\HeaderA{twilight.permute.unpair}{Permutation matrix of unpaired class labels}{twilight.permute.unpair}
\keyword{datagen}{twilight.permute.unpair}
\begin{Description}\relax
The function returns a matrix where each row is a (un)balanced permutation of the input twosample class labels.
\end{Description}
\begin{Usage}
\begin{verbatim}
twilight.permute.unpair(v, m, bal = TRUE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{v}] A binary vector representing class labels in original order. 
\item[\code{m}] A numerical value giving the number of permutations. 
\item[\code{bal}] Logical value. Results in balanced or unbalanced permutations. 
\end{ldescription}
\end{Arguments}
\begin{Value}
Returns a matrix where each row contains one permuted vector of class labels. Note that even for balanced permutations, the first row always contains the original vector.
\end{Value}
\begin{Author}\relax
Stefanie Scheid \url{http://www.molgen.mpg.de/~scheid}
\end{Author}
\begin{References}\relax
Scheid S and Spang R (2004): A stochastic downhill search algorithm for estimating the local false discovery rate, \emph{IEEE TCBB} \bold{1(3)}, 98--108.

Scheid S and Spang R (2005): twilight; a Bioconductor package for estimating the local false discovery rate, \emph{Bioinformatics} \bold{21(12)}, 2921--2922. 

Scheid S and Spang R (2006): Permutation filtering: A novel concept for significance analysis of large-scale genomic data, in: Apostolico A, Guerra C, Istrail S, Pevzner P, and Waterman M (Eds.): \emph{Research in Computational Molecular Biology: 10th Annual International Conference, Proceedings of RECOMB 2006, Venice, Italy, April 2-5, 2006}. Lecture Notes in Computer Science vol. 3909, Springer, Heidelberg, pp. 338-347.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{twilight.permute.pair}{twilight.permute.pair}}, \code{\LinkA{twilight.combi}{twilight.combi}}
\end{SeeAlso}

