compBoostCMA               package:CMA               R Documentation

_C_o_m_p_o_n_e_n_t_w_i_s_e _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.

     'Weak learners' here consist of linear functions in one component
     (variable), as proposed by Buehlmann and Yu (2003).

     It also generates sparsity and can as well be as used for variable
     selection alone. (s. 'GeneSelection').

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

_U_s_a_g_e:

     compBoostCMA(X, y, f, learnind, loss = c("binomial", "exp", "quadratic"), mstop = 100, nu = 0.1, ...)

_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.

    loss: Character specifying the loss function - one of '"binomial"'
          (LogitBoost), '"exp"' (AdaBoost), "quadratic"(L2Boost).

   mstop: Number of boosting iterations, i.e. number of updates to
          perform. The default (100) does not necessarily produce good
          results, therefore usage of 'tune' for this argument is
          highly recommended.

      nu: Shrinkage factor applied to the update steps, defaults to
          0.1. In most cases, it suffices to set 'nu' to a very low
          value and to concentrate on the optimization of 'mstop'.

     ...: Currently unused arguments.

_D_e_t_a_i_l_s:

     The method is partly based on code from the package 'mboost' from
     T. Hothorn and P. Buehlmann.

     The algorithm for the multiclass case is described in Lutz and
     Buehlmann (2006) as 'rowwise updating'.

_V_a_l_u_e:

     An object of class 'clvarseloutput'.

_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:

     Buelmann, P., Yu, B. (2003).

     Boosting with the L2 loss: Regression and Classification.

     _Journal of the American Statistical Association, 98, 324-339_

     Buehlmann, P., Hothorn, T.

     Boosting: A statistical perspective.

     _Statistical Science (to appear)_

     Lutz, R., Buehlmann, P. (2006).

     Boosting for high-multivariate responses in high-dimensional
     linear regression.

     _Statistica Sinica 16, 471-494_.

_S_e_e _A_l_s_o:

     'dldaCMA', 'ElasticNetCMA', 'fdaCMA', 'flexdaCMA', 'gbmCMA',
     '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 componentwise (logit)-boosting (not tuned)
     result <- compBoostCMA(X=golubX, y=golubY, learnind=learnind, mstop = 500)
     ### show results
     show(result)
     ftable(result)
     plot(result)
     ### multiclass example:
     ### 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(ratio*length(khanY)))
     ### run componentwise multivariate (logit)-boosting (not tuned)
     result <- compBoostCMA(X=khanX, y=khanY, learnind=learnind, mstop = 1000)
     ### show results
     show(result)
     ftable(result)
     plot(result)

