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. Assuming that you have stored n observed p-values in an R vector P, and the ith p-value P[i]==.05, then the R syntax to obtain the q-value for P[i] is qvalue(P)$qvalues[i].
If, instead (as I suspect), that .05 is not among your observed p-values, but you want to know what the FDR would be, given your sequence of p-values, if the significance level of every test were .05, then the R syntax would be max(qvalue(P)$qvalues[P<=.05]). On Fri, Jan 13, 2017 at 2:08 AM, Thomas Ryan <tombernardr...@gmail.com> wrote: > 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. > > > > [[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. > [[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.