\HeaderA{uniTest-methods}{A Two-Group Unitest}{uniTest.Rdash.methods}
\aliasA{uniTest}{uniTest-methods}{uniTest}
\aliasA{uniTest<\Rdash}{uniTest-methods}{uniTest<.Rdash.}
\keyword{methods}{uniTest-methods}
\begin{Description}\relax
Unitest performs a a two group uni-test such as the \code{t.test} on each row of the
expression dataframe. \\
The Unitest returns a dataframe containing the results of the test.

\emph{Usage}

\code{uniTest(object)} \\
\code{uniTest(object, value)<-}
\end{Description}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] object of class \code{UniFilter}.
\item[\code{value}] character vector \code{c(type, alternative, correction, numperm, mu, paired, conflevel, varequ)}
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The method \code{uniTest} initializes the following parameters:
\Tabular{lll}{
& \code{type}:& a character string specifying the type of test: currently \code{"t.test"} (default) or \code{"normal.test"}. \\
& \code{alternative}:& a character string specifying the alternative hypothesis,
must be one of \code{"two.sided"} (default), \code{"greater"} or \code{"less"}. \\
& \code{correction}:& a correction to adjust p-values for multiple comparisons: \\
&                   & \code{correction="none"}: no correction (default). \\
&                   & \code{correction="bonferroni"}: Bonferroni correction. \\
&                   & \code{correction="BH" or "fdr"}: correction for false discovery rate (Benjamini \& Hochberg). \\
&                   & \code{correction="BY"}: correction for false discovery rate (Benjamini \& Yekutieli). \\
&                   & \code{correction="hochberg"}: Hochberg correction. \\
&                   & \code{correction="holm"}: Holm correction. \\
&                   & \code{correction="wy"}: Westfall-Young step-down adjusted p-chance (E.Manduchi). \\
& \code{numperm}:& optional number of permutations  used to determine p-chance (default is 0). \\
& \code{mu}:& a number indicating the true value of the difference in means for a two sample test (default is 0). \\
& \code{paired}:& a logical indicating whether you want a paired uni-test (default is FALSE). \\
& \code{conflevel}:& confidence level of the interval (default is 0.95). \\
& \code{varequ}:& a logical variable indicating whether to treat the two variances as being
equal. If \code{TRUE} then the pooled variance is used to estimate the variance otherwise
the Welch (or Satterthwaite) approximation to the degrees of freedom is used (default is FALSE).
}
\end{Details}
\begin{Value}
An initialized \code{\LinkA{UniFilter}{UniFilter.Rdash.class}} object.
\end{Value}
\begin{Author}\relax
Christian Stratowa
\end{Author}
\begin{References}\relax
Benjamini, Y., and Hochberg, Y. (1995).
Controlling the false discovery rate: a practical and powerful
approach to multiple testing.
\emph{Journal of the Royal Statistical Society Series} B, \bold{57}, 289--300.

Benjamini, Y., and Yekutieli, D. (2001).
The control of the false discovery rate in multiple testing under dependency.
\emph{Annals of Statistics} \bold{29}, 1165--1188.


Holm, S. (1979).
A simple sequentially rejective multiple test procedure.
\emph{Scandinavian Journal of Statistics}, \bold{6}, 65--70.

Westfall P.H. and Young S.S. (1993)
Resampling-based multiple testing:examples and methods for p-value adjustment.
\emph{Wiley series in probability and mathematical statistics}; Wiley.

Dudoit S., Yang Y.H., Callow M.J., Speed T.P.  (2000) 
Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. 
\emph{Technical report} \bold{578}; UC Berkeley.

Manduchi E. (2000)
Software: tpWY, see: \url{http://www.cbil.upenn.edu/tpWY/}
\end{References}
\begin{Examples}
\begin{ExampleCode}
unifltr <- UniFilter()
uniTest(unifltr) <- c("t.test","two.sided","none",0,0.0,FALSE,0.98,TRUE)
str(unifltr)
\end{ExampleCode}
\end{Examples}

