mclustDAtest             package:mclust             R Documentation

_M_c_l_u_s_t_D_A _T_e_s_t_i_n_g

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

     Testing phase for MclustDA discriminant analysis.

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

     mclustDAtest(data, models)

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

    data: A numeric vector, matrix, or data frame of observations to be
          classified. 

  models: A list of MCLUST-style models including parameters, usually
          the result of applying 'mclustDAtrain' to some training data.            

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

     Apply 'summary' to the output to obtain the classification  of the
     test data.

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

     A matrix in which the '[i,j]'th entry is the  density for that
     test observation _i_ in the model for class _j_.

_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:

     'summary.mclustDAtest', 'classError', 'mclustDAtrain'

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

     odd <- seq(1, nrow(cross), by = 2)
     train <- mclustDAtrain(cross[odd,-1], labels = cross[odd,1]) ## training step
     summary(train)

     even <- odd + 1
     test <- mclustDAtest(cross[even,-1], train) ## compute model densities
     clEven <- summary(test)$class ## classify training set
     classError(clEven,cross[even,1])

