probhmm             package:HiddenMarkov             R Documentation

_C_o_n_d_i_t_i_o_n_a_l _D_i_s_t_r_i_b_u_t_i_o_n _F_u_n_c_t_i_o_n

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

     Calculates the distribution function at each point for a 'dthmm'
     process given the complete observed process except the given
     point.

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

     probhmm(x, Pi, delta, distn, pm, pn = NULL, adj = 0, ...)

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

       x: is a vector of length n containing the observed process.

      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').

     adj: zero or one, being an adjustment for discrete distributions.

     ...: other arguments. This allows more complicated objects with
          the required arguments, but with redundant components also,
          to be passed to this function.

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

     Let X^{(-i)} denote the entire process, except with the point X_i
     removed. The distribution function at the point X_i is Pr{ X_i <=
     x_i ,|, X^{(-i)} = x^{(-i)} }. This R function calculates the
     distribution function for each point X_i for i=1, cdots, n. This
     is done by using the forward and backward probabilities before and
     after the ith point, respectively.

     In the programming code, note the subtraction of the mean. This is
     to stop underflow when the exponential is taken. Removal of the
     mean is automatically compensated for by the fact that the same
     factor is removed in both the numerator and denominator.

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

     A vector containing the probability.

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

     Zucchini, W. (2005). _Hidden Markov Models Short Course, 3-4 April
     2005._ Macquarie University, Sydney.

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

     'residuals'

