mt.sample.teststat         package:multtest         R Documentation

_P_e_r_m_u_t_a_t_i_o_n _d_i_s_t_r_i_b_u_t_i_o_n _o_f _t_e_s_t _s_t_a_t_i_s_t_i_c_s _a_n_d _r_a_w (_u_n_a_d_j_u_s_t_e_d) _p-_v_a_l_u_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     These functions provide tools to investigate the permutation
     distribution of test statistics, raw (unadjusted) p-values, and
     class labels.

_U_s_a_g_e:

     mt.sample.teststat(V,classlabel,test="t",fixed.seed.sampling="y",B=10000,na=.mt.naNUM,nonpara="n")
     mt.sample.rawp(V,classlabel,test="t",side="abs",fixed.seed.sampling="y",B=10000,na=.mt.naNUM,nonpara="n")
     mt.sample.label(classlabel,test="t",fixed.seed.sampling="y",B=10000)

_A_r_g_u_m_e_n_t_s:

       V: A numeric vector containing the data for one of the variables
          (genes).

classlabel: A vector of integers corresponding to observation (column)
          class labels. For k classes, the labels must be integers
          between 0 and k-1. For the 'blockf' test option, observations
          may be divided into n/k blocks of k observations each. The
          observations are ordered by block, and within each block,
          they are labeled using the integers 0 to k-1. 

    test: A character string specifying the statistic to be used to
          test the null hypothesis of no association between the
          variables and the class labels.
           If 'test="t"', the tests are based on two-sample Welch
          t-statistics (unequal variances).  
           If 'test="t.equalvar"', the tests are based on two-sample
          t-statistics with equal variance for the two samples. The
          square of the t-statistic is equal to an F-statistic for k=2. 
           If 'test="wilcoxon"', the tests are based on standardized
          rank sum Wilcoxon statistics.
           If 'test="f"', the tests are based on F-statistics.
           If 'test="pairt"', the tests are based on paired
          t-statistics. The square of the paired t-statistic is equal
          to a block F-statistic for k=2. 
           If 'test="blockf"', the tests are based on F-statistics
          which adjust for block differences (cf. two-way analysis of
          variance). 

    side: A character string specifying the type of rejection region.
           If 'side="abs"', two-tailed tests, the null hypothesis is
          rejected for large absolute values of the test statistic.
           If 'side="upper"', one-tailed tests, the null hypothesis is
          rejected for large values of the test statistic.
           If 'side="lower"', one-tailed tests,  the null hypothesis is
          rejected for small values of the test statistic. 

fixed.seed.sampling: If 'fixed.seed.sampling="y"', a fixed seed
          sampling procedure is used, which may double the computing
          time, but will not use extra memory to store the
          permutations. If 'fixed.seed.sampling="n"', permutations will
          be stored in memory.  For the 'blockf' test, the option 'n'
          was not implemented as it requires too much memory. 

       B: The number of permutations. For a complete enumeration, 'B'
          should be 0 (zero) or any number not less than the total
          number of permutations. 

      na: Code for missing values (the default is
          '.mt.naNUM=--93074815.62'). Entries with missing values will
          be ignored in the computation, i.e., test statistics will be
          based on a smaller sample size. This feature has not yet
          fully implemented. 

 nonpara: If 'nonpara'="y", nonparametric test statistics are computed
          based on ranked data. 
           If  'nonpara'="n", the original data are used. 

_V_a_l_u_e:

     For 'mt.sample.teststat',  a vector containing 'B' permutation
     test statistics. 

      For 'mt.sample.rawp',  a vector containing 'B' permutation
     unadjusted p-values. 

       For 'mt.sample.label', a matrix containing 'B' sets of permuted
     class labels. Each row corresponds to one permutation.

_A_u_t_h_o_r(_s):

     Yongchao Ge, yongchao.ge@mssm.edu, 
      Sandrine Dudoit, <URL: http://www.stat.berkeley.edu/~sandrine>.

_S_e_e _A_l_s_o:

     'mt.maxT', 'mt.minP', 'golub'.

_E_x_a_m_p_l_e_s:

     # Gene expression data from Golub et al. (1999)
     data(golub)

     mt.sample.label(golub.cl,B=10)

     permt<-mt.sample.teststat(golub[1,],golub.cl,B=1000)
     qqnorm(permt)
     qqline(permt)

     permt<-mt.sample.teststat(golub[50,],golub.cl,B=1000)
     qqnorm(permt)
     qqline(permt)

     permp<-mt.sample.rawp(golub[1,],golub.cl,B=1000)
     hist(permp)