plotEffectSize             package:SSPA             R Documentation

_P_l_o_t_s _t_h_e _d_e_n_s_i_t_y _o_f _e_f_f_e_c_t _s_i_z_e_s _o_f _t_h_e _p_i_l_o_t _d_a_t_a

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

     The function 'plotEffectSize' plots density of effect sizes of the
     pilot data.

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

     plotEffectSize(x, threshold = 0, xlab = "effect size", ylab = "density of effect sizes", main, sub, ...)

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

       x: object of class 'SampleSize-class'

threshold: threshold for truncation of the density of effect-sizes. The
          threshold will be taken symmetrical around the y-axis.

    xlab: a title for the x axis

    ylab: a title for the y axis

    main: an overall title for the plot

     sub: a sub title for the plot

     ...: additional arguments given to 'plot' or 'par'

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

     The density of effect sizes describes the effects observed in the
     pilot data. Usually a bimodal density is observed representing up-
     and down-regulated genes. The way in which the test statistics is
     calculated determines what is meant by up- and down-regulation. A
     small symmetrical region around zero can be defined that will be
     excluded from the density of effect sizes and thereby increases
     the estimated average power.

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

     Ferreira, F.A., Zwinderman, A., (2006). Approximate Power and
     Sample Size Calculations with Microarray Data: An Illustration.
     _Statistical Applications in Genetics and Molecular Biology_ 5,
     (1).

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

     library(multtest)
     data(golub)
     teststat <- mt.teststat(golub, golub.cl)
     table(golub.cl)
     pd <- pilotData(name="golub", testStatistics=teststat, sampleSizeA=11, sampleSizeB=27)
     hist(pd)
     plot(pd)
     ss <- sampleSize(pd)
     plotEffectSize(ss)

