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x
and y
using t-test,
Welch-test (also known as Satterthwaite), Wilcoxon-test,
and a permutation test implemented in compare.2.vectors(x, y, paired = FALSE, na.rm = FALSE,
tests = c("parametric", "nonparametric"), coin = TRUE,
alternative = "two.sided", perm.distribution =
approximate(100000), wilcox.exact = NULL,
wilcox.correct = TRUE)
FALSE
.NA
be removed?
Default is FALSE
.c("parametric",
"nonparametric")
reports both. See details. (Arguments
may be abbreviated)."two.sided"
(default),
"greater"
or "less"
. You can specify just
the initial letter, will be passed to all functions.TRUE
corresponding to all implemented tests.
FALSE
calculates no tests from distribution
argument to
Distribution
or ,
IndependenceTest
. Defaults to
approximate(1
exact
argument to
wilcox.test
.correct
argument to
wilcox.test
.paramteric
and/or nonparamteric
) each containing a
data.frame
with the following columns:
test
, test.statistic
, test.value
,
test.df
, p
.parametric
tests (currently) only contain the
t-test and Welch/Statterwaithe/Smith/unequal
variance t-test implemented in
t.test
. The latter one is only displayed if
paired = FALSE
.
The nonparametric
tests (currently) contain the
Wilcoxon test implemented in wilcox.test
(stats::Wilcoxon
) and (if coin = TRUE
) the
following tests implemented in permutation
testoneway_test
(the only test in this selction
not using a rank transformation),Wilcoxon
testwilcox_test
(coin::Wilcoxon
), andmedian
testmedian_test
.with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2]))
# gives:
## $parametric
## test test.statistic test.value test.df p
## 1 t t -1.861 18.00 0.07919
## 2 Welch t -1.861 17.78 0.07939
##
## $nonparametric
## test test.statistic test.value test.df p
## 1 stats::Wilcoxon W 25.500 NA 0.06933
## 2 permutation Z -1.751 NA 0.08154
## 3 coin::Wilcoxon Z -1.854 NA 0.06487
## 4 median Z 1.744 NA 0.17867
# compare with:
with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2], alternative = "less"))
with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2], alternative = "greater"))
# doesn't make much sense as the data is not paired, but whatever:
with(sleep, compare.2.vectors(extra[group == 1], extra[group == 2], paired = TRUE))
# from ?t.test:
compare.2.vectors(1:10,y=c(7:20, 200))
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