nbinomUnitDeviance {edgeR} | R Documentation

## Negative Binomial Unit Deviance

### Description

Compute unit deviances for the negative binomial distribution.

### Usage

    nbinomUnitDeviance(y, mean, dispersion = 0)

### Arguments

`y` | numeric vector or matrix containing negative binomial counts.
`mean` | numeric vector or matrix of expected values. If a matrix, then of same dimensions as `y`.
`dispersion` | numeric vector or matrix of negative binomial dispersions. Can be a scalar, a vector of length `nrow(y)` or a matrix of same dimensions as `y`.

### Details

The unit deviance of the negative binomial distribution is a measure of the distance between `y` and mean. If `mean` and `dispersion` are the true mean and dispersion of the negative binomial distribution, then the unit deviance follows an approximate chisquare distribution on 1 degree of freedom.

This function computes the unit deviance for each `y` observation. Care is taken to ensure accurate computation in limiting cases when the dispersion is near zero or `mean*dispersion` is very large.

### Value

Numeric vector or matrix of the same size as `y` containing unit deviances.

### Author(s)

Gordon Smyth, Yunshun Chen, Aaron Lun. C++ code by Aaron Lun.

### References

Dunn PK, Smyth GK (2018). Generalized linear models with examples in R. Springer, New York, NY. doi: 10.1007/978-1-4419-0118-7

Jorgensen B (2013). Generalized linear models. Encyclopedia of Environmetrics 3, Wiley. doi: 10.1002/9780470057339.vag010.pub2

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. doi: 10.1093/nar/gks042

### Examples

    y <- 1:4
    names(y) <- letters[1:4]
    nbinomUnitDeviance(y,mean=2.5,dispersion=0.2)

[Package edgeR version 3.34.1 Index]