\HeaderA{gapFilter-methods}{Gap Filter}{gapFilter.Rdash.methods}
\aliasA{gapFilter}{gapFilter-methods}{gapFilter}
\aliasA{gapFilter<\Rdash}{gapFilter-methods}{gapFilter<.Rdash.}
\keyword{methods}{gapFilter-methods}
\begin{Description}\relax
This method initializes the Gap Filter. \\
The \code{gapFilter} looks for genes that might usefully discriminate between two groups.
To do this we look for a gap in the ordered expression values. The gap should come in the
central portion, thus a parameter \code{window} is defined to exclude jumps in the initial
\code{window} values and the final \code{window} values. \\
The Gap Filter flags all rows with: \code{flag = ((gap[i+1] - gap[i])/mean >= cutoff)}

\code{gapFilter(object)} \\
\code{gapFilter(object, value)<-}
\end{Description}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] object of class \code{PreFilter}.
\item[\code{value}] numeric vector \code{c(cutoff, window, trim, epsilon)}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The method \code{gapFilter} initializes the following parameters:
\Tabular{lll}{
& \code{cutoff}:& the cutoff level for the filter. \\
& \code{window}:& trim value for the ordered expression levels (default is \code{window=0.05}). \\
& \code{trim}:& the trim value for trimmed mean (default is \code{trim=0}). \\
& \code{epsilon}:& value to replace mean (default is \code{epsilon=0.01}): \\
&                & \code{epsilon > 0}: replace mean=0 with epsilon. \\
&                & \code{epsilon = 0}: always set mean=1.
}
Note, that for \code{epsilon = 0} the filter flags all rows with: (gap[i+1] - gap[i]) >= cutoff
\end{Details}
\begin{Value}
An initialized \code{PreFilter} object.
\end{Value}
\begin{Author}\relax
Christian Stratowa
\end{Author}
\begin{Examples}
\begin{ExampleCode}
prefltr <- PreFilter()
gapFilter(prefltr) <- c(0.3,0.05,0.0,0.01)
str(prefltr)
\end{ExampleCode}
\end{Examples}

