emControl               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 _t_h_e _E_M _a_l_g_o_r_i_t_h_m.

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

     Supplies a list of values including tolerances for singularity and
     convergence assessment, for use functions inivoling EM within
     _MCLUST_.

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

     emControl(eps, tol, itmax, equalPro) 

_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,sqrt(.Machine$double.eps))'. If only one number is
          supplied, it is used as the tolerance  for the outer
          iterations and the tolerance for the inner iterations is as
          in the default. 

   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'.  If only one
          number is supplied, it is used as the iteration limit for the
          outer iteration only. 

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

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

     'emControl' is provided for assigning values and defaults for EM
     within _MCLUST_.

_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 (2002). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_. 

     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:

     'em',  'estep',  'me',  'mstep',  'mclustBIC'

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

     irisBIC<- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6))
     summary(irisBIC, iris[,-5])

