R: Test for Equal Means in a One-Way Layout

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 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.

### 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.

### 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

    ## 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 2.15.1 Index]