\name{RIcorrect}
\alias{RIcorrect}
\title{ Peak picking from CDF files and RI correction }
\description{
	This function reads from CDF files, finds the apex intensities, converts
	the retention time to retention time index (RI), and writes RI corrected
	text files.
}
\usage{
RIcorrect(samples, rimLimits = NULL, massRange, Window, IntThreshold, 
	pp.method = "ppc", showProgressBar = FALSE, baseline = FALSE,
    baseline.opts = NULL )
}
\arguments{
  \item{samples}{ A \code{tsSample} object created by \code{ImportSamples} function. }
  \item{rimLimits}{ A \code{tsRim} object. If set to \code{NULL}, no retention
	time will be performed. See \code{ImportFameSettings}.}
  \item{massRange}{ A two component vector of m/z range used by the GC-MS machine. }
  \item{Window}{ The window used for smoothing. The number of points actually used
		is \code{2*Window + 1}. }
  \item{IntThreshold}{ Apex intensities lower than this value will be removed from the RI files. }
  \item{pp.method}{ Peak picking method. Options are either "smoothing" or "ppc". See details. }
  \item{showProgressBar}{Logical. Should the progress bar be displayed?}
  \item{baseline}{Logical. Should baseline correction be performed?}
  \item{baseline.opts}{A list of options passed to \code{\link{baselineCorrection}}}.
}
\details{
	There are two pick picking methods available: "ppc" and "smoothing".

	The "ppc" method (default) implements the peak detection method described in the \code{ppc}
	package. It looks for the local maxima within a \code{2*Window + 1} scans for
	every mass trace.

	The "smoothing" method calculates a moving average of \code{2*Window + 1} points
	for every mass trace. Then it looks for	a change of sign (from positive to negative) of
	the difference between two consecutive points. Those points will be returned as
	detected peaks.
}
\value{
	A retention time matrix of the found retention time markers. Every column represents a
	sample and rows RT markers.
}
\examples{
require(TargetSearchData)
# import refLibrary, rimLimits and sampleDescription.
data(TargetSearchData)
# get the CDF files
cdfpath <- file.path(.find.package("TargetSearchData"), "gc-ms-data")
cdfpath
list.files(cdfpath)
# update the CDF path
CDFpath(sampleDescription) <- cdfpath
# run RIcorrect (massScanRange = 85-320; Intensity Threshold = 50;
# peak detection method = "ppc", window = 15)
RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320), 
            Window = 15, pp.method = "ppc", IntThreshold = 50)

# you can try other parameters and other peak picking algorithm.
RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320), 
            Window = 15, pp.method = "smoothing", IntThreshold = 10)

RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320), 
            Window = 15, pp.method = "ppc", IntThreshold = 100)

}
\author{Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig }
\seealso{ \code{\link{ImportSamples}}, \code{\link{ImportFameSettings}}, 
\code{\link{NetCDFPeakFinding}}, \code{\link{FAMEoutliers}},
\code{\linkS4class{tsSample}}, \code{\linkS4class{tsRim}}.}