GenerateFoldMatrix       package:GeneSelector       R Documentation

_A_l_t_e_r_e_d _d_a_t_a_s_e_t_s _v_i_a _k-_J_a_c_k_k_n_i_f_e _o_r _L_a_b_e_l (_c_l_a_s_s) _e_x_c_h_a_n_g_e

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

     Generates an object of class FoldMatrix that is then processed by
     GetRepeatRanking

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

     GenerateFoldMatrix(x, y, k = 1, replicates = ifelse(k==1, ncol(x), 10), type = c("unpaired", "paired", "onesample"), minclassize = 2, balanced = FALSE, control)

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

       x: A 'matrix' of gene expression values with _rows_
          corresponding to genes and columns corresponding to
          observations. 
           Can alternatively an object of class 'ExpressionSet'.
           If 'type = paired', the first half of the columns
          corresponds to  the first measurements and the second half to
          the second ones.  For instance, if there are 10 observations,
          each measured twice, stored in an expression matrix 'expr', 
          then 'expr[,1]' is paired with 'expr[,11]', 'expr[,2]' with
          'expr[,12]', and so on.

       y: If 'x' is a matrix, then 'y' may be a 'numeric' vector or a
          factor with at most two levels.
           If 'x' is an 'ExpressionSet', then 'y' is a character
          specifyig the phenotype variable in the output from 'pData'.
           If 'type = paired', take care that the coding is analogously
          to the requirement concerning 'x'

       k: Number of observations that are removed or whose labels are
          exchanged. Label exchange means that the actual label is
          replaced by the label of the other class  (s.
          GetRepeatRanking).

replicates: Number of replications if 'k>1'.

    type: One of '"paired", "unpaired", "onesample"', depends on the
          type of test to be performed, s. for example RankingTstat.

minclassize: If 'minclassize=k' for some integer 'k', then the number
          of observations in each class are grater then or equal to
          'minclassize' for  each replication.

balanced: If 'balanced=TRUE', then the proportions of the two classes
          are (at least approximately) the same  for each replication.
          It is a shortcut for a certain value of  'minclasssize'. May
          not reasonable, if class proportions are unbalanced.

 control: Further control arguments concerning the generation  process
          of the fold matrix, s. samplingcontrol.

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

     An object of class FoldMatrix.

_w_a_r_n_i_n_g:

     If the generation process (partially) fails, try to reduce the
     constraints or change the argument 'control'.

_N_o_t_e:

     No jackknif-ed dataset will occur more than once, i.e. each
     replication is unique.

_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:

     Davison, A.C., Hinkley, D.V. (1997) 
       Bootstrap Methods and their Application. _Cambridge University
     Press_

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

     GenerateBootMatrix, GetRepeatRanking

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

      ## Load toy gene expression data
     data(toydata)
     ### class labels
     yy <- toydata[1,]
     ### gene expression
     xx <- toydata[-1,]
     ### Generate Leave-One-Out / Exchange-One-Label matrix
     loo <- GenerateFoldMatrix(xx, yy, k=1)
     ### A more complex example
     l3o <- GenerateFoldMatrix(xx, yy, k=3, replicates=30, minclassize=5) 

