Negative Binomial Deviance
Fit the same log-link negative binomial or Poisson generalized linear model (GLM) to each row of a matrix of counts.
nbinomDeviance(y, mean, dispersion=0, weights=NULL)
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 |
dispersion |
numeric vector or matrix of negative binomial dispersions, as for |
weights |
numeric vector or matrix of non-negative weights, as for |
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.
nbinomDeviance returns a numeric vector of length equal to the number of rows of y.
Gordon Smyth, Yunshun Chen, Aaron Lun. C++ code by Aaron Lun.
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
y <- matrix(1:6,3,2) mu <- matrix(3,3,2) nbinomDeviance(y,mu,dispersion=0.2)
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