mt.rawp2adjp            package:multtest            R Documentation

_A_d_j_u_s_t_e_d _p-_v_a_l_u_e_s _f_o_r _s_i_m_p_l_e _m_u_l_t_i_p_l_e _t_e_s_t_i_n_g _p_r_o_c_e_d_u_r_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function computes adjusted p-values for simple multiple
     testing procedures from a vector of raw (unadjusted) p-values. The
     procedures include the Bonferroni, Holm (1979), Hochberg (1988),
     and Sidak procedures for strong control of the family-wise Type I
     error rate (FWER), and the Benjamini & Hochberg (1995) and
     Benjamini & Yekutieli (2001) procedures for (strong) control of
     the false discovery rate (FDR).

_U_s_a_g_e:

     mt.rawp2adjp(rawp, proc=c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY"))

_A_r_g_u_m_e_n_t_s:

    rawp: A vector of raw (unadjusted) p-values for each hypothesis
          under consideration. These could be nominal p-values, for
          example, from t-tables, or permutation p-values as given in
          'mt.maxT' and 'mt.minP'. If the 'mt.maxT' or 'mt.minP'
          functions are used, raw p-values should be given in the
          original data order, 'rawp[order(index)]'.

    proc: A vector of character strings containing the names of the
          multiple testing procedures for which adjusted p-values are
          to be computed. This vector should include any of the
          following: '"Bonferroni"', '"Holm"', '"Hochberg"',
          '"SidakSS"', '"SidakSD"', '"BH"', '"BY"'.  

_D_e_t_a_i_l_s:

     Adjusted p-values are computed for simple FWER and FDR controlling
     procedures based on a vector of raw (unadjusted) p-values.

_B_o_n_f_e_r_r_o_n_i Bonferroni single-step adjusted p-values for strong control
     of the FWER. 

_H_o_l_m Holm (1979) step-down adjusted p-values for strong control of the
     FWER.

_H_o_c_h_b_e_r_g Hochberg (1988) step-up adjusted p-values for  strong control
     of the FWER (for raw (unadjusted) p-values satisfying the Simes
     inequality). 

_S_i_d_a_k_S_S Sidak single-step adjusted p-values for strong control of the
     FWER (for positive orthant dependent test statistics). 

_S_i_d_a_k_S_D Sidak step-down adjusted p-values for strong control of the
     FWER (for positive orthant dependent test statistics). 

_B_H adjusted p-values for the Benjamini & Hochberg (1995) step-up FDR
     controlling procedure (independent and positive regression
     dependent test statistics).

_B_Y adjusted p-values for the Benjamini & Yekutieli (2001) step-up FDR
     controlling procedure (general dependency structures).

_V_a_l_u_e:

     A list with components 

    adjp: A matrix of adjusted p-values, with rows corresponding to
          hypotheses and columns to multiple testing procedures.
          Hypotheses are sorted in increasing order of their raw
          (unadjusted) p-values.

   index: A vector of row indices, between 1 and 'length(rawp)', where
          rows are sorted according to  their raw (unadjusted)
          p-values. To obtain the adjusted p-values in the original
          data order, use 'adjp[order(index),]'.  

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     Y. Benjamini and Y. Hochberg (1995). Controlling the false
     discovery rate: a practical and powerful approach to multiple
     testing. _J. R. Statist. Soc. B_. Vol. 57: 289-300.


     Y. Benjamini and D. Yekutieli (2001). The control of the false
     discovery rate in multiple hypothesis testing under dependency.
     _Annals of Statistics_. Accepted.


     S. Dudoit, J. P. Shaffer, and J. C. Boldrick (Submitted). Multiple
     hypothesis testing in microarray experiments.


     Y. Ge, S. Dudoit, and T. P. Speed. Resampling-based multiple
     testing for microarray data hypothesis, Technical Report #633 of
     UCB Stat. <URL: http://www.stat.berkeley.edu/~gyc>

     Y. Hochberg (1988). A sharper Bonferroni procedure for multiple
     tests of significance, _Biometrika_. Vol. 75: 800-802.


     S. Holm (1979). A simple sequentially rejective multiple test
     procedure. _Scand. J. Statist._. Vol. 6: 65-70.

_S_e_e _A_l_s_o:

     'mt.maxT', 'mt.minP', 'mt.plot', 'mt.reject', 'golub'.

_E_x_a_m_p_l_e_s:

     # Gene expression data from Golub et al. (1999)
     # To reduce computation time and for illustrative purposes, we condider only
     # the first 100 genes and use the default of B=10,000 permutations.
     # In general, one would need a much larger number of permutations
     # for microarray data.

     data(golub)
     smallgd<-golub[1:100,] 
     classlabel<-golub.cl

     # Permutation unadjusted p-values and adjusted p-values for maxT procedure
     res1<-mt.maxT(smallgd,classlabel)
     rawp<-res1$rawp[order(res1$index)]

     # Permutation adjusted p-values for simple multiple testing procedures
     procs<-c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY")
     res2<-mt.rawp2adjp(rawp,procs)