em                  package:mclust                  R Documentation

_E_M _a_l_g_o_r_i_t_h_m _s_t_a_r_t_i_n_g _w_i_t_h _E-_s_t_e_p _f_o_r _p_a_r_a_m_e_t_e_r_i_z_e_d _G_a_u_s_s_i_a_n _m_i_x_t_u_r_e _m_o_d_e_l_s.

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

     Implements the EM algorithm for parameterized Gaussian mixture
     models, starting with the expectation step.

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

     em(modelName, data, parameters, prior = NULL, control = emControl(),
        warn = NULL, ...)

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

modelName: A character string indicating the model. The help file for
          'mclustModelNames' describes the available models. 

    data: A numeric vector, matrix, or data frame of observations.
          Categorical variables are not allowed. If a matrix or data
          frame, rows correspond to observations and columns correspond
          to variables.  

parameters: A names list giving the parameters of the model. The
          components are as follows:

          _p_r_o Mixing proportions for the components of the mixture.  If
               the model includes a Poisson term for noise, there 
               should be one more mixing proportion than the number  of
               Gaussian components.

          _m_e_a_n The mean for each component. If there is more than one
               component, this is a matrix whose kth column is the mean
               of the _k_th component of the mixture model.

          _v_a_r_i_a_n_c_e A list of variance parameters for the model. The
               components of this list depend on the model
               specification. See the help file for 'mclustVariance'
               for details.

          _V_i_n_v An estimate of the reciprocal hypervolume of the data
               region. If set to NULL or a negative value, the default
               is determined by  applying function 'hypvol' to the
               data. Used only when 'pro' includes an additional mixing
               proportion for a noise component.

   prior: Specification of a conjugate prior on the means and
          variances. The default assumes no prior. 

 control: A list of control parameters for EM. The defaults are set by
          the call  'emControl()'. 

    warn: A logical value indicating whether or not a warning should be
          issued when computations fail. The default is 'warn=FALSE'. 

     ...: Catches unused arguments in indirect or list calls via
          'do.call'. 

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

     A list including the following components:  

modelName: A character string identifying the model (same as the input
          argument). 

       z: A matrix whose '[i,k]'th entry is the conditional probability
          of the _i_th observation belonging to the _k_th component of
          the mixture.   

          _p_r_o A vector whose _k_th component is the mixing proportion
               for  the _k_th component of the mixture model. If the
               model includes a Poisson term for noise, there  should
               be one more mixing proportion than the number  of
               Gaussian components.

          _m_e_a_n The mean for each component. If there is more than one
               component, this is a matrix whose kth column is the mean
               of the _k_th  component of the mixture model. 

          _v_a_r_i_a_n_c_e A list of variance parameters for the model. The
               components of this list depend on the model
               specification. See the help file for 'mclustVariance' 
               for details.  

          _V_i_n_v The estimate of the reciprocal hypervolume of the data
               region used in the computation when the input indicates
               the addition of a noise component to the model.

  loglik: The log likelihood for the data in the mixture model.  

        "_i_n_f_o" Information on the iteration.

        "_W_A_R_N_I_N_G" An appropriate warning if problems are encountered in
             the computations.

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

     C. Fraley and A. E. Raftery (2002). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_. 

     C. Fraley and A. E. Raftery (2005). Bayesian regularization for
     normal mixture estimation and model-based clustering. Technical
     Report, Department of Statistics, University of Washington.

     C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal
     Mixture Modeling and Model-Based Clustering,  Technical Report no.
     504, Department of Statistics, University of Washington.

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

     'emE', ..., 'emVVV', 'estep', 'me', 'mstep', 'mclustOptions',
     'do.call'

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

     msEst <- mstep(modelName = "EEE", data = iris[,-5], 
                    z = unmap(iris[,5]))
     names(msEst)

     em(modelName = msEst$modelName, data = iris[,-5],
        parameters = msEst$parameters)
     ## Not run: 
     do.call("em", c(list(data = iris[,-5]), msEst))   ## alternative call
     ## End(Not run)

