rfCMA                  package:CMA                  R Documentation

_C_l_a_s_s_i_f_i_c_a_t_i_o_n _b_a_s_e_d _o_n _R_a_n_d_o_m _F_o_r_e_s_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Random Forests were proposed by Breiman (2001) and are implemented
     in the package 'randomForest'.

     In this package, they can as well be used to rank variables
     according to their importance, s. 'GeneSelection'.

     For 'S4' method information, see rfCMA-methods

_U_s_a_g_e:

     rfCMA(X, y, f, learnind, varimp = TRUE, seed = 111, ...)

_A_r_g_u_m_e_n_t_s:

       X: Gene expression data. Can be one of the following:

             *  A 'matrix'. Rows correspond to observations, columns to
                variables.

             *  A 'data.frame', when 'f' is _not_ missing (s. below).

             *  An object of class 'ExpressionSet'.

       y: Class labels. Can be one of the following:

             *  A 'numeric' vector.

             *  A 'factor'.

             *  A 'character' if 'X' is an 'ExpressionSet' that
                specifies the phenotype variable.

             *  'missing', if 'X' is a 'data.frame' and a proper
                formula 'f' is provided.

          *WARNING*: The class labels will be re-coded to range from
          '0' to 'K-1', where 'K' is the total number of different
          classes in the learning set. 

       f: A two-sided formula, if 'X' is a 'data.frame'. The left part
          correspond to class labels, the right to variables.

learnind: An index vector specifying the observations that belong to
          the learning set. May be 'missing'; in that case, the
          learning set consists of all observations and predictions are
          made on the learning set.

  varimp: Should variable importance measures be computed ? Defauls to
          'TRUE'.

    seed: Fix Random number generator seed to 'seed'. This is useful to
          guarantee reproducibility of the results.

     ...: Further arguments to be passed to 'randomForest' from the
          package of the same name.

_V_a_l_u_e:

     If 'varimp', then an object of class 'clvarseloutput' is returned,
     otherwise an object of class 'cloutput'

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

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

_R_e_f_e_r_e_n_c_e_s:

     Breiman, L. (2001)

     Random Forest.

     _Machine Learning, 45:5-32._

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

      ### load Khan data
     data(khan)
     ### extract class labels
     khanY <- khan[,1]
     ### extract gene expression
     khanX <- as.matrix(khan[,-1])
     ### select learningset
     set.seed(111)
     learnind <- sample(length(khanY), size=floor(2/3*length(khanY)))
     ### run random Forest
     rfresult <- rfCMA(X=khanX, y=khanY, learnind=learnind, varimp = FALSE)
     ### show results
     show(rfresult)
     ftable(rfresult)
     plot(rfresult)

