\name{kroc}
\alias{kroc}
\alias{predict.kroc}
\alias{summary.kroc}

\title{Kernel receiver operating characteristic (ROC) curve}

\description{ Kernel receiver operating characteristic (ROC) curve for 1- to 3-dimensional data.}

\usage{
kroc(x1, x2, H1, h1, hy, gridsize, gridtype, xmin, xmax, supp=3.7, eval.points,
   binned, bgridsize, positive=FALSE, adj.positive, w, verbose=FALSE)

\method{predict}{kroc}(object, ..., x)
\method{summary}{kroc}(object, ...)
}

\arguments{
  \item{x,x1,x2}{vector/matrix of data values}
  \item{H1,h1,hy}{bandwidth matrix/scalar bandwidths. If these are
    missing, \code{Hpi.kcde}, \code{hpi.kcde} is called by default.} 
  \item{gridsize}{vector of number of grid points}
  \item{gridtype}{not yet implemented}
  \item{xmin,xmax}{vector of minimum/maximum values for grid}
  \item{supp}{effective support for standard normal}
  \item{eval.points}{not yet implemented}
  \item{binned}{flag for binned estimation}
  \item{bgridsize}{vector of binning grid sizes}
  \item{positive}{flag if 1-d data are positive. Default is FALSE.}
  \item{adj.positive}{adjustment applied to positive 1-d data}
  \item{w}{vector of weights. Default is a vector of all ones.}
  \item{verbose}{flag to print out progress information. Default is
    FALSE.}
  \item{object}{object of class \code{kroc}, output from \code{kroc}}
  \item{...}{other parameters} 
}

\value{
  A kernel ROC curve is an object of class \code{kroc} which is a list
  with fields:
  \item{x}{list of data values \code{x1, x2} - same as input}
  \item{eval.points}{vector or list of points at which the estimate is evaluated}
  \item{estimate}{ROC curve estimate at \code{eval.points}}
  \item{gridtype}{"linear"}
  \item{gridded}{flag for estimation on a grid}
  \item{binned}{flag for binned estimation}
  \item{names}{variable names}
  \item{w}{weights}
  \item{tail}{"lower.tail"}
  \item{h1}{scalar bandwidth for first sample (1-d only)}
  \item{H1}{bandwidth matrix for first sample}
  \item{hy}{scalar bandwidth for ROC curve}
  \item{indices}{summary indices of ROC curve.}
}



\details{
  In this set-up, the values in the first sample \code{x1} should
  be larger in general that those in the second sample \code{x2}. The
  usual method for computing 1-d ROC curves is not valid for
  multivariate data. Duong (2014), 
  based on Lloyd (1998), develops an alternative formulation
  \eqn{(F_{Y_1}(z), F_{Y_2}(z))}{(F_Y1(z), F_Y2(z))} based on the
  cumulative distribution functions of \eqn{Y_j = \bar{F}_1(\bold{X}_j), j=1,2}{Yj=bar(F)_1(Xj), j=1,2}.

  If the bandwidth \code{H1} is missing from \code{kroc}, then
  the default bandwidth is the  plug-in selector
  \code{Hpi.kcde}. Likewise for missing \code{h1,hy}. A bandwidth matrix
  \code{H1} is required for \code{x1} for d>1, but the second bandwidth \code{hy} is always a scalar since \eqn{Y_j}{Yj} are 1-d variables.

  The effective support, binning, grid size, grid range, positive
  parameters are the same as \code{\link{kde}}. 
   
  --The \code{summary} method for \code{kroc} objects prints out the
  summary indices of the ROC curve, as contained in the \code{indices}
  field, namely the AUC (area under the curve) and Youden index.

}

\references{
  Duong, T. (2016) Non-parametric smoothed estimation of multivariate
  cumulative distribution and survival functions, and receiver operating
  characteristic curves. \emph{Journal of the Korean Statistical
  Society}, \bold{45}, 33-50.

  Lloyd, C. (1998) Using smoothed receiver operating curves to summarize
  and compare diagnostic systems. \emph{Journal of the American
  Statistical Association}, \bold{93}, 1356-1364.
}

\seealso{\code{\link{kcde}}}
 
\examples{
samp <- 1000
x <- rnorm.mixt(n=samp, mus=0, sigmas=1, props=1)
y <- rnorm.mixt(n=samp, mus=0.5, sigmas=1, props=1)
Rhat <- kroc(x1=x, x2=y)
summary(Rhat)
predict(Rhat, x=0.5) 
}

\keyword{ smooth }
