EstimatePi0              package:BGmix              R Documentation

_P_r_o_p_o_r_t_i_o_n _o_f _t_h_e _v_a_r_i_a_b_l_e_s _u_n_d_e_r _t_h_e _n_u_l_l _h_y_p_o_t_h_e_s_i_s

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

     Estimate of the proportion of the variables under the null
     hypothesis using tail posterior probabilities

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

     EstimatePi0(tpp, pp0, plot = T)

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

     tpp: observed tail posterior probability 

     pp0: a vector of tail posterior probability under H0

    plot: if True, estimated pi0 at different locations and the median
          estimate is plotted 

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

     Use Storey (2002) approach to estimate pi0

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

     estimate of pi0 = proportion of non-differentially expressed genes

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

     Natalia Bochkina

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

     Bochkina N., Richardson S. (2007)  Tail posterior probability for
     inference in pairwise and multiclass gene expression data.
     Biometrics (in press).

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

     'TailPP', 'FDRplotTailPP','histTailPP'

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

      data(ybar, ss)
      nreps <- c(8,8)

     ## Note this is a very short MCMC run!
     ## For good analysis need proper burn-in period.
      outdir <- BGmix(ybar, ss, nreps, jstar=-1, nburn=0, niter=100, nthin=1)

      params <- ccParams(outdir)  
      res <-  ccTrace(outdir)
       
      tpp.res <- TailPP(res, nreps, params, plots  = FALSE)
      pi0 <- EstimatePi0(tpp.res$tpp, tpp.res$pp0)

