pcrboot {qpcR}R Documentation

Bootstrapping and jackknifing qPCR (and other) data

Description

Confidence intervals for the estimated parameters and goodness-of-fit measures are calculated for a nonlinear qPCR data fit by either
a) boostrapping the residuals of the fit or
b) jackknifing and refitting the data.

If data of class pcrfit is supplied, confidence intervals are also calculated for all parameters obtained from the efficiency analysis. Works for all models with a model$data item and a fitted and residuals function. See 'Details'.

Usage

pcrboot(object, type = c("boot", "jack"), B = 100, njack = 1,
        plot = TRUE, do.eff = TRUE, conf = 0.95, verbose = TRUE, ...)

Arguments

object an object of class 'pcrfit' or other.
type either bootstrapping or jackknifing.
B numeric. The number of iterations.
njack numeric. In case of type = "jack", how many datapoints to exclude. Defaults to leave-one-out.
plot should the fitting and final results be displayed as a plot?
do.eff logical. If TRUE, efficiency analysis will be performed.
conf the confidence level.
verbose logical. If TRUE, the iterations will be printed on the console.
... other parameters to be passed on to the plotting functions.

Details

Non-parametric bootstrapping is applied using the centered residuals.
1) Obtain the residuals from the fit:

hat{varepsilon_{t}} = y_t - f(x_t, hat{theta})

2) Draw bootstrap pseudodata:

y_{t}^{ast} = f(x_t, hat{theta}) + ε_{t}^{ast}

where ε_{t}^{ast} are i.i.d. from distribution hat{F}, where the residuals from the original fit are centered at zero.
3) Fit hattheta^ast by nonlinear least-squares.
4) Repeat B times, yielding bootstrap replications

hattheta^{ast 1}, hattheta^{ast 2}, ..., hattheta^{ast B}

One can then characterize the EDF and calculate confidence intervals for each parameter:

theta in [EDF^{-1}(α/2), EDF^{-1}(1-α/2)]

The jackknife alternative is to perform the bootstrap on the data-predictor vector, i.e. eliminating a certain number of datapoints.
If the residuals are correlated or have non-constant variance the latter is recommended. This may be the case in qPCR data, as the variance in the low fluorecence region (ground phase) is usually much higher than in the rest of the curve.

Value

A list containing the following items:

ITER a list containing each of the results from the iterations.
CONF a list containing the confidence intervals for each item in ITER.


Each item contains subitems for the coefficients (coef), root-mean-squared error (rse), residual sum-of-squares (rss), goodness-of-fit measures (gof) and the efficiency analysis (eff). If plot = TRUE, all data is plotted as boxplots including confidence intervals.

Author(s)

Andrej-Nikolai Spiess

References

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

Seber GAF and Wild CJ (1989).
Nonlinear regression.
Wiley, New York.

Roy T (1994).
Boostrap accuracy for non-linear regression models.
J Chemometics, 8: 37-44.

Examples

## simple bootstrapping with
## too less iterations...
par(ask = FALSE)
m1 <- pcrfit(reps, 1, 2, l4)
res <- pcrboot(m1, B = 20)

## jackknifing with leaving
## 5 datapoints out
m2 <- pcrfit(reps, 1, 2, l4)
res <- pcrboot(m2, type = "jack", njack = 5, B = 20)

[Package qpcR version 1.2-4 Index]