Test for Equal Means in a One-Way Layout

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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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