[Robast-commits] r727 - branches/robast-1.0/pkg/RobAStBase/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Mar 23 11:50:31 CET 2014
Author: stamats
Date: 2014-03-23 11:50:31 +0100 (Sun, 23 Mar 2014)
New Revision: 727
Modified:
branches/robast-1.0/pkg/RobAStBase/man/0RobAStBase-package.Rd
branches/robast-1.0/pkg/RobAStBase/man/InfoPlotWrapper.Rd
branches/robast-1.0/pkg/RobAStBase/man/outlyingPlotIC.Rd
Log:
some minor updates and correction for version 1.0.
Modified: branches/robast-1.0/pkg/RobAStBase/man/0RobAStBase-package.Rd
===================================================================
--- branches/robast-1.0/pkg/RobAStBase/man/0RobAStBase-package.Rd 2014-03-23 10:29:25 UTC (rev 726)
+++ branches/robast-1.0/pkg/RobAStBase/man/0RobAStBase-package.Rd 2014-03-23 10:50:31 UTC (rev 727)
@@ -1,55 +1,56 @@
-\name{RobAStBase-package}
-\alias{RobAStBase-package}
-\alias{RobAStBase}
-\docType{package}
-\title{
-Robust Asymptotic Statistics
-}
-\description{
-Base S4-classes and functions for robust asymptotic statistics.
-}
-\details{
-\tabular{ll}{
-Package: \tab RobAStBase \cr
-Version: \tab 0.9 \cr
-Date: \tab 2013-09-11 \cr
-Depends: \tab R(>= 2.12.0), methods, distr(>= 2.0), distrEx(>= 2.0),
-distrMod(>= 2.0), RandVar(>= 0.6.3)\cr
-LazyLoad: \tab yes \cr
-License: \tab LGPL-3 \cr
-URL: \tab http://robast.r-forge.r-project.org/\cr
-SVNRevision: \tab 694 \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.
-}
-\seealso{
-\code{\link[distr:0distr-package]{distr-package}},
-\code{\link[distrEx:0distrEx-package]{distrEx-package}},
-\code{\link[distrMod:0distrMod-package]{distrMod-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(RobAStBase)
-
-## some L2 differentiable parametric family from package distrMod, e.g.
-B <- BinomFamily(size = 25, prob = 0.25)
-
-## classical optimal IC
-IC0 <- optIC(model = B, risk = asCov())
-plot(IC0) # plot IC
-checkIC(IC0, B)
-}
-\keyword{package}
+\name{RobAStBase-package}
+\alias{RobAStBase-package}
+\alias{RobAStBase}
+\docType{package}
+\title{
+Robust Asymptotic Statistics
+}
+\description{
+Base S4-classes and functions for robust asymptotic statistics.
+}
+\details{
+\tabular{ll}{
+Package: \tab RobAStBase \cr
+Version: \tab 1.0 \cr
+Date: \tab 2013-09-12 \cr
+Depends: \tab R(>= 2.14.0), rrcov, distr(>= 2.5.2), distrEx(>= 2.5), distrMod(>= 2.5.2),
+RandVar(>= 0.9.2)\cr
+ByteCompile: \tab yes \cr
+LazyLoad: \tab yes \cr
+License: \tab LGPL-3 \cr
+URL: \tab http://robast.r-forge.r-project.org/\cr
+SVNRevision: \tab 726 \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.
+}
+\seealso{
+\code{\link[distr:0distr-package]{distr-package}},
+\code{\link[distrEx:0distrEx-package]{distrEx-package}},
+\code{\link[distrMod:0distrMod-package]{distrMod-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(RobAStBase)
+
+## some L2 differentiable parametric family from package distrMod, e.g.
+B <- BinomFamily(size = 25, prob = 0.25)
+
+## classical optimal IC
+IC0 <- optIC(model = B, risk = asCov())
+plot(IC0) # plot IC
+checkIC(IC0, B)
+}
+\keyword{package}
Modified: branches/robast-1.0/pkg/RobAStBase/man/InfoPlotWrapper.Rd
===================================================================
--- branches/robast-1.0/pkg/RobAStBase/man/InfoPlotWrapper.Rd 2014-03-23 10:29:25 UTC (rev 726)
+++ branches/robast-1.0/pkg/RobAStBase/man/InfoPlotWrapper.Rd 2014-03-23 10:50:31 UTC (rev 727)
@@ -1,49 +1,49 @@
-\name{InfoPlot}
-\alias{InfoPlot}
-\title{Wrapper function for information plot method}
-\usage{
- InfoPlot(IC, data, ..., alpha.trsp = 100,
- with.legend = TRUE, rescale = FALSE, withCall = TRUE)
-}
-\arguments{
- \item{IC}{object of class \code{IC}}
-
- \item{data}{optional data argument --- for plotting
- observations into the plot}
-
- \item{...}{additional parameters (in particular to be
- passed on to \code{plot})}
-
- \item{alpha.trsp}{the transparency argument (0 to 100)
- for ploting the data}
-
- \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 plot method by
- default and gives a user possibility to run the function
- with low number of arguments
-}
-\section{Details}{
- Calls \code{infoPlot} with suitably chosen defaults. If
- \code{withCall == TRUE}, the call to \code{infoPlot} is
- returned
-}
-\examples{
-# Gamma
-fam <- GammaFamily()
-IC <- optIC(model = fam, risk = asCov())
-Y <- distribution(fam)
-data <- r(Y)(1000)
-InfoPlot(IC, data, withCall = FALSE)
-}
-
+\name{InfoPlot}
+\alias{InfoPlot}
+\title{Wrapper function for information plot method}
+\usage{
+ InfoPlot(IC, data, ..., alpha.trsp = 100,
+ with.legend = TRUE, rescale = FALSE, withCall = TRUE)
+}
+\arguments{
+ \item{IC}{object of class \code{IC}}
+
+ \item{data}{optional data argument --- for plotting
+ observations into the plot}
+
+ \item{...}{additional parameters (in particular to be
+ passed on to \code{plot})}
+
+ \item{alpha.trsp}{the transparency argument (0 to 100)
+ for ploting the data}
+
+ \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 plot method by
+ default and gives a user possibility to run the function
+ with low number of arguments
+}
+\section{Details}{
+ Calls \code{infoPlot} with suitably chosen defaults. If
+ \code{withCall == TRUE}, the call to \code{infoPlot} is
+ returned
+}
+\examples{
+# Gamma
+fam <- GammaFamily()
+IC <- optIC(model = fam, risk = asCov())
+Y <- distribution(fam)
+data <- r(Y)(500)
+InfoPlot(IC, data, withCall = FALSE)
+}
+
Modified: branches/robast-1.0/pkg/RobAStBase/man/outlyingPlotIC.Rd
===================================================================
--- branches/robast-1.0/pkg/RobAStBase/man/outlyingPlotIC.Rd 2014-03-23 10:29:25 UTC (rev 726)
+++ branches/robast-1.0/pkg/RobAStBase/man/outlyingPlotIC.Rd 2014-03-23 10:50:31 UTC (rev 727)
@@ -1,103 +1,103 @@
-\name{outlyingPlotIC}
-\alias{outlyingPlotIC}
-\title{Function outlyingPlotIC in Package `RobAStBase' }
-\description{outlyingPlotIC produces an outlyingness plot based on distances applied
-to ICs}
-\usage{
-outlyingPlotIC(data, IC.x, IC.y = IC.x, dist.x = NormType(),
- dist.y, cutoff.y = cutoff.chisq(), cutoff.x = cutoff.sememp(),
- ..., cutoff.quantile.x = 0.95,
- cutoff.quantile.y = cutoff.quantile.x,
- id.n, cex.pts = 1,lab.pts, jitt.pts = 0, alpha.trsp = NA,
- adj =0, cex.idn, col.idn, lty.cutoff, lwd.cutoff, col.cutoff,
- robCov.x = TRUE, robCov.y = TRUE, tf.x = data, tf.y = data,
- jitt.fac = 10,
- main = gettext("Outlyingness \n by means of a distance-distance plot")
- )
-}
-\arguments{
- \item{data}{data coercable to \code{matrix}; the data at which to produce the \code{ddPlot}.}
- \item{IC.x}{object of class \code{IC} the influence curve to produce
- the distances for the \code{x} axis.}
- \item{IC.y}{object of class \code{IC} the influence curve to produce
- the distances for the \code{y} axis.}
- \item{\dots}{further arguments to be passed to \code{plot.default}, \code{text}, and \code{abline}}
- \item{dist.x}{object of class \code{NormType}; the distance for the \code{x} axis.}
- \item{dist.y}{object of class \code{NormType}; the distance for the \code{y} axis.}
- \item{cutoff.x}{object of class \code{cutoff}; the cutoff information for the \code{x} axis
- (the vertical line discriminating 'good' and 'bad' points).}
- \item{cutoff.y}{object of class \code{cutoff}; the cutoff information for the \code{y} axis
- (the horizontal line discriminating 'good' and 'bad' points).}
- \item{cutoff.quantile.x}{numeric; the cutoff quantile for the \code{x} axis.}
- \item{cutoff.quantile.y}{numeric; the cutoff quantile for the \code{y} axis.}
- \item{id.n}{a set of indices (or a corresponding logical vector); to select a subset
- of the data in argument \code{data}.}
- \item{cex.pts}{the corresponding \code{cex} argument for plotted points.}
- \item{lab.pts}{a vector of labels for the (unsubsetted) \code{data}.}
- \item{jitt.pts}{the corresponding \code{jitter} argument for plotted points;
- may be a vector of length 2 -- for separate factors for x- and y-coordinate.}
- \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{adj}{the corresponding argument for \code{\link[graphics]{text}} for
- labelling the outliers.}
- \item{cex.idn}{the corresponding \code{cex} argument for
- \code{\link[graphics]{text}} for labelling the outliers.}
- \item{col.idn}{the corresponding \code{col} argument for
- \code{\link[graphics]{text}} for labelling the outliers.}
- \item{lty.cutoff}{the corresponding \code{lty} argument for
- \code{\link[graphics]{abline}} for drawing the cutoff lines.}
- \item{lwd.cutoff}{the corresponding \code{lwd} argument for
- \code{\link[graphics]{abline}} for drawing the cutoff lines.}
- \item{col.cutoff}{the corresponding \code{col} argument for
- \code{\link[graphics]{abline}} for drawing the cutoff lines.}
- \item{robCov.x}{shall x-distances be based on MCD, i.e.,
- robust covariances (TRUE) or on classical covariance be used?}
- \item{robCov.y}{shall y-distances be based on MCD, i.e.,
- robust covariances (TRUE) or on classical covariance be used?}
- \item{tf.x}{transformation for x axis: a function returning the
- transformed x-coordinates when applied to the data;
- by default identity.}
- \item{tf.y}{transformation for y axis: a function returning the
- transformed y-coordinates when applied to the data;
- by default identity.}
- \item{jitt.fac}{factor for jittering, see \code{\link{jitter}};}
- \item{main}{the main title.}
-}
-\details{
-calls a corresponding \code{\link{ddPlot}} method to produce the plot.
-}
-\author{
- Peter Ruckdeschel \email{Peter.Ruckdeschel at itwm.fraunhofer.de}
- }
-
-\value{
-a list with items
-\item{id.x}{the indices of (possibly transformed) data (within subset \code{id.n}) beyond the \code{x}-cutoff}
-\item{id.y}{the indices of (possibly transformed) data (within subset \code{id.n}) beyond the \code{y}-cutoff}
-\item{id.xy}{the indices of (possibly transformed) data (within subset \code{id.n}) beyond the \code{x}-cutoff and the \code{y}-cutoff}
-\item{qtx}{the quantiles of the distances of the (possibly transformed) data in \code{x} direction}
-\item{qty}{the quantiles of the distances of the (possibly transformed) data in \code{y} direction}
-\item{cutoff.x.v}{the cutoff value in \code{x} direction}
-\item{cutoff.y.v}{the cutoff value in \code{y} direction}
-}
-
-\examples{
-if(require(ROptEst)){
-## generates normal location and scale family with mean = -2 and sd = 3
-N0 <- NormLocationScaleFamily()
-N0.IC0 <- optIC(model = N0, risk = asCov())
-N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 0.5))
-N0.IC1 <- optIC(model = N0.Rob1, risk = asMSE())
-xn <- c(rnorm(100),rcauchy(20)+20)
-outlyingPlotIC(xn, IC.x=N0.IC0)
-outlyingPlotIC(xn, IC.x=N0.IC1)
-}
-}
-\keyword{hplot}
-
+\name{outlyingPlotIC}
+\alias{outlyingPlotIC}
+\title{Function outlyingPlotIC in Package `RobAStBase' }
+\description{outlyingPlotIC produces an outlyingness plot based on distances applied
+to ICs}
+\usage{
+outlyingPlotIC(data, IC.x, IC.y = IC.x, dist.x = NormType(),
+ dist.y, cutoff.y = cutoff.chisq(), cutoff.x = cutoff.sememp(),
+ ..., cutoff.quantile.x = 0.95,
+ cutoff.quantile.y = cutoff.quantile.x,
+ id.n, cex.pts = 1,lab.pts, jitt.pts = 0, alpha.trsp = NA,
+ adj, cex.idn, col.idn, lty.cutoff, lwd.cutoff, col.cutoff,
+ robCov.x = TRUE, robCov.y = TRUE, tf.x = data, tf.y = data,
+ jitt.fac = 10,
+ main = gettext("Outlyingness \n by means of a distance-distance plot")
+ )
+}
+\arguments{
+ \item{data}{data coercable to \code{matrix}; the data at which to produce the \code{ddPlot}.}
+ \item{IC.x}{object of class \code{IC} the influence curve to produce
+ the distances for the \code{x} axis.}
+ \item{IC.y}{object of class \code{IC} the influence curve to produce
+ the distances for the \code{y} axis.}
+ \item{\dots}{further arguments to be passed to \code{plot.default}, \code{text}, and \code{abline}}
+ \item{dist.x}{object of class \code{NormType}; the distance for the \code{x} axis.}
+ \item{dist.y}{object of class \code{NormType}; the distance for the \code{y} axis.}
+ \item{cutoff.x}{object of class \code{cutoff}; the cutoff information for the \code{x} axis
+ (the vertical line discriminating 'good' and 'bad' points).}
+ \item{cutoff.y}{object of class \code{cutoff}; the cutoff information for the \code{y} axis
+ (the horizontal line discriminating 'good' and 'bad' points).}
+ \item{cutoff.quantile.x}{numeric; the cutoff quantile for the \code{x} axis.}
+ \item{cutoff.quantile.y}{numeric; the cutoff quantile for the \code{y} axis.}
+ \item{id.n}{a set of indices (or a corresponding logical vector); to select a subset
+ of the data in argument \code{data}.}
+ \item{cex.pts}{the corresponding \code{cex} argument for plotted points.}
+ \item{lab.pts}{a vector of labels for the (unsubsetted) \code{data}.}
+ \item{jitt.pts}{the corresponding \code{jitter} argument for plotted points;
+ may be a vector of length 2 -- for separate factors for x- and y-coordinate.}
+ \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{adj}{the corresponding argument for \code{\link[graphics]{text}} for
+ labelling the outliers.}
+ \item{cex.idn}{the corresponding \code{cex} argument for
+ \code{\link[graphics]{text}} for labelling the outliers.}
+ \item{col.idn}{the corresponding \code{col} argument for
+ \code{\link[graphics]{text}} for labelling the outliers.}
+ \item{lty.cutoff}{the corresponding \code{lty} argument for
+ \code{\link[graphics]{abline}} for drawing the cutoff lines.}
+ \item{lwd.cutoff}{the corresponding \code{lwd} argument for
+ \code{\link[graphics]{abline}} for drawing the cutoff lines.}
+ \item{col.cutoff}{the corresponding \code{col} argument for
+ \code{\link[graphics]{abline}} for drawing the cutoff lines.}
+ \item{robCov.x}{shall x-distances be based on MCD, i.e.,
+ robust covariances (TRUE) or on classical covariance be used?}
+ \item{robCov.y}{shall y-distances be based on MCD, i.e.,
+ robust covariances (TRUE) or on classical covariance be used?}
+ \item{tf.x}{transformation for x axis: a function returning the
+ transformed x-coordinates when applied to the data;
+ by default identity.}
+ \item{tf.y}{transformation for y axis: a function returning the
+ transformed y-coordinates when applied to the data;
+ by default identity.}
+ \item{jitt.fac}{factor for jittering, see \code{\link{jitter}};}
+ \item{main}{the main title.}
+}
+\details{
+calls a corresponding \code{\link{ddPlot}} method to produce the plot.
+}
+\author{
+ Peter Ruckdeschel \email{Peter.Ruckdeschel at itwm.fraunhofer.de}
+ }
+
+\value{
+a list with items
+\item{id.x}{the indices of (possibly transformed) data (within subset \code{id.n}) beyond the \code{x}-cutoff}
+\item{id.y}{the indices of (possibly transformed) data (within subset \code{id.n}) beyond the \code{y}-cutoff}
+\item{id.xy}{the indices of (possibly transformed) data (within subset \code{id.n}) beyond the \code{x}-cutoff and the \code{y}-cutoff}
+\item{qtx}{the quantiles of the distances of the (possibly transformed) data in \code{x} direction}
+\item{qty}{the quantiles of the distances of the (possibly transformed) data in \code{y} direction}
+\item{cutoff.x.v}{the cutoff value in \code{x} direction}
+\item{cutoff.y.v}{the cutoff value in \code{y} direction}
+}
+
+\examples{
+if(require(ROptEst)){
+## generates normal location and scale family with mean = -2 and sd = 3
+N0 <- NormLocationScaleFamily()
+N0.IC0 <- optIC(model = N0, risk = asCov())
+N0.Rob1 <- InfRobModel(center = N0, neighbor = ContNeighborhood(radius = 0.5))
+N0.IC1 <- optIC(model = N0.Rob1, risk = asMSE())
+xn <- c(rnorm(100),rcauchy(20)+20)
+outlyingPlotIC(xn, IC.x=N0.IC0)
+outlyingPlotIC(xn, IC.x=N0.IC1)
+}
+}
+\keyword{hplot}
+
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