mstepE                package:mclust                R Documentation

_M-_s_t_e_p _f_o_r _a _p_a_r_a_m_e_t_e_r_i_z_e_d _G_a_u_s_s_i_a_n _m_i_x_t_u_r_e _m_o_d_e_l.

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

     Maximization step in the EM algorithm for a parameterized Gaussian
     mixture model.

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

     mstepE( data, z, prior=NULL, warn=NULL, ...)
     mstepV( data, z, prior=NULL,  warn=NULL, ...)
     mstepEII( data, z, prior=NULL, warn=NULL, ...)
     mstepVII( data, z, prior=NULL, warn=NULL, ...)
     mstepEEI( data, z, prior=NULL,  warn=NULL, ...)
     mstepVEI( data, z, prior=NULL, warn=NULL, control=NULL, ...)
     mstepEVI( data, z, prior=NULL, warn=NULL, ...)
     mstepVVI( data, z, prior=NULL, warn=NULL, ...)
     mstepEEE( data, z, prior=NULL, warn=NULL, ...)
     mstepEEV( data, z, prior=NULL, warn=NULL, ...)
     mstepVEV( data, z, prior=NULL, warn=NULL, control=NULL,...)
     mstepVVV( data, z, prior=NULL, warn=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. 

       z: A matrix whose '[i,k]'th entry is the conditional probability
          of the ith observation belonging to the _k_th component of
          the mixture.   In analyses involving noise, this should not
          include the conditional probabilities for the noise
          component.  

   prior: Specification of a conjugate prior on the means and
          variances. The default assumes no prior.   

    warn: A logical value indicating whether or not certain warnings
          (usually related to singularity) should be issued when the
          estimation fails. The default is set in '.Mclust\$warn'. 

 control: Values controling termination for models '"VEI"' and '"VEV"' 
          that have an iterative M-step. This should be a list with
          components named _itmax_ and _tol_. These components can be
          of length 1  or 2; in the latter case, 'mstep' will use the
          second value, under  the assumption that the first applies to
          an outer iteration (as in the  function 'me'). The default
          uses the default values from the function 'emControl', which
          sets no limit on  the number of iterations, and a relative
          tolerance  of 'sqrt(.Machine\$double.eps)' on succesive
          iterates. 

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

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

     A list including the following components:  

modelName: A character string identifying the model (same as the input
          argument). 

          _p_r_o A vector whose _k_th component is the mixing proportion
               for  the _k_th component of the mixture model. If the
               model includes a Poisson term for noise, there  should
               be one more mixing proportion than the number  of
               Gaussian components.

          _m_e_a_n The mean for each component. If there is more than one
               component, this is a matrix whose kth column is the mean
               of the _k_th  component of the mixture model. 

          _v_a_r_i_a_n_c_e A list of variance parameters for the model. The
               components of this list depend on the model
               specification. See the help file for 'mclustVariance' 
               for details.  

          *  '"info"' For those models with iterative M-steps ('"VEI"'
             and '"VEV"'), information  on the iteration.

          *  '"WARNING"' An appropriate warning if problems are
             encountered in the computations.  

_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.

_N_o_t_e:

     This function computes the M-step only for MVN mixtures, so in 
     analyses involving noise, the conditional probabilities input
     should  exclude those for the noise component. 

     In contrast to 'me' for the EM algorithm, computations in 'mstep'
     are carried out unless failure due to overflow would occur. To
     impose stricter tolerances on a single 'mstep', use 'me' with the
     _itmax_ component of the 'control' argument set to 1.

_S_e_e _A_l_s_o:

     'mstep', 'me', 'estep', 'priorControl' 'emControl'

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

     mstepVII(data = iris[,-5], z = unmap(iris[,5]))

