map                  package:mclust                  R Documentation

_C_l_a_s_s_i_f_i_c_a_t_i_o_n _g_i_v_e_n _P_r_o_b_a_b_i_l_i_t_i_e_s

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

     Converts a matrix in which each row sums to _1_  into the nearest
     matrix of _(0,1)_ indicator variables.

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

     map(z, warn=TRUE, ...)

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

       z: A matrix (for example a matrix of conditional probabilities 
          in which each row sums to 1 as produced by the E-step of the
          EM algorithm). 

    warn: A logical variable indicating whether or not a warning should
          be issued when there are some columns of 'z' for which no row
          attains a maximum. 

    ... : 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 integer vector with one entry for each row of z, in which the
     _i_-th value is the column index at which the _i_-th row of 'z'
     attains a maximum.

_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_. 

     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:

     'unmap', 'estep', 'em', 'me'

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

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

     emEst <- me(modelName = "VVV", data = irisMatrix, z = unmap(irisClass))

     map(emEst$z)

