lnested.test  R Documentation 
The test statistic is based on the finite intersection approach.
lnested.test(
y,
group,
location = c("median", "mean", "trim.mean"),
tail = c("right", "left", "both"),
trim.alpha = 0.25,
bootstrap = FALSE,
num.bootstrap = 1000,
correction.method = c("none", "correction.factor", "zero.removal", "zero.correction"),
correlation.method = c("pearson", "kendall", "spearman")
)
y 
a numeric vector of data values. 
group 
factor of the data. 
location 
the default option is 
tail 
the default option is 
trim.alpha 
the fraction (0 to 0.5) of observations to be trimmed from
each end of 
bootstrap 
a logical value identifying whether to implement bootstrap.
The default is 
num.bootstrap 
number of bootstrap samples to be drawn when the 
correction.method 
procedures to make the test more robust;
the default option is 
correlation.method 
measures of correlation; the default option is

The test statistic is based on
the classical Levene's procedure (using the group means),
the modified Brown–Forsythe Levenetype procedure (using the group medians),
or the modified Levenetype procedure (using the group trimmed means).
More robust versions of the test using the correction factor or structural zero
removal method are also available. Two options for calculating critical values,
namely, approximated and bootstrapped, are available.
By default, NA
s are omitted from the data.
A list with the following elements:
T 
the statistic and 
F 
the statistic and 
N 
the statistic and 
L 
the statistic and 
Each of the list elements is a list of class "htest"
with the following elements:
statistic 
the value of the test statistic expressed in terms of correlation (Pearson, Kendall, or Spearman). 
p.value 
the 
method 
type of test performed. 
data.name 
a character string giving the name of the data. 
non.bootstrap.statistic 
the statistic of the test without bootstrap method. 
non.bootstrap.p.value 
the 
Kimihiro Noguchi, W. Wallace Hui, Yulia R. Gel, Joseph L. Gastwirth, Weiwen Miao
levene.test
, ltrend.test
,
mma.test
, neuhauser.hothorn.test
,
robust.mmm.test
data(pot)
lnested.test(pot[,"obs"], pot[, "type"], location = "median", tail = "left",
correction.method = "zero.correction")$N
lnested.test(pot[, "obs"], pot[, "type"], location = "median", tail = "left",
correction.method = "zero.correction",
bootstrap = TRUE, num.bootstrap = 500)$N
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