By using the help page for wilcox.test and looking in the there-mentioned book 
(Hollander and Wolfe), I could find the formula also in the book.
People that might run into the same problem: The formula can also be found 
here, at least if you have access to it via your university or insitution, or 
so: www.springerlink.com/index/a757704707951858.pdf

For those of you who might have stumbled upon
http://reiter1.com/Glossar/Mann-Whitney%20Test.htm ,
if I haven't done any mistake in the calculations, the formula is equivalent 
to the one in R (Hollander and Wolfe).
Especially, if there are no ties, then the formula is equivalent to the 
formula for the variance (sigma-squared) for ranks _without_ties.

Also please notice, as mentioned in an earlier response to this thread, 
results will probably differ if continuity correction in enabled.

When doing your own calculations or trying to compare some results to the ones 
you obtain in R, take care about the distribution that was used in order to 
calculate the p-value: in R for n1 or n2 (respective group sizes) >= 50 a 
normal approximation is used via pnorm(z), whereas for smaller group sizes 
pwilcox(w) is used, which is _exact_.

Thank you all for you help!

Best,

Cedric

On Wednesday, 18. August 2010 11:55:52 Cedric Laczny wrote:
> I was able to trace down the unexpected behavior to the following line
> SIGMA <- sqrt((n.x * n.y/12) * ((n.x + n.y + 1) -
>                 sum(NTIES^3 - NTIES)/((n.x + n.y) * (n.x + n.y -
>                   1))))
> My calculations of the Z-score for the normal approximation where based on
> using the standard deviation for ranks _without_ ties. The above formula
> seems to account for ties and thus, yields a slightly different z-score.
> However, the data seems to include at most 1 tie (based on rnorm), so it
> would be the same result as if it contained no tie (1^3 - 1 has the same
> result as 0^3 - 0, obviously ;) ) and thus I would expect the result to be
> the same as when using the formula for the standard deviation without
> ties.
> Interestigly, this z-score is also different from that reported by
> wilcox_test (exact calculation of p-value).
> I have not been able to find this formula in any textbook nearby or at any
> website. Therefore I am wondering where it does actually com from?
> 
> Best,
> 
> Cedric
> 
> On Tuesday, 17. August 2010 13:48:10 Cedric Laczny wrote:
> > Hi,
> > 
> > I became a little bit confused when working with the Wilcoxon test in R.
> > As far as I understood, there are mainly two versions:
> > 1) wilcox.test{stats}, which is the default and an approximation,
> > especially, when ties are involved
> > 2) wilcox_test{coin}, which does calculate the distribution _exactly_
> > even, with ties.
> > 
> > I have the following scenario:
> > 
> > #---BeginCode---
> > # big example
> > size = 60
> > big1 = rnorm(size, 0, 1)
> > big2 = rnorm(size, 0.5, 1
> > 
> > g1f = rep(1, size)
> > g2f = rep(2, size)
> > big = c(big1, big2)
> > data_frame = data.frame(big, gr=as.factor(c(g1f, g2f)))
> > 
> > wilcox_approx = wilcox.test(big1, big2)
> > wilcox_exact = wilcox_test(big ~ gr, data=data_frame,
> > distribution="exact") #---EndCode---
> > 
> > I found here
> > http://www-stat.stanford.edu/~susan/courses/s141/hononpara.pdf that
> > wilcox.test (at least for the signed rank test) relies on exact
> > (p-)values until N = n1 + n2 = 50.
> > I can reproduce this, when using e.g. size = 15. The p-values then are
> > the same, as I would expect it, having read the info from the link.
> > 
> > #---BeginCode---
> > print(paste("Wilcox approx p-value:", wilcox_approx$p.value), quote=F)
> > print(paste("Wilcox exact p-value:", pvalue(wilcox_exact)), quote=F)
> > #---EndCode---
> > 
> > That said, if I set e.g. size = 60, then the p-values of wilcox.test and
> > wilcox_test differ, as expected.
> > 
> > What's puzzling me particularly is the differing results when wanting to
> > calculate the p-value manually, for bigger sample sizes.
> > 
> > So, if we get the W-score from wilcox.test:
> > 
> > #---BeginCode---
> > W_big = wilcox.test(big1, big2))$statistic
> > #---EndCode---
> > 
> > and "convert" it to a Z-score, like this:
> > 
> > #---BeginCode---
> > mu_big = (size^2)/2
> > sd_big = sqrt(size*size*(size + size + 1)/12)
> > N = size + size
> > sd_big_corr = sqrt( (size * size) / (N * (N - 1)) * (N^3 - N) / 12 )
> > 
> > Z_big = (((W_big - mu_big)/sd_big)
> > #---EndCode---
> > 
> > The Z-Score (Z_big) is equal to the statistic of wilcox_test.
> > So far so good. And now comes the main problem.
> > When I follow the documentation correctly, the p-value for a given
> > W-score/- statistic ist calculated using the normal-approximation with
> > the Z-score. However, when I do that, I get a different result than what
> > I would expect. Because I would expect the p-value of wilcox.test to be
> > equal to
> > 2*pnorm(Z_big), which is in fact _not_ equal. Please see:
> > 
> > #---BeginCode---
> > p_value_manual = 2 * pnorm(Z_big)
> > 
> > print("--- Resulting pvalues --- ", quote=F)
> > print(paste("Wilcox approx p-value:", wilcox_approx$p.value), quote=F)
> > print(paste("Wilcox exact p-value:", pvalue(wilcox_exact)), quote=F)
> > print(paste("P-value manual:", p_value_manual), quote=F)
> > #---EndCode---
> > 
> > So how is the calculation of the p-value performed in wilcox.test, when
> > the sample sizes are big? Because this might explain why the value
> > differs from that being calculated manually.
> > 
> > Best regards,
> > 
> > Cedric
> > 
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html and provide commented,
> > minimal, self-contained, reproducible code.
> 
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html and provide commented,
> minimal, self-contained, reproducible code.


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