normalmixEM             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 _U_n_i_v_a_r_i_a_t_e _N_o_r_m_a_l_s

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

     Return EM algorithm output for mixtures of normal distributions.

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

     normalmixEM(x, lambda = NULL, mu = NULL, sigma = NULL, k = 2,
                 arbmean = TRUE, arbvar = TRUE, epsilon = 1e-08, 
                 maxit = 10000, verb = FALSE)

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

       x: A vector of length n consisting of the data.

  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 'mu'.

      mu: A k-vector of initial values for the mean parameters.   If
          NULL, then the vector is generated from a normal distribution
          according to a binning method done on the data. If both
          'lambda' and 'mu' are NULL, then number of components is
          determined by 'sigma'.

   sigma: A k-vector of initial values for the standard deviation
          parameters.   If NULL, then 1/'sigma'$^2$ has random
          exponential entries according to a binning method done on the
          data.   If 'lambda', 'mu', and 'sigma' are NULL, then number
          of components is determined by 'k'.

       k: Number of components.  Ignored unless 'lambda', 'mu', and
          'sigma' are all NULL.

 arbmean: If TRUE, then the component densities are allowed to have
          different 'mu's. If FALSE, then a scale mixture will be fit.

  arbvar: If TRUE, then the component densities are allowed to have
          different 'sigma's. If FALSE, then a location mixture will be
          fit.

 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:

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

       x: The raw data.

  lambda: The final mixing proportions.

      mu: The final mean parameters.

   sigma: The final standard deviations. If 'arbmean' = FALSE, then
          only the smallest standard deviation is returned. See 'scale'
          below.

   scale: If 'arbmean' = FALSE, then the scale factor for the component
          standard deviations is returned. Otherwise, this is omitted
          from the output.

  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.

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

     'mvnormalmixEM'

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

     ##Analyzing the Old Faithful geyser data with a 2-component mixture of normals.

     data(faithful)
     attach(faithful)
     out<-normalmixEM(waiting, arbvar = FALSE, epsilon = 1e-03)
     out

