fit.normexp {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

    fit.normexp(foreground,background)
    fit.normexp0(foreground,background=NULL,background.matrix=NULL,trace=0,beta.start=NULL)

### Arguments

`foreground` | numeric vector of foreground intensities
`background` | vector of background intensity values
`background.matrix` | option design matrix for regression on background values
`trace` | integer value passed to `optim`. If positive then tracing information on the progress of the optimization is given. Higher values give more information.
`beta.start` | optional numeric vector giving starting values for the regression coefficients

### Details

These functions use 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 foreground intensities in which $μ$ is a linear function of the background. `fit.normexp` uses a BFGS modified Newton-Raphson iteration maxmize the likelihood while `fit.normexp0` uses the Nelder-Mead simplex algorithm, a derivative free method. `fit.normexp` assumes that $μ$ is a constant plus the observed background. `fit.normexp0` allows $μ$ to depend linearly on any set of covariates.

### Value

A list containing the components

`beta` | numeric vector of estimated regression coefficients
`sigma` | numeric scalar giving estimated value of $σ$
`alpha` | numeric scalar giving estimated value of $α$
`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

`signal.normexp`, `optim`

An overview of normalization and background correction functions is given in `4.Normalization`.

### Examples

    f <- c(2,3,1,10,3,20,5,6)
    b <- c(2,2,2,2,2,2,2,2)
    out <- fit.normexp(f,b)

[Package limma version 1.9.6 Index]