\HeaderA{groupCorr-methods}{EIC correlation grouping of LC/ESI-MS data}{groupCorr.Rdash.methods}
\aliasA{groupCorr}{groupCorr-methods}{groupCorr}
\aliasA{groupCorr,xsAnnotate-method}{groupCorr-methods}{groupCorr,xsAnnotate.Rdash.method}
\keyword{methods}{groupCorr-methods}
\begin{Description}\relax
Grouping the peaks after the correlation of the EICs into pseudospectragroups for a xsAnnotate object. Return a xsAnnotate object with group information.
\end{Description}
\begin{Usage}
\begin{verbatim}
 groupCorr(object,cor_eic_th=0.75)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] the \code{xsAnnotate} object
\item[\code{cor\_eic\_th}] correlation threshold for the EIC correlation
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The algorithm correlates the EIC of a every peak with all others, to find the peaks that belong to one substance. LC/MS data should grouped with groupFWHM first. This step reduce the runtime a lot and increased the number of correct classifications.
\end{Details}
\begin{Author}\relax
Carsten Kuhl \textless{}ckuhl@ipb-halle.de\textgreater{}
\end{Author}
\begin{Examples}
\begin{ExampleCode}
 library(CAMERA)
 file <- system.file('mzdata/MM14.mzdata', package = "CAMERA")
 xs   <- xcmsSet(c(file), method="centWave", ppm=30, peakwidth=c(5,10))
 an   <- xsAnnotate(xs)
 an   <- groupFWHM(an)
 an   <- groupCorr(an)
\end{ExampleCode}
\end{Examples}

