dthmm.obsolete         package:HiddenMarkov         R Documentation

_D_i_s_c_r_e_t_e _T_i_m_e _H_M_M - _O_b_s_o_l_e_t_e _F_u_n_c_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     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)

_A_r_g_u_m_e_n_t_s:

       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').

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.

_D_e_t_a_i_l_s:

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

