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 NAs. 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:

 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. doi: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.

### 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 4.3.0 Index]