search for: hochberg

...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" "bonferroni" "BH" "BY" "fdr" [8] "none" BH is Benjamini & Hochberg (1995) and is also called "fdr" (I wish R's documentation said this clearly). BY is Benjamini & Yekutieli (2001). I found a few hits...

Hi- Is anyone working on multiple comparisons of means or post-hoc tests (ie: Tukey, Bonferroni) for R? I saw in the winter 98' archives of the R mailing lists that these tests had not been implemented yet, I was just wondering if I could look foward to having them. ;-) I also looked through the contributed packages and didn't see anything that offered such tests. I guess I could check

...: object is not subsettable In addition: Warning message: In is.na(p) : is.na() applied to non-(list or vector) of type 'closure' > adjust<-sum(pa<0.1) Is there anything i need to change? thanks, k -- View this message in context: http://www.nabble.com/p-adjust-using-Benjamn-and-Hochberg-tp15944523p15944523.html Sent from the R help mailing list archive at Nabble.com.

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 or are there any equivalents? I know that I can use Tukey HSD if the sample sizes are equal and the variances are homogeneous. Thanks! C...

...ollow what this code exactly does (especially the last bit). How would I link it to the ANOVA results? What sort of output would these functions give me - adjusted p values? How would I then interpret these? I'd really appreciate any help!!! ----------------- Code for Bonferroni, Holm and Hochberg post hoc tests: p.adjust.holm <- function(p, n=length(p)) { ##n <- length(p) r <- rank(p) index <- order(p) qi <- p*(n+1-r) for (i in 2:length(p)) { qi[index[i]] <- max(qi[index[i+1]]) } list(adjp=pmin(qi, 1), p=p, method="Holm") } p.a...

Matt, Here's a Bonferroni-corrected multiple one-sample t-test that I wrote some years ago. It took a while to get it into R, as na.omit doesn't seem to handle vectors and I had to write a quick kludge (na.remove). Another more general point was that I discovered that the help page for t.test gives the name "parameters" for the degrees of freedom, as in S. However, the name

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

...- lp:1L o <- order(p, decreasing = TRUE) ro <- order(o) pmin(1, cummin( n / i * p[o] ))[ro] How to explain the first and the fourth row. ====================p.adjust.R======================================= p.adjust.methods <- c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") p.adjust <- function(p, method = p.adjust.methods, n = length(p)) { ## Methods 'Hommel', 'BH', 'BY' and speed improvements contributed by ## Gordo...

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 supplied vector of pvalues and n is the supplied number of comparisons. If n>length(p) p.adjust() gives a war...

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 more powerful than Hochberg's, but the difference is usually small and the Hochberg p-values are fast...

...B5 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 3.46E-10 hsa-miR-200c--PCYOX1 0.721698034 3.48E-10 hsa-miR-200c--WDR68 0.72068017 3.78E-10 And I want to do the Benjamini & Hochberg correction. So I run : rm(list=ls()) a<-read.csv("1-correlation.txt",sep="\t",header=F,quote="") c<-p.adjust(a$V3,"BH") a[,4]<-c write.table(a,"zz.txt",sep="\t") And I got the result: hsa-miR-205--GATA3 0.797882767 1.08E-13 1....

...not actually the correct adjusted p-values, which are unknown, but are the most conservative possible values assuming the worst-case for the missing values. This would become arbitrarily unreasonable as the number of NAs increases. Gordon p.adjust.methods <- c("holm", "hochberg", "hommel", "bonferroni", "yh", "bh", "fdr", "none") p.adjust <- function(p, method = p.adjust.methods) { method <- match.arg(tolower(method),p.adjust.methods) if(method=="fdr") method <- "bh"...

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

...them involved misunderstandings or confusions regarding across which tests the FDR "correction" is being applied. My question is more fundamental and involves how the FDR method is implemented at the level of "p.adjust" (package: stats). I have reread the paper by Benjamini and Hochberg (1995) and nowhere in their paper do they actually "adjust" p values; rather, they develop criteria by which an appropriate p value maximum is chosen such that FDR is expected to be below a certain threshold. To try to get a better handle on this, I wrote the following simple script to...

...them involved misunderstandings or confusions regarding across which tests the FDR "correction" is being applied. My question is more fundamental and involves how the FDR method is implemented at the level of "p.adjust" (package: stats). I have reread the paper by Benjamini and Hochberg (1995) and nowhere in their paper do they actually "adjust" p values; rather, they develop criteria by which an appropriate p value maximum is chosen such that FDR is expected to be below a certain threshold. To try to get a better handle on this, I wrote the following simple script to...

...andings or confusions regarding across which tests the FDR > "correction" is being applied. My question is more fundamental and > involves how the FDR method is implemented at the level of "p.adjust" > (package: stats). > > I have reread the paper by Benjamini and Hochberg (1995) and nowhere in > their paper do they actually "adjust" p values; rather, they develop > criteria by which an appropriate p value maximum is chosen such that FDR > is expected to be below a certain threshold. > > To try to get a better handle on this, I wrote the foll...

...bitrary names (I'll call them a, b, c etc). The set-up is as follows: a b c d .... C 2 C 3 C 5 C 9 C 4 C 5 . N 13 N 16 N 18 . Can someone please show me how to read the data in, do the test on each set (a, b, etc) and then do, for example, a Benjamani and Hochberg correction for multiple tests? That would be great! Cheers -- View this message in context: http://r.789695.n4.nabble.com/Wilcoxon-Rank-Sum-in-R-with-a-multiple-testing-correction-tp3056557p3056557.html Sent from the R help mailing list archive at Nabble.com.

...them involved misunderstandings or confusions regarding across which tests the FDR "correction" is being applied. My question is more fundamental and involves how the FDR method is implemented at the level of "p.adjust" (package: stats). I have reread the paper by Benjamini and Hochberg (1995) and nowhere in their paper do they actually "adjust" p values; rather, they develop criteria by which an appropriate p value maximum is chosen such that FDR is expected to be below a certain threshold. To try to get a better handle on this, I wrote the following simple script to...

> 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

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after Tukey-correction? sorry, if this is an often-answered-question, but i didn??t find it on the list archive... thx a lot, list, Chris Christoph Strehblow, MD Department of Rheumatology, Diabet...