\name{ROC.FCS}
\alias{ROC.FCS}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{ROC (Receiver Operating Characteristic) Curve: 
  Percentage Positives for Flow Cytometry data}
\description{
  This function plots an ROC curve based on cutoff values from the
  observed combined dataset of hivpos and hivneg, which both are vectors of
  patient-specific percentage positives based on the 99.9th percentile of the
  corresponding control sample distribution.  The output contains
  the sensitivities, 1-specificity,and the observed dataset, cutoff values.
}
\usage{
ROC.FCS(hivpos, hivneg, lineopt = 1, colopt = 1, overlay = FALSE)
}
%- maybe also `usage' for other objects documented here.
\arguments{
  \item{hivpos}{numerical vector of percentage positives for the HIV
    positive individuals/samples for a given condition}
  \item{hivneg}{numerical vector of the percentage positives for the HIV
    negative individuals/samples for a given condition}
  \item{lineopt}{numerical value for the lty option of the plot  (line type)}
  \item{colopt}{numerical value for the col option of the plot (color type)}
  \item{overlay}{ Boolean expression as to whether or not the plot is an overlay}
}
\details{
  See 'PerPosROC' in the 'rfcdorig' package for a description of the input data and
  how percentage positives are defined.

  The ROC curve in the example demonstrates that there is higher
  predictive ability of using the GAG stimulated samples rather than
  the PolA or PolB stimulated samples.
}
\value{
  Let T be the the percentage positives, c be a given value in c.obs, and
  HIV+ defined as among HIV positive individuals, and HIV- defined as
  among HIV negative individuals.
  
  \item{sensitivity}{numerical vector of the sensitivity=P(T>c | HIV+)
    calculated corresponding to a given cut-off in c.obs}
  \item{spec.complement}{numerical vector of 1-specificity= P(T>c | HIV
    -)corresponding to a given cut-off in c.obs}
  \item{c.obs}{a numerical vector of the cutoffs which were taken to be
    the values of the observations (the values of the percentage positives 
    of both the HIV positive and HIV negative data)}

}
\references{Zoe Moodie and Mario Roederer}
\author{A.J. Rossini and J.Y. Wan}

\seealso{\code{\link{PercentPos.FCS}}, data 'PerPosROC' in 'rfcdorig'
  package, \code{\link{percentile.FCS}}}



\examples{


if (require(rfcdmin)){

data(PerPosROCmin)

#plotting the gag stimulated 100* percent positives
if (interactive()==TRUE){
GAG<-ROC.FCS(hivpos.gag, hivneg.gag)
#plotting the pola stimulated 100* percent positives
POLA<-ROC.FCS(hivpos.pola, hivneg.pola, lineopt=2, colopt=2, overlay=TRUE)
#plotting the polb stimulated 100* percent positives
POLB<-ROC.FCS(hivpos.polb, hivneg.polb, lineopt=4, colopt=3, overlay=TRUE)
legend(0.7, 0.7, c("gag", "polA", "polB"), col = c(1,2,3), lty=c(1,2,4))
}

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