predict.pcrfit {qpcR}

Value prediction from a fitted qPCR model

Description

After fitting the appropriate model, either the raw fluorescence values can be predicted from the cycle number or vice versa.

Usage

## S3 method for class 'pcrfit'
predict(object, newdata, which = c("y", "x"), 
        interval = c("none", "confidence", "prediction"),
        level = 0.95, ...) 

Arguments

object

an object of class 'pcrfit'.

newdata

a dataframe containing the values to estimate from, using the same variable naming as in the fitted model.

which

either "y" (default) for prediction of the raw fluorescence or "x" for prediction of the cycle number.

interval

if not "none", confidence or prediction intervals are calculated.

level

the confidence level.

...

some methods for this generic require additional arguments. None are used in this method.

Details

y-values (Fluorescence) are estimated from object$MODEL$expr, x-values (Cycles) are estimated from object$MODEL$inv. Confidence intervals are calculated from the gradient of the function and the variance-covariance matrix of object by \nabla f(x) \cdot cov(y) \cdot \nabla f(x) and are based on asymptotic normality (t-distribution).

Value

A dataframe containing the estimated values and (if chosen) standard error/upper confidence limit/lower confidence limit. The gradient is attached to the dataframe and can be accessed with attr.

Note

The estimation of x (cycles) from fluorescence data if which = "x" is problematic in the asymptotic regions of the sigmoidal curves (often gives NaN, due to logarithmation of negative values) and works fairly well in the ascending part.

Author(s)

Andrej-Nikolai Spiess

Examples

m1 <- pcrfit(reps, 1, 2, l5)

## Which raw fluorescence value at cycle number = 17?
predict(m1, newdata = data.frame(Cycles = 17))

## Cycle numbers 20:25, with 95% confidence?
predict(m1, newdata = data.frame(Cycles = 20:25), interval = "confidence")

## Which cycle at Fluo = 4, with 95% prediction?
predict(m1, newdata = data.frame(Fluo = 4), which = "x", interval = "prediction")