\HeaderA{mt.reject}{Identity and number of rejected hypotheses}{mt.reject}
\keyword{htest}{mt.reject}
\begin{Description}\relax
This function returns the identity and number of rejected hypotheses for several multiple testing procedures and different nominal Type I error rates.
\end{Description}
\begin{Usage}
\begin{verbatim}
mt.reject(adjp, alpha)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{adjp}] A matrix of adjusted \emph{p}-values, with rows
corresponding to hypotheses and columns to multiple testing
procedures. This matrix could be obtained from the function
\code{\LinkA{mt.rawp2adjp}{mt.rawp2adjp}}
.
\item[\code{alpha}] A vector of nominal Type I error rates.
\end{ldescription}
\end{Arguments}
\begin{Value}
A list with components
\begin{ldescription}
\item[\code{r}] A matrix containing the number of rejected hypotheses for several multiple testing procedures and different nominal Type I error rates. Rows correspond to Type I error rates and columns to multiple testing procedures.
\item[\code{which}] A matrix of indicators for the rejection of individual hypotheses by different multiple testing procedures for a nominal Type I error rate \code{alpha[1]}. Rows correspond to hypotheses and columns to multiple testing procedures.
\end{ldescription}
\end{Value}
\begin{Author}\relax
Sandrine Dudoit,  \url{http://www.stat.berkeley.edu/~sandrine}, \\
Yongchao Ge, \email{yongchao.ge@mssm.edu}.
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{mt.maxT}{mt.maxT}}, \code{\LinkA{mt.minP}{mt.minP}}, \code{\LinkA{mt.rawp2adjp}{mt.rawp2adjp}}, \code{\LinkA{golub}{golub}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
# Gene expression data from Golub et al. (1999)
# To reduce computation time and for illustrative purposes, we condider only
# the first 100 genes and use the default of B=10,000 permutations.
# In general, one would need a much larger number of permutations
# for microarray data.

data(golub)
smallgd<-golub[1:100,] 
classlabel<-golub.cl

# Permutation unadjusted p-values and adjusted p-values for maxT procedure
res<-mt.maxT(smallgd,classlabel)
mt.reject(cbind(res$rawp,res$adjp),seq(0,1,0.1))$r

\end{ExampleCode}
\end{Examples}

