mclustOptions             package:mclust             R Documentation

_S_e_t _c_o_n_t_r_o_l _v_a_l_u_e_s _f_o_r _u_s_e _w_i_t_h _M_C_L_U_S_T.

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

     Supplies a list of values including tolerances for singularity and
     convergence assessment, and an enumeration of models for use with
     _MCLUST_.

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

     mclustOptions(eps, tol, itmax, equalPro, warnSingular, emModelNames,
                   hcModelName, symbols) 

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

     eps: A scalar tolerance associated with deciding when to terminate
          computations due to computational singularity in covariances.
          Smaller values of 'eps' allow computations to proceed nearer
          to singularity. The default is the relative machine precision
          '.Machine\$double.eps', which is approximately $2e-16$ on
          IEEE-compliant machines.  

     tol: A vector of length two giving relative convergence tolerances
          for the  loglikelihood and for parameter convergence in the
          inner loop for models with iterative M-step ("VEI", "VEE",
          "VVE", "VEV"), respectively. The default is 'c(1.e-5,1.e-5)'. 

   itmax: A vector of length two giving integer limits on the number of
          EM iterations and on the number of iterations in the inner
          loop for models with iterative M-step ("VEI", "VEE", "VVE",
          "VEV"), respectively. The default is 'c(Inf,Inf)' allowing
          termination to be completely governed by 'tol'.  

equalPro: Logical variable indicating whether or not the mixing
          proportions are equal in the model. Default: 'equalPro =
          FALSE'. 

warnSingular: A logical value indicating whether or not a warning
          should be issued whenever a singularity is encountered. The
          default is 'warnSingular = TRUE'.  

emModelNames: A vector of character strings associated with
          multivariate models in MCLUST. The default includes strings
          encoding all of the multivariate models available:

           "EII": spherical, equal volume 
           "VII": spherical, unequal volume 
           "EEI": diagonal, equal volume and shape
            "VEI": diagonal, varying volume, equal shape
            "EVI": diagonal, equal volume, varying shape 
           "VVI": diagonal, varying volume and 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  

hcModelName: A vector of two character strings giving the name of the
          model to be used in the hierarchical clustering phase for
          univariate and multivariate data, respectively, in 'EMclust'
          and 'EMclustN'. The default is 'c("V","VVV")', giving the
          unconstrained model in each case.  

 symbols: A vector whose entries are either integers corresponding to
          graphics symbols or single characters for plotting for
          classifications. Classes are assigned symbols in the given
          order. The default is
          'c(17,0,10,4,11,18,6,7,3,16,2,12,8,15,1,9,14,13,5)'.  

_D_e_t_a_i_l_s:

     'mclustOptions' is provided for assigning values to the '.Mclust'
     list, which is used to supply default values to various functions
     in MCLUST.

     Calls to 'mclustOptions' do not in themselves affect the outcome
     of computations.

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

     A named list in which the names are the names of the arguments and
     the values are the values supplied to the 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:

     '.Mclust'

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

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

     .Mclust
     .Mclust <- mclustOptions(tol = 1.e-6, emModelNames = c("VII", "VVI", "VVV"))
     .Mclust
     irisBic <- EMclust(irisMatrix)
     summary(irisBic, irisMatrix)
     .Mclust <- mclustOptions() # restore default values
     .Mclust

