decideTests              package:limma              R Documentation

_C_o_m_p_u_t_e _M_a_t_r_i_x _o_f _H_y_p_o_t_h_e_s_i_s _T_e_s_t _R_e_s_u_l_t_s

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

     Classify a series of related t-statistics as up, down or not
     significant.

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

     decideTests(object,method="separate",adjust.method="fdr",p.value=0.05)

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

  object: 'MArrayLM' object output from 'eBayes' from which the
          t-statistics may be extracted.

  method: character string specify how probes and contrasts are to be
          combined in the multiple testing strategy.  Choices are
          '"separate"', '"global"', '"heirarchical"', '"nestedF"' or
          any partial string.

adjust.method: character string specifying p-value adjustment method. 
          See 'p.adjust' for possible values.

 p.value: numeric value between 0 and 1 giving the desired size of the
          test

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

     These functions implement multiple testing procedures for
     determining whether each statistic in a matrix of t-statistics
     should be considered significantly different from zero. Rows of
     'tstat' correspond to genes and columns to coefficients or
     contrasts.

     The default settings with 'method="separate"' is equivalent to
     using 'topTable' separately for each coefficient in the linear
     model fit.  'method="global"' will treat the entire matrix of
     t-statistics as a single vector of unrelated tests.
     'method="heirarchical"' adjusts down genes and then across
     contrasts. 'method="nestedF"' adjusts down genes and then uses
     'classifyTestsF' to classify contrasts as significant or not for
     the selected genes.

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

     An object of class 'TestResults'. This is essentially a numeric
     matrix with elements '-1', '0' or '1' depending on whether each
     t-statistic is classified as significantly negative, not
     significant or significantly positive respectively.

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

     Gordon Smyth

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

     An overview of linear model functions in limma is given by
     5.LinearModels.

