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\name{ehplot}
\alias{ehplot}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Engelmann-Hecker-Plot - EH-Plot
}
\description{
This R function provides a convenient way to visualize the distribution of
grouped numerical data.
}
\usage{
ehplot(data, groups, intervals=50, offset=0.1, log=FALSE,
median=TRUE, box=FALSE, boxborder="grey50",
xlab="groups", ylab="values", col="black", ...)
}
\arguments{
\item{data}{
Vector of numerical data.
}
\item{groups}{
Vector of group names which should have the same length as data.
}
\item{intervals}{
The data is splitted into a certain number of intervals. If data
points are in the same interval they are drawn side-by-side.
}
\item{offset}{
x-distance between two data points at the same interval.
}
\item{log}{
Logarithmic display
}
\item{median}{
To show the median of each group. NAs in data are not considered
for calculating the medians.
}
\item{box}{
To underlay a boxplot.
}
\item{boxborder}{
The color of the boxplot if a boxplot is drawn.
}
\item{xlab}{
x-axis label
}
\item{ylab}{
y-axis label
}
\item{col}{
vector of colors for the datapoints. (recycled as necessary).
}
\item{\dots}{
additional plot-parameters will be passed to the plot-function
}
}
\author{
Robby Engelmann <robby.engelmann@med.uni-rostock.de> and
Michael Hecker <michael.hecker@rocketmail.com>
}
\seealso{\link{dendroPlot}}
\examples{
data(iris)
ehplot(iris$Sepal.Length, iris$Species, intervals=20, cex=1.8, pch=20)
ehplot(iris$Sepal.Width, iris$Species, intervals=20, box=TRUE, median=FALSE)
ehplot(iris$Petal.Length, iris$Species, pch=17, col="red", log=TRUE)
ehplot(iris$Petal.Length, iris$Species, offset=0.06, pch=as.numeric(iris$Species))
# Groups don't have to be presorted:
rnd <- sample(150)
plen <- iris$Petal.Length[rnd]
pwid <- abs(rnorm(150, 1.2))
spec <- iris$Species[rnd]
ehplot(plen, spec, pch=19, cex=pwid, col=rainbow(3, alpha=0.6)[as.numeric(spec)])
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ ~kwd1 }
\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
|
