twilight              package:twilight              R Documentation

_E_s_t_i_m_a_t_i_o_n _o_f _t_h_e _l_o_c_a_l _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:

     The function performs the successive exclusion procedure (SEP) as
     described in Scheid and Spang (2004).

_U_s_a_g_e:

     twilight(xin, lambda = NULL, B = 0, boot.ci = 0.95, clus = NULL, verbose = TRUE)

_A_r_g_u_m_e_n_t_s:

     xin: Numerical vector of p-values or a 'twilight' object. 

  lambda: Numerical value denoting the penalty factor. If not
          specified, the function searchs for an appropriate
          regularization parameter. 

       B: Numerical value specifying the number of bootstrap samples.
          If not specified, no bootstrap estimates are calculated. 

 boot.ci: Numerical value denoting the probability value for bootstrap
          confidence intervals of local false discovery rate and prior
          pi0. 

    clus: A list or numerical value to be passed to
          'makeCluster(clus,...)' in 'library(snow)'. If specified,
          bootstrapping is performed in parallel. No checks on 'clus'
          are performed. Please make sure that 'makeCluster(clus)'
          works properly in your environment. 

 verbose: Logical value for message printing. 

_D_e_t_a_i_l_s:

     In short, the successive exclusion procedure divides the set of
     p-values into two parts. The first part is chosen such that it
     resembles a uniform distribution while containing as many p-values
     as possible. This set of p-values represents the set of p-values
     derived from non-induced genes. The height of the uninform
     distribution is a natural estimate for the mixture parameter pi0.
     The p-value not contained in the uniform part serve as
     representatives of p-values derived from induced genes. Their
     distribution is the basis of the final estimate of the local false
     discovery rate.

_V_a_l_u_e:

     Returns a 'twilight' object consisting of a 'data.frame' named
     'result' with variables 

  pvalue: Sorted input vector.

  qvalue: q-values computed as described in Storey and Tibshirani
          (2003) with new estimate 'pi0'.

   index: Index of the original ordering.

     fdr: Local false discovery rate averaged over 10 runs of SEP.

mean.fdr: Bootstrap estimate of local false discovery rate.

lower.fdr: Lower 'boot.ci'-bootstrap confidence bound.

upper.fdr: Upper 'boot.ci'-bootstrap confidence bound.

     Values are sorted by 'pvalue'.

_N_o_t_e:

     Additional output consists of

       'lambda'    Regularization parameter.
       'pi0'       SEP estimate of prior probability.
       'boot.pi0'  Bootstrap estimate and 'boot.ci'-bootstrap confidence bounds.
       'boot.ci'   Passes 'boot.ci' for plotting.
       'effect'    Histogram of effect size distributions averaged over 10 runs of SEP.

     If 'xin' is of class 'twilight', the remaining slots are filled
     with corresponding input values. If 'xin' is not of class
     'twilight', these slots remain free.

_A_u_t_h_o_r(_s):

     Stefanie Scheid <URL: http://www.molgen.mpg.de/~scheid>

_R_e_f_e_r_e_n_c_e_s:

     Scheid S and Spang R (2004): A stochastic downhill search
     algorithm for estimating the local false discovery rate, _IEEE
     TCBB_ *1(3)*, 98-108.

     Storey JD and Tibshirani R (2003): Statistical significance for
     genomewide studies, _PNAS_ *100(16)*, 9440-9445.

_S_e_e _A_l_s_o:

     'twilight.pval', 'plot.twilight', 'exfdr'

_E_x_a_m_p_l_e_s:

     data(exfdr) # twilight object created by twilight with B=1000 bootstraps
     exfdr
     plot(exfdr)

