oneway.test: Test for Equal Means in a One-Way Layout

Description Usage Arguments Details Value References See Also Examples

Description

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

Usage

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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.

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

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## 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))

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