similar to: Adjusted p-values with TukeyHSD (patch)

Attached follows a patch to a minor change in the plot method of the TukeyHSD class (package stats). Basically it defines main= and xlab= as formal arguments of the plot function, with reasonable default values, passing them to title(), instead of using hard-coded only values for main= and xlab=. This way it's possible to change the title and xlab of the plots created using plot.TukeyHSD().

Hello all, I would like to relate this behaviour of: plot.table & plot.TukeyHSD. They don't work with some graphical parameters. Thanks for your attetion. Cleber > ### example: > plot( table(1:10), cex.axis=0.6) > plot( table(1:10), las=2) > > tukaov <- TukeyHSD( aov(breaks ~ wool + tension, data = warpbreaks) ) > > plot( tukaov, main='xxx' ) Error

Hey, I have this TukeyHSD output from which I would like to extract only the P-values (p adj, last number). The problem is that the test output is a character list. How can I "break" this sentence to separate the Pv? Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Fe1$Fe ~ Fe1$genotype) $`Fe1$genotype` diff lwr upr

Dear R-listers, I am trying to compute simultaneous confidence intervals with simint from the package multcomp. 230 measures (abundance) have been taken in 23 sites (factor) of a data.frame (donnees: a file can be sent on request, saved with save(donnees,file="donnees")). I would like to get all pairwise comparisons with : mc<- simint(ren~ID,type="Tukey",data=donnees) I

Dear useRs, I'm working on multiple comparison design on two factor (2 3 levels) ANOVA. Each of the tests I have tried (Tukey, multcomp package) seem to do only with one factor at a time. fm1 <- aov(breaks ~ wool * tension, data = warpbreaks) tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE) $tension diff lwr upr p adj M-L -10.000000 -19.35342

Hi, the function TukeyHSD gives incorrect results for balanced incomplete block designs, as the example below shows, but I can only half fix it. There are two problems, 1. It uses model.tables to estimate treatment means, 2. It uses the wrong standard error The first problem can be fixed using dummy.coef, if the lines > TukeyHSD.aov function (x, which = seq(along = tabs), ordered = FALSE,

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

Using a "by() statement, I am preparing ANOVA's for multiple experiments, and using simint() to generate confidence intervals. This works fine. simint.by.fit <- by(analytes.dfr, list(Assay = analytes.dfr$analyte ), function(data) (simint(value ~ tx, data = data,type='Tukey' ) ) ) I can separately prepare plots of the confidence intervals, and I can prepare separate plots

simint and TukeyHSD work for aov objects. Can someone point me to similar functions for lme objects? Dieter Menne

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

I've never seen anything written about multiple comparisons, as in the multcomp package or with TukeyHSD, but using a glm. Do such procedures exist? Are they sensible? Are there any packages in R that implement such comparisons? Thank you. -- Ken Knoblauch Inserm U371 Cerveau et Vision 18 avenue du Doyen Lepine 69675 Bron cedex France Tel: +33 (0)4 72 91 34 77 Fax: +33 (0)4 72 91 34 61

I have a question about TukeyHSD and the glht function because I'm getting different answers when a covariate is included in model for ANCOVA.? I'm using the cabbages dataset in the 'MASS' package for repeatability.? If I include HeadWt as a covariate, then I get different answers when performing multiple comparisons using TukeyHSD and the glht function. The difference appears

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

I am having what I think is a strange problem with applying TukeyHSD to an aov fit with error strata. TukeyHSD is supposed to take "A fitted model object, usually an 'aov' fit." aov (with error strata) is supposed to generate an object of type aovlist, which is a list of objects of type aov. But I can't seem to feed components of my aovlist to TukeyHSD. I guess I

I'm interested in restricting the pairwise comparisons of interaction effects in a multi-way factorial ANOVA, because I find comparisons of interactions between all different variables different to interpret. For example (supposing a p<0.10 cutoff just to be able to use this example): > summary(fm1 <- aov(breaks ~ wool*tension, data = warpbreaks)) Df Sum Sq Mean Sq F

Dear R gurus, I have the following model: appcov.aov <- aov(yield ~ prevyield + trt + block) where prevyield is a continuous numeric covariate and trt and block are factors (yes, I did factor()!) Now, when I do a TukeyHSD, my diff's are all screwed up! For instance: treatment mean for treatmen "E" is 277.25 and for treatment "O" is 279.5, so I figure the diff O-E

# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor

Hi, I am seeking help with an error when running the example from R Documentation for TukeyHSD. The error occurs with any example I run, from any text book or website. thank you... > plot(TukeyHSD(fm1, "tension")). Error in plot(confint(as.glht(x)), ylim = c(0.5, n.contrasts + 0.5), ...) : error in evaluating the argument 'x' in selecting a method for function

Why do I see this error? > library(stats) > require(stats) [1] TRUE > > tHSD <- TukeyHSD(aov) Error in TukeyHSD(aov) : no applicable method for "TukeyHSD" In case it helps: > aov Call: aov(formula = roi ~ (Cue * Hemisphere) + Error(Subject/(Cue * Hemisphere)), data = roiDataframe) Grand Mean: 8.195069 Stratum 1: Subject Terms: Residuals Sum

Hi, I am running a ANOVA on a factorial design, and using TukeyHSD for post hoc comparisons. I have 2 factors with three levels each: Factor B Factor A 1 2 3 1 2 3 When I look at the Tukey output (on the interaction of the factors) the comparisons come out numbered 1-36. e.g. $"A:B" diff lwr upr [1,] 49.1666667 -160.041022 258.3744 [2,]