\HeaderA{SSgauss}{Gaussian Model}{SSgauss}
\keyword{nonlinear}{SSgauss}
\begin{Description}\relax
This \code{selfStart} model evalueates the Gaussian model and its
gradient. It has an \code{initial} attribute that will evalueate
the inital estimates of the parameters \code{mu}, \code{sigma},
and \code{h}.
\end{Description}
\begin{Usage}
\begin{verbatim}
SSgauss(x, mu, sigma, h)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] a numeric vector of values at which to evaluate the model
\item[\code{mu}] mean  of the distribution function
\item[\code{sigma}] standard deviation of the distribution fuction
\item[\code{h}] height of the distribution function
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Initial values for \code{mu} and \code{h} are chosen from the
maximal value of \code{x}. The initial value for \code{sigma} is
determined from the area under \code{x} divided by \code{h*sqrt(2*pi)}.
\end{Details}
\begin{Value}
A numeric vector of the same length as \code{x}. It is the value
of the expression \code{h*exp(-(x-mu)\textasciicircum{}2/(2*sigma\textasciicircum{}2)}, which is a
modified gaussian function where the maximum height is treated
as a separate parameter not dependent on \code{sigma}. If arguments
\code{mu}, \code{sigma}, and \code{h} are names of objects, the
gradient matrix with respect to these names is attached as an
attribute named \code{gradient}.
\end{Value}
\begin{Author}\relax
Colin A. Smith, \email{csmith@scripps.edu}
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{nls}{nls}},
\code{\LinkA{selfStart}{selfStart}}
\end{SeeAlso}

