mc_build_C: Build the joint covariance matrix

In mcglm: Multivariate Covariance Generalized Linear Models

Build the joint covariance matrix

Description

This function builds the joint variance-covariance matrix using the Generalized Kronecker product and its derivatives with respect to rho, power and tau parameters.

Usage

mc_build_C(
  list_mu,
  list_Ntrial,
  rho,
  list_tau,
  list_power,
  list_Z,
  list_sparse,
  list_variance,
  list_covariance,
  list_power_fixed,
  compute_C = FALSE,
  compute_derivative_beta = FALSE,
  compute_derivative_cov = TRUE
)

Arguments

list_mu	A list with values of the mean.
list_Ntrial	A list with the number of trials. Useful only for binomial responses.
rho	Vector of correlation parameters.
list_tau	A list with values for the tau parameters.
list_power	A list with values for the power parameters.
list_Z	A list of matrix to be used in the matrix linear predictor.
list_sparse	A list with Logical.
list_variance	A list specifying the variance function to be used for each response variable.
list_covariance	A list specifying the covariance function to be used for each response variable.
list_power_fixed	A list of Logical specifying if the power parameters are fixed or not.
compute_C	Logical. Compute or not the C matrix.
compute_derivative_beta	Logical. Compute or not the derivative of C with respect to regression parameters.
compute_derivative_cov	Logical. Compute or not the derivative of C with respect the covariance parameters.

Value

A list with the inverse of the C matrix and the derivatives of the C matrix with respect to rho, power and tau parameters.

Author(s)

Wagner Hugo Bonat

mcglm documentation built on Sept. 16, 2022, 1:06 a.m.