meE                  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 _M-_s_t_e_p _f_o_r _a _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.

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

     Implements the EM algorithm for a parameterized Gaussian mixture
     model, starting with the maximization step.

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

     meE(data, z, prior=NULL, control=emControl(), 
         Vinv=NULL, warn=NULL, ...)
     meV(data, z, prior=NULL, control=emControl(),
         Vinv=NULL, warn=NULL, ...)
     meEII(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meVII(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meEEI(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meVEI(data, z, prior=NULL, control=emControl(),
          Vinv=NULL, warn=NULL, ...)
     meEVI(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meVVI(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meEEE(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meEEV(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meVEV(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)
     meVVV(data, z, prior=NULL, control=emControl(),
           Vinv=NULL, warn=NULL, ...)

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

    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.  

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

   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()'.  

    Vinv: An estimate of the reciprocal hypervolume of the data region,
          when the model is to include a noise term. Set to a negative
          value or zero if a noise term is desired, but an estimate is
          unavailable - in that case function 'hypvol' will be used to
          obtain the estimate. The default is not to assume a noise
          term in the model through the setting 'Vinv=NULL'. 

    warn: A logical value indicating whether or not certain warnings
          (usually related to singularity) should be issued when the
          estimation fails. The default is set in '.Mclust\$warn'. 

    ... : 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.  

             *  '"info"' Information on the iteration.

             *  '"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 (2002a). 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:

     'em', 'me', 'estep', 'mclustOptions'

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

     meVVV(data = iris[,-5], z = unmap(iris[,5]))

