eff {qpcR}

The amplification efficiency curve of a fitted object

Description

Calculates the efficiency curve from the fitted object by E_n = \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, method = c("sigfit", "spline"), sequence = NULL, baseshift = NULL, 
    smooth = FALSE, plot = FALSE)

Arguments

object

an object of class 'pcrfit'.

method

the efficiency curve is either calculated from the sigmoidal fit (default) or a cubic spline interpolation.

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.

baseshift

baseline shift value in case of type = "spline". See documentation to maxRatio.

smooth

logical. If TRUE and type = "spline", invokes a 5-point convolution filter (filter). See documentation to maxRatio.

plot

should the efficiency be plotted?

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 maxRatio.

Value

A list with the following components:

eff.x

the cycle points.

eff.y

the efficiency values at eff.x.

effmax.x

the cycle number with the highest efficiency.

effmax.y

the maximum efficiency.

Author(s)

Andrej-Nikolai Spiess

References

A new method for robust quantitative and qualitative analysis of real-time PCR.
Shain EB & Clemens JM.
Nucleic Acids Research (2008), 36, e91.

Examples

## With default 100 points per cycle.
m1 <- pcrfit(reps, 1, 7, l5)
eff(m1, plot = TRUE) 

## Not all data and only 10 points per cycle.
eff(m1, sequence = c(5, 35, 0.1), plot = TRUE) 

## When using cubic splines it is preferred 
## to use the smoothing option.
#eff(m1, method = "spline", plot = TRUE, smooth = TRUE, baseshift = 0.3)