similar to: closed testing procedure

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

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"

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as 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

Dear all, I was trying to understand how "multcomp" package works by running the examples given in the documentation. However I still don't understand when it comes to multiple comparison set by user (please refer to "Ksub" in the code). Therefore I run 2 other cases along with the original example (case 1), with the expectation I'll get the point from the output. The

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

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

>>>>> "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

Dear useRs, `multcomp' version 0.991-1 will be shortly available from CRAN near you. Nearly all functionality contained in the package has been re-implemented from scratch. The focus of the package has been extended to general linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example

Dear useRs, `multcomp' version 0.991-1 will be shortly available from CRAN near you. Nearly all functionality contained in the package has been re-implemented from scratch. The focus of the package has been extended to general linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example

Hi I'm encountering an error/warning when doing multiple comparisons with the package multcomp on a coxme model. My data: I'm looking at the removal of brood from the nest according to three treatments I applied on the brood. The brood and the workers caring about the brood in the nest, belonged to different colonies. Factor: treatment (3 levels: tx,uv,meta) Random effect 1: origin of

I've been using SAS PROC MULTTEST to perform multiple comparisons on data that are not normally distributed by using the stepdown bootstrap procedures of Westfall and Young (1993). According to the SAS manual, "the bootstrap method creates pseudo-data sets by sampling observations with replacement from each within-stratum pool of observations. An entire data set is thus created, and

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

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

Dear R list., I am running a script to get a compact letter display. library(lme4) library(multcomp) library(gplots) ####### Mixed Effects Model ######### data <- read.table("AJmix.txt",header=TRUE, sep="\t") attach(data) y<-cbind(positive,negative) treatment<-factor(treatment) mouse<-factor(mouse) data$obs<-1:nrow(data) names(data) detach(data) attach(data)

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 users I have a question which is only partly related to R, but I'm sending it to this list in the hope that someone with a better statistical background than mine will be kind enough to help me. It concerns multiple comparison testing in ANOVA. I know this subject has been raised before on this list, and I have looked through the relevant messages and read the article in

Also Consider Bonferroni Hochberg Holm type procedures or . Dunn OJ. Multiple contrasts using rank sum tests. Technometrics 1964;6:241#/52. [[alternative HTML version deleted]]