HiddenMarkov-dthmm-deprecated: Discrete Time HMM - Deprecated Functions
In HiddenMarkov: Hidden Markov Models

HiddenMarkov-dthmm-deprecated

R Documentation

Discrete Time HMM - Deprecated Functions

Description

These functions are deprecated and will ultimately be removed from the package. Please change to the object orientated versions: BaumWelch, simulate or Viterbi.

Usage

Baum.Welch(x, Pi, delta, distn, pm, pn = NULL, nonstat = TRUE,
           maxiter = 500, tol = 1e-05, prt = TRUE,
           posdiff = (distn[1]!="glm"))
sim.hmm(n, initial, Pi, distn, pm, pn = NULL)
sim.hmm1(n, initial, Pi, distn, pm)
sim.markov(n, initial, Pi)
Viterbihmm(x, Pi, delta, distn, pm, pn = NULL)

Arguments

`x`	is a vector of length `n` containing the observed process.
`n`	length of process.
`initial`	integer, being the initial hidden Markov state `(1, \cdots, m)`.
`Pi`	is the `m \times m` transition probability matrix of the hidden Markov chain.
`delta`	is the marginal probability distribution of the `m` hidden states at the first time point.
`distn`	is a character string with the distribution name, e.g. `"norm"` or `"pois"`. If the distribution is specified as `"wxyz"` then a distribution function called `"pwxyz"` should be available, in the standard R format (e.g. `pnorm` or `ppois`).
`pm`	is a list object containing the (Markov dependent) parameter values associated with the distribution of the observed process (see `dthmm`).
`pn`	is a list object containing the observation dependent parameter values associated with the distribution of the observed process (see `dthmm`).
`nonstat`	is logical, `TRUE` if the homogeneous Markov chain is assumed to be non-stationary, default. See “Details” below.
`maxiter`	is the maximum number of iterations, default is 500.
`tol`	is the convergence criterion, being the difference between successive values of the log-likelihood; default is 0.00001.
`prt`	is logical, and determines whether information is printed at each iteration; default is `TRUE`.
`posdiff`	is logical, and determines whether the iterative process stops if a negative log-likelihood difference occurs.