zinb.loglik.regression.gradient: Gradient of the penalized log-likelihood of the ZINB...

In drisso/zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

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

drisso/zinbwave documentation built on March 18, 2024, 5:13 p.m.