\HeaderA{ttest}{A filter function for a t.test}{ttest}
\keyword{manip}{ttest}
\begin{Description}\relax
\code{ttest} returns a function of one argument with bindings for
\code{cov} and \code{p}.
The function, when evaluated, performs a t-test using \code{cov} as
the covariate. It returns \code{TRUE} if the p value for a difference
in means is less than \code{p}.
\end{Description}
\begin{Usage}
\begin{verbatim}
ttest(m, p=0.05, na.rm=TRUE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{m}] If \code{m} is of length one then it is assumed that elements
one through \code{m} of \code{x} will be one group. Otherwise \code{m}
is presumed to be the same length as \code{x} and constitutes the
groups.
\item[\code{p}] The p-value for the test. 
\item[\code{na.rm}] If set to \code{TRUE} any \code{NA}'s will be removed. 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
When the data can be split into two groups (diseased and normal for
example) then we often want to select genes on their ability to
distinguish those two groups. The t-test is well suited to this and
can be used as a filter function.

This helper function creates a t-test (function) for the specified
covariate and considers a gene to have passed the filter if the
p-value for the gene is less than the prespecified \code{p}.
\end{Details}
\begin{Value}
\code{ttest} returns a function with bindings for \code{m} and
\code{p} that will perform a t-test
\end{Value}
\begin{Author}\relax
R. Gentleman
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{kOverA}{kOverA}}, \code{\LinkA{Anova}{Anova}}, \code{\LinkA{t.test}{t.test}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
  dat <- c(rep(1,5),rep(2,5))
  set.seed(5)
  y <- rnorm(10)
  af <- ttest(dat, .01)
  af(y)
  af2 <- ttest(5, .01)
  af2(y)
  y[8] <- NA
  af(y)
  af2(y)
  y[1:5] <- y[1:5]+10
  af(y)
\end{ExampleCode}
\end{Examples}