{limma}R Documentation

Fit Normal+Exp Convolution Model to Observed Intensities


Fit normal+exponential convolution model to observed intensities. The normal part represents the background and the exponential represents the signal intensities. This function is called by backgroundCorrect and is not normally called directly by the user.

Usage, trace=FALSE)


x numeric vector of (background corrected) intensities
trace logical, if TRUE, tracing information on the progress of the optimization is given.


This function uses maximum likelihood estimation to fit a model to the foreground and background intensities. The model is a $normal(μ,σ^2)+exponential(α)$ convolution model for the background corrected intensities.

This is essentially the same model which is used by bg.correct.rma in the affy package. The difference is that the parameters are estimated by maximum likelihood and that the estimated background is subtracted before applying the model-based background.


A list containing the components

par numeric vector giving estimated values of $μ$, $log(σ)$ and $logα$
m2loglik numeric scalar giving minus twice the log-likelihood
convergence integer code indicating successful convergence or otherwise of the optimization. See optim.


Jeremy Silver and Gordon Smyth

See Also


An overview of background correction functions is given in 04.Background.


f <- c(2,3,1,10,3,20,5,6)
b <- c(2,2,2,2,2,2,2,2)
out <-
normexp.signal(out$par, x=f-b)

[Package limma version 2.4.7 Index]