fuzzyBHexact            package:fuzzyFDR            R Documentation

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_F_D_R)

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

     Exact calculation of fuzzy decision rules for multiple testing.
     Controls the FDR (false discovery rate) using the Benjamini and
     Hochberg method.

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

     fuzzyBHexact(pvals, pprev, alpha = 0.05, tol = 1e-05, q.myuni = T, dp = 20)

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

   pvals: observed discrete p-values

   pprev: previously attainable p-values under the null distribution

   alpha: significance level of the FDR procedure

     tol: tolerance for my.match and my.unique

 q.myuni: logical. Use my.match instead of match?

      dp: no. decimal places to round p-values to

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

     my.match and my.unique may be used instead of match and unique if
     there is a problem with calculating the unique set of p-values
     (sometimes a problem with very small p-values)

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

     Data frame containing the p-values and previously attainable
     p-values input to the function, and the tau (fuzzy decision rule)
     output. Also contains the minimum and maximum ranks over
     allocations for each p-value.

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

     Alex Lewin

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

     Kulinsakaya and Lewin (2007).

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

     data(example1)
     names(example1)
     fuzzyBHexact(example1$pvals,example1$pprev,alpha=0.05)
     data(example2)
     names(example2)
     fuzzyBHexact(example2$pvals,example2$pprev,alpha=0.05)

