qplot                 package:qvalue                 R Documentation

_G_r_a_p_h_i_c_a_l _d_i_s_p_l_a_y _o_f _q_v_a_l_u_e _o_b_j_e_c_t_s

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

     Graphical display of qvalue objects

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

     qplot(qobj, rng = c(0, 0.1), ...)
     ## S3 method for class 'qvalue':
     plot(x, ...)

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

 qobj, x: Qvalue object

     rng: Range of q-values to consider (optional)

     ...: Any other arguments

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

     The function qplot allows one to view several plots:

        1.  The estimated pi_0 versus the tuning parameter lambda.

        2.  The q-values versus the p-values

        3.  The number of significant tests versus each q-value cutoff

        4.  The number of expected false positives versus the number of
           significant tests

     This function makes fours plots. The first is a plot of the
     estimate of pi_0 versus its tuning parameter lambda. In most
     cases, as lambda  gets larger, the bias of the estimate decreases,
     yet the variance  increases. Various methods exist for balancing
     this bias-variance  trade-off (Storey 2002, Storey & Tibshirani
     2003, Storey, Taylor  & Siegmund 2004). Comparing your estimate of
     pi_0 to this  plot allows one to guage its quality. The remaining
     three plots show how many tests are significant, as well as how
     many false positives to expect for each q-value cut-off. A
     thorough discussion of these plots can be found in Storey &
     Tibshirani (2003).

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

     Nothing of interest.

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

     John D. Storey jstorey@u.washington.edu

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

     Storey JD. (2002) A direct approach to false discovery rates.
     Journal of the Royal Statistical Society, Series B, 64: 479-498.

     Storey JD and Tibshirani R. (2003) Statistical significance for
     genome-wide experiments. Proceedings of the National Academy of
     Sciences,  100: 9440-9445. 

     Storey JD. (2003) The positive false discovery rate: A Bayesian
     interpretation and the q-value. Annals of Statistics, 31:
     2013-2035.  

     Storey JD, Taylor JE, and Siegmund D. (2004) Strong control,
     conservative point estimation, and simultaneous conservative
     consistency of false discovery rates: A unified approach. Journal
     of the Royal Statistical Society, Series B, 66: 187-205.

     QVALUE Manual <URL:
     http://faculty.washington.edu/~jstorey/qvalue/manual.pdf>

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

     'qvalue', 'qwrite', 'qsummary', 'qvalue.gui'

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

     ## Not run: 
     p <- scan(pvalues.txt) 
     qobj <- qvalue(p) 
     qplot(qobj) 
     qwrite(qobj, filename=myresults.txt)

     # view plots for q-values between 0 and 0.3:
     plot(qobj, rng=c(0.0, 0.3))
     ## End(Not run)

