oneway.test              package:stats              R Documentation

_T_e_s_t _f_o_r _E_q_u_a_l _M_e_a_n_s _i_n _a _O_n_e-_W_a_y _L_a_y_o_u_t

_D_e_s_c_r_i_p_t_i_o_n:

     Test whether two or more samples from normal distributions have
     the same means.  The variances are not necessarily assumed to be
     equal.

_U_s_a_g_e:

     oneway.test(formula, data, subset, na.action, var.equal = FALSE)

_A_r_g_u_m_e_n_t_s:

 formula: a formula of the form 'lhs ~ rhs' where 'lhs' gives the
          sample values and 'rhs' the corresponding groups.

    data: an optional data frame containing the variables in the model
          formula.

  subset: an optional vector specifying a subset of observations to be
          used.

na.action: a function which indicates what should happen when the data
          contain 'NA's.  Defaults to 'getOption("na.action")'.

var.equal: a logical variable indicating whether to treat the variances
          in the samples as equal.  If 'TRUE', then a simple F test for
          the equality of means in a one-way analysis of variance is 
          performed.  If 'FALSE', an approximate method of Welch (1951)
          is used, which generalizes the commonly known 2-sample Welch
          test to the case of arbitrarily many samples.

_V_a_l_u_e:

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

statistic: the value of the test statistic.

parameter: the degrees of freedom of the exact or approximate F
          distribution of the test statistic.

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

  method: a character string indicating the test performed.

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

_R_e_f_e_r_e_n_c_e_s:

     B. L. Welch (1951), On the comparison of several mean values: an
     alternative approach. _Biometrika_, *38*, 330-336.

_S_e_e _A_l_s_o:

     The standard t test ('t.test') as the special case for two
     samples; the Kruskal-Wallis test 'kruskal.test' for a
     nonparametric test for equal location parameters in a one-way
     layout.

_E_x_a_m_p_l_e_s:

     ## Not assuming equal variances
     oneway.test(extra ~ group, data = sleep)
     ## Assuming equal variances
     oneway.test(extra ~ group, data = sleep, var.equal = TRUE)
     ## which gives the same result as
     anova(lm(extra ~ group, data = sleep))

