similar to: FDR

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(),

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

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

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

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>

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

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

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 am using Efron's local fdr procedure. But, I want to change the null from N(0,1) to N(0, 0.002). I can access the function but I have no idea what to change. In other words, I want nulltype to be N(0,0.002) instead of N(0,1) in his function. Anyone has any ideas. This is his code for the local fdr: function (zz, bre = 120, df = 7, pct = 0, pct0 = 1/4, nulltype = 1, type = 0, plot = 1,

Dear colleague, I not sure this R code is correctly ? I would to show the number of Sample Size at Sample Size Axis that line draw from Power Axis (80%) from R code. How I show this and select the most appropriate of this power (.79955687 - 80983575). Thank for your help and answer. Best Regards, Nikom Thanomsieng, Email: nikom at kku.ac.th .... #Power analysis: Sample size for

Hello all, I am doing SAM and the median of positive genes in the permutated sets is = false positives, and the "parent set" gives true positives. FDR = FP/TP * 100. My FDR comes to greater than 100. Is that possible? Please help! Thanks, -D. [[alternative HTML version deleted]]

Dear Dr. Graves Many thanks for your response. FDRs and their associated q values do differ from Type I error rates and P values (See Storey and Tibshirani PNAS 2003;100:9440-5). It is an approach that is rapidly gaining popularity in the analysis of genomic data where we have massive numbers of covariates measured on a comparatively modest number of subjects. To my mind it is a real advance

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

Dear List, We are planning a genotyping study to be analyzed using false discovery rates (FDRs) (See Storey and Tibshirani PNAS 2003; 100:9440-5). I am interested in learning if there is any consensus as to how many features (ie. how many P values) need to be studied before reasonably reliable FDRs can be derived. Does anyone know of a citation where this is discussed? Bill Dupont William D.

I am getting the following error in my script. I am very very new to R and have obtained this script from another person. #read file in (dummy data) starburst.plot<-function(affy.fold, affy.FDR)(ifelse( ((affy.fold) >=0), -1*log10(affy.FDR), 1*log10(affy.FDR))) starburst.plot<-function(meth.fold, meth.FDR)(ifelse( ((meth.fold) >=0), -1*log10(meth.FDR), 1*log10(affy.FDR))) At my next

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

Hi, The smb.conf is the default after the upgrade: cat /etc/samba/smb.conf # Global parameters [global] workgroup = TTU realm = ttu.red netbios name = PDC interfaces = lo, eth0 bind interfaces only = Yes server role = active directory domain controller server services = s3fs, rpc, nbt, wrepl, ldap, cldap, kdc, drepl, winbind, ntp_signd, kcc,

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the