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, …)
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 NAs. Defaults to
getOption("na.action").
further arguments to be passed to or from methods.
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.
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.
# NOT RUN {
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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