What you're doing makes no sense. Given p-values p_i, i=1...n, resulting
from hypothesis tests t_i, i=1...n, the q-value of p_i is the expected
proportion of false positives among all n tests if the significance level
of each test is α=p_i. Thus a q-value is only defined for an observed
p-value.
Jim,
Thanks for the reply. Yes I'm just playing around with the data at the
minute, but regardless of where the p values actually come from, I can't
seem to get a Q value that makes sense.
For example, in one case, I have an actual P value of 0.05. I have a list
of 1,000 randomised p values:
Hi Tom,
>From a quick scan of the docs, I think you are looking for qobj$pi0.
The vector qobj$qvalue seems to be the local false discovery rate for
each of your randomizations. Note that the manual implies that the p
values are those of multiple comparisons within a data set, not
randomizations of
Hi all, I'm wondering if someone could put me on the right path to using
the "qvalue" package correctly.
I have an original p value from an analysis, and I've done 1,000
randomisations of the data set. So I now have an original P value and 1,000
random p values. I want to work out the false
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