oneway.test {stats} R Documentation

Test for Equal Means in a One-Way Layout

Description

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

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 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.

Value

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.

References

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

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

```## 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))
```

[Package stats version 2.2.1 Index]