aic_mcml: Calculates the Akaike Information Criterion for the GLMM

In samuel-watson/glmmr: Design and analysis for generalised linear mixed models in R

Calculates the Akaike Information Criterion for the GLMM

Description

Calculates the Akaike Information Criterion for the GLMM

Usage

aic_mcml(
  Z,
  X,
  y,
  u,
  family,
  link,
  B,
  N_dim,
  N_func,
  func_def,
  N_var_func,
  col_id,
  N_par,
  sum_N_par,
  cov_data,
  beta_par,
  cov_par
)

Arguments

Z	Matrix Z of the GLMM
X	Matrix X of the GLMM
y	Vector of observations
u	Matrix of samples of the random effects. Each column is a sample.
family	Character specifying the family
link	Character specifying the link function
B	Integer specifying the number of blocks in the matrix
N_dim	Vector of integers, which each value specifying the dimension of each block
N_func	Vector of integers specifying the number of functions in the covariance function for each block.
func_def	Matrix of integers where each column specifies the function definition for each function in each block.
N_var_func	Matrix of integers of same size as 'func_def' with each column specying the number of variables in the argument to each function in each block
col_id	3D array (cube) of integers of dimension length(func_def) x max(N_var_func) x B where each slice the respective column indexes of 'cov_data' for each function in the block
N_par	Matrix of integers of same size as 'func_def' with each column specifying the number of parameters in the function in each block
cov_data	3D array (cube) holding the data for the covariance matrix where each of the B slices is the data required for each block
beta_par	Vector specifying the values of the mean function parameters
cov_par	Vector specifying the values of the covariance parameters

Value

A matrix of the Hessian for each parameter

samuel-watson/glmmr documentation built on July 27, 2022, 10:30 p.m.