mclustBIC               package:mclust               R Documentation

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

     BIC for EM initialized by model-based hierarchical clustering for
     parameterized Gaussian mixture models.

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

     mclustBIC(data, G=NULL, modelNames=NULL, prior=NULL, control=emControl(), 
               initialization=list(hcPairs=NULL, subset=NULL, noise=NULL), 
               Vinv=NULL, warn=FALSE, x=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.  

       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', unless the argument 'x' is specified,
           in which case the default is taken from the values
          associated  with 'x'.  

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),
          unless the argument 'x' is specified, in which case the
          default is taken from the values asscoiated with 'x'.  

   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.

        _n_o_i_s_e A logical or numeric vector indicating an initial guess
             as to which observations are noise in the data. If
             supplied, a noise term will be added to the model in the
             estimation.

    Vinv: An estimate of the reciprocal hypervolume of the data region.
          The default is determined by applying function 'hypvol' to
          the data.  Used only if an initial guess as to which
          observations are noise  is supplied. 

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

       x: An object of class '"mclustBIC"'. If supplied, 'mclustBIC'
          will use the settings in 'x' to produce another object of
          class '"mclustBIC"', but with 'G' and 'modelNames' as
          specified in the arguments. Models that have already been
          computed in 'x' are not recomputed. All arguments to
          'mclustBIC'  except 'data', 'G' and 'modelName' are ignored
          and their values are set as specified in the attributes of
          'x'.  Defaults for 'G' and 'modelNames' are taken from 'x'. 

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

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

     Bayesian Information Criterion for the specified mixture models
     numbers of clusters. Auxiliary information returned as attributes.

_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',  'mclustModel', 
     'summary.mclustBIC',  'hc', 'me', 'mclustModelNames',
     'mclustOptions'

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

     irisBIC <- mclustBIC(iris[,-5])
     irisBIC
     plot(irisBIC)

     subset <- sample(1:nrow(iris), 100)
     irisBIC <- mclustBIC(iris[,-5], initialization=list(subset =subset))
     irisBIC
     plot(irisBIC)

     irisBIC1 <- mclustBIC(iris[,-5], G=seq(from=1,to=9,by=2), 
                         modelNames=c("EII", "EEI", "EEE"))
     irisBIC1
     plot(irisBIC1)
     irisBIC2  <- mclustBIC(iris[,-5], G=seq(from=2,to=8,by=2), 
                            modelNames=c("VII", "VVI", "VVV"), x= irisBIC1)
     irisBIC2
     plot(irisBIC2)

     nNoise <- 450
     set.seed(0)
     poissonNoise <- apply(apply( iris[,-5], 2, range), 2, function(x, n) 
                           runif(n, min = x[1]-.1, max = x[2]+.1), n = nNoise)
     set.seed(0)
     noiseInit <- sample(c(TRUE,FALSE),size=nrow(iris)+nNoise,replace=TRUE,
                         prob=c(3,1))
     irisNdata <- rbind(iris[,-5], poissonNoise)
     irisNbic <- mclustBIC(data = irisNdata,
                           initialization = list(noise = noiseInit))
     irisNbic
     plot(irisNbic)

