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 2.4.7

Index]