inmoose.edgepy.nbinomDeviance
- inmoose.edgepy.nbinomDeviance(y, mean, dispersion=0, weights=None)
Residual deviances for row-wise negative binomial GLMs.
This function 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 of
mean * dispersionis very large.See also
nbinomUnitDeviance- Parameters:
y (array_like) – 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 (array_like) – matrix of expected values, of same shape as
y. A vector will be treated as a matrix with one row.dispersion (array_like) – vector or matrix of negative binomial dispersions, as in
glmFit. Can be a scalar, a vector of length equal to the number of rows iny, or a matrix of same shape asy.weights (array_like, optional) – vector or matrix of non-negative weights, as in
glmFit. Can be a scalar, a vector of length equal to the number of columns iny, or a matrix of same shape asy.
- Returns:
vector of length equal to the number of rows in
y- Return type:
ndarray