Performs either a one sample chi-squared test to compare the variance of a vector with a given value or an F test to compare the variances of two samples from normal populations.
VarTest(x, ...)# S3 method for default
VarTest(x, y,
alternative = c("two.sided", "less", "greater"),
ratio = 1, sigma.squared = 1,
conf.level = 0.95, ...)
# S3 method for formula
VarTest(formula, data, subset, na.action, ...)
A list with class "htest"
containing the following components:
the value of the F test statistic.
the degrees of the freedom of the F distribution of the test statistic.
the p-value of the test.
a confidence interval for the ratio of the population variances.
the ratio of the sample variances of x
and
y
.
the ratio of population variances under the null.
a character string describing the alternative hypothesis.
the character string
"F test to compare two variances"
.
a character string giving the names of the data.
numeric vectors of data values.
a character string specifying the alternative
hypothesis, must be one of "two.sided"
(default),
"greater"
or "less"
. You can specify just the initial
letter.
the hypothesized ratio of the population variances of
x
and y
.
a number indicating the true value of the variance, if one sample test is requested.
confidence level for the returned confidence interval.
a formula of the form lhs ~ rhs
where lhs
is a numeric variable giving the data values and rhs
a 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
environment(formula)
.
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when
the data contain NA
s. Defaults to
getOption("na.action")
.
further arguments to be passed to or from methods.
Andri Signorell <andri@signorell.net> (One sample test)
Two Sample test and help text from R-Core.
The formula interface is only applicable for the 2-sample tests.
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
.
var.test
, 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)
# One sample test
VarTest(x, sigma.squared = 2.5)
# two samples
y <- rnorm(30, mean = 1, sd = 1)
VarTest(x, y) # Do x and y have the same variance?
VarTest(lm(x ~ 1), lm(y ~ 1)) # The same.
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