bloodp            package:exactRankTests            R Documentation

_D_i_a_s_t_o_l_i_c _B_l_o_o_d _P_r_e_s_s_u_r_e

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

     Diastolic blood pressure for a two groups of patients.

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

     data(bloodp)

_F_o_r_m_a_t:

     A data frame with 15 observations on the following 2 variables.

     _b_p the diastolic blood pressure.

     _g_r_o_u_p a factor with levels 'group1' and 'group2'.

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

     The data is given in Table 9.6, page 227, of Metha and Pathel
     (2001). Note that there are some tied observations. The
     permutation test using the raw blood pressure values does not lead
     to a rejection of the null hypothesis of exchangeability: p-value
     = 0.1040 (two-sided) and p-value = 0.0564 (one-sided). The
     asymptotic two-sided p-value is 0.1070.

     For the Wilcoxon-Mann-Whitney test, the one-sided p-value is
     0.0542 and the two-sided one is 0.0989 (Metha & Patel, 2001, page
     229).

     The one-sided p-value for the v.d.Waeren test is 0.0462 (Metha &
     Patel, 2001, page 241) and the two-sided p-value is 0.0799.

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

     Cyrus R. Mehta & Nitin R. Patel (2001), _StatXact-5 for Windows._
     Manual, Cytel Software Cooperation, Cambridge, USA

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

     data(bloodp)

     # Permutation test

     perm.test(bp ~ group, data=bloodp)
     perm.test(bp ~ group, data=bloodp, alternative="greater")
     perm.test(bp ~ group, data=bloodp, exact=FALSE)

     # Wilcoxon-Mann-Whitney test

     wilcox.exact(bp ~ group, data=bloodp, conf.int=TRUE, alternative="l")
     wilcox.exact(bp ~ group, data=bloodp, conf.int=TRUE)

     # compute the v.d. Waerden test

     sc <- cscores(bloodp$bp, type="NormalQuantile")
     X <- sum(sc[bloodp$group == "group2"])
     round(pperm(X, sc, 11), 4) 
     round(pperm(X, sc, 11, simulate=TRUE), 4)
     round(pperm(X, sc, 11, alternative="two.sided"), 4) 
     round(pperm(X, sc, 11, alternative="two.sided", simulate=TRUE), 4)

     # use scores mapped into integers (cf. dperm)

     sc <- cscores(bloodp$bp, type="NormalQuantile", int=TRUE)
     X <- sum(sc[bloodp$group == "group2"])
     round(pperm(X, sc, 11), 4)      
     round(pperm(X, sc, 11, alternative="two.sided"), 4)

