control_fit: Optimization Control Parameters Passed to Optim

In rdmatheus/BCNSM: Multivariate Box-Cox Symmetric Distributions Generated by a Normal Scale Mixture Copula

Optimization Control Parameters Passed to Optim

Description

Optimization parameters passed to optim for the fit of a multivariate Box-Cox symmetric distribution generated by a normal scale mixture copula. This function acts in the same spirit as betareg.control from the betareg package. Its primary purpose is to gather all the optimization control arguments in a single function.

Usage

control_fit(
  method = "BFGS",
  maxit = 2000,
  hessian = TRUE,
  start = NULL,
  mu_inits = NULL,
  sigma_inits = NULL,
  lambda_inits = NULL,
  nu_inits = NULL,
  gamma_inits = NULL,
  ...
)

Arguments

method	the method to be used. See 'Details' in optim. The default method ("BFGS") is a quasi-Newton method (also known as a variable metric algorithm), specifically that published simultaneously in 1970 by Broyden, Fletcher, Goldfarb and Shanno.
maxit	the maximum number of iterations of the algorithm. Defaults to 2000.
hessian	logical. Should a numerically differentiated Hessian matrix be returned?
start	an optional vector with starting values for all parameters for fitting a BCNSM distribution. It must be passed in the order: (mu, sigma, lambda, nu, gamma), where mu, sigma, lambda, and nu are parameters associated with marginal distributions and gamma are parameters related to the association matrix.
mu_inits	initial values for the scale parameters of the marginal distributions.
sigma_inits	initial values for the relative dispersion parameters of the marginal distributions.
lambda_inits	initial values for the skewness parameters of the marginal distributions.
nu_inits	initial values for the heavy-tailness parameters of the marginal distributions.
gamma_inits	initial values for the parameters related to the association matrix.
...	further arguments to be passed to optim.

Value

A list with the arguments specified.

Author(s)

Rodrigo M. R. de Medeiros <[e-mail]>

References

Cribari-Neto, F., and Zeileis, A. (2010). Beta regression in R. Journal of statistical software, 34, 1-24.

Vanegas, L. H., and Paula, G. A. (2016). Log-symmetric distributions: statistical properties and parameter estimation. Brazilian Journal of Probability and Statistics, 30, 196-220.

Ferrari, S. L. P., and Fumes, G. (2017). Box-Cox symmetric distributions and applications to nutritional data. AStA Advances in Statistical Analysis, 101, 321-344.

Medeiros, R. M. R. de, and Ferrari, S. L. P. (2024). Multivariate Box-Cox symmetric distributions generated by a normal scale mixture copula.

rdmatheus/BCNSM documentation built on Feb. 8, 2024, 1:28 a.m.