similar to: limma, FDR, and p.adjust

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(),

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 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

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"

Hello, I read a book about statistics in psychology. The authors use SPSS. They talk about post hoc tests after ANOVA finds significant effects: - Gabriel's procedure (for equal or slightly different sample sizes) - Hochberg's GT2 (for different sample sizes) - Games-Howell procedure (for populations with unequal variances) I could not find them in R. Do they not exist in R

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

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

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

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-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

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

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 <-

>>>>> "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

Hi, I have two questions want to ask. 1. If I have a matrix like this, and I want to figure out the rows whose value in the 3rd column are less than 0.05. How can I do it with R. hsa-let-7a--MBTD1 0.528239197 2.41E-05 hsa-let-7a--APOBEC1 0.507869409 5.51E-05 hsa-let-7a--PAPOLA 0.470451884 0.000221774 hsa-let-7a--NF2 0.469280186 0.000231065 hsa-let-7a--SLC17A5

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

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

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, 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

Dear All, I am using the LIMMA package to create 2 contrasts for my data and then calculating the vennCounts of the decideTests from the contrast.fit to be able to create venn Diagrams. The code works fine but the summary(results) shows zeros for all i.e. no gene were up regulated or downregulated. This is not true for my data since toptable output shows Log fold change greater than > 2. I am

Hello, I have just completed an analysis of microarray data using the limma package. The analysis appears to have worked fine. However, when I use the topTable function to get the significant genes, I get the following error: > topTable(fit2,coef=5,adjust="fdr") Error in array(x, c(length(x), 1), if (!is.null(names(x))) list(names(x), : attempt to set an attribute on NULL