zinb.loglik.regression.gradient {zinbwave} | R Documentation |
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
|
The regression model is described in
zinb.loglik.regression
.
The gradient of the penalized log-likelihood.