Ansari-Bradley Test
Usage
ansari.test(x, y, alternative = "two.sided", exact = NULL)
Arguments
x
|
numeric vector of data values.
|
y
|
numeric vector of data values.
|
alternative
|
indicates the alternative hypothesis and must be
one of "two.sided" , "greater" or "less" .
You can specify just the initial letter.
|
exact
|
a logical indicating whether an exact p-value should be
computed.
|
Description
Performs the Ansari-Bradley test for a difference in scale
parameters.Details
Suppose that x
and y
are independent samples from
distributions with densities f((t-m)/s)/s and f(t-m),
respectively, where m is an unknown nuisance parameter and
s is the parameter of interest. The Ansari-Bradley test is
used for testing the null that s equals 1, the two-sided
alternative being that s != 1 (the distributions differ only
in variance), and the one-sided alternatives being s > 1 (the
distribution underlying x
has a larger variance,
"greater"
) or s < 1 ("less"
).
By default (if exact
is not specified), an exact p-value is
computed if both samples contain less than 50 finite values and
there are no ties. Otherwise, a normal approximation is used.
Value
A list with class "htest"
containing the following
components:
statistic
|
the value of the Ansari-Bradley test statistic.
|
p.value
|
the p-value of the test.
|
alternative
|
a character string describing the alternative
hypothesis.
|
method
|
the string "Ansari-Bradley test" .
|
data.name
|
a character string giving the names of the data.
|
References
Myles Hollander & Douglas A. Wolfe (1973),
Nonparametric statistical inference.
New York: John Wiley & Sons.Examples
## Hollander & Wolfe (1973, p. 86f):
## Serum iron determination using Hyland control sera
ramsay <- c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98)
jung.parekh <- c(107, 108, 106, 98, 105, 103, 110, 105, 104,
100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99)
ansari.test(ramsay, jung.parekh)