oneway.test
Test for Equal Means in a OneWay 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 oneway analysis of variance is performed. IfFALSE
, an approximate method of Welch (1951) is used, which generalizes the commonly known 2sample Welch test to the case of arbitrarily many samples.
Details
If the righthand 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 pvalue 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, 330336.
See Also
The standard t test (t.test
) as the special case for two
samples;
the KruskalWallis test kruskal.test
for a nonparametric
test for equal location parameters in a oneway 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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