stat.diag.da               package:sma               R Documentation

_D_i_a_g_o_n_a_l _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 function implements a simple Gaussian maximum likelihood
     discriminant rule, for diagonal class covariance matrices.

_U_s_a_g_e:

     stat.diag.da(ls, cll, ts, pool=1)

_A_r_g_u_m_e_n_t_s:

      ls: learning set data matrix, with rows corresponding to cases
          (i.e., mRNA samples) and columns to predictor variables 
          (i.e., genes).

     cll: class labels for learning set, must be consecutive integers.

      ts: test set data matrix, with rows corresponding to cases and
          columns to predictor variables.

    pool: logical flag. If 'pool=1', the covariance matrices are
          assumed to be constant across classes and the discriminant
          rule is linear in the data. If 'pool=0', the covariance
          matrices may vary across classes and the discriminant rule is
          quadratic in the data.

_V_a_l_u_e:

     List containing the following components  

    pred: vector of class predictions for the test set.

_A_u_t_h_o_r(_s):

     Sandrine Dudoit, sandrine@stat.berkeley.edu 
      Jane Fridlyand, janef@stat.berkeley.edu

_R_e_f_e_r_e_n_c_e_s:

     S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of
     Discrimination Methods for the Classification of Tumors Using Gene
      Expression Data. June 2000. (Statistics, UC Berkeley, Tech Report
     #576).

