\HeaderA{emp.f}{Create a guassian kernel density}{emp.f}
\keyword{distribution}{emp.f}
\begin{Description}\relax
emp.f creates a guassian kernel density estimate for x using a bandwidth h
\end{Description}
\begin{Usage}
\begin{verbatim}
emp.f(x, h)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] the data vector 
\item[\code{h}] the bandwidth, should be on scale of standardized x's  
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
the definition of bandwidth is different than R's density function, thus will not give you the same reult.  Also, emp.f finds the density estimate at every 0.02 values of x.  Also, this rescales x by median and the mad for a comparable unit
\end{Details}
\begin{Value}
\begin{ldescription}
\item[\code{f}] the density at specific x
\item[\code{x}] the values along the x axis every 0.02 values, going from midpoint between minimum and 2nd smallest to the largest and 2nd largest values of x
\end{ldescription}

...
\end{Value}
\begin{Author}\relax
Kevin Rader
\end{Author}
\begin{References}\relax
B.W. Silverman (1981),Using Kernel Density Estimates to
Investigate Multimodatlity. J.R. Statist. Soc. B,43,1,97-99.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{get.h}{get.h}}, \code{\LinkA{get.p}{get.p}}, \code{\LinkA{get.num.modes}{get.num.modes}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
 set.seed(12345)
 x<-runif(50)
 f<-emp.f(x,0.5)
\end{ExampleCode}
\end{Examples}

