| HMM {regmhmm} | R Documentation |
Fitting Hidden Markov Models using Expectation-Maximization (EM) Algorithm
Description
Fit Hidden Markov Models (HMMs) to the provided data using an iterative Expectation-Maximization (EM) algorithm. This method alternates between the E-step (Expectation) and M-step (Maximization) to iteratively optimize model parameters. The algorithm ensures convergence and provides robust estimates for latent state probabilities and transition probabilities, contributing to the accurate characterization of underlying patterns in the data.
Usage
HMM(delta, Y_mat, A, B, X_cube, family, trace, ...)
Arguments
delta |
a vector of length S specifying the initial probabilities. |
Y_mat |
a matrix of observations of size N x T. |
A |
a matrix of size S x S specifying the transition probabilities. |
B |
a matrix of size S x (p + 1) specifying the GLM parameters of the emission probabilities. |
X_cube |
a design array of size T x p x N. |
family |
the family of the response. |
trace |
logical indicating if detailed output should be produced during the fitting process. |
... |
other arguments to be passed to the next method. |
Value
A list object with the following slots:
delta_hat |
the estimate of delta. |
A_hat |
the estimate of A. |
B_hat |
the estimate of B. |
log_likelihood |
the log-likelihood of the model. |
Examples
# Example usage of the function
seed_num <- 1
p_noise <- 2
N <- 100
N_persub <- 10
parameters_setting <- list(
init_vec = c(0.5, 0.5),
trans_mat = matrix(c(0.7, 0.3, 0.2, 0.8), nrow = 2, byrow = TRUE),
emis_mat = matrix(c(1, 0.5, 0.5, 2), nrow = 2, byrow = TRUE)
)
simulated_data <- simulate_HMM_data(seed_num, p_noise, N, N_persub, parameters_setting)
init_start = c(0.5, 0.5)
trans_start = matrix(c(0.5, 0.5, 0.5, 0.5), nrow = 2)
emis_start = matrix(rep(1, 8), nrow = 2)
HMM_fit <- HMM(delta=as.matrix(init_start),
Y_mat=simulated_data$y_mat,
A=trans_start,
B=emis_start,
X_cube=simulated_data$X_array,
family="P",
eps=1e-4,
trace = 0
)