A test for a monotonic trend in variances Mudholkar_etal_1995lawstat.
The test statistic is based on
a combination of the finite intersection approach and the two-sample \(t\)-test
using Miller's transformation. By default, NA
s are omitted.
robust.mmm.test(y, group, tail = c("right", "left", "both"))
A list with the following elements:
the statistic and \(p\)-value of the test based on the Tippett \(p\)-value combination.
the statistic and \(p\)-value of the test based on the Fisher \(p\)-value combination.
the statistic and \(p\)-value of the test based on the Liptak \(p\)-value combination.
the statistic and \(p\)-value of the test based on the Mudholkar-George \(p\)-value combination.
Each of the list elements is a list of class "htest"
with the following elements:
the value of the test statistic.
the \(p\)-value of the test.
type of test performed.
a character string giving the name of the data.
a numeric vector of data values.
factor of the data.
the default option is "right"
, corresponding to an increasing
trend in variances as the one-sided alternative; "left"
corresponds to a
decreasing trend in variances, and "both"
corresponds to any
(increasing or decreasing) monotonic trend in variances as the two-sided alternative.
Kimihiro Noguchi, Yulia R. Gel
neuhauser.hothorn.test
, levene.test
,
lnested.test
, ltrend.test
, mma.test
data(pot)
robust.mmm.test(pot[, "obs"], pot[, "type"], tail = "left")$N
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