Eigen.HMM_fit {MatrixHMM} | R Documentation |
Fitting Parsimonious Hidden Markov Models for Matrix-Variate Longitudinal Data
Description
Fits parsimonious Hidden Markov Models for matrix-variate longitudinal data using ECM algorithms. The models are based on the matrix-variate normal, matrix-variate t, and matrix-variate contaminated normal distributions. Parallel computing is implemented and highly recommended for faster model fitting.
Usage
Eigen.HMM_fit(
Y,
init.par = NULL,
tol = 0.001,
maxit = 500,
nThreads = 1,
verbose = FALSE
)
Arguments
Y |
An array with dimensions |
init.par |
A list of initial values for starting the algorithms, as generated by the |
tol |
A numeric value specifying the tolerance level for the ECM algorithms' convergence. |
maxit |
A numeric value specifying the maximum number of iterations for the ECM algorithms. |
nThreads |
A positive integer indicating the number of cores to use for parallel processing. |
verbose |
A logical value indicating whether to display the running output. |
Value
A list containing the following elements:
results |
A list of the results from the fitted models. |
c.time |
A numeric value providing information on the computational time required to fit all models for each state. |
models |
A data frame listing the models that were fitted. |
Examples
data(simData)
Y <- simData$Y
init <- Eigen.HMM_init(Y = Y, k = 2, density = "MVT", mod.row = "EEE", mod.col = "EE", nstartR = 10)
fit <- Eigen.HMM_fit(Y = Y, init.par = init, nThreads = 1)