p.adjust                package:base                R Documentation

_A_d_j_u_s_t _p _v_a_l_u_e_s _f_o_r _m_u_l_t_i_p_l_e _c_o_m_p_a_r_i_s_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     Given a set of p values, returns p values adjusted using one of
     several methods.

_U_s_a_g_e:

     p.adjust(p, method=p.adjust.methods, n=length(p))
     p.adjust.methods # c("holm", "hochberg", "bonferroni","none")

_A_r_g_u_m_e_n_t_s:

       p: vector of p values

  method: correction method

       n: number of comparisons

_D_e_t_a_i_l_s:

     The adjustment methods include the Bonferroni correction in which
     the p values are multiplied by the number of comparisons. Two less
     conservative corrections by Holm, respectively Hochberg, are also
     included. A pass-through option `"none"' is also included. The set
     of methods are contained in the `p.adjust.methods' vector for the
     benefit of methods that need to have the method as an option and
     pass it on to `p.adjust'.

_V_a_l_u_e:

     A vector of corrected p values.

_N_o_t_e:

     The Hochberg method is only proved to work if the p values are
     independent, although simulations have indicated that it works in
     correlated cases as well. Hence the Holm method is the default.

_R_e_f_e_r_e_n_c_e_s:

     S Paul Wright: Adjusted P-values for simultaneous inference, 
     Biometrics 48, 1005-1013

_S_e_e _A_l_s_o:

     `pairwise.*' functions in the `ctest' package, such as
     `pairwise.t.test'.

_E_x_a_m_p_l_e_s:

       p <- runif(50)
       p.adjust(p)

