| oneway.test {stats} | R Documentation |
Test whether two or more samples from normal distributions have the same means. The variances are not necessarily assumed to be equal.
oneway.test(formula, data, subset, na.action, var.equal = FALSE)
formula |
a formula of the form lhs ~ rhs where lhs
gives the sample values and rhs the corresponding groups. |
data |
an optional data frame containing the variables in the model 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. |
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. |
B. L. Welch (1951), On the comparison of several mean values: an alternative approach. Biometrika, 38, 330–336.
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.
## 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))