\name{fitNorm2}
\alias{fitNorm2}
\title{Fit bivariate normal distribution.}
\description{Fits a bivariate normal distribution into a data set of
  paired values and selects data points according to their standard
  deviation from the fitted distribution.
}
\usage{fitNorm2(x, y=NA, scalefac=1, method="covMcd", noise, gateName = "fitNorm")
}
\arguments{
  \item{x}{Numeric vector containing x-value or n by 2 matrix containing x and
    y values or object of class \code{cytoFrame}.}
  \item{y}{Numeric vector containing y-value (optional). The length of \code{x}
    must be the same as that of \code{y}.}
  \item{scalefac}{Numeric vector giving factor of standard deviations
    used for data selection (all points within \code{scalefac} standard
    deviations are selected).}
  \item{method}{One of \code{covMcd} or \code{cov.rob} defining method
    used for computation of covariance matrix.}
  \item{noise}{Numeric or logical index vector defining value pairs in x
that are not used for fitting of distributions. Can be used to deal with
noisy data.}
 \item{gateName}{Character giving the name of the gate object.}
}

\details{Computes the densities of a bivariate normal distribution from
  the covariance matrix of the paired data. Covariance matrices are
  acquired either by function \code{covMcd}
  (considerably faster) or by function \code{\link[MASS]{cov.rob}}.
}

\value{A list containing items \code{mu} (midpoint of distribution),
  \code{S} (covariance matrix), \code{p} (density values for each
  data pair), \code{sel} (selection of data points), \code{scalefac}
  (factor of standard deviations used for data selection), \code{data}
  (x and y values of data points) and \code{gate}, an object of class
  \code{gate} containing the selection.}

\seealso{\code{\link[MASS:cov.rob]{cov.rob}},
  \code{covMcd},
  \code{\link{plotNorm2}}}

\author{Florian Hahne}

\examples{
 sampdat <- readFCS(system.file("extdata",
   "fas-Bcl2-plate323-04-04.A01", package="prada"))
 nfit    <- fitNorm2(exprs(sampdat[,1:2]), scalefac=2)
 plotNorm2(nfit, selection=TRUE, ellipse=TRUE) 
}