normexp.fit {limma} | R Documentation |
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.
normexp.fit(x, 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
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.fit(f-b) normexp.signal(out$par, x=f-b)