estepE                package:mclust                R Documentation

_E-_s_t_e_p _i_n _t_h_e _E_M _a_l_g_o_r_i_t_h_m _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 expectation step in the EM algorithm for a 
     parameterized Gaussian mixture model.

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

     estepE(data, parameters, warn = NULL, ...)
     estepV(data, parameters, warn = NULL, ...)
     estepEII(data, parameters, warn = NULL, ...)
     estepVII(data, parameters, warn = NULL, ...)
     estepEEI(data, parameters, warn = NULL, ...)
     estepVEI(data, parameters, warn = NULL, ...)
     estepEVI(data, parameters, warn = NULL, ...)
     estepVVI(data, parameters, warn = NULL, ...)
     estepEEE(data, parameters, warn = NULL, ...)
     estepEEV(data, parameters, warn = NULL, ...)
     estepVEV(data, parameters, warn = NULL, ...)
     estepVVV(data, parameters, 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. 

parameters: The parameters of the model:

             *  An argument describing the variance (depends on the
                model):

             _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_u The mean for each component. If there is more than one
                  component, this is a matrix whose columns are the
                  means of the  components.

             _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 not supplied or set to 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 certain warnings should
          be issued. 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: Character string identifying the model. 

       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 logliklihood 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_. 

     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:

     'estep', 'em', 'mstep', 'do.call', 'mclustOptions',
     'mclustVariance'

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

     msEst <- mstepEII(data = iris[,-5], z = unmap(iris[,5]))
     names(msEst)

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

