# oneway.test

##### 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.

- Keywords
- htest

##### Usage

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

##### Arguments

- formula
a formula of the form

`lhs ~ rhs`

where`lhs`

gives the sample values and`rhs`

the corresponding groups.- data
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)`

.- 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.

##### Details

If the right-hand side of the formula contains more than one term, their interaction is taken to form the grouping.

##### Value

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.

##### References

B. L. Welch (1951).
On the comparison of several mean values: an alternative approach.
*Biometrika*, **38**, 330--336.
10.2307/2332579.

##### See Also

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.

##### Examples

`library(stats)`

```
# 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))
# }
```

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*