oneway.test
Test for Equal Means in a One-Way Layout
Test whether two or more samples from normal distributions have the same means. The variances are not necessarily assumed to be equal.
- Keywords
- htest
Usage
oneway.test(formula, data, subset, na.action, var.equal = FALSE)
Arguments
- formula
- a formula of the form
lhs ~ rhs
wherelhs
gives the sample values andrhs
the corresponding groups. - data
- an optional matrix or data frame (or similar: see
model.frame
) containing the variables in the formulaformula
. By default the variables are taken fromenvironment(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 togetOption("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. IfFALSE
, 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
- 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.
"htest"
containing the following components:
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
library(stats)
## 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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