Dear Stefan,
See two comments inserted below.
Stefan Grosse wrote:
On Sun, 1 Nov 2009 00:47:50 -0700 (PDT) jomni <jom...@gmail.com> wrote:
J> So do I write the function as wilcox.test(original, test,
J> alternative="l")? or wlcox.test(original, test, alternative = "g")?
J> or wilcox.test(test, original, alternative="g")?
J> or wilcox.test(test, original, alternative="l")?
J> How do I interpret the p-value given my criteria?
J> Do I reject null when p-value less than 0.05?
J> or greater than 0.95?
The interpretation of the p depends on how you have tested the
hypothesis.
J> Not a statistics major here so I'm really confused.
You don't need to be that but please read the documentation and try the
given examples in the documentation.
Comment 1:
As you point out, one should at least scan the documentation.
Here's a quote from ?wilcox.test:
'the one-sided alternative "greater" is that x is shifted
to the right of y'
That's pretty unambiguous.
If you would have typed example(wilcox.test) you would have seen for
example:
wlcx.t> ## Two-sample test.
wlcx.t> ## Hollander & Wolfe (1973), 69f.
wlcx.t> ## Permeability constants of the human chorioamnion (a placental
wlcx.t> ## membrane) at term (x) and between 12 to 26 weeks gestational
wlcx.t> ## age (y). The alternative of interest is greater
permeability
wlcx.t> ## of the human chorioamnion for the term
pregnancy.
wlcx.t> x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91,
1.64, 0.73, 1.46)
wlcx.t> y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
wlcx.t> wilcox.test(x, y, alternative = "g") # greater
Wilcoxon rank sum test
data: x and y
W = 35, p-value = 0.1272
alternative hypothesis: true location shift is greater than 0
This I think makes it very easy to interprete. Here it is tested as the
text says whether x is greater than y. So if you want to test the
hypothesis that x is smaller than y so you do
wilcox.test(x,y,alternative="less")
then the lower your p is the higher is the probability that the samples
are different. hence p<0.05 would match your confidence level. Now the
Comment 2:
I know that you know better, but with p-values it's always
best to be careful with the language. "... the probability
that the _samples_ are different" makes little sense. The
samples _are_ different, period (or why do the test?). The
p-value says something about the distribution from which
the samples are obtained.
Cheers,
Peter Ehlers
surprising news:
wilcox.test(y,x,alternative="greater")
would work as well!
If you are in doubt create an x and an y where you are sure that x is
smaller than y.
One final remark: if you have ties (several identical values in one
sample) you should use wilcox_test of the coin package.
hth
Stefan
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