\name{Statistic}
\alias{Statistic}
\alias{print.Statistic}
\alias{toString.Statistic}
\title{Representation of a Statisic which can be used as a score.}
\description{
  Creates a statistic object to represent a potential score which can be
  calculated from an assessment.
}
\usage{
Statistic(name, class, model, reportingVars = character(0),
          reportOnUpdate = TRUE, scale=1, offset=0)
print.Statistic(x, ...)                               
toString.Statistic(x, ...)
}
\arguments{
  \item{name}{String giving the identifier for the statistic.}
  \item{class}{String giving the logical implementation class for this
    statisic type.  StatShop maps this to a Java class.}
  \item{model}{String giving the name of the Student Model this
    statistic operates on.}
  \item{reportingVars}{Character vector giving the names of the Student
    Model variables this statistics uses in its calculations.}
  \item{reportOnUpdate}{A boolean variable specifying whether this
    statistic should be calculated automatically.}
  \item{scale}{Multiplier used for raw statistic.}
  \item{offset}{Constant value added to statistic.}
  \item{x}{Dummy argument to \code{print} or \code{toString}.}
  \item{\dots}{Additional arguments to \code{print} or \code{toString}.}
}
\value{
  An object of class \code{MissingCode} whose fields are the same as the
  entities given above. 
}
\details{
  StatShop uses a Bayesian scoring algorithm in which the \dfn{Scoring
  Model} for each examinee provides the posterior distribution over the
  \dfn{reporting variables} in the Student Model.  Any statitic of that
  distribution is a potentially reportable score.  

}
\references{
  \url{http://research.ets.org/~ralmond/StatShop/dataFormats.html}
}
\note{
  Because of the internal coding for StatShop, mean statistics usually
  make more sense if they have \code{scale=-1}.
}
\author{Russell Almond }
\seealso{\code{\link{amd}}, \code{\link{parseStatistic}},
	 \code{\link{xmlStatistic}}}
\examples{
   Statistic(name="margin(Skill1)", class="Bayes Net Margin",
             reportOnUpdate=TRUE, model="System",
             reportingVars="Skill 1")

   Statistic(name="sd(Skill2)", class="Bayes Net Standard Deviation",
             reportOnUpdate=TRUE, model="System",
             reportingVars="Skill 2")

   Statistic(name="EAP(Skill1)", class="Bayes Net Mean",
             reportOnUpdate=TRUE, model="System",
             reportingVars="Skill 1", offset=2, scale=-1)

   Statistic(name="SkillsMastered", class="Sum at Level",
             reportOnUpdate=FALSE, model="System",
             reportingVars=c("Skill 1", "Skill 2", "Skill 3"))




}
\keyword{classes}% at least one, from doc/KEYWORDS