mood.test {stats}R Documentation

Mood Two-Sample Test of Scale


Performs Mood's two-sample test for a difference in scale parameters.


mood.test(x, ...)

## Default S3 method:
mood.test(x, y,
          alternative = c("two.sided", "less", "greater"), ...)

## S3 method for class 'formula'
mood.test(formula, data, subset, na.action, ...)


x, y

numeric vectors of data values.


indicates the alternative hypothesis and must be one of "two.sided" (default), "greater" or "less" all of which can be abbreviated.


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.


The underlying model is that the two samples are drawn from f(x-l) and f((x-l)/s)/s, respectively, where l is a common location parameter and s is a scale parameter.

The null hypothesis is s = 1.

There are more useful tests for this problem.

In the case of ties, the formulation of Mielke (1967) is employed.


A list with class "htest" containing the following components:


the value of the test statistic.


the p-value of the test.


a character string describing the alternative hypothesis. You can specify just the initial letter.


the character string "Mood two-sample test of scale".

a character string giving the names of the data.


William J. Conover (1971), Practical nonparametric statistics. New York: John Wiley & Sons. Pages 234f.

Paul W. Mielke, Jr. (1967). Note on some squared rank tests with existing ties. Technometrics, 9/2, 312–314. doi:10.2307/1266427.

See Also

fligner.test for a rank-based (nonparametric) k-sample test for homogeneity of variances; ansari.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.


## Same data as for the Ansari-Bradley test:
## 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)
mood.test(ramsay, jung.parekh)
## Compare this to ansari.test(ramsay, jung.parekh)

[Package stats version 4.4.0 Index]