14.6 years ago by
The statistic is the same between multtest and t.test (for t-tests) or
wilcox.test (for wilcoxen tests), but the p-value is computed
The functions in t.test and wilcox.test use tabled distributions to
p-value for each test. These assume a certain distribution for the
statistics and also treat each test independently. The multtest
provides methods that use the bootstrap (MTP function) or permutations
estimate the joint null distribution of the test statistics and then
compute p-values from these. These are empirical null distributions
differ from those used in t.test and wilcox.test in two important
(i) they do not assume a particular parametric form for the test
statistics distribution, and (ii) they account for correlations
test statistics by using the *joint* null distribution. For these
the p-values computed can be very different from those using tabled,
Hope this helps,
> Message: 18
> Date: Fri, 29 Apr 2005 11:58:26 +0200
> From: "Dipl.-Ing. Johannes Rainer" <firstname.lastname@example.org>
> Subject: [BioC] multtest problem...
> To: "email@example.com"
> Message-ID: <firstname.lastname@example.org>
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> i used the mt.teststat function from the package multtest and wonder
> why the p values that the function calculates differ from those i
> using the wilcox.test or t.test function.
> i tried it with the golub data set (data(golub)) and
> returns a p-value = 0.8987
> while teststat<-mt.teststat(golub,golub.cl,test="wilcoxon")
> teststat = 1.75419
> i thought that the mt.teststat funciton uses simple t tests or
> test to calculate p values...
> perhaps i am wrong with the class labels?
> from the help i understood, that if i have two groups in my samples
> submit as classlabels for examples c(0,0,0,1,1,1) and this means
> the first 3 columns of my data correspond to group 0 and the other 3
> group 1. is this correct?
> thanks, jo