\name{error.crosses}
\alias{error.crosses}
\title{ Plot x and y error bars }
\description{Given two vectors of data (X and Y), plot the means and show standard errors in both X and Y directions. 
}
\usage{
error.crosses(x,y,labels=NULL,main=NULL,xlim=NULL,ylim= NULL,xlab=NULL,ylab=NULL,pos=NULL,offset=1,arrow.len=.2,alpha=.05,sd=FALSE,...)  
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{x}{ A vector of data or summary statistics (from Describe) }
  \item{y}{ A second vector of data or summary statistics (also from Describe)}
  \item{labels}{the names of each pair -- defaults to rownames of x }
  \item{main}{The title for the graph}
  \item{xlim}{xlim values if desired-- defaults to min and max mean(x) +/- 2 se}
  \item{ylim}{ylim values if desired  -- defaults to min and max mean(y) +/- 2 se}
  \item{xlab}{label for x axis -- grouping variable 1}
  \item{ylab}{label for y axis -- grouping variable 2}
 
  \item{pos}{Labels are located where with respect to the mean?  }
  \item{offset}{Labels are then offset from this location}
  \item{arrow.len}{ Arrow length }
  \item{alpha}{alpha level of error bars }
  \item{sd}{if sd is TRUE, then draw means +/- 1 sd)}
  \item{\dots}{ Other parameters for plot  }
}
\details{For an example of two way error bars describing the effects of mood manipulations upon positive and negative affect, see \url{http://personality-project.org/revelle/publications/happy-sad-appendix/FIG.A-6.pdf}

The second example shows how error crosses can be done for multiple variables where the grouping variable is found dynamically. 
}

\author{ William Revelle \cr     
 \email{revelle@northwestern.edu}
}

\seealso{To draw error bars for single variables  \code{\link{error.bars}}, or by groups \code{\link{error.bars.by}}, or to find descriptive statistics \code{\link{describe}}  or descriptive statistics by a grouping variable \code{\link{describe.by}}  }
\examples{

#just draw one pair of variables
desc <- describe(attitude)
x <- desc[1,]
y <- desc[2,]
error.crosses(x,y,xlab=rownames(x),ylab=rownames(y))   

#now for a bit more complicated plotting 
data(bfi)
desc <- describe.by(bfi[1:25],bfi$gender) #select a high and low group
error.crosses(desc$'1',desc$'2',ylab="female scores",xlab="male scores",main="BFI scores by gender")
 abline(a=0,b=1)
 
#do it from summary statistics  (using standard errors) 
g1.stats <- data.frame(n=c(10,20,30),means=c(10,12,18),se=c(2,3,5))
g2.stats <- data.frame(n=c(15,20,25),means=c(6,14,15),se =c(1,2,3))
error.crosses(g1.stats,g2.stats)

#Or, if you prefer to draw +/- 1 sd. instead of 95% confidence
g1.stats <- data.frame(n=c(10,20,30),means=c(10,12,18),sd=c(2,3,5))
g2.stats <- data.frame(n=c(15,20,25),means=c(6,14,15),sd =c(1,2,3))
error.crosses(g1.stats,g2.stats,sd=TRUE)

#and seem even fancy plotting: This is taken from a study of mood
#four films were given (sad, horror, neutral, happy)
#with a pre and post test
data(affect)
colors <- c("black","red","white","blue")
films <- c("Sad","Horror","Neutral","Happy")
map.mat <- describe.by(maps[10:17],maps$Film,mat=TRUE)
 error.crosses(map.mat[c(1:4,17:20),],map.mat[c(5:8,21:24),],labels=films[map.mat$group1],xlab="Energetic Arousal",ylab="Tense Arousal",col=colors[map.mat$group1],pch=16,cex=2)

}
\keyword{ multivariate }% at least one, from doc/KEYWORDS
\keyword{ hplot }% __ONLY ONE__ keyword per line