EMrun_bes: EMrun_bes

In vpnsctl/bbreg: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data

Description

Function to run the Expectation-Maximization algorithm for the bessel regression.

Usage

EMrun_bes(
  kap,
  lam,
  z,
  x,
  v,
  link.mean,
  link.precision,
  em_controls = list(maxit = 5000, em_tol = 10^(-5)),
  optim_method = "L-BFGS-B",
  optim_controls = list()
)

Arguments

kap	initial values for the coefficients in kappa related to the mean parameter.
lam	initial values for the coefficients in lambda related to the precision parameter.
z	response vector with 0 < z_i < 1.
x	matrix containing the covariates for the mean submodel. Each column is a different covariate.
v	matrix containing the covariates for the precision submodel. Each column is a different covariate.
link.mean	a string containing the link function for the mean. The possible link functions for the mean are "logit","probit", "cauchit", "cloglog".
link.precision	a string containing the link function the precision parameter. The possible link functions for the precision parameter are "identity", "log", "sqrt".
em_controls	a list containing two elements: maxit that contains the maximum number of iterations of the EM algorithm, the default is set to 5000; em_tol that defines the tolerance value to control the convergence criterion in the EM-algorithm, the default is set to 10^(-5).
optim_method	main optimization algorithm to be used. The available methods are the same as those of optim function. The default is set to "L-BFGS-B".
optim_controls	a list of control arguments to be passed to the optim function in the optimization of the model. For the control options, see the 'Details' in the help of optim for the possible arguments.

Value

Vector containing the estimates for kappa and lambda in the bessel regression.

vpnsctl/bbreg documentation built on March 14, 2021, 12:11 a.m.