nb.loglik.regression.gradient: Gradient of the log-likelihood of the NB regression model

In drisso/learn2count: Structure Learning for Count Data

Gradient of the log-likelihood of the NB regression model

Description

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

Usage

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

Arguments

alpha	the vectors of parameters a.mu concatenated
Y	the vector of counts
A.mu	matrix of the model (see Details, default=empty)
C.theta	matrix of the model (see Details, default=zero)

Details

The regression model is described in nb.loglik.regression.

Value

The gradient of the log-likelihood.

See Also

nb.loglik.regression

drisso/learn2count documentation built on July 15, 2024, 11:13 p.m.