ElasticNetCMA-methods          package:CMA          R Documentation

_C_l_a_s_s_f_i_c_a_t_i_o_n _a_n_d _v_a_r_i_a_b_l_e _s_e_l_e_c_t_i_o_n _b_y _t_h_e _E_l_a_s_t_i_c_N_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     Zou and Hastie (2004) proposed a combined L1/L2 penalty for
     regularization and variable selection. The Elastic Net penalty
     encourages a grouping effect, where strongly correlated predictors
     tend to be in or out of the model together. The computation is
     done with the function 'glmpath' from the package of the same
     name.

_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 references, further argument and output information, consult
     'ElasticNetCMA'

