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=NULL,background.matrix=NULL,trace=0,beta.start=NULL)

Arguments

foreground numeric vector of foreground intensities
background optional 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

Uses Nelder-Mead simplex algorithm to maximize likelihood based on $normal(μ,σ^2)+exponential(α)$ convolution model for the foreground intensities. The values $μ$ may depend on any covariates, for example the observed background values.

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)

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 1.8.6 Index]