Mclust                package:mclust                R Documentation

_M_o_d_e_l-_B_a_s_e_d _C_l_u_s_t_e_r_i_n_g

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

     The optimal model according to BIC for EM initialized by
     hierarchical  clustering for parameterized Gaussian mixture
     models.

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

     Mclust(data, G=NULL, modelNames=NULL, prior=NULL, control=emControl(), 
            initialization=NULL, warn=FALSE, ...)

_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.  

       G: An integer vector specifying the numbers of mixture
          components (clusters) for which the BIC is to be calculated. 
          The default is 'G=1:9'.  

modelNames: A vector of character strings indicating the models to be
          fitted  in the EM phase of clustering. The help file for
          'mclustModelNames' describes the available models. The
          default is 'c("E", "V")' for univariate data and
          'mclustOptions()\$emModelNames' for multivariate data (n >
          d), the spherical and diagonal models  'c("EII", "VII",
          "EEI", "EVI", "VEI", "VVI")'  for multivariate data (n <= d). 

   prior: The default assumes no prior, but this argument allows
          specification of a  conjugate prior on the means and
          variances through the function  'priorControl'. 

 control: A list of control parameters for EM. The defaults are set by
          the call 'emControl()'.  

initialization: A list containing zero or more of the following
          components:

        _h_c_P_a_i_r_s A matrix of merge pairs for hierarchical clustering
             such as produced by function 'hc'. For multivariate data,
             the default is to compute a hierarchical clustering tree
             by applying function 'hc' with 'modelName = "VVV"' to the
             data or a subset as indicated by the 'subset' argument.
             The hierarchical clustering results are to start EM. For
             univariate data, the default is to use quantiles to start
             EM.

        _s_u_b_s_e_t A logical or numeric vector specifying a subset of the
             data to be used in the initial hierarchical clustering
             phase.

    warn: A logical value indicating whether or not certain warnings 
          (usually related to singularity) should be issued. The
          default is to suppress these warnings. 

    ... : Catches unused arguments in indirect or list calls via
          'do.call'. 

_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 at which the optimal
          BIC occurs. 

       n: The number of observations in the data. 

       d: The dimension of the data. 

       G: The optimal number of mixture components. 

     BIC: All BIC values. 

     bic: 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. 

classification: 'map(z)': The classification corresponding to 'z'. 

uncertainty: The uncertainty associated with the classification. 

Attributes:: The input parameters other than the data. 

_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 (2005). Bayesian regularization for
     normal mixture estimation and model-based clustering. Technical
     Report, Department of Statistics, University of Washington. 

     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:

     'priorControl',  'emControl',  'mclustBIC',  'mclustModelNames',
     'mclustOptions'

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

     irisMclust <- Mclust(iris[,-5])
     ## Not run: 
      plot(irisMclust)
     ## End(Not run)

