LBE                   package:LBE                   R Documentation

_E_s_t_i_m_a_t_i_o_n _o_f _t_h_e _f_a_l_s_e _d_i_s_c_o_v_e_r_y _r_a_t_e.

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

     LBE is an efficient procedure for estimating the proportion of
     true null hypotheses, the false discovery rate and the q-values.

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

     LBE(pval, a = NA, l = 0.05, ci.level = 0.95, qvalues = TRUE, plot.type = "main", FDR.level = 0.05, n.significant = NA)

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

    pval: Numerical vector of p-values (only necessary input). 

       a: Real value used in [-ln (1-pi)]^a  (see details). If a == NA
          (default), then the value of a is automatically calculated as
          the greatest value such that the upper bound of the
          asymptotic standard deviation of the estimator of pi0 is
          smaller than the threshold l.  If a >= 1, the value of a is
          used in [-ln (1-pi)]^a (see details).  If a < 1, the identity
          function is used for transforming the p-values. 

       l: Threshold for the upper bound of the asymptotic standard
          deviation (only used if a == NA). 

ci.level: Level for the confidence interval of pi0.  

 qvalues: Logical value for estimating the qvalues and the FDR. If
          qvalues = FALSE, only the proportion pi0 of true null
          hypotheses is estimated. 

plot.type: If plot.type = "none", no graphic is displayed. If plot.type
          = "main", the estimated q-values versus the p-values are
          plotted together with the histogram of the p-values.    If
          plot.type = "multiple", several graphics are displayed: 1.
          The histogram of the p-values 2. The estimated q-values
          versus the p-values 3. The number of significant tests versus
          each qvalue cutoff 4. The number of expected false positives
          versus the number of significant tests.  

FDR.level: Level at which to control the FDR (only used if
          n.significant == NA). 

n.significant: If specified, the FDR is estimated for the rejection
          region defined by the "n.significant" smallest p-values. 

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

     The procedure LBE is based on the expectation of a particular
     transformation of the p-values leading to a straightforward
     estimation of the key quantity pi0 that is the proportion of true
     null hypotheses: 
     pi0(a)={(1/m)*sum_{i=1}^m[-ln(1-pi)]^a}/Gamma(a+1), where a
     belongs to the interval [1;inf).

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

     A list containing: 

   call : Function call.

    FDR : Level at which to control the FDR (if n.significant == NA) or
          estimated FDR (if n.significant != NA).

    pi0 : Estimated value of pi0, the proportion of true null
          hypotheses.

 pi0.ci : Confidence interval for pi0.

 ci.level : Level for the confidence interval of pi0.

      a : Value used in [-ln (1-pi)]^a (see details).

      l : Upper bound of the asymptotic standard deviation for pi0.

 qvalues : Vector of the estimated q-values.

 pvalues : Vector of the original p-values.

 significant : Indicator of wether the null hypothesis is rejected.

 n.significant : Number of rejected null hypotheses.

_N_o_t_e:

     LBE is an alternative method to the one proposed by Storey and
     Tibshirani (2003) for estimating the q-values, this latter method
     being implemented in the package 'qvalue'.

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

     Cyril Dalmasso

_R_e_f_e_r_e_n_c_e_s:

     Dalmasso C, Broet P, Moreau T (2005). A simple procedure for
     estimating the false discovery rate. Bioinformatics.
     Bioinformatics, 21: 660 - 668.  

     Storey JD and Tibshirani R. (2003). Statistical significance for
     genome-wide studies. Proc Natl Acad Sci, 100, 9440-9445.

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

     'LBEplot', 'LBEsummary', 'LBEwrite', 'LBEa'

_E_x_a_m_p_l_e_s:

     ## start
     data(hedenfalk.pval)
     res=LBE(hedenfalk.pval)
     data(golub.pval)
     res=LBE(golub.pval)
     ## end

