getpar               package:adaptTest               R Documentation

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

     This function calculates the parameter that specifies the
     conditional error function running through a given point (p1,p2),
     based on a chosen family of conditional error functions.

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

     getpar(typ, p1 = NA, p2 = p1, c = FALSE)

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

     typ: type of test: '"b"' for Bauer and Koehne (1994), '"l"' for
          Lehmacher and Wassmer (1999), '"v"' for Vandemeulebroecke
          (2006) and '"h"' for the horizontal conditional error
          function

      p1: the p-value p1 of the test after the first stage

      p2: the p-value p2 of the test after the second stage, defaults
          to 'p1'

       c: logical determining whether the parameter alpha2 or the
          parameter c is returned (alpha2 is the default).

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

     See 'parconv' for more information on the two alternative
     parameterizations by alpha2 and c.

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

     'getpar' returns the parameter (either alpha2 or c, depending on
     the chosen parameterization) that specifies the conditional error
     function running through (p1,p2).

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

     Marc Vandemeulebroecke

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

     Bauer, P., Koehne, K. (1994). Evaluation of experiments with
     adaptive interim analyses. _Biometrics_ 50, 1029-1041.

     Lehmacher, W., Wassmer, G. (1999). Adaptive sample size
     calculations in group sequential trials. _Biometrics_ 55,
     1286-1290.

     Vandemeulebroecke, M. (2006). An investigation of two-stage tests.
     _Statistica Sinica_ 16, 933-951.

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

     'adaptTest' package description, 'parconv', 'CEF'

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

     ## Plot the conditional error function of the Lehmacher-Wassmer (1999) type that runs through (p1,p2)=(0.3,0.7)
     alpha2 <- getpar(typ="l", p1=0.3, p2=0.7)
     plotCEF(typ="l", a2=alpha2, add=FALSE)

     ## Other ways of doing the same as above
     plotCEF(typ="l", p1=0.3, p2=0.7, add=FALSE) 
     plot(CEF(typ="l", p1=0.3, p2=0.7), xlim=0:1)

