Test for Equal Means in a One-Way Layout
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 ~ rhswhere
lhsgives the sample values and
rhsthe 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
- an optional vector specifying a subset of observations to be used.
- a function which indicates what should happen when
the data contain
NAs. Defaults to
- 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.
If the right-hand side of the formula contains more than one term, their interaction is taken to form the grouping.
A list with class
- 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.
"htest"containing the following components:
B. L. Welch (1951), On the comparison of several mean values: an alternative approach. Biometrika, 38, 330--336.
## 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))