ansari.test              package:ctest              R Documentation

_A_n_s_a_r_i-_B_r_a_d_l_e_y _T_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     Performs the Ansari-Bradley two-sample test for a difference in
     scale parameters.

_U_s_a_g_e:

     ansari.test(x, y, alternative = c("two.sided", "less", "greater"),
                 exact = NULL, conf.int = TRUE, conf.level = 0.95)

_A_r_g_u_m_e_n_t_s:

       x: numeric vector of data values.

       y: numeric vector of data values.

alternative: indicates the alternative hypothesis and must be one of
          `"two.sided"', `"greater"' or `"less"'.  You can specify just
          the initial letter.

   exact: a logical indicating whether an exact p-value should be
          computed.

conf.int: a logical,indicating whether a confidence interval should be
          computed.

conf.level: confidence level of the interval.

_D_e_t_a_i_l_s:

     Suppose that `x' and `y' are independent samples from
     distributions with densities f((t-m)/s)/s and f(t-m),
     respectively, where m is an unknown nuisance parameter and s is
     the parameter of interest.  The Ansari-Bradley test is used for
     testing the null that s equals 1, the two-sided alternative being
     that s != 1 (the distributions differ only in variance), and the
     one-sided alternatives being s > 1 (the distribution underlying
     `x' has a larger variance, `"greater"') or s < 1 (`"less"').

     By default (if `exact' is not specified), an exact p-value is
     computed if both samples contain less than 50 finite values and
     there are no ties.  Otherwise, a normal approximation is used.

     Optionally, a nonparametric confidence interval for s is computed.
      If exact p-values are available, an exact confidence interval is
     obtained by the algorithm described in Bauer (1972). Otherwise, an
     asymptotic confidence interval is returned.

_V_a_l_u_e:

     A list with class `"htest"' containing the following components: 

statistic: the value of the Ansari-Bradley test statistic.

 p.value: the p-value of the test.

alternative: a character string describing the alternative hypothesis.

  method: the string `"Ansari-Bradley test"'.

data.name: a character string giving the names of the data.

conf.int: a confidence interval for the scale parameter. (Only present
          if argument `conf.int = TRUE'.)

_R_e_f_e_r_e_n_c_e_s:

     Myles Hollander & Douglas A. Wolfe (1973), Nonparametric
     statistical inference. New York: John Wiley & Sons. Pages 83-92.

     David F. Bauer (1972), Constructing confidence sets using rank
     statistics. Journal of the American Statistical Association 67,
     687-690.

_S_e_e _A_l_s_o:

     `fligner.test' for a rank-based (nonparametric) k-sample test for
     homogeneity of variances; `mood.test' for another rank-based
     two-sample test for a difference in scale parameters; `var.test'
     and `bartlett.test' for parametric tests for the homogeneity in
     variance.

_E_x_a_m_p_l_e_s:

     ## Hollander & Wolfe (1973, p. 86f):
     ## Serum iron determination using Hyland control sera
     ramsay <- c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
                 101, 96, 97, 102, 107, 113, 116, 113, 110, 98)
     jung.parekh <- c(107, 108, 106, 98, 105, 103, 110, 105, 104,
                 100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99)
     ansari.test(ramsay, jung.parekh)

     ansari.test(rnorm(10), rnorm(10, 0, 2), conf.int = TRUE)

