search for: yekuti

L.S. In the current version of ?p.adjust.Rd, one needs to scroll down to the examples section to find confirmation of one's guess that "fdr" is an alias of "BH". Please find a patch in attachment which mentions this explicitly. Best, Tobias -------------- next part -------------- A non-text attachment was scrubbed... Name: p.adjust.Rd.patch Type: text/x-patch Size: 633

Dear R contributors, I suggest adding a new method to `p.adjust` ("Adjust P-values for Multiple Comparisons", https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html). This new method is published in Benjamini, Krieger, Yekutieli 2016 Adaptive linear step-up procedures that control the false discovery rate (Biometrika). https://doi.org/10.1093/biomet/93.3.491 This paper described multiple methods for adjusting p-values, where the "TST" method (Definition 6) performed the best when test statistics are positivel...

...s [1] "holm" "hochberg" "hommel" "bonferroni" "BH" "BY" "fdr" [8] "none" BH is Benjamini & Hochberg (1995) and is also called "fdr" (I wish R's documentation said this clearly). BY is Benjamini & Yekutieli (2001). I found a few hits from Google on Dunn-Sidak, but I'm curious if anyone can tell me on a "conservative-liberal" scale, where the Dunn-Sidak method falls? My guess is it's less conservative than Bonferroni (but aren't all the other methods?), but how does it compare...

I append below a suggested update for p.adjust(). 1. A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188. 2. I've re-named the "fdr" method to "bh" but kept "fdr" as a synonym for backward compatability. 3. Upper case values for method &...

Hello, I am not sure about the p.adjust( , fdr). How do these adjusted p-values get? I have read papers of BH method. For independent case, we compare the ordered p-values with the alfa*i/m, where m is the number of tests. But I have checked that result based on the adjusted p-values is different with that by using the independent case method. Then how do the result of p.adjust( , fdr) come? And