pcropt2 {qpcR} | R Documentation |
The qPCR curve containing n cycles is refitted n-1 times, each time leaving out one cycle. The difference of the new coefficients of the fit in comparison to the original coefficients is calculated and those cycles are eliminated that have a weak (strong) influence on change of coefficients. A new model is returned with the selected cycles left out.
pcropt2(object, plot = TRUE, which.par = "all", quan = 0.1, delete = c("low", "high"), ...)
object |
an object of class 'pcrfit'. |
plot |
logical. If TRUE , the refitting and the final result are plotted. |
which.par |
The coefficient(s) to be analysed. Either "all" for all coefficients, or the coefficient name, i.e. "b" . |
quan |
the quantile for selecting the cycles exhibiting weak (strong) influence on the coefficient estimation. |
delete |
which cycles to delete. Those with low influence on the coefficients or those with a high one. |
... |
other parameters to be passed on to the plotting functions. |
For each deletion of cycle i = 1, ..., n, the qPCR data is refitted yielding new parameter estimates
hattheta^{ast 1}, ..., hattheta^{ast i}
The difference to the original coefficients hattheta is calculated by
crit = frac{<=ft|hattheta - hattheta^{ast i}right|}{s.e.(hattheta)}
with s.e. = standard error. The user then chooses the cycles with F^{-1}(p) = inf{crit in R: F(crit) >= p} with p = the selected quantile.
A new model of class 'pcrfit' and 'nls' with the corresponding cycles removed.
Andrej-Nikolai Spiess
Bates DM and Watts DG (1988).
Nonlinear regression analysis and its applications.
Wiley, Chichester, UK.
The function pcropt1
that removes cycles sequentially from both sides of the curve.
m <- pcrfit(reps, 1, 2, l4) ## which cycles have low influence ## on parameter 'c' (the lower ## asymptote)? pcropt2(m, which.par = "c", quan = 0.3, delete = "low") ## and on 'b' and 'e'? m <- pcrfit(reps, 1, 2, l4) pcropt2(m, which.par = c("b", "e"), quan = 0.3, delete = "low") ## very high influence on 'd' ## (upper asymptote)? m <- pcrfit(reps, 1, 2, l4) m2 <- pcropt2(m, which.par = c("d"), quan = 0.1, delete = "high") ## plot new model plot(m2)