distrMod(>= 2.5.2),
+RandVar(>= 0.9.2), RobAStBase(>= 0.9) \cr
+Suggests: \tab RobLox, MASS \cr
+Imports: \tab startupmsg \cr
+ByteCompile: \tab yes \cr
+License: \tab LGPL-3 \cr
+URL: \tab http://robast.r-forge.r-project.org/\cr
+SVNRevision: \tab 728 \cr
+}
+}
+\author{
+Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de},\cr%
+Matthias Kohl \email{Matthias.Kohl at stamats.de}\cr
+
+Maintainer: Matthias Kohl \email{matthias.kohl at stamats.de}}
+\references{
+ M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness.
+ Dissertation. University of Bayreuth.
+
+ M. Kohl, P. Ruckdeschel, H. Rieder (2010). Infinitesimally Robust Estimation in
+ General Smoothly Parametrized Models. Statistical Methods and Application 19(3):333–354.
+}
+\seealso{
+\code{\link[distr:0distr-package]{distr-package}},
+\code{\link[distrEx:0distrEx-package]{distrEx-package}},
+\code{\link[distrMod:0distrMod-package]{distrMod-package}},
+\code{\link[RandVar:0RandVar-package]{RandVar-package}},
+\code{\link[RobAStBase:0RobAStBase-package]{RobAStBase-package}}
+}
+\section{Package versions}{
+Note: The first two numbers of package versions do not necessarily reflect
+ package-individual development, but rather are chosen for the
+ RobAStXXX family as a whole in order to ease updating "depends"
+ information.
+}
+\examples{
+library(ROptEst)
+
+## Example: Rutherford-Geiger (1910); cf. Feller~(1968), Section VI.7 (a)
+x <- c(rep(0, 57), rep(1, 203), rep(2, 383), rep(3, 525), rep(4, 532),
+ rep(5, 408), rep(6, 273), rep(7, 139), rep(8, 45), rep(9, 27),
+ rep(10, 10), rep(11, 4), rep(12, 0), rep(13, 1), rep(14, 1))
+
+## ML-estimate from package distrMod
+MLest <- MLEstimator(x, PoisFamily())
+MLest
+## confidence interval based on CLT
+confint(MLest)
+
+## compute optimally (w.r.t to MSE) robust estimator (unknown contamination)
+robEst <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
+estimate(robEst)
+## check influence curve
+pIC(robEst)
+checkIC(pIC(robEst))
+## plot influence curve
+plot(pIC(robEst))
+## confidence interval based on LAN - neglecting bias
+confint(robEst)
+## confidence interval based on LAN - including bias
+confint(robEst, method = symmetricBias())
+}
+\keyword{package}
Modified: branches/robast-1.0/pkg/ROptEst/man/CniperPointPlotWrapper.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/CniperPointPlotWrapper.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/CniperPointPlotWrapper.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -1,49 +1,49 @@
-\name{CniperPointPlot}
-\alias{CniperPointPlot}
-\title{Wrapper function for cniperPointPlot - Computation and Plot
- of Cniper Contamination and Cniper Points}
-\usage{
- CniperPointPlot(fam, ...,
- lower = getdistrOption("DistrResolution"),
- upper = 1 - getdistrOption("DistrResolution"),
- with.legend = TRUE, rescale = FALSE, withCall = TRUE)
-}
-\arguments{
- \item{fam}{object of class L2ParamFamily}
-
- \item{...}{additional parameters (in particular to be
- passed on to \code{plot})}
-
- \item{lower}{the lower end point of the contamination
- interval}
-
- \item{upper}{the upper end point of the contamination
- interval}
-
- \item{with.legend}{the flag for showing the legend of the
- plot}
-
- \item{rescale}{the flag for rescaling the axes for better view of the plot}
-
- \item{withCall}{the flag for the call output}
-}
-\value{
- invisible(NULL)
-}
-\description{
- The wrapper takes most of arguments to the
- cniperPointPlot function by default and gives a user
- possibility to run the function with low number of
- arguments
-}
-\section{Details}{
- Calls \code{cniperPointPlot} with suitably chosen
- defaults; if \code{withCall == TRUE}, the call to
- \code{cniperPointPlot} is returned.
-}
-\examples{
-L2fam <- NormLocationScaleFamily()
-CniperPointPlot(fam=L2fam, main = "Normal location and scale",
- lower = 0, upper = 2.5, withCall = FALSE)
-}
-
+\name{CniperPointPlot}
+\alias{CniperPointPlot}
+\title{Wrapper function for cniperPointPlot - Computation and Plot
+ of Cniper Contamination and Cniper Points}
+\usage{
+ CniperPointPlot(fam, ...,
+ lower = getdistrOption("DistrResolution"),
+ upper = 1 - getdistrOption("DistrResolution"),
+ with.legend = TRUE, rescale = FALSE, withCall = TRUE)
+}
+\arguments{
+ \item{fam}{object of class L2ParamFamily}
+
+ \item{...}{additional parameters (in particular to be
+ passed on to \code{plot})}
+
+ \item{lower}{the lower end point of the contamination
+ interval}
+
+ \item{upper}{the upper end point of the contamination
+ interval}
+
+ \item{with.legend}{the flag for showing the legend of the
+ plot}
+
+ \item{rescale}{the flag for rescaling the axes for better view of the plot}
+
+ \item{withCall}{the flag for the call output}
+}
+\value{
+ invisible(NULL)
+}
+\description{
+ The wrapper takes most of arguments to the
+ cniperPointPlot function by default and gives a user
+ possibility to run the function with low number of
+ arguments
+}
+\section{Details}{
+ Calls \code{cniperPointPlot} with suitably chosen
+ defaults; if \code{withCall == TRUE}, the call to
+ \code{cniperPointPlot} is returned.
+}
+\examples{
+L2fam <- NormLocationScaleFamily()
+CniperPointPlot(fam=L2fam, main = "Normal location and scale",
+ lower = 0, upper = 2.5, withCall = FALSE)
+}
+
Modified: branches/robast-1.0/pkg/ROptEst/man/asL1-class.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/asL1-class.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/asL1-class.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -32,7 +32,7 @@
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for
General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
}
-\author{Peter Ruckdeschel \email{peter.ruckdeschel at itwm.fraunhofer.de}}
+\author{Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de}}
%\note{}
\seealso{\code{\link{asGRisk-class}}, \code{\link{asMSE}}, \code{\link{asMSE-class}}, \code{\link{asL4-class}}, \code{\link{asL1}}}
\examples{
Modified: branches/robast-1.0/pkg/ROptEst/man/asL1.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/asL1.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/asL1.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -17,7 +17,7 @@
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for
General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
}
-\author{Peter Ruckdeschel \email{peter.ruckdeschel at itwm.fraunhofer.de}}
+\author{Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de}}
%\note{}
\seealso{\code{\link{asL1-class}}, \code{\link{asMSE}}, \code{\link{asL4}}}
\examples{
Modified: branches/robast-1.0/pkg/ROptEst/man/asL4-class.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/asL4-class.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/asL4-class.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -32,7 +32,7 @@
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for
General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
}
-\author{Peter Ruckdeschel \email{peter.ruckdeschel at itwm.fraunhofer.de}}
+\author{Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de}}
%\note{}
\seealso{\code{\link{asGRisk-class}}, \code{\link{asMSE}}, \code{\link{asMSE-class}}, \code{\link{asL1-class}}, \code{\link{asL4}}}
\examples{
Modified: branches/robast-1.0/pkg/ROptEst/man/asL4.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/asL4.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/asL4.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -17,7 +17,7 @@
Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for
General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
}
-\author{Peter Ruckdeschel \email{peter.ruckdeschel at itwm.fraunhofer.de}}
+\author{Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de}}
%\note{}
\seealso{\code{\link{asL4-class}}, \code{\link{asMSE}}, \code{\link{asL1}}}
\examples{
Modified: branches/robast-1.0/pkg/ROptEst/man/cniperCont.Rd
===================================================================
--- branches/robast-1.0/pkg/ROptEst/man/cniperCont.Rd 2015-06-16 07:42:13 UTC (rev 843)
+++ branches/robast-1.0/pkg/ROptEst/man/cniperCont.Rd 2015-06-16 07:43:12 UTC (rev 844)
@@ -1,204 +1,211 @@
-\name{cniperCont}
-\alias{cniperCont}
-\alias{cniperPoint}
-\alias{cniperPointPlot}
-\title{ Functions for Computation and Plot of Cniper Contamination
- and Cniper Points. }
-\description{
- These functions and their methods can be used to determine cniper
- contamination as well as cniper points. That is, under which (Dirac)
- contamination is the risk of one procedure larger than the risk of some
- other procedure.
-}
-\usage{
-cniperCont(IC1, IC2, data = NULL, ...,
- neighbor, risk, lower = getdistrOption("DistrResolution"),
- upper = 1-getdistrOption("DistrResolution"), n = 101,
- scaleX = FALSE, scaleX.fct, scaleX.inv,
- scaleY = FALSE, scaleY.fct = pnorm, scaleY.inv=qnorm,
- scaleN = 9, x.ticks = NULL, y.ticks = NULL,
- cex.pts = 1, cex.pts.fun = NULL, col.pts = par("col"),
- pch.pts = 19, jitter.fac = 1, with.lab = FALSE,
- lab.pts = NULL, lab.font = NULL, alpha.trsp = NA,
- which.lbs = NULL, which.Order = NULL,
- return.Order = FALSE, withSubst = TRUE)
-
-
-cniperPoint(L2Fam, neighbor, risk, lower, upper)
-
-cniperPointPlot(L2Fam, data=NULL, ..., neighbor, risk= asMSE(),
- lower=getdistrOption("DistrResolution"),
- upper=1-getdistrOption("DistrResolution"), n = 101,
- withMaxRisk = TRUE,
- scaleX = FALSE, scaleX.fct, scaleX.inv,
- scaleY = FALSE, scaleY.fct = pnorm, scaleY.inv=qnorm,
- scaleN = 9, x.ticks = NULL, y.ticks = NULL,
- cex.pts = 1, cex.pts.fun = NULL, col.pts = par("col"),
- pch.pts = 19, jitter.fac = 1, with.lab = FALSE,
- lab.pts = NULL, lab.font = NULL, alpha.trsp = NA,
- which.lbs = NULL, which.Order = NULL,
- return.Order = FALSE, withSubst = TRUE)
-
-}
-\arguments{
- \item{IC1}{ object of class \code{IC} }
- \item{IC2}{ object of class \code{IC} }
- \item{L2Fam}{ object of class \code{L2ParamFamily} }
- \item{neighbor}{ object of class \code{Neighborhood} }
- \item{risk}{ object of class \code{RiskType} }
- \item{\dots}{ additional parameters (in particular to be passed on to \code{plot}). }
- \item{data}{data to be plotted in}
- \item{lower, upper}{ the lower and upper end points of the
- contamination interval (in prob-scale). }
- \item{n}{ number of points between \code{lower} and \code{upper}}
- \item{withMaxRisk}{logical; if \code{TRUE}, for risk comparison
- uses the maximal risk of the classically optimal IC \eqn{\psi}{psi} in all
- situations with contamination in Dirac points 'no larger' than
- the respective evaluation point and the optimally-robust
- IC \eqn{\eta}{eta} at its least favorable contamination situation
- ('over all real Dirac contamination points'). This is the default and
- was the behavior prior to package version 0.9).
- If \code{FALSE} it uses exactly the situation
- with Dirac contamination in the evaluation point for both ICs
- \eqn{\psi}{psi} and \eqn{\eta}{eta} which amounts to calling \code{cniperCont}
- with \code{IC1=psi}, \code{IC2=eta}.}
- \item{scaleX}{logical; shall X-axis be rescaled (by default according to the cdf of
- the underlying distribution)?}
- \item{scaleY}{logical; shall Y-axis be rescaled (by default according to a probit scale)?}
- \item{scaleX.fct}{an isotone, vectorized function mapping the domain of the IC(s)
- to [0,1]; if \code{scaleX} is \code{TRUE} and \code{scaleX.fct} is
- missing, the cdf of the underlying observation distribution.}
- \item{scaleX.inv}{the inverse function to \code{scale.fct}, i.e., an isotone,
- vectorized function mapping [0,1] to the domain of the IC(s)
- such that for any \code{x} in the domain,
- \code{scaleX.inv(scaleX.fct(x))==x}; if \code{scaleX} is \code{TRUE}
- and \code{scaleX.inv} is
- missing, the quantile function of the underlying observation distribution.}
- \item{scaleY.fct}{an isotone, vectorized function mapping for each coordinate the
- range of the respective coordinate of the IC(s)
- to [0,1]; defaulting to the cdf of \eqn{{\cal N}(0,1)}{N(0,1)}.}
- \item{scaleY.inv}{an isotone, vectorized function mapping for each coordinate
- the range [0,1] into the range of the respective coordinate of the IC(s);
- defaulting to the quantile function of \eqn{{\cal N}(0,1)}{N(0,1)}.}
- \item{scaleN}{integer; defaults to 9; on rescaled axes, number of x
- and y ticks if drawn automatically;}
- \item{x.ticks}{numeric; defaults to NULL; (then ticks are chosen automatically);
- if non-NULL, user-given x-ticks (on original scale);}
- \item{y.ticks}{numeric; defaults to NULL; (then ticks are chosen automatically);
- if non-NULL, user-given y-ticks (on original scale);}
- \item{cex.pts}{size of the points of the second argument plotted}
- \item{cex.pts.fun}{rescaling function for the size of the points to be plotted;
- either \code{NULL} (default), then \code{log(1+abs(x))} is used for
- the rescaling, or a function which is then used for the
- rescaling.}
- \item{col.pts}{color of the points of the second argument plotted}
- \item{pch.pts}{symbol of the points of the second argument plotted}
- \item{with.lab}{logical; shall labels be plotted to the observations?}
- \item{lab.pts}{character or NULL; labels to be plotted to the observations; if \code{NULL}
- observation indices;}
- \item{lab.font}{font to be used for labels}
- \item{alpha.trsp}{alpha transparency to be added ex post to colors
- \code{col.pch} and \code{col.lbl}; if one-dim and NA all colors are
- left unchanged. Otherwise, with usual recycling rules \code{alpha.trsp}
- gets shorted/prolongated to length the data-symbols to be plotted.
- Coordinates of this vector \code{alpha.trsp} with NA are left unchanged,
- while for the remaining ones, the alpha channel in rgb space is set
- to the respective coordinate value of \code{alpha.trsp}. The non-NA
- entries must be integers in [0,255] (0 invisible, 255 opaque).}
- \item{jitter.fac}{jittering factor used in case of a \code{DiscreteDistribution}
- for plotting points of the second argument in a jittered fashion.}
- \item{which.lbs}{either an integer vector with the indices of the observations
- to be plotted into graph or \code{NULL} --- then no observation is excluded}
- \item{which.Order}{we order the observations (descending) according to the norm given by
- \code{normtype(object)}; then \code{which.Order}
- either is an integer vector with the indices of the \emph{ordered}
- observations (remaining after a possible reduction by argument \code{which.lbs})
- to be plotted into graph or \code{NULL} --- then no (further) observation
- is excluded.}
- \item{return.Order}{logical; if \code{TRUE}, an order vector
- is returned; more specifically, the order of the (remaining) observations
- given by their original index is returned (remaining means: after a possible
- reduction by argument \code{which.lbs}, and ordering is according to the norm given by
- \code{normtype(object)});
- otherwise we return \code{invisible()} as usual.}
- \item{withSubst}{logical; if \code{TRUE} (default) pattern substitution for
- titles and lables is used; otherwise no substitution is used. }
-}
-\details{
- In case of \code{cniperCont} the difference between the risks of two ICs
- is plotted.
-
- The function \code{cniperPoint} can be used to determine cniper
- points. That is, points such that the optimally robust estimator
- has smaller minimax risk than the classical optimal estimator under
- contamination with Dirac measures at the cniper points.
-
- As such points might be difficult to find, we provide the
- function \code{cniperPointPlot} which can be used to obtain a plot
- of the risk difference; in this function the usual arguments for
- \code{plot} can be used. For arguments \code{col}, \code{lwd},
- vectors can be used; then the first coordinate is taken for the
- curve, the second one for the balancing line. For argument \code{lty},
- a list can be used; its first component is then taken for the
- curve, the second one for the balancing line.
-
- If argument \code{withSubst} is \code{TRUE}, in all title
- and axis lable arguments of \code{cniperCont} and \code{cniperPointPlot},
- the following patterns are substituted:
- \describe{
- \item{\code{"\%C"}}{class of argument \code{L2Fam} (for \code{cniperPointPlot})}
- \item{\code{"\%A"}}{deparsed argument \code{L2Fam} (for \code{cniperPointPlot})}
- \item{\code{"\%C1"}}{class of argument \code{IC1} (for \code{cniperCont})}
- \item{\code{"\%A1"}}{deparsed argument \code{IC1} (for \code{cniperCont})}
- \item{\code{"\%C2"}}{class of argument \code{IC2} (for \code{cniperCont})}
- \item{\code{"\%A2"}}{deparsed argument \code{IC2} (for \code{cniperCont})}
- \item{\code{"\%D"}}{time/date-string when the plot was generated}
- }
-
- For more details about cniper contamination and cniper points we refer
- to Section~3.5 of Kohl et al. (2008) as well as Ruckdeschel (2004) and
- the Introduction of Kohl (2005).
-}
-\value{invisible() resp. cniper point is returned.}
-\references{
- Kohl, M. and Ruckdeschel, H. and Rieder, H. (2008). Infinitesimally
- Robust Estimation in General Smoothly Parametrized Models. Unpublished
- Manuscript.
-
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-
- Ruckdeschel, P. (2004). Higher Order Asymptotics for the MSE of M-Estimators
- on Shrinking Neighborhoods. Unpublished Manuscript.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-%\seealso{ ~~objects to See Also as \code{\link{help}}, ~~~ }
-\examples{
-## cniper contamination
-P <- PoisFamily(lambda = 4)
-RobP1 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 0.1))
-IC1 <- optIC(model=RobP1, risk=asMSE())
-RobP2 <- InfRobModel(center = P, neighbor = ContNeighborhood(radius = 1))
-IC2 <- optIC(model=RobP2, risk=asMSE())
-cniperCont(IC1 = IC1, IC2 = IC2,
- neighbor = ContNeighborhood(radius = 0.5),
- risk = asMSE(),
- lower = 0, upper = 8, n = 101)
-
-## cniper point plot
-cniperPointPlot(P, neighbor = ContNeighborhood(radius = 0.5),
- risk = asMSE(), lower = 0, upper = 10)
-
-## Don't run to reduce check time on CRAN
-\dontrun{
-## cniper point
-cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
- risk = asMSE(), lower = 0, upper = 4)
-cniperPoint(P, neighbor = ContNeighborhood(radius = 0.5),
- risk = asMSE(), lower = 4, upper = 8)
-}
-}
-\concept{cniper contamination}
-\concept{cniper point}
-\keyword{robust}
+\name{cniperCont}
+\alias{cniperCont}
+\alias{cniperPoint}
+\alias{cniperPointPlot}
+\title{ Functions for Computation and Plot of Cniper Contamination
+ and Cniper Points. }
+\description{
+ These functions and their methods can be used to determine cniper
+ contamination as well as cniper points. That is, under which (Dirac)
+ contamination is the risk of one procedure larger than the risk of some
+ other procedure.
+}
+\usage{
+cniperCont(IC1, IC2, data = NULL, ...,
+ neighbor, risk, lower = getdistrOption("DistrResolution"),
+ upper = 1-getdistrOption("DistrResolution"), n = 101,
+ scaleX = FALSE, scaleX.fct, scaleX.inv,
+ scaleY = FALSE, scaleY.fct = pnorm, scaleY.inv=qnorm,
+ scaleN = 9, x.ticks = NULL, y.ticks = NULL,
+ cex.pts = 1, cex.pts.fun = NULL, col.pts = par("col"),
+ pch.pts = 19, jitter.fac = 1, with.lab = FALSE,
+ lab.pts = NULL, lab.font = NULL, alpha.trsp = NA,
+ which.lbs = NULL, which.Order = NULL,
+ return.Order = FALSE,
+ draw.nonlbl = TRUE, cex.nonlbl=0.3, pch.nonlbl=".",
+ withSubst = TRUE)
+
+
+cniperPoint(L2Fam, neighbor, risk, lower, upper)
+
+cniperPointPlot(L2Fam, data=NULL, ..., neighbor, risk= asMSE(),
+ lower=getdistrOption("DistrResolution"),
+ upper=1-getdistrOption("DistrResolution"), n = 101,
+ withMaxRisk = TRUE,
+ scaleX = FALSE, scaleX.fct, scaleX.inv,
+ scaleY = FALSE, scaleY.fct = pnorm, scaleY.inv=qnorm,
+ scaleN = 9, x.ticks = NULL, y.ticks = NULL,
+ cex.pts = 1, cex.pts.fun = NULL, col.pts = par("col"),
+ pch.pts = 19, jitter.fac = 1, with.lab = FALSE,
+ lab.pts = NULL, lab.font = NULL, alpha.trsp = NA,
+ which.lbs = NULL, which.Order = NULL,
+ return.Order = FALSE,
+ draw.nonlbl = TRUE, cex.nonlbl=0.3, pch.nonlbl=".",
+ withSubst = TRUE)
+
+}
+\arguments{
+ \item{IC1}{ object of class \code{IC} }
+ \item{IC2}{ object of class \code{IC} }
+ \item{L2Fam}{ object of class \code{L2ParamFamily} }
+ \item{neighbor}{ object of class \code{Neighborhood} }
+ \item{risk}{ object of class \code{RiskType} }
+ \item{\dots}{ additional parameters (in particular to be passed on to \code{plot}). }
+ \item{data}{data to be plotted in}
+ \item{lower, upper}{ the lower and upper end points of the
+ contamination interval (in prob-scale). }
+ \item{n}{ number of points between \code{lower} and \code{upper}}
+ \item{withMaxRisk}{logical; if \code{TRUE}, for risk comparison
+ uses the maximal risk of the classically optimal IC \eqn{\psi}{psi} in all
+ situations with contamination in Dirac points 'no larger' than
+ the respective evaluation point and the optimally-robust
+ IC \eqn{\eta}{eta} at its least favorable contamination situation
+ ('over all real Dirac contamination points'). This is the default and
+ was the behavior prior to package version 0.9).
+ If \code{FALSE} it uses exactly the situation
+ with Dirac contamination in the evaluation point for both ICs
+ \eqn{\psi}{psi} and \eqn{\eta}{eta} which amounts to calling \code{cniperCont}
+ with \code{IC1=psi}, \code{IC2=eta}.}
+ \item{scaleX}{logical; shall X-axis be rescaled (by default according to the cdf of
+ the underlying distribution)?}
+ \item{scaleY}{logical; shall Y-axis be rescaled (by default according to a probit scale)?}
+ \item{scaleX.fct}{an isotone, vectorized function mapping the domain of the IC(s)
+ to [0,1]; if \code{scaleX} is \code{TRUE} and \code{scaleX.fct} is
+ missing, the cdf of the underlying observation distribution.}
+ \item{scaleX.inv}{the inverse function to \code{scale.fct}, i.e., an isotone,
+ vectorized function mapping [0,1] to the domain of the IC(s)
+ such that for any \code{x} in the domain,
+ \code{scaleX.inv(scaleX.fct(x))==x}; if \code{scaleX} is \code{TRUE}
+ and \code{scaleX.inv} is
+ missing, the quantile function of the underlying observation distribution.}
+ \item{scaleY.fct}{an isotone, vectorized function mapping for each coordinate the
+ range of the respective coordinate of the IC(s)
+ to [0,1]; defaulting to the cdf of \eqn{{\cal N}(0,1)}{N(0,1)}.}
+ \item{scaleY.inv}{an isotone, vectorized function mapping for each coordinate
+ the range [0,1] into the range of the respective coordinate of the IC(s);
+ defaulting to the quantile function of \eqn{{\cal N}(0,1)}{N(0,1)}.}
+ \item{scaleN}{integer; defaults to 9; on rescaled axes, number of x
+ and y ticks if drawn automatically;}
+ \item{x.ticks}{numeric; defaults to NULL; (then ticks are chosen automatically);
+ if non-NULL, user-given x-ticks (on original scale);}
+ \item{y.ticks}{numeric; defaults to NULL; (then ticks are chosen automatically);
+ if non-NULL, user-given y-ticks (on original scale);}
+ \item{cex.pts}{size of the points of the second argument plotted}
+ \item{cex.pts.fun}{rescaling function for the size of the points to be plotted;
+ either \code{NULL} (default), then \code{log(1+abs(x))} is used for
+ the rescaling, or a function which is then used for the
+ rescaling.}
+ \item{col.pts}{color of the points of the second argument plotted}
+ \item{pch.pts}{symbol of the points of the second argument plotted}
+ \item{with.lab}{logical; shall labels be plotted to the observations?}
+ \item{lab.pts}{character or NULL; labels to be plotted to the observations; if \code{NULL}
+ observation indices;}
+ \item{lab.font}{font to be used for labels}
+ \item{alpha.trsp}{alpha transparency to be added ex post to colors
+ \code{col.pch} and \code{col.lbl}; if one-dim and NA all colors are
+ left unchanged. Otherwise, with usual recycling rules \code{alpha.trsp}
+ gets shorted/prolongated to length the data-symbols to be plotted.
+ Coordinates of this vector \code{alpha.trsp} with NA are left unchanged,
+ while for the remaining ones, the alpha channel in rgb space is set
+ to the respective coordinate value of \code{alpha.trsp}. The non-NA
+ entries must be integers in [0,255] (0 invisible, 255 opaque).}
+ \item{jitter.fac}{jittering factor used in case of a \code{DiscreteDistribution}
+ for plotting points of the second argument in a jittered fashion.}
+ \item{which.lbs}{either an integer vector with the indices of the observations
+ to be plotted into graph or \code{NULL} --- then no observation is excluded}
+ \item{which.Order}{we order the observations (descending) according to the norm given by
+ \code{normtype(object)}; then \code{which.Order}
+ either is an integer vector with the indices of the \emph{ordered}
+ observations (remaining after a possible reduction by argument \code{which.lbs})
+ to be plotted into graph or \code{NULL} --- then no (further) observation
+ is excluded.}
+ \item{return.Order}{logical; if \code{TRUE}, an order vector
+ is returned; more specifically, the order of the (remaining) observations
+ given by their original index is returned (remaining means: after a possible
+ reduction by argument \code{which.lbs}, and ordering is according to the norm given by
+ \code{normtype(object)});
+ otherwise we return \code{invisible()} as usual.}
+ \item{draw.nonlbl}{logical; should non-labelled observations be drawn?}
+ \item{cex.nonlbl}{character expansion(s) for non-labelled observations}
+ \item{pch.nonlbl}{plotting symbol(s) for non-labelled observations}
+ \item{withSubst}{logical; if \code{TRUE} (default) pattern substitution for
+ titles and lables is used; otherwise no substitution is used. }
+}
+\details{
+ In case of \code{cniperCont} the difference between the risks of two ICs
+ is plotted.
+
+ The function \code{cniperPoint} can be used to determine cniper
+ points. That is, points such that the optimally robust estimator
+ has smaller minimax risk than the classical optimal estimator under
+ contamination with Dirac measures at the cniper points.
+
+ As such points might be difficult to find, we provide the
+ function \code{cniperPointPlot} which can be used to obtain a plot
+ of the risk difference; in this function the usual arguments for
+ \code{plot} can be used. For arguments \code{col}, \code{lwd},
+ vectors can be used; then the first coordinate is taken for the
+ curve, the second one for the balancing line. For argument \code{lty},
+ a list can be used; its first component is then taken for the
+ curve, the second one for the balancing line.
+
+ If argument \code{withSubst} is \code{TRUE}, in all title
+ and axis lable arguments of \code{cniperCont} and \code{cniperPointPlot},
+ the following patterns are substituted:
+ \describe{
+ \item{\code{"\%C"}}{class of argument \code{L2Fam} (for \code{cniperPointPlot})}
+ \item{\code{"\%A"}}{deparsed argument \code{L2Fam} (for \code{cniperPointPlot})}
+ \item{\code{"\%C1"}}{class of argument \code{IC1} (for \code{cniperCont})}
+ \item{\code{"\%A1"}}{deparsed argument \code{IC1} (for \code{cniperCont})}
+ \item{\code{"\%C2"}}{class of argument \code{IC2} (for \code{cniperCont})}
+ \item{\code{"\%A2"}}{deparsed argument \code{IC2} (for \code{cniperCont})}
+ \item{\code{"\%D"}}{time/date-string when the plot was generated}
+ }
+
+ For more details about cniper contamination and cniper points we refer
+ to Section~3.5 of Kohl et al. (2008) as well as Ruckdeschel (2004) and
+ the Introduction of Kohl (2005).
+}
+\value{invisible() resp. cniper point is returned.}
+\references{
+ Kohl, M. and Ruckdeschel, H. and Rieder, H. (2008). Infinitesimally
+ Robust Estimation in General Smoothly Parametrized Models. Unpublished
+ Manuscript.
+
[TRUNCATED]
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