Performs Mood's two-sample test for a difference in scale parameters.
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, …)
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 NA
s. Defaults to
getOption("na.action")
.
further arguments to be passed to or from methods.
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.
The underlying model is that the two samples are drawn from
The null hypothesis is
There are more useful tests for this problem.
In the case of ties, the formulation of Mielke (1967) is employed.
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.
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.
# 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)
# }
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