mclustModel              package:mclust              R Documentation

_B_e_s_t _m_o_d_e_l _b_a_s_e_d _o_n _B_I_C.

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

     Determines the best model from clustering via 'mclustBIC' for a
     given set of model parameterizations and numbers of components.

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

     mclustModel(data, BICvalues, G, modelNames, ...)

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

    data: The matrix or vector of observations used to generate
          `object'. 

BICvalues: An '"mclustBIC"' object,  which is the result of applying
          'mclustBIC'  to 'data'. 

       G: A vector of integers giving the numbers of mixture components
          (clusters) from which the best model according to BIC will be
          selected  ('as.character(G)' must be a subset of the row
          names of  'BICvalues'). The default is to select the best
          model for all numbers  of mixture components used to obtain
          'BICvalues'. 

modelNames: A vector of integers giving the model parameterizations
          from which the best model according to BIC will be selected 
          ('as.character(model)' must be a subset of the column names
          of  'BICvalues'). The default is to select the best model for
          parameterizations used to obtain 'BICvalues'. 

     ...: Not used. For generic/method consistency. 

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

     A list giving the optimal (according to BIC) parameters,
     conditional probabilities 'z', and loglikelihood, together with
     the associated classification and its uncertainty.

     The details of the output components are as follows:

modelName: A character string denoting the model corresponding to the
          optimal BIC. 

       n: The number of observations in the data. 

       d: The dimension of the data. 

       G: The number of mixture components in the model corresponding
          to the optimal BIC. 

     bic: The optimal BIC value. 

  loglik: The loglikelihood corresponding to the optimal BIC. 

       z: A matrix whose _[i,k]_th entry is the probability that
          observation _i_ in the test data belongs to the _k_th class. 

_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:

     'mclustBIC'

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

     irisBIC <- mclustBIC(iris[,-5])
     mclustModel(iris[,-5], irisBIC)
     mclustModel(iris[,-5], irisBIC, G = 1:6, modelNames = c("VII", "VVI", "VVV"))

