get_initial_values: Initialization of numerical likelihood optimization

In fHMM: Fitting Hidden Markov Models to Financial Data

Initialization of numerical likelihood optimization

Description

This helper function generates a set of initial values for the numerical optimization of the model likelihood function.

Usage

get_initial_values(
  data,
  ncluster = 1,
  seed = NULL,
  verbose = TRUE,
  initial_estimate = NULL
)

Arguments

data	An object of class fHMM_data.
ncluster	Set the number of clusters for parallel optimization runs to reduce optimization time. By default, ncluster = 1 (no clustering).
seed	Set a seed for the generation of initial values. No seed by default.
verbose	Set to TRUE to print progress messages.
initial_estimate	Optionally defines an initial estimate for the numerical likelihood optimization. Good initial estimates can improve the optimization process. Can be: NULL (the default), in this case applies a heuristic to calculate a good initial estimate or uses the true parameter values (if available and data$controls$origin is TRUE) or an object of class parUncon (i.e., a numeric of unconstrained model parameters), for example the estimate of a previously fitted model (i.e. the element model$estimate).

Value

A list, where each element is an object of class parUncon.

fHMM documentation built on April 3, 2025, 5:49 p.m.