cloutput-class              package:CMA              R Documentation

"_c_l_o_u_t_p_u_t"

_D_e_s_c_r_i_p_t_i_o_n:

     Object returned by one of the classifiers (functions ending with
     'CMA')

_S_l_o_t_s:


     '_l_e_a_r_n_i_n_d': Vector of indices that indicates which observations
          where used in the learning set.

     '_y': Actual (true) class labels of predicted observations.

     '_y_h_a_t': Predicted class labels by the classifier.

     '_p_r_o_b': A 'numeric' 'matrix' whose rows equals the number of
          predicted observations (length of 'y'/'yhat') and whose
          columns equal the number of different classes in the learning
          set. Rows add up to one. Entry 'j,k' of this matrix contains
          the probability for the 'j'-th predicted observation to
          belong to class 'k'. Can be a matrix of 'NA's, if the
          classifier used does not provide any probabilities

     '_m_e_t_h_o_d': Name of the classifer used.

     '_m_o_d_e': 'character', one of '"binary"' (if the number of classes
          in the learning set is two) or 'multiclass' (if it is more
          than two).

_M_e_t_h_o_d_s:


     _s_h_o_w Use 'show(cloutput-object)' for brief information

     _f_t_a_b_l_e Use 'ftable(cloutput-object)' to obtain a confusion
          matrix/cross-tabulation of 'y' vs. 'yhat', s.
          'ftable,cloutput-method'.

     _p_l_o_t Use 'plot(cloutput-object)' to generate a probability plot of
          the matrix 'prob' described above, s. 'plot,cloutput-method'

     _r_o_c Use 'roc(cloutput-object)' to compute the empirical ROC curve
          and the Area Under the Curve (AUC) based on the predicted
          probabilities, s.'roc,cloutput-method'

_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:

     'clvarseloutput' 'compBoostCMA', 'dldaCMA', 'ElasticNetCMA',
     'fdaCMA', 'flexdaCMA', 'gbmCMA', 'knnCMA', 'ldaCMA', 'LassoCMA',
     'nnetCMA', 'pknnCMA', 'plrCMA', 'pls_ldaCMA', 'pls_lrCMA',
     'pls_rfCMA', 'pnnCMA', 'qdaCMA', 'rfCMA', 'scdaCMA',
     'shrinkldaCMA', 'svmCMA'

