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: range of these randomised p values is 0.002
to 0.795, average of the randomised p values is 0.399 and the median of the
randomised p values is 0.45.
So I thought it would be reasonable to expect the FDR Q Value (i.e the
number of expected false positives over the number of significant results) to
be at least over 0.05, given that 869 of the randomised p values are >
0.05?
When I run the code:
library(qvalue)
list1 <-scan("ListOfPValues")
qobj <-qvalue(p=list1)
qobj$pi0
The answer is 0.0062. That's why I thought qobj$pi0 isn't the right
variable to be looking at? So my problem (or my mis-understanding) is that
I have an actual P value of 0.05, but then a Q value that is lower, 0.006?
Thanks again for your help,
Tom
On Thu, Jan 12, 2017 at 9:27 PM, Jim Lemon <drjimle...@gmail.com> wrote:
> 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 the data, so I'm not sure that your usage is valid
> for the function..
>
> Jim
>
>
> On Fri, Jan 13, 2017 at 4:12 AM, Thomas Ryan <tombernardr...@gmail.com>
> wrote:
> > 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 discovery rate (FDR) (Q; as
> > described by Storey and Tibshriani in 2003) for my original p value,
> > defined as the number of expected false positives over the number of
> > significant results for my original P value.
> >
> > So, for my original P value, I want one Q value, that has been calculated
> > as described above based on the 1,000 random p values.
> >
> > I wrote this code:
> >
> > pvals <- c(list_of_p_values_obtained_from_randomisations)
> > qobj <-qvalue(p=pvals)
> > r_output1 <- qobj$pvalue
> > r_output2 <- qobj$qvalue
> >
> > r_output1 is the list of 1,000 p values that I put in, and r_output2 is
> a q
> > value for each of those p values (i.e. so there are 1,000 q values).
> >
> > The problem is I don't want there to be 1,000 Q values (i.e one for each
> > random p value). The Q value should be the false discovery rate (FDR)
> (Q),
> > defined as the number of expected false positives over the number of
> > significant results. So I want one Q value for my original P value, and
> to
> > calculate that one Q value using the 1,000 random P values I have
> generated.
> >
> > Could someone please tell me where I'm going wrong.
> >
> > Thanks
> > Tom
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
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