gbmCMA                  package:CMA                  R Documentation

_T_r_e_e-_b_a_s_e_d _G_r_a_d_i_e_n_t _B_o_o_s_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Roughly speaking, Boosting combines 'weak learners' in a weighted
     manner in a stronger ensemble. This method calls the function
     'gbm.fit' from the package 'gbm'. The 'weak learners' are simple
     trees that need only very few splits (default: 1).

     For 'S4' method information, see 'gbmCMA-methods'.

_U_s_a_g_e:

     gbmCMA(X, y, f, learnind, ...)

_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.

     ...: Further arguments passed to the function 'gbm.fit' from the
          package of the same name. Worth mentionning are

          '_n_t_r_e_e_s' Number of trees to fit (size of the ensemble),
               defaults to 100. This parameter should be optimized
               using 'tune'.

          '_s_h_r_i_n_k_a_g_e' The learning rate (default is 0.001). Usually
               fixed to a very low value.

          '_d_i_s_t_r_i_b_u_t_i_o_n' Loss function to be used. Default is
               '"bernoulli"', i.e. 'LogitBoost', a (less robust)
               alternative is '"adaboost"'.

          '_i_n_t_e_r_a_c_t_i_o_n._d_e_p_t_h' Number of splits used by the 'weak
               learner' (single decision tree). Default is '1'.

_V_a_l_u_e:

     An onject of class 'cloutput'.

_N_o_t_e:

     Up to now, this method can only be applied to binary
     classification.

_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:

     Ridgeway, G. (1999).

     The state of boosting.

     _Computing Science and Statistics, 31:172-181_

     Friedman, J. (2001).

     Greedy Function Approximation: A Gradient Boosting Machine.

     _Annals of Statistics 29(5):1189-1232_.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     ### load Golub AML/ALL data
     data(golub)
     ### extract class labels
     golubY <- golub[,1]
     ### extract gene expression
     golubX <- as.matrix(golub[,-1])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run tree-based gradient boosting (no tuning)
     gbmresult <- gbmCMA(X=golubX, y=golubY, learnind=learnind, n.trees = 500)
     show(gbmresult)
     ftable(gbmresult)
     plot(gbmresult)

