estep                 package:mclust                 R Documentation

_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 expectation step of EM algorithm for parameterized
     Gaussian mixture models.

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

       estep( modelName, data, parameters, 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.

    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.    

parameters: The input parameters. 

  loglik: The loglikelihood for the data in the mixture model.  

             *  '"WARNING"': 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 (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:

     'estepE', ..., 'estepVVV', 'em', 'mstep', 'mclustOptions'
     'mclustVariance'

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

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

     estep(modelName = msEst$modelName, data = iris[,-5],
           parameters = msEst$parameters)           

