similar to: Ic TukeyHSD

Dear R-developeRs, Attached follows a patch against svn 34959 that adds the printing of p-values to the TukeyHSD.aov function in stats package. I also updated the corresponding documentation file and added a 'see also' reference to the simint function of the multcomp package. As it was already brought up in a previous thread [1] in R-help, one can obtain the adjusted

Dear all, I wanted to compare Bonferroni vs TukeyHSD correction over a range of groups and group sizes, and wanted to use the function qtukey. In the help documentation it says qtukey(p, nmeans, df, nranges = 1, lower.tail = TRUE, log.p = FALSE) Arguments q vector of quantiles. p vector of probabilities. nmeans sample size for range (same for each group). df degrees of freedom for s (see

We cannot help you understand what you are doing if you do not show us what you are doing. Here are some discussions about how to communicate questions about R [1][2][3]. [1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [2] http://adv-r.had.co.nz/Reproducibility.html [3] https://cran.r-project.org/web/packages/reprex/index.html -- Sent from my phone.

Well, it is clear enough that the problem is in interpreting the documentation. However, when you claim you tested something, and found it inconsistent with tables, it would be advisable to back it up with examples! The description in the help files and in the sources is admittedly confusing. The original paper has this, rather more clear, description in the abstract: "We consider the

Dear all, I'm using R to perform multiple comparison testing on agriculture genotype trials. To perform the Duncan's MRT, I use the qtukey() function with the following syntax: qtukey(p = ((1 - 0.05) ^ (pos - 1)), nmeans = pos, df = ni) I experience a strange behaviour when the number of means in the trial and the number of residual degrees of freedom (ni) becomes high

I am using R 1.1 with Redhat 6.2 and RW 1.001 with Win98 (the upkey doesn't work on my IBM either as has been previously reported by others). The function aov doesn't return either the residuals or the residual degrees of freedom for split-plot designs. If you use the following code from Baron and Li's "Notes on the use of R for psycology experiments and questionnaires"

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,

Hello R People: Here's the Saturday night goofy question. I would like to see the code for TukeyHSD function and I tried the following: > getAnywhere("TukeyHSD") A single object matching ?TukeyHSD? was found It was found in the following places package:stats namespace:stats with value function (x, which, ordered = FALSE, conf.level = 0.95, ...)

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 R community, I'm trying to understand this behavior of TukeyHSD. My goal is to obtain defensible, labelled multiple comparisons of an interaction term. Firstly, if I plot the TukeyHSD from the model that calculates its own interactions, then the y-axis labels appear to be reflected on their median when compared to the text output of the TukeyHSD statement. The labels are integers.

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().

I can prove I've done this before, but I recently installed Rexcel (and it was easiest to reinstall R and some other bits to make it work) and now its no longer working. Before I would do an ANOVA and a tukey post-hoc like this: >data1.aov=aov(result~factor1*factor2, data=data1) then... >TukeyHSD(summary(data1.aov)) and it would give me a nice tukey table of all the pairwise

pairwise.t.test is returning NAs when one of the samples only has one entry, while TukeyHSD returns results (maybe not trustworthy or believable, but results). I stumbled on this because I did not realize one of my samples only had one entry while most of the others had several hundred, so I realize this is not a desirable situation. I'm really just curious about the difference between how

Dear R-Helpers, I was calling the TukeyHSD function and not getting confidence intervals or p-values. It turns out this was caused by missing data and the fact that I had previously turned on R Commander (Rcmdr). John Fox knew that Rcmdr sets na.action to na.exclude, which causes the problem. If you have this problem, you can either exit Rcmdr before calling TukeyHSD or you can set na.action to

Hello, When plotting the results of a TukeyHSD multiple comparisons procedure with an ANOVA (lm) object, an extra line appears in the confidence intervals that contain 0. For example (this is straight from the TukeyHSD helpfile): > summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)) > TukeyHSD(fm1, "tension", ordered = TRUE) > plot(TukeyHSD(fm1,

R Version 2.0.0 Under development (unstable) (2004-05-07) I have been wanting to use TukeyHSD for two and three way aov's, as they are especially simple for students to use correctly. I realize model simplification is usually a preferred methodology, but my disciplinary enertia and simplicity of TukeyHSD is also compelling. However, the lack of names on the comparisons for the higher order

Hello, I am trying to determine the most appropriate way to run post-hoc comparisons on my lme model. I had originally planned to use Tukey HSD method as I am interested in all possible comparisons between my treatment levels. TukeyHSD, however, does not work with lme. The only other code that I was able to find, and which also seems to be widely used, is glht specified with Tukey:

I would like to do a one way anova and then a tukeyHSD. I have three vectors A,B and C. In a previous help message, I was told to do the following for the anova: y = c(A,B,C) group = factor(rep(a:3,c(7,9,13))) #provided there a 7 elements in A,9 in B and 13 in C and then anova(lm(y~group)) Looking at the tukeyHSD method it looks like it wants the aov method which I don't understand.

Dear R people, I have a couple of questions about post-doc analyses for 2 by 2 within subjects ANOVA. I conducted a psycholinguistic study that combined a 2 by 2 design and a latin square design. Specifically, I had 32 items each of which generated 4 conditions. Participants saw each of the 32 items only once: 8 in Condition A, 8 in B, 8 in C, and 8 in D. The table below serves as an example.

Hello everyone, So I ran an anova with aov and then I want to run post-hoc comparisons but keep receiving this message : > no applicable method for "TukeyHSD" Here is my code: > d<-read.table("d.txt") > d > Obs subj Hand Gaze RT > 1 1 s1 1 1 401.4 > 2 2 s2 1 1 363.3...... > summary(ano <-