clvarseloutput-class           package:CMA           R Documentation

"_c_l_v_a_r_s_e_l_o_u_t_p_u_t"

_D_e_s_c_r_i_p_t_i_o_n:

     Object returned by all classifiers that can peform variable
     selection or compute variable importance. These are:

        *  Random Forest, s. 'rfCMA',

        *  Componentwise Boosting, s. 'compBoostCMA',

        *  LASSO-logistic regression, s. 'LassoCMA',

        *  ElasticNet-logistic regression, s. 'ElasticNetCMA'

_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).

     '_v_a_r_s_e_l': 'numeric' vector of variable importance measures (for
          Random Forest) or absolute values of regression coefficients
          (for the other three methods mentionned above) (from which
          the majority will be zero).

_E_x_t_e_n_d_s:

     Class '"cloutput"', directly. Class '"varseloutput"', directly.

_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:

     'rfCMA', 'compBoostCMA', 'LassoCMA', 'ElasticNetCMA'

