| var.test | R Documentation |
Performs an F test to compare the variances of two samples from normal populations.
var.test(x, ...)
## Default S3 method:
var.test(x, y, ratio = 1,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, ...)
## S3 method for class 'formula'
var.test(formula, data, subset, na.action, ...)
x, y |
numeric vectors of data values, or fitted linear model
objects (inheriting from class |
ratio |
the hypothesized ratio of the population variances of
|
alternative |
a character string specifying the alternative
hypothesis, must be one of |
conf.level |
confidence level for the returned confidence interval. |
formula |
a formula of the form |
data |
an optional matrix or data frame (or similar: see
|
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 |
... |
further arguments to be passed to or from methods. |
The null hypothesis is that the ratio of the variances of the
populations from which x and y were drawn, or in the
data to which the linear models x and y were fitted, is
equal to ratio.
A list with class "htest" containing the following components:
statistic |
the value of the F test statistic. |
parameter |
the degrees of the freedom of the F distribution of the test statistic. |
p.value |
the p-value of the test. |
conf.int |
a confidence interval for the ratio of the population variances. |
estimate |
the ratio of the sample variances of |
null.value |
the ratio of population variances under the null. |
alternative |
a character string describing the alternative hypothesis. |
method |
the character string
|
data.name |
a character string giving the names of the data. |
bartlett.test for testing homogeneity of variances in
more than two samples from normal distributions;
ansari.test and mood.test for two rank
based (nonparametric) two-sample tests for difference in scale.
x <- rnorm(50, mean = 0, sd = 2) y <- rnorm(30, mean = 1, sd = 1) var.test(x, y) # Do x and y have the same variance? var.test(lm(x ~ 1), lm(y ~ 1)) # The same.
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