nbinomDeviance {edgeR} | R Documentation |
Fit the same log-link negative binomial or Poisson generalized linear model (GLM) to each row of a matrix of counts.
nbinomUnitDeviance(y, mean, dispersion=0) nbinomDeviance(y, mean, dispersion=0, weights=NULL)
y |
numeric vector or matrix containing the negative binomial counts. If a matrix, then rows for genes and columns for libraries. |
mean |
numeric vector matrix of expected values, of same dimension as |
dispersion |
numeric vector or matrix of negative binomial dispersions.
Can be a scalar, or a vector of length equal to the number of genes, or a matrix of same dimensions as |
weights |
numeric vector or matrix of non-negative weights, as for |
nbinomUnitDeviance
computes the unit deviance for each y
observation.
nbinomDeviance
computes the total residual deviance for each row of y
observation, i.e., weighted row sums of the unit deviances.
Care is taken to ensure accurate computation for small dispersion values.
nbinomUnitDeviance
returns a numeric vector or matrix of the same size as y
.
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. http://nar.oxfordjournals.org/content/40/10/4288
y <- matrix(1:6,3,2) mu <- matrix(3,3,2) nbinomUnitDeviance(y,mu,dispersion=0.2) nbinomDeviance(y,mu,dispersion=0.2)