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Hi, I would like to derive p-values for pair-wise comparison (Tukey's) of effects when the response is a count. I am trying a test case where y ~ Po( lambda(x) ). x has three levels : A, B and C with lambda(x) = 10, 20 and 20 respectively. Hence, p-values for the contrast C - B should distribute uniformally. I have implemented this test case as below but do not get uniform

...justed p-values for all-pairwise comparisons in a one-way layout ### set up ANOVA model amod <- aov(response ~ trt, data = cholesterol) ### set up multiple comparisons object for all-pair comparisons cht <- glht(amod, linfct = mcp(trt = "Tukey")) summary(cht, test = adjusted("Westfall")) ### use only a subset of all pairwise hypotheses K <- contrMat(table(cholesterol$trt), type="Tukey") Ksub <- rbind(K[c(1,2,5),],"D - test" = c(-1, -1, -1, 3, 0),"E - test" = c(-1, -1, -1, 0, 3)) ### reproduce results in Westfall et al. (1999, page 172...

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 many-to-one comparisons o...

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 many-to-one comparisons o...

Could someone kindly help me with the following question: when I analyze microarray data I need to fit multiple linear regression models between genes and clinical patameters followed by estimation of the p-values. What's the solution to implement Westfall and Young's algorithm + resampling into the scheme: lm -> stepAIC -> anova. Actually permcor works fine for me in the case of one regression variable, I need extension for multiple regression models. Any ideas are very much wellcome! Sincerely yours, Serguei Baranov Research "Obe...

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 p-values for all tests are computed on this pseudo-data set. A counte...

Dear R users, I am wondering about the following results: > p.adjust(c(0.05,0.05,0.05),"fdr") [1] 0.05 0.05 0.05 > p.adjust(c(0.05,0.04,0.03),"fdr") [1] 0.05 0.05 0.05 Why does p.adjust(..., "fdr") not adjust p-values, if they are constant? Does somebody have an explanation or can point to a reference? Thanks in advance, Will

...to the samba in the hq-dmz comes up. If the line is already up, theres no difference at all (last week we had one service-tech who backuped a users data to the r: drive - regardless of the users existing home dir - well it was a little slow for some 100 Megs... over a 128kb line.) -- Dirk Westfal (EDV/NETADMIN) <dirk.westfal@frankfurter-verein.de> Key fingerprint = 8D7F 71D3 239A FA48 5E73 F1DE D460 E97F 83B0 1F89 "assumption is the mother of all screw-ups" (unknown)

...propriate of this power (.79955687 - 80983575). Thank for your help and answer. Best Regards, Nikom Thanomsieng, Email: nikom at kku.ac.th .... #Power analysis: Sample size for Chi-Square 2x2, RxC: R software #Concept from SAS program to calculate power of ANOVA F-test #http://www2.tltc.ttu.edu/Westfall/images/5347/power_analysis_of_anova_f_test.htm # and Cohen,J (1977). Statistical Power Analysis for Behavioral Sciences. #New York: Academic Press. #Asst. Prof. Nikom Thanomsieng. 29/09/2000 #Department of Biostatistics & Demography. Faculty of Public Health. #Khon Kaen University. Thailand....

...and bug reports are more than welcome. Best wishes, Torsten ___________________________________________________________________________ Package: multcomp Title: Simultaneous Inference for General Linear Hypotheses Version: 0.991-1 Date: 2006-10-16 Author: Torsten Hothorn, Frank Bretz and Peter Westfall Description: Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. Maintainer: Torsten Hothorn <Torsten.Hothorn at R-project.org> Depends: mvtnorm (>= 0.7-5) La...

Hi, My query is regarding permutation test and reshuffling of genotype/phenotype data I have been using the haplo.stats package of R. for haplotype analysis and I would like to perform an analysis which I'm requesting your advice. I have a data set of individuals genotyped for 12 SNP and a dichotomous phenotype. At first, I have tested each of those SNP independently in order to bypass

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

...and bug reports are more than welcome. Best wishes, Torsten ___________________________________________________________________________ Package: multcomp Title: Simultaneous Inference for General Linear Hypotheses Version: 0.991-1 Date: 2006-10-16 Author: Torsten Hothorn, Frank Bretz and Peter Westfall Description: Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. Maintainer: Torsten Hothorn <Torsten.Hothorn at R-project.org> Depends: mvtnorm (>= 0.7-5) La...

...tment) mouse<-factor(mouse) data$obs<-1:nrow(data) names(data) detach(data) attach(data) Mixmodel<-lmer(y~treatment+(1|mouse:treatment)+(1|obs) ,family=binomial) summary(Mixmodel) model.mc<-glht(Mixmodel,linfct=mcp(treatment="Tukey")) summary(model.mc, test=adjusted(type="Westfall")) model.ci <- confint(model.mc, level= 0.95) model.ci par(mar=c(5,10,3,2)+.1) plot(model.ci,main="AJ Pairwise Differences of Logit(Proportion) OggI", xlab="Difference in logit(Proportion) Between Treatments") model.cld<-cld(model.mc) par(mar=c(3,4,6,2)+.1) plot(mod...

...uv - tx == 0 21.4694 Global Test: Chisq DF Pr(>Chisq) 1 71.94 1 2.217e-17 Warning message: In cov2cor(covm) : diag(.) had 0 or NA entries; non-finite result is doubtful and all pairwise comparisons between treatments are then not possible: summary(pp.coxme.glht,test=adjusted("Westfall")) Error in if (!chkcorr(corr)) stop(sQuote("corr"), " is not a correlation matrix") : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In cov2cor(covm) : diag(.) had 0 or NA entries; non-finite result is doubtful 2: In cov2cor(covm) : diag...

Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the

I am a new R user but a long time SAS user. I searched for a response to this question but no luck, so forgive me if this topic has been covered before. I am running a TukeyHSD post hoc test after running an ANOVA. I get the results of all pairwise comparisons, no problem. However, the output table is a little "busy", and I'd like to make the output easier to read. Specifically, I

I currently work on a draft of an aquatic bioassessment. The conditions tested are the following: ER river water T dechlorinated water control 0.5 + 0.5mg / L of malate T + 1 dechlorinated water control + 1g / L of malate T ED dechlorinated water control SED + ER + river water sediment SED ED + sediment + water dechlorinated. It is the result of AChE in muscle (fillet of fish). The production of

Dear R community, I am using 6 variables to test for an effect (by linear regression). These 6 variables are strongly correlated among each other and I would like to find out the number of independent test that I perform in this calcuation. For this I calculated a matrix of correlation coefficients between the variables (see below). But to find the rank of the table in R is not the right

...bited. > > [[alternative HTML version deleted]] > > > > > > > > > > From: Julien Gagneur <julien.gagneur@embl.de> > Date: 16 June 2009 1:58:56 AM > To: r-help@r-project.org > Subject: Re: [R] multcomp: contrasts for count data > > > Peter Westfall has answered me giving useful links. Here is the > exchange: > > > thanks for your quick answer and the refs. My misconception was that i > treated the p-values as if they were non adjusted p-values. > Discreteness was a minor problem. The p-values look much more uniform > onc...