GenerateBootMatrix       package:GeneSelector       R Documentation

_A_l_t_e_r_e_d _d_a_t_a_s_e_t_s _v_i_a _b_o_o_t_s_t_r_a_p

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

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

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

     GenerateBootMatrix(x, y, replicates = 50, type = c("unpaired", "paired", "onesample"), maxties = NULL, minclassize = 2, balancedclass = FALSE, balancedsample = 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'

replicates: Number of bootstrap replicates to be generated. Should
          rarely exceed 50.

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

 maxties: The maximum number of ties allowed per observation. For
          example, 'maxties=2' means that no observation occurs more
          than 'maxties+1 = 3' times in a bootstrap sample.

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 bootstrap sample.

balancedclass: If 'balancedclass=TRUE', then the proportions of the two
          classes are the same for each bootstrap sample. It is a
          shortcut for a certain value of  'minclasssize'. May not
          reasonable, if class proportions are unbalanced in the
          original sample.

balancedsample: Should balanced bootstrap (s.details) be performed ?

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

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

     For the case that 'balancedsample=TRUE', all other contstraints as
     imposed by 'maxties', 'minclassize' and so on are ignored.
     Balanced Bootstrap (s. reference below) means that each
     observation occurs equally frequently (with respect to all
     bootstrap replications).

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

     An object of class 'BootMatrix'

_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 bootstrap sample 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:

     GenerateFoldMatrix, 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 Boot Matrix, maximum number of ties=3, 
     ### minimum classize=5, 30 replications:
     boot <- GenerateBootMatrix(xx, yy, maxties=3, minclassize=5, repl=30)

