d_lik_optim: Optimises the log-likelihood of the random effects

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

Optimises the log-likelihood of the random effects

Description

Optimises the log-likelihood of the random effects

Usage

d_lik_optim(
  B,
  N_dim,
  N_func,
  func_def,
  N_var_func,
  col_id,
  N_par,
  sum_N_par,
  cov_data,
  u,
  start,
  lower,
  upper,
  trace = 0L
)

Arguments

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
u	Matrix of samples of the random effects. Each column is a sample.
start	Vector of starting values for the optmisation
lower	Vector of lower bounds for the covariance parameters
upper	Vector of upper bounds for the covariance parameters
trace	Integer indicating what to report to the console, 0= nothing, 1-3=detailed output

Value

A vector of covariance parameters that maximise the log likelihood

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