pcropt2 {qpcR}R Documentation

Elimination of qPCR cycles with low (high) impact on fitted parameters

Description

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.

Usage

pcropt2(object, plot = TRUE, which.par = "all", quan = 0.1, 
        delete = c("low", "high"), ...)

Arguments

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.

Details

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.

Value

A new model of class 'pcrfit' and 'nls' with the corresponding cycles removed.

Author(s)

Andrej-Nikolai Spiess

References

Bates DM and Watts DG (1988).
Nonlinear regression analysis and its applications.
Wiley, Chichester, UK.

See Also

The function pcropt1 that removes cycles sequentially from both sides of the curve.

Examples

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) 

[Package qpcR version 1.2-4 Index]