bic                  package:mclust                  R Documentation

_B_I_C _f_o_r _P_a_r_a_m_e_t_e_r_i_z_e_d _M_V_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:

     Compute the BIC (Bayesian Information Criterion) for parameterized
     mixture models  given the loglikelihood, the dimension of the
     data, and number of mixture components in the model.

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

     bic(modelName, loglik, n, d, G, ...)

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

modelName: A character string indicating the model. Possible models: 

           "E" for spherical, equal variance  (one-dimensional)  
           "V" for spherical, variable variance (one-dimensional)  
           "EII": spherical, equal volume  
           "VII": spherical, unequal volume  
           "EEI": diagonal, equal volume, equal shape  
           "VEI": diagonal, varying volume, equal shape  
           "EVI": diagonal, equal volume, varying shape  
           "VVI": diagonal, varying volume, varying shape  
           "EEE": ellipsoidal, equal volume, shape, and orientation  
           "EEV": ellipsoidal, equal volume and equal shape 
           "VEV": ellipsoidal, equal shape  
           "VVV": ellipsoidal, varying volume, shape, and orientation  

  loglik: The loglikelihood for a data set with respect to the MVN
          mixture model specified in the 'modelName' argument. 

       n: The number of observations in the data use to compute
          'loglik'. 

       d: The dimension of the data used to compute 'loglik'. 

       G: The number of components in the MVN mixture model used to
          compute 'loglik'. 

     ...: Arguments for diagonal-specific methods, in particular

          _e_q_u_a_l_P_r_o A logical variable indicating whether or not the
               components in the model are assumed to be present in
               equal proportion. The default is '.Mclust\$equalPro'. 

          _n_o_i_s_e A logical variable indicating whether or not the model
               includes and optional Poisson noise component. The
               default is to assume that the model does not include a
               noise component.

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

     The BIC or Bayesian Information Criterion for the given input
     arguments.

_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_.  See <URL:
     http://www.stat.washington.edu/mclust>. 

     C. Fraley and A. E. Raftery (2002b). MCLUST:Software for
     model-based clustering, density estimation and  discriminant
     analysis.  Technical Report, Department of Statistics, University
     of Washington.  See <URL: http://www.stat.washington.edu/mclust>.

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

     'bicE', ..., 'bicVVV', 'EMclust', 'estep', 'mclustOptions',
     'do.call'.

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

     data(iris)
     irisMatrix <- as.matrix(iris[,1:4])
     irisClass <- iris[,5]

     n <- nrow(irisMatrix)
     d <- ncol(irisMatrix)
     G <- 3

     emEst <- me(modelName="VVI", data=irisMatrix, unmap(irisClass))
     names(emEst)

     args(bic)
     bic(modelName="VVI",loglik=emEst$loglik,n=n,d=d,G=G)
     ## Not run: do.call("bic", emEst)    ## alternative call

