[Robast-commits] r1299 - in pkg/ROptEst: . man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Feb 7 08:24:55 CET 2024
Author: stamats
Date: 2024-02-07 08:24:55 +0100 (Wed, 07 Feb 2024)
New Revision: 1299
Modified:
pkg/ROptEst/DESCRIPTION
pkg/ROptEst/man/0ROptEst-package.Rd
pkg/ROptEst/man/getInfGamma.Rd
Log:
minor changes for submittsion
Modified: pkg/ROptEst/DESCRIPTION
===================================================================
--- pkg/ROptEst/DESCRIPTION 2024-02-07 02:50:34 UTC (rev 1298)
+++ pkg/ROptEst/DESCRIPTION 2024-02-07 07:24:55 UTC (rev 1299)
@@ -1,9 +1,9 @@
Package: ROptEst
-Version: 1.3.2
-Date: 2024-02-06
+Version: 1.3.3
+Date: 2024-02-07
Title: Optimally Robust Estimation
Description: R infrastructure for optimally robust estimation in general smoothly
- parameterized models using S4 classes and methods as decribed Kohl, M.,
+ parameterized models using S4 classes and methods as described Kohl, M.,
Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in
Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.
Depends: R(>= 3.4), methods, distr(>= 2.8.0), distrEx(>= 2.8.0), distrMod(>= 2.8.1),
Modified: pkg/ROptEst/man/0ROptEst-package.Rd
===================================================================
--- pkg/ROptEst/man/0ROptEst-package.Rd 2024-02-07 02:50:34 UTC (rev 1298)
+++ pkg/ROptEst/man/0ROptEst-package.Rd 2024-02-07 07:24:55 UTC (rev 1299)
@@ -1,87 +1,87 @@
-\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 1.3.2 \cr
-Date: \tab 2024-02-06 \cr
-Depends: \tab R(>= 3.4), methods, distr(>= 2.8.0), distrEx(>= 2.8.0), distrMod(>= 2.8.1),RandVar(>= 1.2.0), RobAStBase(>= 1.2.0) \cr
-Suggests: \tab RobLox \cr
-Imports: \tab startupmsg, MASS, stats, graphics, utils, grDevices \cr
-ByteCompile: \tab yes \cr
-Encoding: \tab latin1 \cr
-License: \tab LGPL-3 \cr
-URL: \tab https://robast.r-forge.r-project.org/\cr
-VCS/SVNRevision: \tab 1286 \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. \url{https://epub.uni-bayreuth.de/id/eprint/839/2/DissMKohl.pdf}.
- M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in
- General Smoothly Parametrized Models. Statistical Methods and Applications \emph{19}(3): 333-354.
- \doi{10.1007/s10260-010-0133-0}.
- H. Rieder (1994): Robust Asymptotic Statistics. Springer.
- \doi{10.1007/978-1-4684-0624-5}
- H. Rieder, M. Kohl, and P. Ruckdeschel (2008). The Costs of Not Knowing the Radius.
- Statistical Methods and Applications \emph{17}(1): 13-40. \doi{10.1007/s10260-007-0047-7}
- P. Ruckdeschel (2005). Optimally One-Sided Bounded Influence Curves.
- Mathematical Methods of Statistics \emph{14}(1), 105-131.
- P. Ruckdeschel and H. Rieder (2004). Optimal Influence Curves for
- General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
- \doi{10.1524/stnd.22.3.201.57067}
-}
-\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{
-## don't test to reduce check time on CRAN
-\donttest{
-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 1.3.3 \cr
+Date: \tab 2024-02-07 \cr
+Depends: \tab R(>= 3.4), methods, distr(>= 2.8.0), distrEx(>= 2.8.0), distrMod(>= 2.8.1),RandVar(>= 1.2.0), RobAStBase(>= 1.2.0) \cr
+Suggests: \tab RobLox \cr
+Imports: \tab startupmsg, MASS, stats, graphics, utils, grDevices \cr
+ByteCompile: \tab yes \cr
+Encoding: \tab latin1 \cr
+License: \tab LGPL-3 \cr
+URL: \tab https://robast.r-forge.r-project.org/\cr
+VCS/SVNRevision: \tab 1286 \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. \url{https://epub.uni-bayreuth.de/id/eprint/839/2/DissMKohl.pdf}.
+ M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in
+ General Smoothly Parametrized Models. Statistical Methods and Applications \emph{19}(3): 333-354.
+ \doi{10.1007/s10260-010-0133-0}.
+ H. Rieder (1994): Robust Asymptotic Statistics. Springer.
+ \doi{10.1007/978-1-4684-0624-5}
+ H. Rieder, M. Kohl, and P. Ruckdeschel (2008). The Costs of Not Knowing the Radius.
+ Statistical Methods and Applications \emph{17}(1): 13-40. \doi{10.1007/s10260-007-0047-7}
+ P. Ruckdeschel (2005). Optimally One-Sided Bounded Influence Curves.
+ Mathematical Methods of Statistics \emph{14}(1), 105-131.
+ P. Ruckdeschel and H. Rieder (2004). Optimal Influence Curves for
+ General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
+ \doi{10.1524/stnd.22.3.201.57067}
+}
+\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{
+## don't test to reduce check time on CRAN
+\donttest{
+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: pkg/ROptEst/man/getInfGamma.Rd
===================================================================
--- pkg/ROptEst/man/getInfGamma.Rd 2024-02-07 02:50:34 UTC (rev 1298)
+++ pkg/ROptEst/man/getInfGamma.Rd 2024-02-07 07:24:55 UTC (rev 1299)
@@ -1,119 +1,119 @@
-\name{getInfGamma}
-\alias{getInfGamma}
-\alias{getInfGamma-methods}
-\alias{getInfGamma,UnivariateDistribution,asGRisk,ContNeighborhood,BiasType-method}
-\alias{getInfGamma,UnivariateDistribution,asGRisk,TotalVarNeighborhood,BiasType-method}
-\alias{getInfGamma,RealRandVariable,asMSE,ContNeighborhood,BiasType-method}
-\alias{getInfGamma,RealRandVariable,asMSE,TotalVarNeighborhood,BiasType-method}
-\alias{getInfGamma,UnivariateDistribution,asUnOvShoot,ContNeighborhood,BiasType-method}
-\alias{getInfGamma,UnivariateDistribution,asMSE,ContNeighborhood,onesidedBias-method}
-\alias{getInfGamma,UnivariateDistribution,asMSE,ContNeighborhood,asymmetricBias-method}
-
-\title{Generic Function for the Computation of the Optimal Clipping Bound}
-\description{
- Generic function for the computation of the optimal clipping bound.
- This function is rarely called directly. It is called by \code{getInfClip}
- to compute optimally robust ICs.
-}
-\usage{
-getInfGamma(L2deriv, risk, neighbor, biastype, ...)
-
-\S4method{getInfGamma}{UnivariateDistribution,asGRisk,ContNeighborhood,BiasType}(L2deriv,
- risk, neighbor, biastype, cent, clip)
-
-\S4method{getInfGamma}{UnivariateDistribution,asGRisk,TotalVarNeighborhood,BiasType}(L2deriv,
- risk, neighbor, biastype, cent, clip)
-
-\S4method{getInfGamma}{RealRandVariable,asMSE,ContNeighborhood,BiasType}(L2deriv,
- risk, neighbor, biastype, Distr, stand, cent, clip, power = 1L, ...)
-
-\S4method{getInfGamma}{RealRandVariable,asMSE,TotalVarNeighborhood,BiasType}(L2deriv,
- risk, neighbor, biastype, Distr, stand, cent, clip, power = 1L, ...)
-
-\S4method{getInfGamma}{UnivariateDistribution,asUnOvShoot,ContNeighborhood,BiasType}(L2deriv,
- risk, neighbor, biastype, cent, clip)
-
-\S4method{getInfGamma}{UnivariateDistribution,asMSE,ContNeighborhood,onesidedBias}(L2deriv,
- risk, neighbor, biastype, cent, clip)
-
-\S4method{getInfGamma}{UnivariateDistribution,asMSE,ContNeighborhood,asymmetricBias}(L2deriv,
- risk, neighbor, biastype, cent, clip)
-}
-\arguments{
- \item{L2deriv}{ L2-derivative of some L2-differentiable family
- of probability measures. }
- \item{risk}{ object of class \code{"RiskType"}. }
- \item{neighbor}{ object of class \code{"Neighborhood"}. }
- \item{biastype}{ object of class \code{"BiasType"}. }
- \item{\dots}{ additional parameters, in particular for expectation \code{E}. }
- \item{cent}{ optimal centering constant. }
- \item{clip}{ optimal clipping bound. }
- \item{stand}{ standardizing matrix. }
- \item{Distr}{ object of class \code{"Distribution"}. }
- \item{power}{ exponent for the integrand; by default \code{1}, but
- may also be \code{2}, for optimization in \code{getLagrangeMultByOptim}. }
-}
-\details{
- The function is used in case of asymptotic G-risks; confer
- Ruckdeschel and Rieder (2004).
-}
-\value{The optimal clipping height is computed. More spefically, the optimal
- clipping height \eqn{b} is determined in a zero search of a certain function
- \eqn{\gamma}{gamma}, where the respective \code{getInf}-method will return
- the value of \eqn{\gamma(b)}{gamma(b)}. The actual function \eqn{\gamma}{gamma}
- varies according to whether the parameter is one dimensional or higher dimensional,
- according to the risk, according to the neighborhood, and according to the
- bias type, which leads to the different methods.}
-\section{Methods}{
-\describe{
- \item{L2deriv = "UnivariateDistribution", risk = "asGRisk",
- neighbor = "ContNeighborhood",
- biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
-
- \item{L2deriv = "UnivariateDistribution", risk = "asGRisk",
- neighbor = "TotalVarNeighborhood",
- biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
-
- \item{L2deriv = "RealRandVariable", risk = "asMSE",
- neighbor = "ContNeighborhood",
- biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
-
- \item{L2deriv = "RealRandVariable", risk = "asMSE",
- neighbor = "TotalVarNeighborhood",
- biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
-
- \item{L2deriv = "UnivariateDistribution", risk = "asUnOvShoot",
- neighbor = "ContNeighborhood",
- biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
-
- \item{L2deriv = "UnivariateDistribution", risk = "asMSE",
- neighbor = "ContNeighborhood",
- biastype = "onesidedBias"}{ used by \code{getInfClip} for onesided bias. }
-
- \item{L2deriv = "UnivariateDistribution", risk = "asMSE",
- neighbor = "ContNeighborhood",
- biastype = "asymmetricBias"}{ used by \code{getInfClip} for asymmetric bias. }
-}}
-\references{
- Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. \bold{8}: 106--115.
-
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
-
- Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for
- General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
-
- Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves.
- Mathematical Methods in Statistics \emph{14}(1), 105-131.
-
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de},
- Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de}}
-%\note{}
-\seealso{\code{\link[distrMod]{asGRisk-class}}, \code{\link[distrMod]{asMSE-class}},
- \code{\link[distrMod]{asUnOvShoot-class}}, \code{\link[RobAStBase]{ContIC-class}},
- \code{\link[RobAStBase]{TotalVarIC-class}}}
-%\examples{}
-\concept{influence curve}
-\keyword{robust}
+\name{getInfGamma}
+\alias{getInfGamma}
+\alias{getInfGamma-methods}
+\alias{getInfGamma,UnivariateDistribution,asGRisk,ContNeighborhood,BiasType-method}
+\alias{getInfGamma,UnivariateDistribution,asGRisk,TotalVarNeighborhood,BiasType-method}
+\alias{getInfGamma,RealRandVariable,asMSE,ContNeighborhood,BiasType-method}
+\alias{getInfGamma,RealRandVariable,asMSE,TotalVarNeighborhood,BiasType-method}
+\alias{getInfGamma,UnivariateDistribution,asUnOvShoot,ContNeighborhood,BiasType-method}
+\alias{getInfGamma,UnivariateDistribution,asMSE,ContNeighborhood,onesidedBias-method}
+\alias{getInfGamma,UnivariateDistribution,asMSE,ContNeighborhood,asymmetricBias-method}
+
+\title{Generic Function for the Computation of the Optimal Clipping Bound}
+\description{
+ Generic function for the computation of the optimal clipping bound.
+ This function is rarely called directly. It is called by \code{getInfClip}
+ to compute optimally robust ICs.
+}
+\usage{
+getInfGamma(L2deriv, risk, neighbor, biastype, ...)
+
+\S4method{getInfGamma}{UnivariateDistribution,asGRisk,ContNeighborhood,BiasType}(L2deriv,
+ risk, neighbor, biastype, cent, clip)
+
+\S4method{getInfGamma}{UnivariateDistribution,asGRisk,TotalVarNeighborhood,BiasType}(L2deriv,
+ risk, neighbor, biastype, cent, clip)
+
+\S4method{getInfGamma}{RealRandVariable,asMSE,ContNeighborhood,BiasType}(L2deriv,
+ risk, neighbor, biastype, Distr, stand, cent, clip, power = 1L, ...)
+
+\S4method{getInfGamma}{RealRandVariable,asMSE,TotalVarNeighborhood,BiasType}(L2deriv,
+ risk, neighbor, biastype, Distr, stand, cent, clip, power = 1L, ...)
+
+\S4method{getInfGamma}{UnivariateDistribution,asUnOvShoot,ContNeighborhood,BiasType}(L2deriv,
+ risk, neighbor, biastype, cent, clip)
+
+\S4method{getInfGamma}{UnivariateDistribution,asMSE,ContNeighborhood,onesidedBias}(L2deriv,
+ risk, neighbor, biastype, cent, clip)
+
+\S4method{getInfGamma}{UnivariateDistribution,asMSE,ContNeighborhood,asymmetricBias}(L2deriv,
+ risk, neighbor, biastype, cent, clip)
+}
+\arguments{
+ \item{L2deriv}{ L2-derivative of some L2-differentiable family
+ of probability measures. }
+ \item{risk}{ object of class \code{"RiskType"}. }
+ \item{neighbor}{ object of class \code{"Neighborhood"}. }
+ \item{biastype}{ object of class \code{"BiasType"}. }
+ \item{\dots}{ additional parameters, in particular for expectation \code{E}. }
+ \item{cent}{ optimal centering constant. }
+ \item{clip}{ optimal clipping bound. }
+ \item{stand}{ standardizing matrix. }
+ \item{Distr}{ object of class \code{"Distribution"}. }
+ \item{power}{ exponent for the integrand; by default \code{1}, but
+ may also be \code{2}, for optimization in \code{getLagrangeMultByOptim}. }
+}
+\details{
+ The function is used in case of asymptotic G-risks; confer
+ Ruckdeschel and Rieder (2004).
+}
+\value{The optimal clipping height is computed. More specifically, the optimal
+ clipping height \eqn{b} is determined in a zero search of a certain function
+ \eqn{\gamma}{gamma}, where the respective \code{getInf}-method will return
+ the value of \eqn{\gamma(b)}{gamma(b)}. The actual function \eqn{\gamma}{gamma}
+ varies according to whether the parameter is one dimensional or higher dimensional,
+ according to the risk, according to the neighborhood, and according to the
+ bias type, which leads to the different methods.}
+\section{Methods}{
+\describe{
+ \item{L2deriv = "UnivariateDistribution", risk = "asGRisk",
+ neighbor = "ContNeighborhood",
+ biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
+
+ \item{L2deriv = "UnivariateDistribution", risk = "asGRisk",
+ neighbor = "TotalVarNeighborhood",
+ biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
+
+ \item{L2deriv = "RealRandVariable", risk = "asMSE",
+ neighbor = "ContNeighborhood",
+ biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
+
+ \item{L2deriv = "RealRandVariable", risk = "asMSE",
+ neighbor = "TotalVarNeighborhood",
+ biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
+
+ \item{L2deriv = "UnivariateDistribution", risk = "asUnOvShoot",
+ neighbor = "ContNeighborhood",
+ biastype = "BiasType"}{ used by \code{getInfClip} for symmetric bias. }
+
+ \item{L2deriv = "UnivariateDistribution", risk = "asMSE",
+ neighbor = "ContNeighborhood",
+ biastype = "onesidedBias"}{ used by \code{getInfClip} for onesided bias. }
+
+ \item{L2deriv = "UnivariateDistribution", risk = "asMSE",
+ neighbor = "ContNeighborhood",
+ biastype = "asymmetricBias"}{ used by \code{getInfClip} for asymmetric bias. }
+}}
+\references{
+ Rieder, H. (1980) Estimates derived from robust tests. Ann. Stats. \bold{8}: 106--115.
+
+ Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
+
+ Ruckdeschel, P. and Rieder, H. (2004) Optimal Influence Curves for
+ General Loss Functions. Statistics & Decisions \emph{22}, 201-223.
+
+ Ruckdeschel, P. (2005) Optimally One-Sided Bounded Influence Curves.
+ Mathematical Methods in Statistics \emph{14}(1), 105-131.
+
+ Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
+ Bayreuth: Dissertation.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de},
+ Peter Ruckdeschel \email{peter.ruckdeschel at uni-oldenburg.de}}
+%\note{}
+\seealso{\code{\link[distrMod]{asGRisk-class}}, \code{\link[distrMod]{asMSE-class}},
+ \code{\link[distrMod]{asUnOvShoot-class}}, \code{\link[RobAStBase]{ContIC-class}},
+ \code{\link[RobAStBase]{TotalVarIC-class}}}
+%\examples{}
+\concept{influence curve}
+\keyword{robust}
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