\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}