l2boost                package:boost                R Documentation

_l_2_b_o_o_s_t

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

     An implementation of the LogitBoost algorithm for binary
     classification

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

     l2boost(xlearn, ylearn, xtest, presel = 200, mfinal = 100)

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

  xlearn: A (n x p)-matrix, where rows correspond to training instances
          and columns contain the predictor variables.

  ylearn: A vector of length n containing the class labels, which need
          to be coded by 0 and 1.

   xtest: A (m x p)-matrix, where rows correspond to test instances and
          columns contain the predictor variables.

  presel: An integer, giving the number of features to be pre-selected
          according to the Wilcoxon test statistic. Default is
          presel=200 features. If presel=0, no feature preselection is
          carried out.

  mfinal: An integer, the number of iterations for which boosting is
          run. Defaults to mfinal=100 iterations

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

     The function outputs an array, whose rows contain out-of-sample
     probabilities that the class labels are predicted as being of
     class 1, for every boosting iteration.

_A_u_t_h_o_r(_s):

     Marcel Dettling

_R_e_f_e_r_e_n_c_e_s:

_o "Boosting for Tumor Classification with Gene Expression Data", Marcel
     Dettling and Peter B\"uhlmann. Bioinformatics (2003), Vol. 19, p.
     1061-1069.

_o "BagBoosting for Tumor Classification with Gene Expression Data",
     Marcel Dettling. To appear in Bioinformatics (2005).

_o Further information is available from the webpage <URL:
     http://stat.ethz.ch/~dettling>

_E_x_a_m_p_l_e_s:

     data(leukemia, package = "boost")

     ## Dividing the leukemia dataset into training and test data
     xlearn <- leukemia.x[c(1:20, 34:38),]
     ylearn <- leukemia.y[c(1:20, 34:38)]
     xtest  <- leukemia.x[21:33,]
     ytest  <- leukemia.y[21:33]

     ## Classification with l2boost
     fit <- l2boost(xlearn, ylearn, xtest, presel=50, mfinal=20)
     summarize(fit, ytest)

