| normexp {limma} | R Documentation |
Marginal log-likelihood of foreground values for normal + exponential model and its derivatives.
This function is called by backgroundCorrect and is not normally called directly by the user.
normexp.m2loglik(theta,foreground,background=0) normexp.grad(theta,foreground,background=0)
theta |
numeric vector of parameters |
foreground |
numeric vector of foreground intensities |
background |
optional vector of background intensity values |
Computes minus twice the log-likelihood based on the $normal(μ,σ^2)+exponential(α)$ convolution model for the foreground intensities.
The last two elements of theta are $log(σ)$ and $log(α)$.
The other elements are the vector $β$.
The vector $β$ is related to $μ$ through $μ=Xβ$ if $X=$background.matrix is non-null.
Otherwise $μ=β_1+β_2$background if background is non-null.
If both background and background.matrix are null, then $μ=β_1$.
normexp.m2loglik is minus-twice the log-likelihood.
normexp.grad is the gradient vector.
Numeric scalar giving minus twice the log-likelihood
Jeremy Silver and Gordon Smyth
An overview of normalization and background correction functions is given in 05.Normalization.