mstepE                package:mclust                R Documentation

_M-_s_t_e_p _i_n _t_h_e _E_M _a_l_g_o_r_i_t_h_m _f_o_r _a _p_a_r_a_m_e_t_e_r_i_z_e_d _M_V_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 MVN
     mixture model.

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

     mstepE(data, z, equalPro, noise = FALSE, ...)
     mstepV(data, z, equalPro, noise = FALSE, ...)
     mstepEII(data, z, equalPro, noise = FALSE, ...)
     mstepVII(data, z, equalPro, noise = FALSE, ...)
     mstepEEI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
     mstepVEI(data, z, equalPro, noise = FALSE, eps, tol, itmax, warnSingular, ...)
     mstepEVI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
     mstepVVI(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
     mstepEEE(data, z, equalPro, noise = FALSE, ...)
     mstepEEV(data, z, equalPro, noise = FALSE, eps, warnSingular, ...)
     mstepVVV(data, z, equalPro, noise = 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. 

       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.   

equalPro: A logical value indicating whether or not the components in
          the model are  present in equal proportions. The default is
          '.Mclust\$equalPro'.  

   noise: A logical value indicating whether or not the model includes
          a Poisson noise component. The default assumes there is no
          noise component. 

     eps: A scalar tolerance for deciding when to terminate
          computations due to computational singularity in covariances.
          Smaller values of 'eps' allows computations to proceed nearer
          to singularity.  The default is '.Mclust\$eps'. 

          Not used for models "EII", "VII", "EEE", "VVV". 

     tol: For models with iterative M-step ("VEI", "VEE", "VVE",
          "VEV"),  a scalar tolerance for relative convergence of the
          parameters.  The default is '.Mclust\$tol'.  

   itmax: For models with iterative M-step ("VEI", "VEE", "VVE",
          "VEV"),  an integer limit on the number of EM iterations. 
          The default is '.Mclust\$itmax'.  

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

          Not used for models "EII", "VII", "EEE", "VVV". 

    ... : Provided to allow lists with elements other than the
          arguments can be passed in indirect or list calls with
          'do.call'. 

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

     A list including the following components: 

      mu: A matrix whose kth column is the mean of the _k_th component
          of the mixture model. 

   sigma: For multidimensional models, a three dimensional array  in
          which the '[,,k]'th entry gives the the covariance for the
          _k_th group in the best model. <br> For one-dimensional
          models, either a scalar giving a common variance for the
          groups or a vector whose entries are the variances for each
          group in the best model. 

     pro: A vector whose _k_th component is the mixing proportion for
          the _k_th component of the mixture model. 

       z: A matrix whose '[i,k]'th entry is the conditional probability
          of the _i_th observation belonging to the _k_th component of
          the mixture.   

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

          *  '"info"' Information on the iteration.

          *  '"warn"' 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 (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:

     'mstep', 'me', 'estep', 'mclustOptions'

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

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

     mstepVII(data = irisMatrix, z = unmap(irisClass))

