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