zinb.loglik.regression.gradient: Gradient of the penalized log-likelihood of the ZINB...
In zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

Description Usage Arguments Details Value See Also

This function computes the gradient of the penalized log-likelihood of a ZINB regression model given a vector of counts.

zinb.loglik.regression.gradient(
  alpha,
  Y,
  A.mu = matrix(nrow = length(Y), ncol = 0),
  B.mu = matrix(nrow = length(Y), ncol = 0),
  C.mu = matrix(0, nrow = length(Y), ncol = 1),
  A.pi = matrix(nrow = length(Y), ncol = 0),
  B.pi = matrix(nrow = length(Y), ncol = 0),
  C.pi = matrix(0, nrow = length(Y), ncol = 1),
  C.theta = matrix(0, nrow = length(Y), ncol = 1),
  epsilon = 0
)

`alpha`	the vectors of parameters c(a.mu, a.pi, b) concatenated
`Y`	the vector of counts
`A.mu`	matrix of the model (see Details, default=empty)
`B.mu`	matrix of the model (see Details, default=empty)
`C.mu`	matrix of the model (see Details, default=zero)
`A.pi`	matrix of the model (see Details, default=empty)
`B.pi`	matrix of the model (see Details, default=empty)
`C.pi`	matrix of the model (see Details, default=zero)
`C.theta`	matrix of the model (see Details, default=zero)
`epsilon`	regularization parameter. A vector of the same length as `alpha` if each coordinate of `alpha` has a specific regularization, or just a scalar is the regularization is the same for all coordinates of `alpha`. Default=`O`.