cv1EMtrain              package:mclust              R Documentation

_S_e_l_e_c_t _d_i_s_c_r_i_m_i_n_a_n_t _m_o_d_e_l_s _u_s_i_n_g _c_r_o_s_s _v_a_l_i_d_a_t_i_o_n

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

     For the ten available discriminant models the leave-one-out cross
     validation error is calulated. The models for one-dimensional data
     are "E" and "V"; for higher dimensions they are "EII", "VII",
     "EEI", "VEI", "EVI", "VVI", "EEE", "EEV", "VEV" and "VVV".

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

     cv1EMtrain(data, labels, modelNames)

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

    data: A data matrix

  labels: Labels for each row in the data matrix

modelNames: Vector of model names that should be tested.

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

     Returns a vector where each element is the error rate for the
     corresponding model.

_A_u_t_h_o_r(_s):

     C. Fraley

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

     'bicEMtrain'

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

     data(lansing)
     odd <- seq(from=1, to=nrow(lansing), by=2)
     round(cv1EMtrain(data=lansing[odd,-3], labels=lansing[odd,3]), 3)

     cv1Modd <- mstepEEV(data=lansing[odd,-3], z=unmap(lansing[odd,3]))
     cv1Zodd <- do.call("estepEEV", c(cv1Modd, list(data=lansing[odd,-3])))$z
     compareClass(map(cv1Zodd), lansing[odd,3])

     even <- (1:nrow(lansing))[-odd]
     cv1Zeven <- do.call("estepEEV", c(cv1Modd, list(data=lansing[even,-3])))$z
     compareClass(map(cv1Zodd), lansing[odd,3])$error

