An exponential model is fit to a window of defined size on the qPCR raw data. The window is identified either by the second derivative maximum 'cpD2' (default), 'studentized outlier' method as described in Tichopad et al. (2003), the 'midpoint' method (Peirson et al., 2003) or by subtracting the difference of cpD1 and cpD2 from cpD2 ('ERBCP', unpublished).
expfit(object, method = c("cpD2", "outlier", "midpoint", "ERBCP"),
model = c("exp", "linexp"), offset = 0, pval = 0.05, n.outl = 3,
n.ground = 1:5, corfact = 1, fix = c("top", "bottom", "middle"),
nfit = 5, plot = TRUE, ...)
an object of class 'pcrfit'.
one of the four possible methods to be used for defining the position of the fitting window.
for method = "cpD2"
, the cycle offset from second derivative maximum.
for method = "outlier"
, the p-value for the outlier test.
for method = "outlier"
, the number of successive outlier cycles.
for method = "midpoint"
, the number of cycles in the noisy ground phase to calculate the standard deviation from.
for method = "ERBCP"
, the correction factor for finding the exponential region. See 'Details'.
for methods "midpoint" and "ERBCP", the orientation of the fitting window based on the identified point. See 'Details'.
the size of the fitting window.
logical. If TRUE
, a graphical display of the curve and the fitted region is shown.
other parameters to be passed to the plotting function.
A list with the following components:
the point within the exponential region as identified by one of the three methods.
the cycles of the identified region.
the efficiency calculated from the exponential fit.
the Akaike Information Criterion of the fit.
the residual variance of the fit.
the root-mean-squared-error of the fit.
the initial template fluorescence.
the exponential model of class 'nls'.
The exponential growth function \(f(x) = a \cdot exp(b \cdot x) + c\) is fit to a subset of the data. Calls efficiency
for calculation of the second derivative maximum, takeoff
for calculation of the studentized residuals and 'outlier' cycle, and midpoint
for calculation of the exponential phase 'midpoint'. For method 'ERBCP' (Exponential Region By Crossing Points), the exponential region is calculated by \(expR = cpD2 - \code{corfact} \cdot (cpD1-cpD2)\). The efficiency is calculated from the exponential fit with \(E = exp(b)\) and the inital template fluorescence \(F_0 = a\).
Standardized determination of real-time PCR efficiency from a single reaction set-up. Tichopad A, Dilger M, Schwarz G & Pfaffl MW. Nucleic Acids Research (2003), 31:e122.
Comprehensive algorithm for quantitative real-time polymerase chain reaction. Zhao S & Fernald RD. J Comput Biol (2005), 12:1047-64.
# NOT RUN {
## Using default SDM method.
m1 <- pcrfit(reps, 1, 2, l5)
expfit(m1)
## Using 'outlier' method.
expfit(m1, method = "outlier")
## Linear exponential model.
expfit(m1, model = "linexp")
# }
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