similar to: typo in p.adjust (PR#2231)

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 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

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

>>>>> "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

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

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 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

Pat Burns makes a good point. -Peter -------- Original Message -------- Subject: Re: [R] Using seq_len() vs 1:n Date: Fri, 12 Feb 2010 09:01:20 +0000 From: Patrick Burns <pburns at pburns.seanet.com> To: Peter Ehlers <ehlers at ucalgary.ca> References: <4B746AEF.10900 at ucalgary.ca> If you want your code to be compatible with S+, then 'seq_len' isn't going to work.

Folks: I know this may be overreaching, but are we missing what's important? WHY do the zeros occur? Are they values less then a known or unknown LOD? -- and/or is there positive mass on zero? In either case, using logs to calculate a geometric mean may not make sense. Paraphrasing Greg Snow, what is the scientific question? What is the model? Cheers, Bert On Mon, Jan 17, 2011 at 9:13 AM,

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 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

Thanks, Writing plot(addTA()) worked fine. I find myself with such mixed feelings about R. After finding that addTA worked fine at the command line but not in a function, I puzzled for a long time about what kind of virtual machine structure could possibly account for that. I couldn't think of any. It turns out that this isn't due to an R virtual machine structure. The reason addTA adds

Dear Rexperts: (R 2.0.1 on Windows XP Pro) Is the following problem unique to my setup? If it's a known problem, I didn't see it at http://bugs.r-project.org/ nor find discussion in the archives. plot(1:10) loc <- c(5, 6) text(loc, labels = "a") Produces expected results according to ?xy.coords. plot(1:10) loc <- list(x = 5, y = 6) text(loc, labels = "a") No

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(),

Note the edited subject line! I don't know why I typed it as it was before. This says that as.numeric(as.character(f)) will work regardless of the implementation, and I agree. It's the recommendation to use as.numeric(levels(f))[f] that has me wondering about section 2.3.1 of the language definition. I expect that this idiom is in widespread use, and perhaps the language definition

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

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 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

Hi,?are the methods for multiple testing p value adjustment (Shaffer, Westfall, free) implemented in the function adjusted() in multcomp package so called closed testing procedure? what about those methods (holm, hochberg, hommel, BH, BY) implemented in the p.adjust() in the stats package? ? Thanks ? John