On 2012-07-17 05:13, R. Michael Weylandt wrote:
On Mon, Jul 16, 2012 at 3:39 PM, Oxenstierna <david.chert...@gmail.com> wrote:
lapply(thing, function(x) x[['p.value']]) --works very well, thank you.
Not to be a chore, but I'm interested in comparing the results of
wilcox.test--and the methodology we've employed so far--with the results and
methodology of wilcox_test (library("coin")). So, I'd like to compare
There should not be any differences between the p-values obtained using
'wilcox.test' and 'wilcox_test' in the asymptotic case. However, the
latter function allows you to use the exact null distribution even in
the presence of ties, or use an Monte Carlo approximation of the exact
null distribution. Using the approximately exact null distribution is
particularly helpful when the asymptotics doesn't work well, say, large
but unbalanced data, and/or the exact computations are too time consuming.
groups 5 and 6 across A through H using wilcox_test, and then I'd like to
extract the p-values. Going through the same methodology as above, but
replacing wilcox.test with wilcox_test has failed, and so has the p.value
extraction method: lapply(thing, function(x) x[['p.value']]) .
I believe the latter failure has to do with the fact that the coin package
has a built-in class and built-in extraction method (pvalue() to extract and
class "IndependenceTest"), but I don't know how to work around it. For
example, for a single comparison: wilcox_test(A~Group, Dtb) works fine, and
pvalue(wilcox.test(A~Group, Dtb)) extracts the p-value.
So, any ideas about how to compare groups 5 and 6 across A through H using
wilcox_test?
Well, since you're doing multiple tests here (A, C, ..., H vs Group) you
should consider adjusting for multiplicity and 'coin' allows you to do
that easily and efficiently. A multivariate Wilcoxon rank-sum test can
be constructed using
> set.seed(711109) # for reproducibility
> it <- independence_test(A + C + D + E + F + G + H ~ Group, data = Dtb,
ytrafo = function(data)
trafo(data, numeric_trafo = rank),
distribution = approximate(B = 100000))
approximating the exact null distribution using 100,000 Monte Carlo
replicates.
Step-down adjusted p-values taking account of the dependence structure
between the test statistics and possible discreteness in the null
distribution are available through
> (psd <- pvalue(it, "step-down"))
A C D E F G H
5 0.08598 0.08598 0.08598 0.20018 0.08598 0.08598 0.34182
and using the development version of 'coin', available at R-Forge, we
can get the unadjusted p-values from
> (pu <- pvalue(it, "unadjusted"))
A C D E F G H
5 0.02894 0.02894 0.02894 0.11512 0.02894 0.02894 0.34182
If we look at the ratio of step-down adjusted and unadjusted p-values,
> psd / pu
A C D E F G H
5 2.970974 2.970974 2.970974 1.738881 2.970974 2.970974 1
we can see that this type of adjustment is pretty powerful compared to
step-down methods like Bonferroni-Holm that doesn't take account of the
correlation nor the discreteness
> p.adjust(pu) / pu
A C D E F G H
5 7 7 7 2 7 7 1
HTH,
Henric
I think there are a few things at play here.
1) coin uses so-called S4 objects, so `[[` style subsetting isn't
going to work. The "right way" is, as you have found to use the
pvalue() function.
2) It looks like you need to use the formula intervace for
wilcox_test. Unfortunately, this makes things a little more
complicated as you'll need to construct the formula programmatically.
A one liner looks something like this.
lapply(LETTERS[1:8], function(x)
pvalue(wilcox_test(as.formula(paste(x, "~ Group ")), Dtb)))
Where lapply loops over the letters A,B, etc. and makes the string `A
~ Group`, converts it to a formula, passes that to wilcox_test, then
gets the pvalue and returns it.
In two lines you could do:
thing <- lapply(LETTERS[1:8], function(x)
wilcox_test(as.formula(paste(x, "~ Group")), Dtb))
thing2 <- lapply(thing, pvalue)
Where thing has all the test result objects, and thing2 collects the pvalues.
Hope this helps,
Michael
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and provide commented, minimal, self-contained, reproducible code.
