set_fhmm: 'S3' Class for Hidden Markov Models with functional response.

In hmmhdd: Hidden Markov Models for High Dimensional Data

Description

This function creates an object of S3 class fhmm, which contains the elements of a Hidden Markov Model with multivariate response whose components can be correlated through a gaussian copula.

Usage

set_fhmm(Obs, bT = NULL, nStates = NULL, nu = 1/rep(nStates,
  nStates), A = matrix(1/nStates, nStates, nStates), centroids = NULL)

Arguments

Obs	your data, it can be a matrix (multivariate data) or a funData object (functional data)
bT	the vector of the beginning times for the statistical units
nStates	an integer representing the number of states of the Hidden Markov Model.
nu	a vector of the initial probabilities of the Hidden Markov Model.
A	the transition matrix of the Hidden Markov Model.
centroids	the initialization of the centers.

Value

The function returns an object of S3 class fhmm containing the initialization and the data of your HMM

References

Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286. Martino A., Guatteri, G. and Paganoni A. M., Hidden Markov Models for multivariate functional data, MOX Report 21/2019, 2019

See Also

fitBM_fhmm

Examples

data(simulatedFD)
FD <- simulatedFD
n <- 20
n_tot <- dim(FD$data[[1]])[1]
bt <- seq(1, n_tot, by = n)
hmm <- set_fhmm(FD, nStates = 3, bT = bt)

hmmhdd documentation built on Sept. 4, 2019, 5:03 p.m.