flexdaCMA-methods            package:CMA            R Documentation

_F_l_e_x_i_b_l_e _D_i_s_c_r_i_m_i_n_a_n_t _A_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     This method is experimental.

     It is easy to show that, after appropriate scaling of the
     predictor matrix 'X', Fisher's Linear Discriminant Analysis is
     equivalent to Discriminant Analysis in the space of the  fitted
     values from the linear regression of the 'nlearn x K' indicator
     matrix of the class labels on 'X'. This gives rise to 'nonlinear
     discrimant analysis' methods that expand 'X' in a suitable, more
     flexible basis. In order to avoid overfitting, penalization is
     used. In the implemented version, the linear model is replaced by
     a generalized additive one, using the package 'mgcv'.

_M_e_t_h_o_d_s:



     _X = "_m_a_t_r_i_x", _y = "_n_u_m_e_r_i_c", _f = "_m_i_s_s_i_n_g" signature 1

     _X = "_m_a_t_r_i_x", _y = "_f_a_c_t_o_r", _f = "_m_i_s_s_i_n_g" signature 2

     _X = "_d_a_t_a._f_r_a_m_e", _y = "_m_i_s_s_i_n_g", _f = "_f_o_r_m_u_l_a" signature 3

     _X = "_E_x_p_r_e_s_s_i_o_n_S_e_t", _y = "_c_h_a_r_a_c_t_e_r", _f = "_m_i_s_s_i_n_g" signature 4

     For further argument and output information, consult 'flexdaCMA'.

