On Fri, 21 Apr 2017, peter dalgaard wrote:
Also, as far as I know just for historical consistency, the test
statistic in R is the rank sum of the first group MINUS its minimum
possible value: W = 110.5 - sum(1:13) = 19.5
Ah, yes, I meant to add that remark. And coin::wilcox_test always computes
a standardized test statistic as opposed to the (adjusted) rank sum. But
these are all "simple" transformations of the test statistic and hence do
not influence the p-values.
See also the "Note" in ?wilcox.test on the difference between so-called
Wilcoxon and Mann-Whitney statistics.
On 21 Apr 2017, at 14:54 , Achim Zeileis <achim.zeil...@uibk.ac.at> wrote:
On Fri, 21 Apr 2017, Tripoli Massimiliano wrote:
Dear R users,
Why the result of Wilcoxon sum rank test by R is different from sas
https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_npar1way_sect022.htm
The code is next:
sampleA <- c(1.94, 1.94, 2.92, 2.92, 2.92, 2.92, 3.27, 3.27, 3.27, 3.27,
3.7, 3.7, 3.74)
sampleB <- c(3.27, 3.27, 3.27, 3.7, 3.7, 3.74)
wilcox.test(A,B,paired = F)
There are different ways how to compute or approximate the asymptotic or exact
conditional distribution of the test statistic:
SAS reports an asymptotic normal approximation (apparently without continuity
correction along with an asymptotic t approximation and the exact conditional
distribution.
Base R's stats::wilcox.test can either report the exact conditional
distribution (but only if there are no ties) or the asymptotic normal
distribution (with or without continuity correction). In small samples the
default is to use the former but a warning is issued when there are ties (as in
your case).
Furthermore, coin::wilcox_test can report either the asymptotic normal
distribution (without continuity correction) or the exact conditional
distribution (even in the presence of ties).
Thus:
## collect data in data.frame
d <- data.frame(
y = c(sampleA, sampleB),
x = factor(rep(0:1, c(length(sampleA), length(sampleB))))
)
## asymptotic normal distribution without continuity correction
## (p = 0.0764)
stats::wilcox.test(y ~ x, data = d, exact = FALSE, correct = FALSE)
coin::wilcox_test(y ~ x, data = d, distribution = "asymptotic")
## exact conditional distribution (p = 0.1054)
coin::wilcox_test(y ~ x, data = d, distribution = "exact")
These match SAS's results. The default result of stats::wilcox.test is
different as explained by the warning issued.
hth,
Z
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--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: pd....@cbs.dk Priv: pda...@gmail.com
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