normexp {limma} | R Documentation

## Normal + Exponential Log-Likelihood

### Description

Marginal log-likelihood of foreground values for the normal + exponential convolution model and its derivatives. These functions are called by `normexp.fit` and are not normally called directly by the user.

### Usage

    normexp.m2loglik(par,x)
    normexp.m2loglik.saddle(par,x)
    normexp.grad(par,x)

### Arguments

`par` | numeric vector of parameters
`x` | numeric vector of (background corrected) intensities

### Details

The parameter vector `par` holds the normal mean, the normal log-standard deviation and the exponential mean.

`normexp.m2loglik` computes minus twice the log-likelihood, and `normexp.grad` it is derivative, based on the $normal(μ,σ^2)+exponential(α)$ convolution model for the intensities. The elements of `par` are $μ$, $log(σ)$ and $log(α)$.

`normexp.m2loglik` is the saddle-point approximation to the log-logelihood, which is generally prefered because it is numerically more stable.

### Value

`normexp.m2loglik` returns a numeric scalar holding minus-twice the log-likelihood. `normexp.grad` returns a numeric vector holding the derivatives with respect to the elements of `par`.

### Author(s)

Jeremy Silver and Gordon Smyth

### References

Ritchie, M. E., Silver, J., Oshlack, A., Silver, J., Holmes, M., Diyagama, D., Holloway, A., and Smyth, G. K. (2007). A comparison of background correction methods for two-colour microarrays. Bioinformatics http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm412

### See Also

An overview of background correction functions is given in `04.Background`.

[Package limma version 2.12.0 Index]