F Test to Compare Two Variances
Performs an F test to compare the variances of two samples from normal populations.
## S3 method for class 'default': 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, \dots)
- x, y
- numeric vectors of data values, or fitted linear model
objects (inheriting from class
- the hypothesized ratio of the population variances of
- a character string specifying the alternative
hypothesis, must be one of
"less". You can specify just the initial letter.
- confidence level for the returned confidence interval.
- a formula of the form
lhs ~ rhswhere
lhsis a numeric variable giving the data values and
rhsa factor with two levels giving the corresponding groups.
- 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
- an optional vector specifying a subset of observations to be used.
- a function which indicates what should happen when
the data contain
NAs. Defaults to
- further arguments to be passed to or from methods.
The null hypothesis is that the ratio of the variances of the
populations from which
y were drawn, or in the
data to which the linear models
y were fitted, is
- 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
"F test to compare two variances".
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;
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.