similar to: SAM FDR

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,

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

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

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

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

Hi all, Here's an interesting (for me, at least!) problem I came across: I have a correlation matrix, let's say with 6 variables, A to F, as column headings and the same 6 as row headings. The matrix is filled with correlation coefficients. Therefore, the diagonal is all 1's, and each of the two triangles formed by the diagonal has the same 15 correlation coefficients. I need to

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(),

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 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

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

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

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, I am performing Cox proportional hazards regression on a microarray dataset with 15000 genes. The p values generated from the Cox regression (based on normal distribution of large sample theory) showed only 2 genes have a p value less than 0.05. However, when I did a permutation on the dataset to obtained permutated p values, and it turned out about 750 genes had a permutated p value less than

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 all, I am running R 2.4.1. > library(siggenes); > library(multtest); > cl<-rep(c(0,1),c(3,3)); > sub<-exprs(AffyExpData[,c(1:3,7:9)]); > gn<-geneNames(AffyRAwData); > sam.out<-sam(sub,cl,rand=123,gene.names=gn); We're doing 20 complete permutations > sam.out SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances Delta p0

Dear All: Significance Analysis of Microarrays(SAM) As we know sam do multiple t.test as following ## Default S3 method: t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),mu = 0, paired = FALSE, var.equal = FALSE,conf.level = 0.95, ...) var.equal: a logical variable indicating whether to treat the two variances as being equal. If 'TRUE'

Hi, I am new to R...a recent convert from SAS. I have a dataset that looks like this: SEQ A1 A2 A 532.5 554.5 B 25.5 35.5 C 265.2 522.2 D 245.55 521.56 E 546.52 141.52 F 243.25 32.56 G 452.55 635.56 H 15.14 16.54 I 543.4 646.56 J 54.4 654.5 K 646.5 64.54 L 645.4 614.46 M 646.54 634.46 I want to make a histogram

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>

Hi all, My name is Amy, I am a masters student in Bioinformatics at North Carolina State University. I am working on a project and I am trying to use the lumi R package for microarray data analysis. I have shown the sample code here and have questions about modifying the sample code for my own data. lumi package in R, example.lumi, the sample data has 8000 features and 4 samples I have