mps                package:hmm.discnp                R Documentation

_M_o_s_t _p_r_o_b_a_b_l_e _s_t_a_t_e_s.

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

     Calculates the most probable hidden state underlying each
     observation.

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

     mps(y, object = NULL, tpm, Rho, ispd=NULL)

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

       y: The observations for which the underlying most probable
          hidden states are required.  May be a sequence of
          observations, or a list each component of which constitutes a
          (replicate) sequence of observations.  If 'y' is missing it
          is set equal to the 'y' component of 'object', given that
          that object and that component exist.  Otherwise an error is
          given.

  object: An object describing a fitted hidden Markov model, as
          returned by 'hmm()'.  In order to make any kind of sense,
          'object' should bear some reasonable relationship to 'y'.

     tpm: The transition probability matrix for a hidden Markov model;
          ignored if 'object' is non-null. Should bear some reasonable
          relationship to 'y'.

     Rho: A matrix specifying the probability distributions of the
          observations for a hidden Markov model; ignored if 'object'
          is non-null. Should bear some reasonable relationship to 'y'.

    ispd: A vector specifying the initial state probability
          distribution for a hidden Markov model; ignored if 'object'
          is non-null. Should bear some reasonable relationship to 'y'.
          If both 'ispd' and 'object' are 'NULL' then 'ispd' is taken
          to be the stationary distribution of the chain, calculated
          from 'tpm'.

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

     For each t the maximum value of gamma_t(i), i.e. of the
     (estimated) probability that the state at time t is equal to i, is
     calculated, and the corresponding index returned.  These indices
     are interpreted as the values of the (most probable) states.  I.e.
     the states are assumed to be 1, 2, ..., K, for some K.

_V_a_l_u_e:

     If 'y' is a single observation sequence, then the value is a
     vector of corresponding most probable states.

     If 'y' is a matrix of replicate sequences, then the value is a
     matrix, the j-th column of which constitutes the vector of most
     probable states underlying the j-th replicate sequence.

_W_a_r_n_i_n_g:

     The _sequence of most probable states_ as calculated by this
     function will not in general be the _most probable sequence of
     states_.  It may not even be a _possible_ sequence of states. This
     function looks at the state probabilities separately for each time
     t, and not at the states in their sequential context.

     To obtain the most probable sequence of states use 'viterbi()'.

_A_u_t_h_o_r(_s):

     Rolf Turner r.turner@auckland.ac.nz
      <URL: http://www.math.unb.ca/~rolf>

_R_e_f_e_r_e_n_c_e_s:

     Rabiner, L. R., "A tutorial on hidden Markov models and selected
     applications in speech recognition," Proc. IEEE vol. 77, pp. 257 -
     286, 1989.

_S_e_e _A_l_s_o:

     'hmm()', 'sim.hmm()', 'viterbi()'

_E_x_a_m_p_l_e_s:

     # See the help for sim.hmm() for how to generate y.num.
     ## Not run: 
     fit.num <- hmm(y.num,K=2,verb=TRUE)
     s.1 <- mps(y.num,fit.num)
     s.2 <- mps(y.num,tpm=P,ispd=c(0.25,0.75),Rho=R) # P and R as in the help
                                                       # for sim.hmm().
     # The order of the states has gotten swapped; 3-s.1[,1] is much
     # more similar to s.2[,1] than is s.1[,1].
     ## End(Not run)

