On Tue, 15 Sep 2009, Keo Ormsby wrote:
Hi Murat,
I am not an expert in either statistics nor R, but I can imagine that since the
default is exact=TRUE, It numerically computes the probability, and it may
indeed be 0. if you use wilcox.test(x, y, exact=FALSE) it will give you a
normal aproximation, which will most likely be different from zero.
No, the exact p-value can't be zero for a discrete distribution. The smallest
possible value in this case would, I think, be
1/choose(length(x)+length(y),length(x)), or perhaps twice that.
More generally, the approach used by format.pvalue() is to display very small p-values as
<2e-16, where 2e-16 is machine epsilon. I wouldn't want to claim optimality for this
choice, but it seems a reasonable way to represent "very small".
-thomas
Hope this helps.
Keo.
Murat Tasan escribió:
hi, folks,
how have you gone about reporting a p-value from a test when the
returned value from a test (in this case a rank-sum test) is
numerically equal to 0 according to the machine?
the next lowest value greater than zero that is distinct from zero on
the machine is likely algorithm-dependent (the algorithm of the test
itself), but without knowing the explicit steps of the algorithm
implementation, it is difficult to provide any non-zero value. i
initially thought to look at .mach...@double.xmin, but i'm not
comfortable with reporting p < .mach...@double.xmin, since without
knowing the specifics of the implementation, this may not be true!
to be clear, if i have data x, and i run the following line, the
returned value is TRUE.
wilcox.test(x)$p.value == 0
thanks for any help on this!
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Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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