crossval             package:LogitBoost             R Documentation

_R_u_n_s _v-_f_o_l_d _c_r_o_s_s _v_a_l_i_d_a_t_i_o_n _w_i_t_h _L_o_g_i_t_B_o_o_s_t

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

     The data are divided into v non-overlapping subsets of roughly
     equal size. Then, feature selection is applied on (v-1) of the
     subsets, which are also used to fit the LogitBoost classifier.
     Then, predictions are made for the left out subsets, and the
     process is repeated for each of the v subsets.

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

     crossval(x, y, v=length(y), mfinal=100, presel=0, estimate=0, verbose=F)

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

       x: A matrix with n rows (different individuals) and p columns
          (different genes) containing expression values.

       y: A vector of length n containing the class labels from
          individuals of K different classes. The labels need to be
          coded by consecutive integers from 0 to (K-1).

       v: An integer, specifying the type of v-fold cross validation.
          The default, v=length(y) means leave-one-out cross
          validation. Besides this, every value between 2 and length(y)
          is valid and means that roughly every v-th observation is
          left out. Make sure that (especially for multiclass problems)
          this is a sensible partition into training and test data.

  mfinal: An integer, describing the number of iterations for which
          boosting should be run. The default value is mfinal=100,
          which is a reasonable choice for gene expression data.

  presel: An integer, giving the number of features to be used for
          classification. If presel=0, no feature preselection is
          carried out.

estimate: An integer, specifying the v of an additional, internal
          v-fold cross validation on the respective training data for
          stopping parameter estimation. Please note that this is
          (especially for larger values of 'estimate') extremly time
          consuming. The default value of estimate=0 means no stopping
          parameter estimation.

 verbose: Logical, indicates whether comments should be given.

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

     The computation of the stopping parameter estimate is
     computationally very expensive and time consuming.

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

   probs: Array, whose rows contain out of sample probabilities that
          the class labels are predicted as 1, for every boosting
          iteration. For multiclass problems, the third dimension of
          the array are the probabilites for the K binary
          one-against-all partitions of the data.

loglikeli: Array, contains the log-likelihood across the training
          instances for determination of the stopping parameter if
          estimate>0. For multiclass problems, the third dimension of
          the array contains the values for the K binary
          one-against-all partitions of the data.

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

     Marcel Dettling

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

     See "Boosting for Tumor Classification of Gene Expression Data",
     Dettling and Buhlmann (2002), available on the web page
     http://stat.ethz.ch/~dettling/boosting.html

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

     'logitboost', 'summarize'

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

     data(leukemia)

     ## An example without stopping parameter estimation
     fit <- crossval(leukemia.x,leukemia.y,v=5,mfinal=100,presel=75,verbose=TRUE)
     summarize(fit,leukemia.y)

     ## 4-fold cross validation with stopping estimation by 3-fold-cv
     fit <- crossval(leukemia.x,leukemia.y,v=4,presel=50,estimate=3,verbose=TRUE)
     summarize(fit,leukemia.y)

