search for: benjaminy

The Partek package (www.partek.com) allows only two selections for Multiple Test Correction: Bonferroni and Dunn-Sidak. Can anyone suggest why Partek implemented Dunn-Sidak and not the other methods that R has? Is there any particular advantage to the Dunn-Sidak method? R knows about these methods (in R 2.1.1): > p.adjust.methods [1] "holm" "hochberg" "hommel"

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

You asked the same question on the Bioconductor mailing list back in August. At that time, you suggested yourself a solution for how the adjusted p-values should be interpreted. I answered your query and told you that your interpretation was correct. So I'm not sure what more can be said, except that you should read the article Wright (1992), which is cited in the help entry for p.adjust(),

In The American Statistician vol 42 (1988) pages 257 - 280, Yoav Benjamini investigates some variations on the box plot, including vaseplots, which maek the width of each box vary proportionally to he estimated density at a particular point. Has anyone implemented these in R ? Thanks as always Peter

Mark, there is a fdr website link via Yoav Benjamini's homepage which is: http://www.math.tau.ac.il/%7Eroee/index.htm On it you can download an S-Plus function (under the downloads link) which calculates the false discovery rate threshold alpha level using stepup, stepdown, dependence methods etc. Some changes are required to the plotting code when porting it to R. I removed the

I am posting this to both R and BioC communities because I believe there is a lot of confusion on this topic in both communities (having searched the mail archives of both) and I am hoping that someone will have information that can be shared with both communities. I have seen countless questions on the BioC list regarding limma (Bioconductor) and its calculation of FDR. Some of them involved

I am posting this to both R and BioC communities because I believe there is a lot of confusion on this topic in both communities (having searched the mail archives of both) and I am hoping that someone will have information that can be shared with both communities. I have seen countless questions on the BioC list regarding limma (Bioconductor) and its calculation of FDR. Some of them involved

Hi, There is a question that I am confused. I have a set of data like this: hsa-miR-205--GATA3 0.797882767 1.08E-13 hsa-miR-205--ITGB4 0.750217593 1.85E-11 hsa-miR-187--PGF 0.797604155 3.24E-11 hsa-miR-205--SERPINB5 0.744124886 3.28E-11 hsa-miR-205--PBX1 0.734487224 7.89E-11 hsa-miR-205--MCC 0.72499934 1.80E-10 hsa-miR-205--WNT5B 0.717705259 3.33E-10 hsa-miR-200c--PKN2 0.721746815

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

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

I am looking to replicate the functionality of the JMP diamond plot in R. I have found the following enhancement and no other references(see below). I have tried to contact the author via email and I have had no luck. Does any know if this functionality exists in R? If it does not does anyone know how to contact the author of the enhancement request? "Hi, I experimented with a set of S

dear all, I recently came across the following issue and I was not sure whether it is intentionally or not: using p.adjust to adjust p-values for multiple hypothesis testing using the method from Benjamini and Hochberg removes all NA values from the input vector and does not account for them in the adjustment, i.e. in a vector of 23 p-values with 20 of them being NA it adjusts the 3 non-NA

Dear list, I have performed several tests for the hypergeometric distribution using phyper() for some gene annotation categories as follows >phyper(26,830,31042,337, lower.tail=F) >phyper(16,387,31042,337, lower.tail=F) . . . I am only running some selected categories but I would like to correct this value for multiple testing since I have 3121 possible tests according to 3121

Hi all, I am having a problem with the function "p.adjust" in stats. I have looked at the manuals and searched the R site, but didn't get anything that seems directly relevant. Can anybody throw any light on it or confirm my suspicion that this might be a bug? I am trying to use the p.adjust() function to do Benjamini/Hochberg FDR control on a vector of p-values that are the

Benjamin Ylvisaker <benjaminy at alumni.cmu.edu> writes: > I am interested in working on a little architecture project that > involves modifying an ISA in some non-trivial ways and seeing what > impact it has on instruction frequencies (and other such metrics). > Clearly I'll need to hack on a compile...

I am interested in working on a little architecture project that involves modifying an ISA in some non-trivial ways and seeing what impact it has on instruction frequencies (and other such metrics). Clearly I'll need to hack on a compiler backend, and I thought that LLVM might be a good choice since among mature compiler infrastructures it's fairly young and presumably

Dear all, Myself Vijaykumar Muley working as senior research fellow. By training I am a computational biologist with not a strong knowledge of statistics. I have done some analysis which is explained as follows, I have 10340 (X) profiles of binary vectors with same length(N=845), I will call then "gene profiles" for example... v1 v2 v3 v4.....vN a 1 0 1 0 1 b 0

On Jun 9, 2011, at 8:09 PM, David A. Greene wrote: > Note that things like instruction frequencies are highly ISA- > dependent. If possible, it is best to evaluate your ideas on more > than one target, just to see what the effects are. What other sorts > of things do you want to study? > > If, long-term, you are planning to do serious studies of performance >