Tagged: fdr correction
June 29, 2018 at 8:57 pm #22276johnsojpParticipant
Hello again Mite and company,
I’m a little confused about the fdr values that are shown when I look at group comparisons of nodal measures. I’m comparing two groups using weighted undirected graphs with anticorrelations set to zero and I have noticed that the fdr(1-tailed) and fdr(2-tailed) are the same across all of the regions.
I’m guessing that these do NOT reflect fdr-corrected p-values, but maybe represent the cutoff for significance after fdr correction–is that correct?
In other words, if the p(2-tailed) for global efficiency is .002 and the fdr(2-tailed) = .012, would it be correct to interpret that as a significant result (i.e., global efficiency for this region is significantly different between my groups)? On the other hand, if global efficiency = .02, the result would not be significant because the value is greater than the fdr of .012?
If all of that is correct, what does it mean when the fdr value is 0? Is that simply telling me there are no significant results for that measure after fdr correction?
Thank you very much for the clarification!
JeffJuly 9, 2018 at 11:45 am #22318Mite MijalkovKeymaster
Yes, your conclusions that you mentioned above are correct. In fact, we discussed these issues with the p-values and fdr correction table in another post. There, I offered a detailed explanation about how to interpret the fdr values shown in the tables in Braph:
I hope that this information helps you. If you need to discuss any other possible problem, please do not hesitate to contact me.
July 12, 2018 at 6:49 pm #22335JeffGuest
- This reply was modified 1 year, 5 months ago by Mite Mijalkov.
Thanks for your response Mite–very much appreciated!October 28, 2019 at 2:28 pm #33977DionGuest
So what does it mean if the p(1-tailed) shows 0.1 and the value 1 is 0 ?
This is the same for p(2-tailed) and value 2.
DionNovember 7, 2019 at 5:14 pm #34250Mite MijalkovKeymaster
I think that you are referring for the global measures table. If that is the case, then the relevant part of the table goes in the following way:
difference: denotes the difference between the average measure values in the two groups (this difference is tested whether is significantly different that zero)
p(1-tailed): 1 tailed p-value outputted from the permutation test
p(2-tailed): 2 tailed p-value outputted from the permutation test
value1: average value of the corresponding measure in group 1
value2: average value of the corresponding measure in group 2
Hope this helps. If there is anything more I can help you with, please do not hesitate to ask me.