mood.test
Mood Two-Sample Test of Scale
Performs Mood's two-sample test for a difference in scale parameters.
- Keywords
- htest
Usage
mood.test(x, …)# S3 method for default
mood.test(x, y,
alternative = c("two.sided", "less", "greater"), …)
# S3 method for formula
mood.test(formula, data, subset, na.action, …)
Arguments
- x, y
numeric vectors of data values.
- alternative
indicates the alternative hypothesis and must be one of
"two.sided"(default),"greater"or"less"all of which can be abbreviated.- formula
a formula of the form
lhs ~ rhswherelhsis a numeric variable giving the data values andrhsa 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 formulaformula. By default the variables are taken fromenvironment(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 togetOption("na.action").- …
further arguments to be passed to or from methods.
Details
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.
Value
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.
References
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. 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.
Examples
library(stats)
# NOT RUN {
## 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)
# }