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 setting 'method="separate"' is equivalent to using 'topTable'
     separately for each coefficient in the linear model fit, and will
     give the same lists of probes if 'adjust.method' is the same. Note
     that the defaults for 'adjust.method' are different for
     'decideTests' and 'topTable'. '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.

