Test for a monotonic trend in variances for normal samples. The test statistic
is based on a combination of the finite intersection approach and the classical
\(F\) (variance ratio) test Mudholkar_etal_1993lawstat.
By default, NA
s are omitted.
mma.test(y, group, tail = c("right", "left", "both"))
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.
A list with the following components:
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.
neuhauser.hothorn.test
, levene.test
,
lnested.test
, ltrend.test
, robust.mmm.test
# NOT RUN {
data(pot)
mma.test(pot[, "obs"], pot[, "type"], tail = "left")$N
# }
Run the code above in your browser using DataLab