fit.normexp {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.
fit.normexp(foreground,background) fit.normexp0(foreground,background=NULL,background.matrix=NULL,trace=0,beta.start=NULL)
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 |
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.
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 . |
Jeremy Silver and Gordon Smyth
An overview of normalization and background correction functions is given in 4.Normalization
.
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)