nbinomDeviance {edgeR} | R Documentation

## Negative Binomial Deviance

### Description

Fit the same log-link negative binomial or Poisson generalized linear model (GLM) to each row of a matrix of counts.

### Usage

    nbinomDeviance(y, mean, dispersion=0, weights=NULL)

### Arguments

`y` | numeric matrix containing the negative binomial counts, with rows for genes and columns for libraries. A vector will be treated as a matrix with one row.
`mean` | numeric matrix of expected values, of same dimension as `y`. A vector will be treated as a matrix with one row.
`dispersion` | numeric vector or matrix of negative binomial dispersions, as for `glmFit`. Can be a scalar, a vector of length equal to `nrow(y)`, or a matrix of same dimensions as `y`.
`weights` | numeric vector or matrix of non-negative weights, as for `glmFit`. Can be a scalar, a vector of length equal to `ncol(y)`, or a matrix of same dimensions as `y`.

### Details

Computes the total residual deviance for each row of `y`, i.e., weighted row sums of the unit deviances.

Care is taken to ensure accurate computation in limiting cases when the dispersion is near zero or `mean*dispersion` is very large.

### Value

`nbinomDeviance` returns a numeric vector of length equal to the number of rows of `y`.

### Author(s)

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

### References

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

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. https://doi.org/10.1093/nar/gks042

### See Also

`nbinomUnitDeviance`

### Examples

    y <- matrix(1:6,3,2)
    mu <- matrix(3,3,2)
    nbinomDeviance(y,mu,dispersion=0.2)

[Package edgeR version 3.32.1 Index]