ansari.test {stats} R Documentation

### Description

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

### Usage

```ansari.test(x, ...)

## Default S3 method:
ansari.test(x, y, alternative = c("two.sided", "less", "greater"),
exact = NULL, conf.int = FALSE, conf.level = 0.95, ...)

## S3 method for class '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 data frame containing the variables in the model 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 `NA`s. 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 != 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.

### 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

Myles Hollander & Douglas A. Wolfe (1973), Nonparametric statistical inference. New York: John Wiley & Sons. Pages 83–92.

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

`fligner.test` for a rank-based (nonparametric) k-sample test for homogeneity of variances; `mood.test` for another rank-based two-sample test for a difference in scale parameters; `var.test` and `bartlett.test` for parametric tests for the homogeneity in variance.

### 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)

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

[Package stats version 2.2.1 Index]