normexp.fit {limma} | R Documentation

## Fit Normal+Exp Convolution Model to Observed Intensities

### Description

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

    normexp.fit(x, trace=FALSE)

### Arguments

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

### Details

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.

### Value

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`.

### Author(s)

Jeremy Silver and Gordon Smyth

### See Also

`normexp.signal`

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

### Examples

    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)

[Package limma version 2.10.5 Index]