HiddenMarkov-dthmm-deprecated: Discrete Time HMM - Deprecated Functions

Description Usage Arguments Details

Description

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

Usage

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Baum.Welch(x, Pi, delta, distn, pm, pn = NULL, nonstat = TRUE,
           maxiter = 500, tol = 1e-05, prt = TRUE,
           posdiff = (distn[1]!="glm"))
residualshmm(x, Pi, delta, distn, pm, pn = NULL, discrete = FALSE)
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*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).

discrete

is logical, and is TRUE if distn is a discrete distribution.

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.

Details

The function sim.hmm1 will run faster for cases where the argument pn is NULL.


HiddenMarkov documentation built on Nov. 17, 2017, 6:59 a.m.