similar to: [BioC] limma, FDR, and p.adjust

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

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

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

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

>>>>> "GS" == Gordon K Smyth <smyth@wehi.edu.au> >>>>> on Sat, 8 Jan 2005 01:11:30 +1100 (EST) writes: <.............> GS> p.adjust() unfortunately gives incorrect results when GS> 'p' includes NAs. The results from topTable are GS> correct. topTable() takes care to remove NAs before GS> passing

Dear R-Help, I am using the function "topTable" from the LIMMA package. To estimate adjusted P-values there are several options (adjust="fdr" , adjust="BH") as shown below: topTable(fit, number = 10, adjust = "BH", fit$Name) I guess any of these options (fdr, BH, etc.) is using a default of FDR=0.05 which is quite conservative (i.e., very

I try to send this message To Gordon Smyth at smyth at vehi,edu.au but it bounced back, so here it is to r-help I am trying to use limma, just downloaded it from CRAN. I use R 2.0.1 on Win XP see the following: > library(RODBC) > chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls") > dd <- sqlFetch(chan1,"Raw") # all data 12000 > # > nzw <-

Hola a todos, Tengo un pequeño problemilla... Tengo unas 9000 variables que he contrastado con 1 en concreto con el test de wilcoxon. He calculado el p-valor, y queria corregirlo con el permutation-based FDR. He encontrado una funcion con R comp.fdr()que hace esta corrección, pero te pide que le pongas las variables con las observaciones y te hace el test (según he entendido). Yo solo quiero

I have developed a problem with the postscript output of plot on Windows. My code still works properly with R 2.3 but, with R 2.4, the white text on red background does not show up. It does, however, show up when output is sent to the screen. Below is my code and sessionInfo. R version 2.4.0 Under development (unstable) (2006-08-29 r39012) i386-pc-mingw32 locale: LC_COLLATE=English_United

Hello, I've a question about the fdr method in p.adjust: What is the threshold of the FDR, and is it possible to change this threshold? As I understand the FDR (please correct) it adjusts the p-values so that for less than N% (say the cutoff is 25%) of the alternative hypothesis the Null is in fact true. thanks a lot for help, +regards, Arne

On Wed, December 22, 2004 12:11 am, r.ghezzo at staff.mcgill.ca said: > ----- Forwarded message from r.ghezzo at staff.mcgill.ca ----- > Date: Mon, 20 Dec 2004 15:45:11 -0500 > From: r.ghezzo at staff.mcgill.ca > Reply-To: r.ghezzo at staff.mcgill.ca > Subject: [R] problems with limma > To: r-help at stat.math.ethz.ch > > I try to send this message To Gordon

Dear R users, I am wondering about the following results: > p.adjust(c(0.05,0.05,0.05),"fdr") [1] 0.05 0.05 0.05 > p.adjust(c(0.05,0.04,0.03),"fdr") [1] 0.05 0.05 0.05 Why does p.adjust(..., "fdr") not adjust p-values, if they are constant? Does somebody have an explanation or can point to a reference? Thanks in advance, Will

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

Hello, Also, I’ve noticed that FDR mode doesn’t flush to a log unless programmatically configured to do so unlike basic mode, which flushes by default. Would it be possible to add this feature as well? Thanks, Henry -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20180611/295c3dba/attachment.html>

Hallo, > fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12 ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value

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

Hello, I am initializing FDR mode and finalizing/flushing the buffers manually. XRay does not log calls from the main thread unless there is a function call after __xray_log_finalize(). This behavior is abnormal since one would expect the trace file to contain all function calls made up to the point when __xray_log_finalize() is called. To demonstrate this behavior, I have taken the test case