multmixEM              package:mixtools              R Documentation

_E_M _A_l_g_o_r_i_t_h_m _f_o_r _M_i_x_t_u_r_e_s _o_f _M_u_l_t_i_n_o_m_i_a_l_s

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

     Return EM algorithm output for mixtures of multinomial
     distributions.

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

     multmixEM(y, lambda = NULL, theta = NULL, k = 2,
               maxit = 10000, epsilon = 1e-08, verb = FALSE)

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

       y: An nxp matrix of data (multinomial counts), where n is the
          sample size and p is the number of multinomial bins.

  lambda: Initial value of mixing proportions.  Entries should sum to
          1.  This determines number of components.  If NULL, then
          'lambda' is random from uniform Dirichlet and number of
          components is determined by 'theta'.

   theta: Initial value of 'theta' parameters.  Should be a kxp matrix,
          where p is the number of columns of y and k is number of
          components. Each row of 'theta' should sum to 1. If NULL,
          then each row is random from uniform Dirichlet. If both
          'lambda' and 'theta' are NULL, then number of components  is
          determined by k.

       k: Number of components.  Ignored unless 'lambda' and 'theta'
          are NULL.

 epsilon: The convergence criterion.

   maxit: The maximum number of iterations.

    verb: If TRUE, then various updates are printed during each
          iteration of the algorithm.

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

     'multmixEM' returns a list of class 'mixEM' with items: 

       y: The raw data.

  lambda: The final mixing proportions.

   theta: The final multinomial parameters.

  loglik: The final log-likelihood.

posterior: An nxk matrix of posterior probabilities for observations.

all.loglik: A vector of each iteration's log-likelihood.

restarts: The number of times the algorithm restarted due to
          unacceptable choice of initial values.

      ft: A character vector giving the name of the function.

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

     McLachlan, G. J. and Peel, D. (2000) _Finite Mixture Models_, John
     Wiley & Sons, Inc.

     Elmore, R. T., Hettmansperger, T. P. and Xuan, F. (2004) The Sign
     Statistic, One-Way Layouts and Mixture Models, _Statistical
     Science_ *19(4)*, 579-587.

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

     'compCDF', 'makemultdata', 'multmixmodel.sel'

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

     ## The sulfur content of the coal seams in Texas

     A<-c(1.51, 1.92, 1.08, 2.04, 2.14, 1.76, 1.17)
     B<-c(1.69, 0.64, .9, 1.41, 1.01, .84, 1.28, 1.59) 
     C<-c(1.56, 1.22, 1.32, 1.39, 1.33, 1.54, 1.04, 2.25, 1.49) 
     D<-c(1.3, .75, 1.26, .69, .62, .9, 1.2, .32) 
     E<-c(.73, .8, .9, 1.24, .82, .72, .57, 1.18, .54, 1.3)

     dis.coal<-makemultdata(A, B, C, D, E, 
                            cuts = median(c(A, B, C, D, E)))
     em.out<-multmixEM(dis.coal$y, epsilon = 1e-3)
     em.out[1:4]

