[Robast-commits] r1304 - in branches/robast-1.3/pkg: ROptEst RobExtremes RobLox RobLox/man RobLoxBioC RobLoxBioC/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Feb 11 14:38:40 CET 2024
Author: ruckdeschel
Date: 2024-02-11 14:38:39 +0100 (Sun, 11 Feb 2024)
New Revision: 1304
Modified:
branches/robast-1.3/pkg/ROptEst/DESCRIPTION
branches/robast-1.3/pkg/RobExtremes/DESCRIPTION
branches/robast-1.3/pkg/RobLox/DESCRIPTION
branches/robast-1.3/pkg/RobLox/man/0RobLox-package.Rd
branches/robast-1.3/pkg/RobLox/man/finiteSampleCorrection.Rd
branches/robast-1.3/pkg/RobLox/man/rlOptIC.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.AL.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An1.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An2.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.AnMad.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.BM.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Ha3.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Ha4.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.HaMad.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Hu1.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Hu2.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Hu2a.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Hu3.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.HuMad.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.M.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.MM2.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Tu1.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.Tu2.Rd
branches/robast-1.3/pkg/RobLox/man/rlsOptIC.TuMad.Rd
branches/robast-1.3/pkg/RobLox/man/roblox.Rd
branches/robast-1.3/pkg/RobLox/man/rowRoblox.Rd
branches/robast-1.3/pkg/RobLox/man/rsOptIC.Rd
branches/robast-1.3/pkg/RobLox/man/showdown.Rd
branches/robast-1.3/pkg/RobLoxBioC/DESCRIPTION
branches/robast-1.3/pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
Log:
branch 1.3: + backported changes in trunk in RobLox and RobLoxBioC;
+ corrected doi references in Description tag in DESCRIPTION of RobExtremes
+ backported changes from trunk in rev 1299 in ROptEst
Modified: branches/robast-1.3/pkg/ROptEst/DESCRIPTION
===================================================================
--- branches/robast-1.3/pkg/ROptEst/DESCRIPTION 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/ROptEst/DESCRIPTION 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,18 +1,21 @@
Package: ROptEst
-Version: 1.3.1
-Date: 2019-04-07
+Version: 1.3.4
+Date: 2024-02-11
Title: Optimally Robust Estimation
-Description: Optimally robust estimation in general smoothly parameterized models using S4
- classes and methods.
+Description: R infrastructure for optimally robust estimation in general smoothly
+ 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),
RandVar(>= 1.2.0), RobAStBase(>= 1.2.0)
Imports: startupmsg, MASS, stats, graphics, utils, grDevices
Suggests: RobLox
Authors at R: c(person("Matthias", "Kohl", role=c("cre", "cph"),
- email="Matthias.Kohl at stamats.de"), person("Mykhailo", "Pupashenko", role="ctb",
- comment="contributed wrapper functions for diagnostic plots"), person("Gerald",
- "Kroisandt", role="ctb", comment="contributed testing routines"), person("Peter",
- "Ruckdeschel", role=c("aut", "cph")))
+ email="Matthias.Kohl at stamats.de", comment = c(ORCID = "0000-0001-9514-8910")),
+ person("Mykhailo", "Pupashenko", role="ctb", comment="contributed wrapper
+ functions for diagnostic plots"), person("Gerald", "Kroisandt", role="ctb",
+ comment="contributed testing routines"), person("Peter", "Ruckdeschel",
+ role=c("aut", "cph"), comment = c(ORCID = "0000-0001-7815-4809")))
ByteCompile: yes
License: LGPL-3
URL: https://r-forge.r-project.org/projects/robast/
Modified: branches/robast-1.3/pkg/RobExtremes/DESCRIPTION
===================================================================
--- branches/robast-1.3/pkg/RobExtremes/DESCRIPTION 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobExtremes/DESCRIPTION 2024-02-11 13:38:39 UTC (rev 1304)
@@ -5,8 +5,8 @@
Description: Optimally robust estimation for extreme value distributions using S4 classes and
methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and
- Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and
- \doi{10.1007/s00184-011-0366-4}.
+ Horbenko, (2013 and 2012), <doi:10.1080/02331888.2011.628022> and
+ <doi:10.1007/s00184-011-0366-4>.
Depends: R(>= 3.4), methods, distrMod(>= 2.8.0), ROptEst(>= 1.2.0), robustbase, evd
Suggests: RUnit(>= 0.4.26), ismev(>= 1.39)
Enhances: fitdistrplus(>= 1.0-9)
Modified: branches/robast-1.3/pkg/RobLox/DESCRIPTION
===================================================================
--- branches/robast-1.3/pkg/RobLox/DESCRIPTION 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/DESCRIPTION 2024-02-11 13:38:39 UTC (rev 1304)
@@ -3,7 +3,8 @@
Date: 2019-03-13
Title: Optimally Robust Influence Curves and Estimators for Location and Scale
Description: Functions for the determination of optimally robust influence curves and
- estimators in case of normal location and/or scale.
+ estimators in case of normal location and/or scale
+ (see Chapter 8 in Kohl (2005) <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).
Depends: R(>= 3.4), stats, distrMod(>= 2.8.0), RobAStBase(>= 1.2.0)
Imports: methods, lattice, RColorBrewer, Biobase, RandVar(>= 1.2.0), distr(>= 2.8.0)
Suggests: MASS
Modified: branches/robast-1.3/pkg/RobLox/man/0RobLox-package.Rd
===================================================================
--- branches/robast-1.3/pkg/RobLox/man/0RobLox-package.Rd 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/man/0RobLox-package.Rd 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,75 +1,64 @@
-\name{RobLox-package}
-\alias{RobLox-package}
-\alias{RobLox}
-\docType{package}
-\title{
-Optimally robust influence curves and estimators for location and scale
-}
-\description{
-Functions for the determination of optimally robust influence curves and
-estimators in case of normal location and/or scale.
-}
-\details{
-\tabular{ll}{
-Package: \tab RobLox \cr
-Version: \tab 1.3.0 \cr
-Date: \tab 2019-03-13 \cr
-Depends: \tab R(>= 3.4), stats, distrMod(>= 2.8.0), RobAStBase(>= 1.2.0) \cr
-Imports: \tab methods, lattice, RColorBrewer, Biobase, RandVar(>= 1.2.0),
- distr(>= 2.8.0) \cr
-Suggests: \tab MASS \cr
-ByteCompile: \tab yes \cr
-License: \tab LGPL-3 \cr
-URL: \tab http://robast.r-forge.r-project.org/\cr
-VCS/SVNRevision: \tab 1205 \cr
-}
-}
-\author{Matthias Kohl \email{matthias.kohl at stamats.de}}
-\references{
- M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness.
- Dissertation. University of Bayreuth.
-
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
-
- Rieder, H., Kohl, M. and Ruckdeschel, P. (2008). The Costs of not Knowing
- the Radius. \emph{Statistical Methods and Applications} \bold{17}(1) 13-40.
- Extended version: \url{http://r-kurs.de/RRlong.pdf}
-
- M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation
- in General Smoothly Parametrized Models. \emph{Statistical Methods and Application},
- \bold{19}(3):333-354.
-}
-\seealso{ \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(RobLox)
-ind <- rbinom(100, size=1, prob=0.05)
-x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9)
-roblox(x)
-
-res <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE)
-estimate(res)
-confint(res)
-confint(res, method = symmetricBias())
-pIC(res)
-
-## don't run to reduce check time on CRAN
-\dontrun{
-checkIC(pIC(res))
-Risks(pIC(res))
-Infos(pIC(res))
-plot(pIC(res))
-infoPlot(pIC(res))
-}
-
-## row-wise application
-ind <- rbinom(200, size=1, prob=0.05)
-X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2)
-rowRoblox(X)
-}
-\keyword{package}
+\name{RobLox-package}
+\alias{RobLox-package}
+\alias{RobLox}
+\docType{package}
+\title{
+Optimally robust influence curves and estimators for location and scale
+}
+\description{
+Functions for the determination of optimally robust influence curves and
+estimators in case of normal location and/or scale
+(see Chapter 8 in Kohl (2005) <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).
+}
+\author{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}.
+
+ 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}
+
+ 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}.
+
+ M. Kohl and H.P. Deigner (2010). Preprocessing of gene expression data by
+ optimally robust estimators. BMC Bioinformatics \emph{11}, 583.
+ \doi{10.1186/1471-2105-11-583}.
+
+ M. Kohl (2012). Bounded influence estimation for regression and scale.
+ Statistics, \bold{46}(4): 437-488. \doi{10.1080/02331888.2010.540668}
+}
+\seealso{ \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(RobLox)
+ind <- rbinom(100, size=1, prob=0.05)
+x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9)
+roblox(x)
+res <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE)
+estimate(res)
+confint(res)
+confint(res, method = symmetricBias())
+pIC(res)
+## don't run to reduce check time on CRAN
+\dontrun{
+checkIC(pIC(res))
+Risks(pIC(res))
+Infos(pIC(res))
+plot(pIC(res))
+infoPlot(pIC(res))
+}
+## row-wise application
+ind <- rbinom(200, size=1, prob=0.05)
+X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2)
+rowRoblox(X)
+}
+\keyword{package}
Modified: branches/robast-1.3/pkg/RobLox/man/finiteSampleCorrection.Rd
===================================================================
--- branches/robast-1.3/pkg/RobLox/man/finiteSampleCorrection.Rd 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/man/finiteSampleCorrection.Rd 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,54 +1,54 @@
-\name{finiteSampleCorrection}
-\Rdversion{1.1}
-\alias{finiteSampleCorrection}
-\title{Function to compute finite-sample corrected radii}
-\description{
-Given some radius and some sample size the function computes
-the corresponding finite-sample corrected radius.
-}
-\usage{
-finiteSampleCorrection(r, n, model = "locsc")
-}
-\arguments{
- \item{r}{ asymptotic radius (non-negative numeric) }
- \item{n}{ sample size }
- \item{model}{ has to be \code{"locsc"} (for location and scale),
- \code{"loc"} (for location) or \code{"sc"} (for scale), respectively. }
-}
-\details{
-The finite-sample correction is based on empirical results obtained via
-simulation studies.
-
-Given some radius of a shrinking contamination neighborhood which leads
-to an asymptotically optimal robust estimator, the finite-sample empirical
-MSE based on contaminated samples was minimized for this class of
-asymptotically optimal estimators and the corresponding finite-sample
-radius determined and saved.
-
-The computation is based on the saved results of these Monte-Carlo simulations.
-}
-\value{Finite-sample corrected radius.}
-\references{
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
-
- Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing
- the Radius. Statistical Methods and Applications \emph{17}(1) 13-40.
- Extended version: \url{https://uol.de/f/5/inst/mathe/personen/peter.ruckdeschel/Rr.pdf}
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-\seealso{\code{\link{roblox}}, \code{\link{rowRoblox}},
- \code{\link{colRoblox}} }
-\examples{
-finiteSampleCorrection(n = 3, r = 0.001, model = "locsc")
-finiteSampleCorrection(n = 10, r = 0.02, model = "loc")
-finiteSampleCorrection(n = 250, r = 0.15, model = "sc")
-}
-\concept{normal location}
-\concept{normal scale}
-\concept{normal location and scale}
-\concept{finite-sample correction}
-\keyword{robust}
+\name{finiteSampleCorrection}
+\Rdversion{1.1}
+\alias{finiteSampleCorrection}
+\title{Function to compute finite-sample corrected radii}
+\description{
+Given some radius and some sample size the function computes
+the corresponding finite-sample corrected radius.
+}
+\usage{
+finiteSampleCorrection(r, n, model = "locsc")
+}
+\arguments{
+ \item{r}{ asymptotic radius (non-negative numeric) }
+ \item{n}{ sample size }
+ \item{model}{ has to be \code{"locsc"} (for location and scale),
+ \code{"loc"} (for location) or \code{"sc"} (for scale), respectively. }
+}
+\details{
+The finite-sample correction is based on empirical results obtained via
+simulation studies.
+
+Given some radius of a shrinking contamination neighborhood which leads
+to an asymptotically optimal robust estimator, the finite-sample empirical
+MSE based on contaminated samples was minimized for this class of
+asymptotically optimal estimators and the corresponding finite-sample
+radius determined and saved.
+
+The computation is based on the saved results of these Monte-Carlo simulations.
+}
+\value{Finite-sample corrected radius.}
+\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}.
+
+ H. Rieder (1994): Robust Asymptotic Statistics. Springer. \doi{10.1007/978-1-4684-0624-5}
+
+ M. Kohl and H.P. Deigner (2010). Preprocessing of gene expression data by
+ optimally robust estimators. BMC Bioinformatics \emph{11}, 583.
+ \doi{10.1186/1471-2105-11-583}.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
+%\note{}
+\seealso{\code{\link{roblox}}, \code{\link{rowRoblox}},
+ \code{\link{colRoblox}} }
+\examples{
+finiteSampleCorrection(n = 3, r = 0.001, model = "locsc")
+finiteSampleCorrection(n = 10, r = 0.02, model = "loc")
+finiteSampleCorrection(n = 250, r = 0.15, model = "sc")
+}
+\concept{normal location}
+\concept{normal scale}
+\concept{normal location and scale}
+\concept{finite-sample correction}
+\keyword{robust}
Modified: branches/robast-1.3/pkg/RobLox/man/rlOptIC.Rd
===================================================================
--- branches/robast-1.3/pkg/RobLox/man/rlOptIC.Rd 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/man/rlOptIC.Rd 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,54 +1,58 @@
-\name{rlOptIC}
-\alias{rlOptIC}
-\title{Computation of the optimally robust IC for AL estimators}
-\description{
- The function \code{rlOptIC} computes the optimally robust IC for
- AL estimators in case of normal location and (convex) contamination
- neighborhoods. The definition of these estimators can be found
- in Rieder (1994) or Kohl (2005), respectively.
-}
-\usage{
-rlOptIC(r, mean = 0, sd = 1, bUp = 1000, computeIC = TRUE)
-}
-\arguments{
- \item{r}{ non-negative real: neighborhood radius. }
- \item{mean}{ specified mean.}
- \item{sd}{ specified standard deviation.}
- \item{bUp}{ positive real: the upper end point of the
- interval to be searched for the clipping bound b. }
- \item{computeIC}{ logical: should IC be computed. See details below. }
-}
-\details{
- If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a', and
- 'b' contained in the optimally robust IC are computed.
-}
-\value{
- If 'computeIC' is 'TRUE' an object of class \code{"ContIC"} is returned,
- otherwise a list of Lagrange multipliers
- \item{A}{ standardizing constant }
- \item{a}{ centering constant; always '= 0' is this symmetric setup }
- \item{b}{ optimal clipping bound }
-}
-\references{
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
-
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-\seealso{\code{\link[RobAStBase]{ContIC-class}}, \code{\link{roblox}}}
-\examples{
-IC1 <- rlOptIC(r = 0.1)
-distrExOptions("ErelativeTolerance" = 1e-12)
-checkIC(IC1)
-distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default
-Risks(IC1)
-cent(IC1)
-clip(IC1)
-stand(IC1)
-plot(IC1)
-}
-\concept{normal location}
-\concept{influence curve}
-\keyword{robust}
+\name{rlOptIC}
+\alias{rlOptIC}
+\title{Computation of the optimally robust IC for AL estimators}
+\description{
+ The function \code{rlOptIC} computes the optimally robust IC for
+ AL estimators in case of normal location and (convex) contamination
+ neighborhoods. The definition of these estimators can be found
+ in Rieder (1994) or Kohl (2005), respectively.
+}
+\usage{
+rlOptIC(r, mean = 0, sd = 1, bUp = 1000, computeIC = TRUE)
+}
+\arguments{
+ \item{r}{ non-negative real: neighborhood radius. }
+ \item{mean}{ specified mean.}
+ \item{sd}{ specified standard deviation.}
+ \item{bUp}{ positive real: the upper end point of the
+ interval to be searched for the clipping bound b. }
+ \item{computeIC}{ logical: should IC be computed. See details below. }
+}
+\details{
+ If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a', and
+ 'b' contained in the optimally robust IC are computed.
+}
+\value{
+ If 'computeIC' is 'TRUE' an object of class \code{"ContIC"} is returned,
+ otherwise a list of Lagrange multipliers
+ \item{A}{ standardizing constant }
+ \item{a}{ centering constant; always '= 0' is this symmetric setup }
+ \item{b}{ optimal clipping bound }
+}
+\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}.
+
+ H. Rieder (1994): Robust Asymptotic Statistics. Springer. \doi{10.1007/978-1-4684-0624-5}
+
+ 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}.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
+%\note{}
+\seealso{\code{\link[RobAStBase]{ContIC-class}}, \code{\link{roblox}}}
+\examples{
+IC1 <- rlOptIC(r = 0.1)
+distrExOptions("ErelativeTolerance" = 1e-12)
+checkIC(IC1)
+distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default
+Risks(IC1)
+cent(IC1)
+clip(IC1)
+stand(IC1)
+plot(IC1)
+}
+\concept{normal location}
+\concept{influence curve}
+\keyword{robust}
Modified: branches/robast-1.3/pkg/RobLox/man/rlsOptIC.AL.Rd
===================================================================
--- branches/robast-1.3/pkg/RobLox/man/rlsOptIC.AL.Rd 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/man/rlsOptIC.AL.Rd 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,106 +1,110 @@
-\name{rlsOptIC.AL}
-\alias{rlsOptIC.AL}
-\title{Computation of the optimally robust IC for AL estimators}
-\description{
- The function \code{rlsOptIC.AL} computes the optimally robust IC for
- AL estimators in case of normal location with unknown scale and
- (convex) contamination neighborhoods. The definition of
- these estimators can be found in Section 8.2 of Kohl (2005).
-}
-\usage{
-rlsOptIC.AL(r, mean = 0, sd = 1, A.loc.start = 1, a.sc.start = 0,
- A.sc.start = 0.5, bUp = 1000, delta = 1e-6, itmax = 100,
- check = FALSE, computeIC = TRUE)
-}
-\arguments{
- \item{r}{ non-negative real: neighborhood radius. }
- \item{mean}{ specified mean.}
- \item{sd}{ specified standard deviation.}
- \item{A.loc.start}{ positive real: starting value for
- the standardizing constant of the location part. }
- \item{a.sc.start}{ real: starting value for centering
- constant of the scale part. }
- \item{A.sc.start}{ positive real: starting value for
- the standardizing constant of the scale part. }
- \item{bUp}{ positive real: the upper end point of the
- interval to be searched for the clipping bound b. }
- \item{delta}{ the desired accuracy (convergence tolerance). }
- \item{itmax}{ the maximum number of iterations. }
- \item{check}{ logical: should constraints be checked. }
- \item{computeIC}{ logical: should IC be computed. See details below. }
-}
-\details{The Lagrange multipliers contained in the expression
- of the optimally robust IC can be accessed via the
- accessor functions \code{cent}, \code{clip} and \code{stand}.
- If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a',
- and 'b' contained in the optimally robust IC are computed.
-}
-\value{
- If 'computeIC' is 'TRUE' an object of class \code{"ContIC"} is returned,
- otherwise a list of Lagrange multipliers
- \item{A}{ standardizing matrix }
- \item{a}{ centering vector }
- \item{b}{ optimal clipping bound }
-}
-\references{
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
-
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-\seealso{\code{\link[RobAStBase]{ContIC-class}}, \code{\link{roblox}}}
-\examples{
-IC1 <- rlsOptIC.AL(r = 0.1, check = TRUE)
-distrExOptions("ErelativeTolerance" = 1e-12)
-checkIC(IC1)
-distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default
-Risks(IC1)
-cent(IC1)
-clip(IC1)
-stand(IC1)
-
-## don't run to reduce check time on CRAN
-\dontrun{
-plot(IC1)
-infoPlot(IC1)
-
-## k-step estimation
-## better use function roblox (see ?roblox)
-## 1. data: random sample
-ind <- rbinom(100, size=1, prob=0.05)
-x <- rnorm(100, mean=0, sd=(1-ind) + ind*9)
-mean(x)
-sd(x)
-median(x)
-mad(x)
-
-## 2. Kolmogorov(-Smirnov) minimum distance estimator (default)
-## -> we use it as initial estimate for one-step construction
-(est0 <- MDEstimator(x, ParamFamily = NormLocationScaleFamily()))
-
-## 3.1 one-step estimation: radius known
-IC1 <- rlsOptIC.AL(r = 0.5, mean = estimate(est0)[1], sd = estimate(est0)[2])
-(est1 <- oneStepEstimator(x, IC1, est0))
-
-## 3.2 k-step estimation: radius known
-## Choose k = 3
-(est2 <- kStepEstimator(x, IC1, est0, steps = 3L))
-
-## 4.1 one-step estimation: radius unknown
-## take least favorable radius r = 0.579
-## cf. Table 8.1 in Kohl(2005)
-IC2 <- rlsOptIC.AL(r = 0.579, mean = estimate(est0)[1], sd = estimate(est0)[2])
-(est3 <- oneStepEstimator(x, IC2, est0))
-
-## 4.2 k-step estimation: radius unknown
-## take least favorable radius r = 0.579
-## cf. Table 8.1 in Kohl(2005)
-## choose k = 3
-(est4 <- kStepEstimator(x, IC2, est0, steps = 3L))
-}
-}
-\concept{normal location and scale}
-\concept{influence curve}
-\keyword{robust}
+\name{rlsOptIC.AL}
+\alias{rlsOptIC.AL}
+\title{Computation of the optimally robust IC for AL estimators}
+\description{
+ The function \code{rlsOptIC.AL} computes the optimally robust IC for
+ AL estimators in case of normal location with unknown scale and
+ (convex) contamination neighborhoods. The definition of
+ these estimators can be found in Section 8.2 of Kohl (2005).
+}
+\usage{
+rlsOptIC.AL(r, mean = 0, sd = 1, A.loc.start = 1, a.sc.start = 0,
+ A.sc.start = 0.5, bUp = 1000, delta = 1e-6, itmax = 100,
+ check = FALSE, computeIC = TRUE)
+}
+\arguments{
+ \item{r}{ non-negative real: neighborhood radius. }
+ \item{mean}{ specified mean.}
+ \item{sd}{ specified standard deviation.}
+ \item{A.loc.start}{ positive real: starting value for
+ the standardizing constant of the location part. }
+ \item{a.sc.start}{ real: starting value for centering
+ constant of the scale part. }
+ \item{A.sc.start}{ positive real: starting value for
+ the standardizing constant of the scale part. }
+ \item{bUp}{ positive real: the upper end point of the
+ interval to be searched for the clipping bound b. }
+ \item{delta}{ the desired accuracy (convergence tolerance). }
+ \item{itmax}{ the maximum number of iterations. }
+ \item{check}{ logical: should constraints be checked. }
+ \item{computeIC}{ logical: should IC be computed. See details below. }
+}
+\details{The Lagrange multipliers contained in the expression
+ of the optimally robust IC can be accessed via the
+ accessor functions \code{cent}, \code{clip} and \code{stand}.
+ If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a',
+ and 'b' contained in the optimally robust IC are computed.
+}
+\value{
+ If 'computeIC' is 'TRUE' an object of class \code{"ContIC"} is returned,
+ otherwise a list of Lagrange multipliers
+ \item{A}{ standardizing matrix }
+ \item{a}{ centering vector }
+ \item{b}{ optimal clipping bound }
+}
+\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}.
+
+ H. Rieder (1994): Robust Asymptotic Statistics. Springer. \doi{10.1007/978-1-4684-0624-5}
+
+ 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}.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
+%\note{}
+\seealso{\code{\link[RobAStBase]{ContIC-class}}, \code{\link{roblox}}}
+\examples{
+IC1 <- rlsOptIC.AL(r = 0.1, check = TRUE)
+distrExOptions("ErelativeTolerance" = 1e-12)
+checkIC(IC1)
+distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default
+Risks(IC1)
+cent(IC1)
+clip(IC1)
+stand(IC1)
+
+## don't run to reduce check time on CRAN
+\dontrun{
+plot(IC1)
+infoPlot(IC1)
+
+## k-step estimation
+## better use function roblox (see ?roblox)
+## 1. data: random sample
+ind <- rbinom(100, size=1, prob=0.05)
+x <- rnorm(100, mean=0, sd=(1-ind) + ind*9)
+mean(x)
+sd(x)
+median(x)
+mad(x)
+
+## 2. Kolmogorov(-Smirnov) minimum distance estimator (default)
+## -> we use it as initial estimate for one-step construction
+(est0 <- MDEstimator(x, ParamFamily = NormLocationScaleFamily()))
+
+## 3.1 one-step estimation: radius known
+IC1 <- rlsOptIC.AL(r = 0.5, mean = estimate(est0)[1], sd = estimate(est0)[2])
+(est1 <- oneStepEstimator(x, IC1, est0))
+
+## 3.2 k-step estimation: radius known
+## Choose k = 3
+(est2 <- kStepEstimator(x, IC1, est0, steps = 3L))
+
+## 4.1 one-step estimation: radius unknown
+## take least favorable radius r = 0.579
+## cf. Table 8.1 in Kohl(2005)
+IC2 <- rlsOptIC.AL(r = 0.579, mean = estimate(est0)[1], sd = estimate(est0)[2])
+(est3 <- oneStepEstimator(x, IC2, est0))
+
+## 4.2 k-step estimation: radius unknown
+## take least favorable radius r = 0.579
+## cf. Table 8.1 in Kohl(2005)
+## choose k = 3
+(est4 <- kStepEstimator(x, IC2, est0, steps = 3L))
+}
+}
+\concept{normal location and scale}
+\concept{influence curve}
+\keyword{robust}
Modified: branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An1.Rd
===================================================================
--- branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An1.Rd 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An1.Rd 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,46 +1,46 @@
-\name{rlsOptIC.An1}
-\alias{rlsOptIC.An1}
-\title{Computation of the optimally robust IC for An1 estimators}
-\description{
- The function \code{rlsOptIC.An1} computes the optimally robust IC for
- An1 estimators in case of normal location with unknown scale and
- (convex) contamination neighborhoods. The definition of
- these estimators can be found in Subsection 8.5.3 of Kohl (2005).
-}
-\usage{
-rlsOptIC.An1(r, aUp = 2.5, delta = 1e-06)
-}
-\arguments{
- \item{r}{ non-negative real: neighborhood radius. }
- \item{aUp}{ positive real: the upper end point of the interval
- to be searched for a. }
- \item{delta}{ the desired accuracy (convergence tolerance). }
-}
-\details{The optimal value of the tuning constant a can be read off
- from the slot \code{Infos} of the resulting IC.}
-\value{Object of class \code{"IC"}}
-\references{
- Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J.,
- Rogers, W.H. and Tukey, J.W. (1972) \emph{Robust estimates of location}.
- Princeton University Press.
-
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-\seealso{\code{\link[RobAStBase]{IC-class}}}
-\examples{
-IC1 <- rlsOptIC.An1(r = 0.1)
-checkIC(IC1)
-Risks(IC1)
-Infos(IC1)
-## don't run to reduce check time on CRAN
-\dontrun{
-plot(IC1)
-infoPlot(IC1)
-}
-}
-\concept{normal location and scale}
-\concept{influence curve}
-\keyword{robust}
+\name{rlsOptIC.An1}
+\alias{rlsOptIC.An1}
+\title{Computation of the optimally robust IC for An1 estimators}
+\description{
+ The function \code{rlsOptIC.An1} computes the optimally robust IC for
+ An1 estimators in case of normal location with unknown scale and
+ (convex) contamination neighborhoods. The definition of
+ these estimators can be found in Subsection 8.5.3 of Kohl (2005).
+}
+\usage{
+rlsOptIC.An1(r, aUp = 2.5, delta = 1e-06)
+}
+\arguments{
+ \item{r}{ non-negative real: neighborhood radius. }
+ \item{aUp}{ positive real: the upper end point of the interval
+ to be searched for a. }
+ \item{delta}{ the desired accuracy (convergence tolerance). }
+}
+\details{The optimal value of the tuning constant a can be read off
+ from the slot \code{Infos} of the resulting IC.}
+\value{Object of class \code{"IC"}}
+\references{
+ Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J.,
+ Rogers, W.H. and Tukey, J.W. (1972) \emph{Robust estimates of location}.
+ Princeton University Press.
+
+ 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}.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
+%\note{}
+\seealso{\code{\link[RobAStBase]{IC-class}}}
+\examples{
+IC1 <- rlsOptIC.An1(r = 0.1)
+checkIC(IC1)
+Risks(IC1)
+Infos(IC1)
+## don't run to reduce check time on CRAN
+\dontrun{
+plot(IC1)
+infoPlot(IC1)
+}
+}
+\concept{normal location and scale}
+\concept{influence curve}
+\keyword{robust}
Modified: branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An2.Rd
===================================================================
--- branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An2.Rd 2024-02-11 10:25:58 UTC (rev 1303)
+++ branches/robast-1.3/pkg/RobLox/man/rlsOptIC.An2.Rd 2024-02-11 13:38:39 UTC (rev 1304)
@@ -1,51 +1,51 @@
-\name{rlsOptIC.An2}
-\alias{rlsOptIC.An2}
-\title{Computation of the optimally robust IC for An2 estimators}
-\description{
- The function \code{rlsOptIC.An2} computes the optimally robust IC for
- An2 estimators in case of normal location with unknown scale and
- (convex) contamination neighborhoods. The definition of
- these estimators can be found in Subsection 8.5.3 of Kohl (2005).
-}
-\usage{
-rlsOptIC.An2(r, a.start = 1.5, k.start = 1.5, delta = 1e-06, MAX = 100)
-}
-\arguments{
- \item{r}{ non-negative real: neighborhood radius. }
- \item{a.start}{ positive real: starting value for a. }
- \item{k.start}{ positive real: starting value for k. }
- \item{delta}{ the desired accuracy (convergence tolerance). }
- \item{MAX}{ if a or k are beyond the admitted values,
- \code{MAX} is returned. }
-}
-\details{
- The computation of the optimally robust IC for An2 estimators
- is based on \code{optim} where \code{MAX} is used to
- control the constraints on a and k. The optimal values of the
- tuning constants a and k can be read off from the slot
- \code{Infos} of the resulting IC.
-}
-%\details{}
-\value{Object of class \code{"IC"}}
-\references{
- Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J.,
- Rogers, W.H. and Tukey, J.W. (1972) \emph{Robust estimates of location}.
- Princeton University Press.
-
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-\seealso{\code{\link[RobAStBase]{IC-class}}}
-\examples{
-IC1 <- rlsOptIC.An2(r = 0.1)
-checkIC(IC1)
-Risks(IC1)
-Infos(IC1)
-plot(IC1)
-infoPlot(IC1)
-}
-\concept{normal location and scale}
-\concept{influence curve}
-\keyword{robust}
+\name{rlsOptIC.An2}
+\alias{rlsOptIC.An2}
+\title{Computation of the optimally robust IC for An2 estimators}
+\description{
+ The function \code{rlsOptIC.An2} computes the optimally robust IC for
+ An2 estimators in case of normal location with unknown scale and
+ (convex) contamination neighborhoods. The definition of
+ these estimators can be found in Subsection 8.5.3 of Kohl (2005).
+}
+\usage{
+rlsOptIC.An2(r, a.start = 1.5, k.start = 1.5, delta = 1e-06, MAX = 100)
+}
+\arguments{
+ \item{r}{ non-negative real: neighborhood radius. }
+ \item{a.start}{ positive real: starting value for a. }
+ \item{k.start}{ positive real: starting value for k. }
+ \item{delta}{ the desired accuracy (convergence tolerance). }
+ \item{MAX}{ if a or k are beyond the admitted values,
+ \code{MAX} is returned. }
+}
+\details{
+ The computation of the optimally robust IC for An2 estimators
+ is based on \code{optim} where \code{MAX} is used to
+ control the constraints on a and k. The optimal values of the
+ tuning constants a and k can be read off from the slot
+ \code{Infos} of the resulting IC.
+}
+%\details{}
+\value{Object of class \code{"IC"}}
+\references{
+ Andrews, D.F., Bickel, P.J., Hampel, F.R., Huber, P.J.,
+ Rogers, W.H. and Tukey, J.W. (1972) \emph{Robust estimates of location}.
+ Princeton University Press.
+
+ 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}.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
+%\note{}
+\seealso{\code{\link[RobAStBase]{IC-class}}}
+\examples{
+IC1 <- rlsOptIC.An2(r = 0.1)
+checkIC(IC1)
+Risks(IC1)
+Infos(IC1)
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/robast -r 1304
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