This function applies the bootstrap method to generate a set of simulated samples, estimates model parameters for each sample using the EM algorithm (or MLE), and calculates the confidence intervals for the parameters.
Usage
CI_Bootstrap(
it = 100,
final_par,
model,
core,
types,
n,
m,
init_param,
period = c(0, 1),
t_list,
u_list,
alpha_value = 0.05,
tol1,
max_iter,
parallel = TRUE,
n_points,
n_samples,
n_intervals,
show_progress = FALSE,
approx.method
)Arguments
- it
Integer, number of bootstrap iterations (default is 100).
- final_par
Numeric vector, the final estimated parameters of the model.
- model
Character string, model type, one of "M0", "M1", "M2", "M3", or "M4".
- core
Integer, number of cores to use for parallel computation.
- types
Character string, the type of data (e.g., "type1", "type2", etc.).
- n
Number of units.
- m
Number of time.
- init_param
Numeric vector, initial parameters for the EM algorithm.
- period
Numeric vector, the time interval for the analysis, default is `c(0, 1)`.
- t_list
Numeric vector, time points.
- u_list
Numeric vector, cycle points.
- alpha_value
Numeric, significance level for the confidence interval, default is 0.05.
- tol1
Numeric, tolerance for the convergence of the EM algorithm.
- max_iter
Integer, maximum number of iterations for the EM algorithm.
- parallel
Logical, whether to use parallel computation, default is `TRUE`.
- n_points
Integer, the number of points used in the computation.
- n_samples
Integer, the number of samples to use in the computation.
- n_intervals
Integer, the number of intervals used in the computation.
- show_progress
Logical, whether to display the computation progress, default is `FALSE`.
- approx.method
Character string, the approximation method to use.