decomp2sigma             package:mclust             R Documentation

_C_o_n_v_e_r_t _m_i_x_t_u_r_e _c_o_m_p_o_n_e_n_t _c_o_v_a_r_i_a_n_c_e_s _t_o _m_a_t_r_i_x _f_o_r_m.

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

     Converts a set of covariances from a parameterization by 
     eigenvalue decomposition to representation as a 3-D array.

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

     decomp2sigma(d, G, scale, shape, orientation, ...)

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

       d: The dimension of the data.  

       G: The number of components in the mixture model.  

   scale: Either a _G_-vector giving the scale of the covariance (the
          _d_th root of its determinant) for each component in the
          mixture model, or a single numeric value if the scale is the
          same for each component.   

   shape: Either a _G_ by _d_ matrix in which the _k_th column is the
          shape of the covariance matrix (normalized to have
          determinant 1) for the _k_th component, or a _d_-vector
          giving a common shape for all components.  

orientation: Either a _d_ by _d_ by _G_ array whose '[,,k]'th entry is
          the orthonomal matrix of eigenvectors of the covariance
          matrix of the _k_th component, or a _d_ by _d_ orthonormal
          matrix if the mixture components have a common orientation.
          The 'orientation' component of 'decomp' can be omitted in
          spherical and diagonal models, for which the principal
          components are parallel to the coordinate axes so that the
          orientation matrix is the identity.   

     ...: Catch unused arguments from a 'do.call' call. 

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

     A 3-D array whose '[,,k]'th component is the  covariance matrix of
     the _k_th component in an MVN mixture model.

_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:

     'sigma2decomp'

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

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

     meEst <- meVEV(irisMatrix, unmap(irisClass)) 
     names(meEst)
     meEst$decomp
     meEst$sigma

     dec <- meEst$decomp
     decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale,
                  orientation = dec$orientation)
     ## Not run: 
     do.call("decomp2sigma", meEst$decomp)  ## alternative call
     ## End(Not run)

