According to the documentation for MTP.  A Mann-Whitney test can be
obtained by setting test="t.twosamp.equalvar".  It describes a
'conversion' but I am assuming this is what has been done and not what
needs to be done?
 
From the docs:
t.twosamp.equalvar: Wilcoxon rank sum or Mann-Whitney,
          'wilcox.test', 
           conversion: n2*(num+mean(r1)) - n2*(n2+1)/2
 
My main confusion arises from reading the Vignettes (MTP.pdf,
multtest.pdf, MTPALL.pdf). In all the vignettes you see something like
the following:
Step-down minP FWER-controlling MTP with robust two-sample t-statistics
and bootstrap null distribution The Wilcoxon rank sum statistic (also
known as the Mann-Whitney statistic) is a robust alternative to the
usual two-sample t-statistic.
> cyto.wilcox <- MTP(X = filtALL, Y = "cyto.normal", robust = TRUE,
alternative = "less", B = 100, method = "sd.minP", seed = seed)
 
In the command above, a 'test' is not specified and therefore, the
default 't.twosamp.unequalvar' instead of 't.twosamp.equalvar' (which
according to above is the correct function for Mann-Whitney) would be
used.
 
From the docs:
test: Character string specifying the test statistics to use, by default
't.twosamp.unequalvar'. See details (below) for a list of tests.
 
Is this just a typographical error in the Vignettes?  Or am I missing
something?  What is the correct procedure for a Mann-Whitney test in
MTP?
 
Any help will be greatly appreciated.
 
Obi

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