importance           package:randomForest           R Documentation

_E_x_t_r_a_c_t _v_a_r_i_a_b_l_e _i_m_p_o_r_t_a_n_c_e _m_e_a_s_u_r_e

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

     This is the extractor function for variable importance measures as
     produced by 'randomForest'.

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

     ## S3 method for class 'randomForest':
     importance(x, type=NULL, class=NULL, scale=TRUE, ...)

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

       x: an object of class 'randomForest'

    type: either 1 or 2, specifying the type of importance measure
          (1=mean decrease in accuracy, 2=mean decrease in node
          impurity).

   class: for classification problem, which class-specific measure to
          return.

   scale: For permutation based measures, should the measures be
          divided their ``standard errors''?

     ...: not used.

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

     See the documentation for 'randomForest' for explanation of how
     the importance measures are computed.

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

     If 'class' and 'type' are 'NULL', A matrix of 'p' rows and 'nclass
     + 2' columns (where 'p' is the number of variables in the data and
     'nclass' is the number of classes) for classification problem, or
     'p' rows and '2' columns for regression.  In the classification
     case, the first 'nclass' columns are the class-specific importance
     measures (based on permutation of out-of-bag data).  The
     'nclass+1'st column is the overall importance, and the last column
     is the overall measure based on the decrease in node purity (or
     `Gini-based' measure).

     If either 'type' or 'class' is given, it returns a (named) vector
     of importance measure, one for each predictor variable.

_S_e_e _A_l_s_o:

     'randomForest', 'varImpPlot'

