roc                   package:CMA                   R Documentation

_R_e_c_e_i_v_e_r _O_p_e_r_a_t_o_r _C_h_a_r_a_c_t_e_r_i_s_t_i_c

_D_e_s_c_r_i_p_t_i_o_n:

     The empirical Receiver Operator Characteristic (ROC) is widely
     used for the evaluation of diagnostic tests, but also for the
     evaluation of classfiers. In this implementation, it can only be
     used for the binary classification case. The input are a numeric
     vector of class probabilities (which play the role of a test
     result) and the true class labels. Note that misclassifcation
     performance can (partly widely) differ from the Area under the ROC
     (AUC). This is due to the fact that misclassifcation rates are
     always computed for the threshold 'probability = 0.5'.

_A_r_g_u_m_e_n_t_s:

  object: An object of 'cloutput'.

    plot: Should the ROC curve be plotted ? Default is 'TRUE'.

     ...: Argument to specifiy further graphical options.

_V_a_l_u_e:

     The empirical area under the curve (AUC).

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

     Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

_S_e_e _A_l_s_o:

     'evaluation'

