ansari.test

From stats v3.5.0 by R-core R-core@R-project.org

Ansari-Bradley Test

Performs the Ansari-Bradley two-sample test for a difference in scale parameters.

Keywords: htest

Usage

ansari.test(x, …)
# S3 method for default
ansari.test(x, y,
            alternative = c("two.sided", "less", "greater"),
            exact = NULL, conf.int = FALSE, conf.level = 0.95,
            …)
# S3 method for formula
ansari.test(formula, data, subset, na.action, …)

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.
conf.int: a logical,indicating whether a confidence interval should be computed.
conf.level: confidence level of the interval.
formula: 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.
data: 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).
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 NAs. Defaults to getOption("na.action").
…: further arguments to be passed to or from methods.

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 \(s\), the ratio of scales, 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 \(s \ne 1\) (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.

Optionally, a nonparametric confidence interval and an estimator for \(s\) are computed. If exact p-values are available, an exact confidence interval is obtained by the algorithm described in Bauer (1972), and the Hodges-Lehmann estimator is employed. Otherwise, the returned confidence interval and point estimate are based on normal approximations.

Note that mid-ranks are used in the case of ties rather than average scores as employed in Hollander & Wolfe (1973). See, e.g., Hajek, Sidak and Sen (1999), pages 131ff, for more information.

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.

null.value

the ratio of scales \(s\) under the null, 1.

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.

conf.int

a confidence interval for the scale parameter. (Only present if argument conf.int = TRUE.)

estimate

an estimate of the ratio of scales. (Only present if argument conf.int = TRUE.)

Note

To compare results of the Ansari-Bradley test to those of the F test to compare two variances (under the assumption of normality), observe that \(s\) is the ratio of scales and hence \(s^2\) is the ratio of variances (provided they exist), whereas for the F test the ratio of variances itself is the parameter of interest. In particular, confidence intervals are for \(s\) in the Ansari-Bradley test but for \(s^2\) in the F test.

References

David F. Bauer (1972). Constructing confidence sets using rank statistics. Journal of the American Statistical Association, 67, 687--690. 10.1080/01621459.1972.10481279.

Jaroslav Hajek, Zbynek Sidak and Pranab K. Sen (1999). Theory of Rank Tests. San Diego, London: Academic Press.

Myles Hollander and Douglas A. Wolfe (1973). Nonparametric Statistical Methods. New York: John Wiley & Sons. Pages 83--92.

Examples

library(stats)

# NOT RUN {
## 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)

ansari.test(rnorm(10), rnorm(10, 0, 2), conf.int = TRUE)

## try more points - failed in 2.4.1
ansari.test(rnorm(100), rnorm(100, 0, 2), conf.int = TRUE)
# }

Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0

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