kda.kde.rd

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\name{kda.kde}
\alias{kda.kde}

\title{Kernel density estimate for kernel discriminant analysis for multivariate data}
\description{
  Kernel density estimate for kernel discriminant analysis for 1- to 6-dimensional data
}
\usage{
kda.kde(x, x.group, Hs, hs, prior.prob=NULL, gridsize, xmin, xmax,
        supp=3.7, eval.points=NULL, binned=FALSE, bgridsize)
}

\arguments{
  \item{x}{matrix of training data values}
  \item{x.group}{vector of group labels for training data}
  \item{Hs}{(stacked) matrix of bandwidth matrices}
  \item{hs}{vector of scalar bandwidths}
  \item{prior.prob}{vector of prior probabilities}
  \item{gridsize}{vector of number of grid points}
  \item{xmin}{vector of minimum values for grid}
  \item{xmax}{vector of maximum values for grid}
  \item{supp}{effective support for standard normal is [\code{-supp, supp}]}
  \item{eval.points}{points at which density estimate is evaluated}
  \item{binned}{flag for binned kernel estimation}
  \item{bgridsize}{vector of binning grid sizes - only required if
	\code{binned=TRUE}}
}
  

\value{
  The kernel density estimate for kernel discriminant analysis is
  based on \code{\link{kde}}, one density estimate for each group. 

  The result from \code{kda.kde} is a density estimate
  for discriminant analysis is an object of class \code{kda.kde} which is a
  list with 6 fields
  \item{x}{data points - same as input}
  \item{x.group}{group labels - same as input}
  \item{eval.points}{points that density estimate is evaluated at}
  \item{estimate}{density estimate at \code{eval.points}}  
  \item{prior.prob}{prior probabilities}
  \item{H}{bandwidth matrices (>1-d only)  or } 
  \item{h}{bandwidths (1-d only)}
}

\details{
  For d = 1, 2, 3, 4, 
  and if \code{eval.points} is not specified, then the
  density estimate is computed over a grid 
  defined by \code{gridsize} (if \code{binned=FALSE}) or
  by \code{bgridsize} (if \code{binned=TRUE}).

  For d = 1, 2, 3, 4, 
  and if \code{eval.points} is specified, then the
  density estimate is computed is computed exactly at \code{eval.points}.
  
  For d > 4, the kernel density estimate is computed exactly 
  and \code{eval.points} must be specified.

  If you have prior probabilities then set \code{prior.prob} to these.
  Otherwise \code{prior.prob=NULL} is the default i.e. use the sample
  proportions as estimates of the prior probabilities.

  The default \code{xmin} is \code{min(x) - Hmax*supp} and \code{xmax}
  is \code{max(x) + Hmax*supp}  where \code{Hmax} is the maximim of the
  diagonal elements of \code{H}. 
}

\references{ Wand, M.P. \& Jones, M.C. (1995) \emph{Kernel Smoothing}.
  Chapman \& Hall. London. } 

\seealso{\code{\link{plot.kda.kde}}}

\examples{
### See examples in ? plot.kda.kde
}
\keyword{smooth}