bicE                 package:mclust                 R Documentation

_B_I_C _f_o_r _a _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

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

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

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

     bicE(loglik, n, G, equalPro, noise = FALSE, ...)
     bicV(loglik, n, G, equalPro, noise = FALSE, ...)
     bicEII(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicVII(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicEEI(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicVEI(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicEVI(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicVVI(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicEEE(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicEEV(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicVEV(loglik, n, d, G, equalPro, noise = FALSE, ...)
     bicVVV(loglik, n, d, G, equalPro, noise = FALSE, ...)

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

  loglik: The loglikelihood for a data set with respect to the MVN
          mixture model. 

       n: The number of observations in the data used 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'. 

equalPro: 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'. 

   noise: 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.  

     ...: Catch unused arguments from a 'do.call' call. 

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

     The BIC or Bayesian Information Criterion for the MVN mixture
     model and data set corresponding to the 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:

     'bic', 'EMclust', 'estepE', 'mclustOptions', 'do.call'

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

     ## To run an example, see man page for bic
     ## Not run: 
     data(iris)
     irisMatrix <- as.matrix(iris[,1:4])
     irisClass <- iris[,5]

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

     emEst <- meVVI(data=irisMatrix, unmap(irisClass))
     names(emEst)

     bicVVI(loglik=emEst$loglik, n=n, d=d, G=G)
     do.call("bicVVI", emEst)  ## alternative call
     ## End(Not run)

