zinb.loglik.regression.gradient {zinbwave}R Documentation

Gradient of the penalized log-likelihood of the ZINB regression model

Description

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

Usage

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)

Arguments

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.

Details

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

Value

The gradient of the penalized log-likelihood.

See Also

zinb.loglik.regression


[Package zinbwave version 1.6.0 Index]