\HeaderA{segment}{Fit a piecewise constant curve: segmentation by dynamic
programming}{segment}
\keyword{manip}{segment}
\begin{Description}\relax
The function fits a piecewise constant curve to one or multiple
sequences of measurements,
using a least squares cost function and an O(n) dynamic programming
algorithm (see references).
\end{Description}
\begin{Usage}
\begin{verbatim}
segment(y, maxseg, maxk)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{y}] Numeric matrix. Rows correspond to the
\code{x}-variable, columns to replicate measurements at the same
value of \code{x}. Breakpoints are fitted along the \code{x}-axis.
For example, the \code{x}-variable can be genomic coordinates or
time. The segmentation will be along the rows of \code{y}.
\item[\code{maxseg}] integer of length 1, maximum number of segments (= 1 + maximum
number of change points).
\item[\code{maxk}] integer of length 1, maximum length of a single segment.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The complexity of the algorithm is 
\code{length(x)*maxk} in memory and
\code{length(x)*maxk*maxseg} in time.
\end{Details}
\begin{Value}
An object of class \code{\LinkA{segmentation}{segmentation}}.
\end{Value}
\begin{Author}\relax
W. Huber \email{huber@ebi.ac.uk}
\end{Author}
\begin{References}\relax
[1] Transcript mapping with high-density oligonucleotide tiling
arrays. Huber W, Toedling J, Steinmetz, L.
Bioinformatics 22, 1963-1970 (2006).

[2] A statistical approach for CGH microarray data analysis.
Franck Picard, Stephane Robin, Marc Lavielle, Christian Vaisse,
Gilles Celeux, Jean-Jacques Daudin.
BMC Bioinformatics. 2005 Feb 11; 6:27.
\end{References}
\begin{Examples}
\begin{ExampleCode}
  x = rep( sin((0:4)/2*pi), each=3) + rnorm(3*5, sd=0.1)
  res = segment(x, maxseg=6, maxk=15)
\end{ExampleCode}
\end{Examples}

