ucpm                  package:klaR                  R Documentation

_U_s_c_h_i'_s _c_l_a_s_s_i_f_i_c_a_t_i_o_n _p_e_r_f_o_r_m_a_n_c_e _m_e_a_s_u_r_e_s

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

     Function to calculate the Correctness Rate, the Accuracy, the
     Ability to Seperate and the Confidence of  a classification rule.

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

     ucpm(m, tc, ec = NULL)

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

       m: matrix of (scaled) membership values

      tc: vector of true classes

      ec: vector of estimated classes (only required if scaled
          membership values are used)

_D_e_t_a_i_l_s:

        *  The _correctness rate_ is the estimator for the correctness
           of a classification rule (1-error rate). 

        *  The _accuracy_ is based on the euclidean distances between
           (scaled) membership vectors and the vectors  representing
           the true class corner. These distances are standardized so
           that a measure of 1 is achieved if all vectors lie in the
           correct corners and 0 if they all lie in the center. 

        *  Analougously, the _ability to seperate_ is based on the
           distances between (scaled) membership  vectors and the
           vector representing the corresponding assigned class corner. 

        *  The _confidence_ is the mean of the membership values of the
           assigned classes. 

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

     A list with elements: 

      CR: Correctness Rate

      AC: Accuracy

      AS: Ability to Seperate

      CF: Confidence

   CFvec: Confidence for each (true) class

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

     Karsten Luebke, luebke@statistik.uni-dortmund.de

_R_e_f_e_r_e_n_c_e_s:

     Garczarek, Ursula Maria (2002): Classification rules in
     standardized partition spaces. Dissertation, University of
     Dortmund.  URL <URL:
     http://eldorado.uni-dortmund.de:8080/FB5/ls7/forschung/2002/Garcza
     rek>

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

     library(MASS)
     data(iris)
     ucpm(predict(lda(Species ~ ., data = iris))$posterior, iris$Species)

