similar to: p.adjust on a vector including NA values

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"

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

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

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(),

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

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

Full_Name: Ludo Pagie Version: 2.8.1 OS: linux Submission from: (NULL) (194.171.7.39) p.adjust in stats seems to have a bug in handling n>length(p) for (at least) the methods 'holm' and 'hochberg'. For method 'holm' the relevant code: i <- 1:n o <- order(p) ro <- order(o) pmin(1, cummax((n - i + 1) * p[o]))[ro] where p is the

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, I am trying to use the p.adjust function for multiple testing. here is what i have 9997 201674_s_at 0.327547396 9998 221013_s_at 0.834211067 9999 221685_s_at 0.185099475 I import them from excel have have the gene symbol as well as the pvalue here is the issue > pa<-p.adjust(pt,method="BH") Error in p[nna] : object is not

Full_Name: Peter Ehlers Version: 1.6.0 OS: Windows 2000 Pro Submission from: (NULL) (136.159.61.178) In: p.adjust package:base R Documentation In the paragraph: Hochberg's and Hommel's methods are valid when the hypothesis tests are independent or when they are non-negatively associated (Sarkar, 1998; Sarker and Chang, 1997). Hommel's method is

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 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 every GNU's, I have a question about mt.plot, on multtest package. I'm wondering how do a plot with test like bonferroni, holm, hochberg,... and also résults of SAM(Significiance Analysis Microarrays) as we can see on several document of Sandrine Dudoit. Thanks a lot and Have a nice day Sandrine --------------------------------------------------------------------------------

Dear R community I sent a message out a while ago asking for help with multiple comparison tests for ANOVA's, but haven't had any response yet. I'm sending a final desperate plea. If I can't get this done in R I'm going to have to redo a whole lot of stuff in a commercial package, which I'm REALLY not keen to do! My problem is how to implement these tests in R. Below

You can get help on the whole package by > help(package="multtest") which is likely pretty close to what you want. There's the index etc for the package on the web as well. You can also just look in the package's installation directory. If it's loaded you can do an ls(2) say if it loaded in position 2, to get all objects in the package. Reid -----Original Message-----

To All How does one get a list of functions from a loaded package so that one can then get the appropriate help for each of the functions. Currently my method is based on a lot of trial-and-error. Here's an example of what I mean... >From this forum I learn that an interesting package called "multtest" exists on Bioconductor. I then use R Console's "Packages" --

> dim(data.sub) [1] 10000 140 #####extracting all differentially express genes########## library(multtest) two_side<- (1-pt(abs(data.sub),50))*2 diff<- mt.rawp2adjp(two_side) all_differ<-diff[[1]][37211:10000,] all_differ #####list of differentially expressed genes########## > probe.names<- + all_differ[[2]][all_differ[[1]][,"BY"]<=0.01] Error in