Test whether two or more samples from normal distributions have the same means. The variances are not necessarily assumed to be equal.
oneway.test(formula, data, subset, na.action, var.equal = FALSE)
a formula of the form lhs ~ rhs
where lhs
gives the sample values and rhs
the corresponding groups.
an optional matrix or data frame (or similar: see
model.frame
) containing the variables in the
formula formula
. By default the variables are taken from
environment(formula)
.
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when
the data contain NA
s. Defaults to
getOption("na.action")
.
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.
A list with class "htest"
containing the following components:
the value of the test statistic.
the degrees of freedom of the exact or approximate F distribution of the test statistic.
the p-value of the test.
a character string indicating the test performed.
a character string giving the names of the data.
If the right-hand side of the formula contains more than one term, their interaction is taken to form the grouping.
B. L. Welch (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38, 330--336. 10.2307/2332579.
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.
# NOT RUN {
## 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))
# }
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