\name{PRIM.real.data.min}
\alias{PRIM.real.data.min}
\alias{out.expand}
\alias{out.peel}
\alias{out.cv}
\alias{Rule1.idx}
\alias{TD1}
\alias{TD2}
\alias{box.idx.list}
\alias{box.idx.listTD1}
\alias{box.idx.listTD2}
\alias{box.support.ES}
\alias{box.support.PS}
\alias{means.ES}
\alias{means.PS}
\alias{type.ES}
\alias{type.PS}
\alias{best.box.X.ranges}
\docType{data}
\title{PRIM output from VRCmin data analysis}
\description{
  The HIV-protein stimulated \link{st.1829} FCS R-object and the
  unstimulated \link{unst.1829} FCS R-object from the
  \link{VRCmin} data were used to exemplify the PRIM algorithm on a real
  dataset.  The three steps for the finding of a single rule were saved
  as output.  The \link{out.peel} is the 'PRIM.step' class object of the
  Peeling step.  The \link{out.expand} is the 'PRIM.step' class object
  of the Expansion Step.  The \link{out.cv} is the 'PRIM.crossval.step'
  class object of the cross-validation step.  From these output objects
  we obtain the necessary indices and vectors for making plots that are
  exemplified in PRIM.pdf.
}
\usage{data(PRIM.real.data.min)}
\format{

  \item{box.idx.list}{Peeling Step: a list of five boxes that were in
    the peeling sequence of 'out.peel' which used the whole data 'X'} 
  
  \item{box.support.PS}{Peeling Step: The vector of proportions of X
    that lie in the peeled box for each iteration of the expansion
    step from 'out.peel'}
  
  \item{means.PS}{Peeling Step: The vector of means(Y) of each peeled
    box for each iteration from 'out.peel'}
  
  \item{type.PS}{Peeling  Step: The vector of peeling types,
    denoting the direction and variable for the addition of new points
    into the box for each iteration from 'out.peel'}

  \item{box.support.ES}{Expansion Step: The vector of proportions of X
    that lie in the expanded box for each iteration of the expansion
    step from 'out.expand'}
  
  \item{means.ES}{Expansion Step: The vector of means(Y) of each expanded
    box for each iteration from 'out.expand'}
  
  \item{type.ES}{Expansion  Step: The vector of expansion types,
    denoting the direction and variable for the addition of new points
    into the box for each iteration from 'out.expand'}
  
  \item{best.box.X.ranges}{Expansion Step: The X-variable ranges of the
    final rule
    estimated after the Expansion Step from 'out.expand'}

  \item{Rule1.idx}{Expansion Step: The index of the final rule obtained
    after the expansion step from out.expand}
  
  \item{TD1}{Cross-Validation Step: vector of positional row
    indices corresponding to the first
    testdata set which is subset from the original 'X' data
    (which is constructed in the example below)}
  
  \item{TD2}{Cross-Validation Step: vector of positional row
    indices corresponding to the second
    testdata set which is subset from the original 'X' data
  (which is constructed in the example below)}
  
  \item{box.idx.listTD1}{Cross-Validation Step: a list of five boxes in
    the peeling sequence of the first Testdata set}
  
  \item{box.idx.listTD2}{Cross-Validation Step: a list of five boxes in
    the peeling sequence of the second Testdata set}
 

}
\details{
  The class information, extraction, plotting, implementation
  tools for the 'PRIM.step' and the
  'PRIM.crossval.step' class objects are detailed in the
  \pkg{rfcprim} package.
}
\source{
  See \link{VRCmin}
}
\references{
  See \link{VRCmin} and \pkg{rfcprim}.
}
\examples{
data(PRIM.real.data)

## the following code was used to generate the output
if (FALSE){

  data(VRCmin)
  ## the HIV-protein stimulation status
  Y <- c(rep(1, dim.FCS(st.1829)[1]), 
       rep(0, dim.FCS(unst.1829)[1]))

  ## the dataset
  X <- rbind(as(st.1829, "matrix"), as(unst.1829, "matrix"))
  if (require(rfcprim)){
  out.peel <- peel.step(X,
                      Y,
                      min.box.size = 500,
                      alpha=0.10,
                      verbose=TRUE)


  out.expand <- expand.step(X,
                          Y,
                          out.peel@best.box.idx,
                          beta=0.01,
                          verbose=TRUE)


  out.cv <- crossval.step(X,
                        Y,
                        num.testdata=2,
                        prob.testdata=c(0.50, 0.50),
                        alpha=0.10,
                        target.mu.Y=1,
                        min.box.size=500,
                        choose.best.box.decision=c("max.mean.box", "final.box"),
                       
                        beta=0.01,
                        verbose=TRUE)

  box.idx.list <- list(out.peel@best.box.idx,
     out.expand@best.box.idx)

  box.support.PS <- out.peel@box.support.vec

  means.PS <- out.peel@means.vec

  type.PS <- out.peel@type.vec

  box.support.ES <- out.expand@box.support.vec

  means.ES <- out.expand@means.vec

  type.ES <- out.expand@type.vec

  best.box.X.ranges <- out.expand@best.box.X.ranges

  Rule1.idx <- out.expand@best.box.idx

  TD1 <- out.cv[1, "testdata.idx.list"]
  TD2 <- out.cv[2, "testdata.idx.list"]

  box.idx.listTD1 <- list(out.cv[1,"step.set.list"]@peel.step@best.box.idx,
      out.cv[1, "step.set.list"]@expand.step@best.box.idx)

  box.idx.listTD2 <- list(out.cv[2,"step.set.list"]@peel.step@best.box.idx,
      out.cv[2, "step.set.list"]@expand.step@best.box.idx)
  }
 }

}
\keyword{datasets}