Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/adjustedProfileLik.R

Compute adjusted profile-likelihoods for estimating the dispersion parameters of genewise negative binomial glms.

1 2 |

`dispersion` |
numeric scalar or vector of dispersions. |

`y` |
numeric matrix of counts. |

`design` |
numeric matrix giving the design matrix. |

`offset` |
numeric matrix of same size as |

`weights` |
optional numeric matrix giving observation weights. |

`adjust` |
logical, if |

`start` |
numeric matrix of starting values for the GLM coefficients, to be passed to |

`get.coef` |
logical, specifying whether fitted GLM coefficients should be returned. |

For each row of data, compute the adjusted profile-likelihood for estimating the dispersion parameter of the negative binomial glm. The adjusted profile likelihood is described by McCarthy et al (2012), and is based on the method of Cox and Reid (1987).

The adjusted profile likelihood is an approximate log-likelihood for the dispersion parameter, conditional on the estimated values of the coefficients in the NB log-linear models. The conditional likelihood approach is a technique for adjusting the likelihood function to allow for the fact that nuisance parameters have to be estimated in order to evaluate the likelihood. When estimating the dispersion, the nuisance parameters are the coefficients in the linear model.

This implementation calls the LAPACK library to perform the Cholesky decomposition during adjustment estimation.

The purpose of `start`

and `get.coef`

is to allow hot-starting for multiple calls to `adjustedProfileLik`

, when only the `dispersion`

is altered.
Specifically, the returned GLM coefficients from one call with `get.coef==TRUE`

can be used as the `start`

values for the next call.

If `get.coef==FALSE`

, a vector of adjusted profile log-likelihood values is returned containing one element for each row of `y`

.

Otherwise, a list is returned containing `apl`

, the aforementioned vector of adjusted profile likelihoods; and `beta`

, a numeric matrix of fitted GLM coefficients.

Yunshun Chen, Gordon Smyth, Aaron Lun

Cox, DR, and Reid, N (1987). Parameter orthogonality and approximate conditional inference. *Journal of the Royal Statistical Society Series B* 49, 1-39.

McCarthy, DJ, Chen, Y, Smyth, GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.
*Nucleic Acids Research* 40, 4288-4297.
http://nar.oxfordjournals.org/content/40/10/4288

1 2 3 4 5 |

edgeR documentation built on March 18, 2018, 2:35 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.