\name{eff}
\alias{eff}

\title{The amplification efficiency curve of a fitted object}

\description{
Calculates the efficiency curve from the fitted object by \eqn{E = \frac{F(n)}{F(n-1)}},
 with E = efficiency, F = raw fluorescence, n = Cycle number. Alternatively, a cubic spline interpolation 
  can be used on the raw data as in Shain et al. (2008). 
}

\usage{
eff(object, type = c("sigfit", "spline"), sequence = NULL, baseshift = NULL, 
    smooth = FALSE, plot = FALSE)
}

\arguments{
  \item{object}{an object of class 'pcrfit'.}
  \item{type}{the efficiency curve is either calculated from the sigmoidal fit (default) or a cubic spline interpolation.}
  \item{sequence}{a 3-element vector (from, to, by) defining the sequence for the efficiency curve. Defaults to [min(Cycles), max(Cycles)] with 100 points per cycle.}
  \item{baseshift}{baseline shift value in case of \code{type = "spline"}. See documentation to \code{\link{maxRatio}}.}
  \item{smooth}{logical. If \code{TRUE} and \code{type = "spline"}, invokes a 5-point convolution filter (\code{\link{filter}}). See documentation to \code{\link{maxRatio}}.} 
  \item{plot}{should the efficiency be plotted?}
 }

\value{
A list with the following components:
  \item{eff.x}{the cycle points.}
  \item{eff.y}{the efficiency values at \code{eff.x}.}   
  \item{effmax.x}{the cycle number with the highest efficiency.}
  \item{effmax.y}{the maximum efficiency.}   
}

\details{
For more information about the curve smoothing, baseline shifting and cubic spline interpolation for the method as in Shain et al. (2008),
 see 'Details' in \code{\link{maxRatio}}.
}

\author{
Andrej-Nikolai Spiess
}    

\references{
A new method for robust quantitative and qualitative analysis of real-time PCR.\cr
Shain & Clemens, \emph{Nucleic Acids Research}, 2008, \bold{36}, e91.
}  

\examples{
m <- pcrfit(reps, 1, 7, l5)

## with default 100 points per cycle
eff(m, plot = TRUE) 

## not all data and only 10 points per cycle
eff(m, sequence = c(5, 35, 0.1), plot = TRUE) 

## using cubic splines
## it is preferred to use the
## smoothing option 
eff(m, type = "spline", plot = TRUE, smooth = TRUE, baseshift = 0.3)  
}

\keyword{models}
\keyword{nonlinear}